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WP30: E3 transfer complete, all results sections, abstract, conclusion, claim ledger, FINAL_REPORT draft, page inspection

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Oct 30, 2026) parent 3a436c8 Browse files

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1+# WP30 — FINAL REPORT
2+
3+**Status: COMPLETE_WITHIN_DECLARED_SCOPE** (with the explicit exclusion of cross-family cache transfer; see "Not executed").
4+
5+Paper: `paper/WP30_Boucher_Cache_Compression_Knowledge_Transfer.pdf` (LaTeX in `paper/`). Protocol frozen 2026-10-30 04:32 UTC
6+(`protocol/frozen.json`, hash `404c52b3…`); amendments A-001–A-004 in `protocol/hypotheses.md`; dated decisions D-001–D-019 in `DECISIONS.md`.
7+
8+## What was actually executed (all from Claude Code, real model inference)
9+
10+| Campaign | What | Trials / runs | Where | Status |
11+|---|---|---|---|---|
12+| E0 | cache correctness suite (identity, prefix equivalence, positions, packed bytes, loader rejection, isolation) | 12 tests + 20 unit tests | laptop | 32/32 pass |
13+| E1_pilot | dev calibration, 31 conditions × 2 pairs | 1 037 | laptop | done (dev only) |
14+| E1_core | 22 conditions × 2 pairs × 160 test documents | 13 280 (+240 A8 retries) | laptop (Qwen pair, Llama public) + M3U96a (Llama synthetic/exec) | complete |
15+| E3_core (+sender) | Qwen2.5-1.5B→3B ridge mapper v2, identity control, text handoffs, sender-alone | 1 536 + 512 | M3U96a | complete |
16+| E4_core | relays 1/2/4/8 handoffs, closed/repairable, Qwen and Qwen/Llama, fact ledger | 7 200 | laptop + M3U96b (document parity shards) | complete |
17+| E5_core | fan-in k=2,4 with superseded revision + irrelevant source; 5 compositions incl. cache-concat negative control | 1 600 | laptop | complete |
18+| E6_core | genuine two-node transfer M3U96a→M3U96b (1 GbE, 940 Mbit/s): bf16/zstd/8-bit/4-bit caches vs remote text prefill vs local | 324 | cluster | complete |
19+| ED_hosted_core | A0–A6 messages → claude-haiku-4-5 and gpt-5-nano, provider prefix caching | 672 (0.53 USD; pilots 0.19 USD) | M3U96b + APIs | complete |
20+| E8 | router fitted on dev pilot, evaluated off-policy on the full-factorial test records | 640 instances × 2 regimes | laptop | complete |
21+| E9 | minimal reproduction from a clean process | 64 trials | laptop | 64/64 identical outputs and scores |
22+| E7_qtypes | EXPLORATORY: question types 6–15 (negation, revision, temporal, aggregation, units, unanswerable) | 5 600 planned | laptop | see STATUS |
23+
24+Models: `mlx-community/Qwen2.5-3B-Instruct-bf16`, `Llama-3.2-3B-Instruct-bf16`, `Qwen2.5-1.5B-Instruct-bf16` (revisions and weight hashes in
25+`evidence/models.json`). Hardware: MacBook Pro M5 Max 48 GB (laptop), two Mac Studio M3 Ultra 96 GB (M3U96a/b) on a wired 1 GbE LAN.
26+Runtimes: mlx 0.32.4, mlx-lm 0.32.2, torch 2.15 (MPS, LLMLingua-2 only), Python 3.12.13. Total wall time ≈ 10 h of campaigns (2026-10-29 23:00 →
27+2026-10-30 09:30 UTC) plus development; API spend 0.72 USD (cap 25 USD).
28+
29+## Principal findings (each with a claim-ledger row in `results/claim_ledger.csv`)
30+1. Lossless representations (text, zstd text, native cache, reloaded cache, 8-bit packed cache) are indistinguishable in quality; their cost
31+ ranking inverts between cold (text wins) and reused (cache wins) regimes.
32+2. 4-bit packed KV quantization: Llama-3.2-3B ≥ 97 %, Qwen2.5-3B 0 % (native mlx-lm path reproduces it) — H2 confirmed.
33+3. SnapKV-style eviction at 1/4 with the question keeps 78–98 % of private-fact accuracy; with a generic window 12–19 %; sink+recent 18–20 %;
34+ random 0 %; every eviction scores 0 on executable tasks.
35+4. At a 512-token sender budget, prose summaries (38–44 %) beat structured evidence with spans (12–15 %): the budget is spent on quotes — H1 rejected.
36+5. Relays: loss at hop 1, then nearly lossless to hop 8 (H5 rejected); no invented identifiers; metered repair +6 pts retention for 5.9 kB.
37+6. Fan-in: concatenated summaries ≈ full context on joins; merged structured records fail; cache concatenation (invalid) still answers 70 % at k=2 — a warning.
38+7. Two-node: bf16 cache 215 MB at 8k tokens moves in 1.8 s; 8-bit cache answers in 1.7 s vs 3.7 s for remote text prefill on an M3 Ultra receiver.
39+8. Same-family ridge mapper: 68 % of value variance reconstructed, 0–4 % content transfer, coherent "unknown" abstention — H10 confirmed.
40+9. Hosted receivers recover everything from full text and the same ~30 % from the local summary; Anthropic caching needs ≥ 4 096 tokens.
41+10. Router: equal quality at half the amortized latency (blind); no gain and higher cost (aware).
42+
43+## Not executed / limitations
44+- Cross-family cache transfer (SwaT: no checkpoint, no licence; training a 300M mapper exceeded the budget) → the transfer study is same-family only.
45+- Attention-Matching compaction, C2C, LatentMAS, H2O, KIVI kernels: audited, not run (CUDA/vLLM code; see Table 1 and `literature/method_notes.md`).
46+- One sender budget (512), one retention ratio (1/4) in the confirmatory family; 2-bit dropped from the core; public QA subsets of 40 documents.
47+- Llama-pair latencies for synthetic/exec come from M3U96a, public from the laptop (hardware recorded per record; within-pair, within-family comparisons only).
48+- Hosted sampling is provider default (temperature not accepted); A8 for the Llama pair was retried on the laptop after a missing-runtime failure on M3U96a.
49+- No human validation, no LLM judge; deterministic scorers only. Synthetic facts are new; public texts may be in pre-training corpora.
50+
51+## Reproduction
52+```bash
53+uv venv --python 3.12 .venv && source .venv/bin/activate && uv pip install -r reproduction/requirements-lock.txt && uv pip install -e .
54+make reproduce-analysis # tables, figures, provenance, audit (no inference)
55+python -m wp30 reproduce --profile reproduction/minimal.yaml # re-run 64 trials, compare outputs (needs the two checkpoints, ~15 GB)
56+make paper
57+```
58+Full campaigns: `configs/core*.yaml`, `chains_core*.yaml`, `fanin_core.yaml`, `hosted_core.yaml`; two-node: `scripts/run_e6_core.sh`.
59+
60+## Execution disclosure
61+Operator: Claude Code (claude-fable-5-1). Human interventions: none during execution (brief provided at start). Defects found and fixed are listed in
62+the paper (Section 9) and in `DECISIONS.md`. No screenshots were captured; machine-readable logs (`runs/logs`) and rendered page images
63+(`evidence/pages`) are the execution evidence.
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1 # STATUS.md — WP30 1 # STATUS.md — WP30
2 2
3−**Phase:** 8 (core single-handoff campaign running) — protocol frozen 2026-10-30 04:32 UTC, hash 404c52b34d964693… 3+**Phase:** 11 (analysis) / 12 (manuscript) — core campaigns complete; two tails running
4−**Completed gates:** 0 environment · 1 literature (34 sources) · 2 datasets (800 items, keys isolated) · 3 live inference · 4 cache correctness (12/12) · 4+**Completed:** E0 correctness · E1_core 13 280 trials (both pairs) · E4_core 7 200 (chains, 2 machines) · E5_core 1 600 (fan-in) · E6_core 324 two-node runs ·
5−5 text/evidence methods (A0–A8) · 6 advanced methods (B5/B6 faithful re-implementations; linear mapper v1/v2 trained on M3U96b; SwaT/C2C/LatentMAS/H2O/Compaction audited, not run) · 5+ED_hosted_core 672 (0.53 USD) · E8 router evaluated on test · E9 minimal reproduction 64/64 identical · protocol frozen 404c52b3…, amendments A-001…A-004
6−7 pilot (E1_pilot 1 037 trials dev; E3_pilot/v2; E4_pilot; ED_hosted_pilot) + freeze 6+**Running:** E3_core (Qwen1.5B→3B mapper, M3U96a) · laptop queue2 (A8 Llama retry, then exploratory E7_qtypes)
7−**Running:** E1_core Qwen shard (laptop, started 04:32 UTC); E4_pilot (M3U96a, finishing); ED_hosted_pilot (M3U96b) 7+**Next:** collect E3 + E3_sender → results_transfer.tex · E7 → appendix · abstract/conclusion · claim ledger · page-by-page PDF inspection · audit-artifacts · FINAL_REPORT.md · spbgit push
8−**Queued:** E1_core Llama shard (M3U96a) · E6_core (M3U96a→M3U96b) · E4_core, E5_core, E3_core(+sender) (M3U96b) · ED_hosted_core · E8 router eval · analysis · paper 8+**Resume:** `./scripts/collect_remote_results.sh && make reproduce-analysis && make paper`
9−**Blockers:** none
10−**Resume commands:** laptop `python -m wp30 run --config configs/core_qwen.yaml --resume`; M3U96a `python -m wp30 run --config configs/core_llama.yaml --resume`;
11−M3U96b `python -m wp30.cli_extra chains configs/chains_core.yaml --resume`, `fanin configs/fanin_core.yaml --resume`, `python -m wp30 run --config configs/core_transfer.yaml --resume`
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modified paper/appendix/appendix.tex +1 −1
@@ -8,7 +8,7 @@
8 \label{app:audit} 8 \label{app:audit}
9 Thirty-four sources were verified on 2026-10-29 from their arXiv pages, repositories and documentation (\texttt{literature/sources.csv}; 9 Thirty-four sources were verified on 2026-10-29 from their arXiv pages, repositories and documentation (\texttt{literature/sources.csv};
10 notes in \texttt{literature/audit\_group\{1,2,3\}.md}). Table~\ref{tab:audit} summarizes the core methods. 10 notes in \texttt{literature/audit\_group\{1,2,3\}.md}). Table~\ref{tab:audit} summarizes the core methods.
11−\begin{table}[h]\centering\scriptsize 11+\begin{table}[H]\centering\scriptsize
12 \caption{Audited core methods: availability of code and checkpoints, backend, question access, and WP30 status.}\label{tab:audit} 12 \caption{Audited core methods: availability of code and checkpoints, backend, question access, and WP30 status.}\label{tab:audit}
13 \begin{tabular}{@{}llllll@{}}\toprule 13 \begin{tabular}{@{}llllll@{}}\toprule
14 Method & Code & Checkpoint & Backend & Question access & WP30 \\\midrule 14 Method & Code & Checkpoint & Backend & Question access & WP30 \\\midrule
modified paper/appendix/extra_tables.tex +26 −1
@@ -1 +1,26 @@
1−% filled by analysis 1+\subsection{Core results by question type}
2+\begin{table}[H]\centering\scriptsize
3+\caption{Synthetic family, core run, accuracy (\%) by question type (both pairs pooled; 95\,\% cluster-bootstrap CI).}
4+\adjustbox{max width=\textwidth}{\input{tables/table_qtype_core}}
5+\end{table}
6+
7+\subsection{Attempt outcomes by method}
8+\begin{table}[H]\centering\scriptsize
9+\caption{All attempts of the core run by status (retries included).}
10+\input{tables/table_failures}
11+\end{table}
12+
13+\subsection{Exploratory: question types not covered by the core (A-004)}
14+\begin{figure}[H]\centering\includegraphics[width=\textwidth]{figA_qtypes_exploratory.pdf}
15+\caption{EXPLORATORY. Accuracy on negation, revision/precedence, temporal order, aggregation, unit conversion and unanswerable questions
16+(questions 6--15 of the 40 synthetic test documents) for a subset of methods; no confirmatory test attached.}\end{figure}
17+\begin{table}[H]\centering\scriptsize
18+\caption{EXPLORATORY question-type extension (\%, 95\,\% CI).}
19+\adjustbox{max width=\textwidth}{\input{tables/table_qtypes_exploratory}}
20+\end{table}
21+
22+\subsection{Question-access panels}
23+\begin{figure}[H]\centering\includegraphics[width=\textwidth]{figA_quality_vs_bytes_blind.pdf}
24+\caption{Question-blind conditions only (reusable messages).}\end{figure}
25+\begin{figure}[H]\centering\includegraphics[width=\textwidth]{figA_quality_vs_bytes_aware.pdf}
26+\caption{Question-aware conditions only (query-specific messages).}\end{figure}
modified paper/appendix/hypotheses_verbatim.tex +106 −32
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1 \begin{footnotesize}\begin{verbatim} 1 \begin{footnotesize}\begin{verbatim}
2−# WP30 -- Research questions and falsifiable hypotheses (registered before any held-out outcome was examined) 2+# WP30 -- Research questions and falsifiable hypotheses (registered before any held-out
3+ outcome was examined)
3 4
4 Registered: 2026-10-29 (Phase 1). Amendments only as dated entries at the bottom. 5 Registered: 2026-10-29 (Phase 1). Amendments only as dated entries at the bottom.
5−Test-split outcomes had **not** been examined at registration (no test split existed yet; only the E0 smoke run on a 6+Test-split outcomes had **not** been examined at registration (no test split existed yet;
7+ only the E0 smoke run on a
6 hand-written 3-sentence memo had been executed, see `runs/logs/`). 8 hand-written 3-sentence memo had been executed, see `runs/logs/`).
7 9
8 ## Research questions 10 ## Research questions
9 11
10−- **RQ1 (compression frontier)** How do text shortening (A2-A4, A8), packed KV quantization (B2), token eviction (B3-B6) and 12+- **RQ1 (compression frontier)** How do text shortening (A2-A4, A8), packed KV quantization
11− learned compaction (B7, if run) trade task quality against *measured* serialized bytes and resident cache bytes? 13+ (B2), token eviction (B3-B6) and
12−- **RQ2 (transfer compatibility)** How much performance is preserved by same-checkpoint reuse (B1, cross-process, cross-node) 14+ learned compaction (B7, if run) trade task quality against *measured* serialized bytes and
13− versus learned mappings between distinct checkpoints (Track C), relative to the receiver's own full-context answer? 15+ resident cache bytes?
14−- **RQ3 (resource accounting)** Do rankings of methods under a token budget change when the budget is bytes, end-to-end latency, 16+- **RQ2 (transfer compatibility)** How much performance is preserved by same-checkpoint
17+ reuse (B1, cross-process, cross-node)
18+ versus learned mappings between distinct checkpoints (Track C), relative to the receiver's
19+ own full-context answer?
20+- **RQ3 (resource accounting)** Do rankings of methods under a token budget change when the
21+ budget is bytes, end-to-end latency,
15 peak memory or amortized cost over N reuses? 22 peak memory or amortized cost over N reuses?
16−- **RQ4 (repeated handoffs)** How do correct-fact retention, unsupported additions and task accuracy evolve after 1, 2, 4, 8 handoffs, 23+- **RQ4 (repeated handoffs)** How do correct-fact retention, unsupported additions and task
24+ accuracy evolve after 1, 2, 4, 8 handoffs,
17 for prose summaries vs structured evidence, in closed vs repairable chains? 25 for prose summaries vs structured evidence, in closed vs repairable chains?
18−- **RQ5 (evidence)** Under an equal byte budget, does structured evidence with source spans (A5) retain exact facts (identifiers, 26+- **RQ5 (evidence)** Under an equal byte budget, does structured evidence with source spans
19− numbers, dates, negations) better than an abstractive summary (A4)? Does metered retrieval (A6) repair losses, and at what cost? 27+ (A5) retain exact facts (identifiers,
20−- **RQ6 (locality)** When is running the next query where the cache resides cheaper than moving the cache or recomputing from text, 28+ numbers, dates, negations) better than an abstractive summary (A4)? Does metered retrieval
29+ (A6) repair losses, and at what cost?
30+- **RQ6 (locality)** When is running the next query where the cache resides cheaper than
31+ moving the cache or recomputing from text,
21 as a function of context length, payload bytes, measured bandwidth and queue delay? 32 as a function of context length, payload bytes, measured bandwidth and queue delay?
22−- **RQ7 (adaptive policy)** Can a router fitted on development data improve the held-out quality/cost frontier over the best 33+- **RQ7 (adaptive policy)** Can a router fitted on development data improve the held-out
34+ quality/cost frontier over the best
23 development-selected static method, once routing overhead and fallbacks are counted? 35 development-selected static method, once routing overhead and fallbacks are counted?
24 36
25 ## Hypotheses (each may be rejected) 37 ## Hypotheses (each may be rejected)
26 38
27−- **H1** Lossy compression degrades exact identifiers, decimal numbers, negation and temporal order more than paraphrasable content: 39+- **H1** Lossy compression degrades exact identifiers, decimal numbers, negation and
28− on the synthetic private-fact family, the accuracy drop from A1 to A4 at a 512-token budget is larger (>= 10 percentage points) 40+ temporal order more than paraphrasable content:
29− for `exact_copy`, `negation`, and `revision` question types than for `single_fact` questions. 41+ on the synthetic private-fact family, the accuracy drop from A1 to A4 at a 512-token
30−- **H2** Packed KV quantization is model-dependent: at 4 bits/group 64, Llama-3.2-3B retains >= 95 % of its B0 accuracy while 42+ budget is larger (>= 10 percentage points)
31− Qwen2.5-3B loses >= 20 percentage points (motivated by E0 smoke: Qwen2.5-1.5B produced garbage at 4-bit in both the native 43+ for `exact_copy`, `negation`, and `revision` question types than for `single_fact`
44+ questions.
45+- **H2** Packed KV quantization is model-dependent: at 4 bits/group 64, Llama-3.2-3B retains
46+ >= 95 % of its B0 accuracy while
47+ Qwen2.5-3B loses >= 20 percentage points (motivated by E0 smoke: Qwen2.5-1.5B produced
48+ garbage at 4-bit in both the native
32 mlx_lm path and ours; Llama-3.2-3B did not). 8-bit retains >= 97 % for both. 49 mlx_lm path and ours; Llama-3.2-3B did not). 8-bit retains >= 97 % for both.
33−- **H3** Sink+recent eviction (B3) at <= 1/4 retention is worse than a *question-aware* attention-informed selection (B5-aware) 50+- **H3** Sink+recent eviction (B3) at <= 1/4 retention is worse than a *question-aware*
34− at equal retained positions, but **not** reliably better than random selection (B4) on questions whose answer lies in the 51+ attention-informed selection (B5-aware)
52+ at equal retained positions, but **not** reliably better than random selection (B4) on
53+ questions whose answer lies in the
35 middle of the document. 54 middle of the document.
36−- **H4** Serialized cache bytes exceed raw UTF-8 document bytes by >= 2 orders of magnitude for 3B models; hence B1 transfer only 55+- **H4** Serialized cache bytes exceed raw UTF-8 document bytes by >= 2 orders of magnitude
37− wins on total latency when (a) the receiver's prefill cost exceeds transport time, which on the measured LAN requires contexts 56+ for 3B models; hence B1 transfer only
38− above a break-even length we will estimate (prediction: several thousand tokens), or (b) the cache is reused >= N* times. 57+ wins on total latency when (a) the receiver's prefill cost exceeds transport time, which
39−- **H5** Repeated prose handoffs lose facts monotonically; after 4 handoffs the retained-correct-fact rate of A4 chains falls below 58+ on the measured LAN requires contexts
40− 70 % while A5 (structured, explicit unknowns) stays above 85 %; unsupported additions appear mainly in prose chains. 59+ above a break-even length we will estimate (prediction: several thousand tokens), or (b)
41−- **H6** Metered retrieval (A6, repairable chains) restores most of the lost exact facts but adds fetched bytes comparable to 60+ the cache is reused >= N* times.
61+- **H5** Repeated prose handoffs lose facts monotonically; after 4 handoffs the
62+ retained-correct-fact rate of A4 chains falls below
63+ 70 % while A5 (structured, explicit unknowns) stays above 85 %; unsupported additions
64+ appear mainly in prose chains.
65+- **H6** Metered retrieval (A6, repairable chains) restores most of the lost exact facts but
66+ adds fetched bytes comparable to
42 the message itself. 67 the message itself.
43−- **H7** Question-aware compression dominates question-blind compression at equal budget for *all* lossy methods; the gap is 68+- **H7** Question-aware compression dominates question-blind compression at equal budget for
69+ *all* lossy methods; the gap is
44 largest for attention-based selection (which is query-dependent by construction). 70 largest for attention-based selection (which is query-dependent by construction).
45−- **H8** A lossless transport codec (A7, zstd) on text yields >= 2x byte savings with < 5 ms overhead but **no** change in receiver 71+- **H8** A lossless transport codec (A7, zstd) on text yields >= 2x byte savings with < 5 ms
72+ overhead but **no** change in receiver
46 latency or quality; on bf16 KV tensors the codec yields < 1.3x (high-entropy payload). 73 latency or quality; on bf16 KV tensors the codec yields < 1.3x (high-entropy payload).
47−- **H9** The adaptive router (E8) will not reliably beat the best development-selected static method on held-out data once 74+- **H9** The adaptive router (E8) will not reliably beat the best development-selected
75+ static method on held-out data once
48 overhead is included (we expect a null or small, uncertain gain). 76 overhead is included (we expect a null or small, uncertain gain).
49−- **H10** Cross-family cache transfer without a trained mapper is not possible; with a mapper trained under our budget (if feasible), 77+- **H10** Cross-family cache transfer without a trained mapper is not possible; with a
50− quality is below the receiver's own A4 summary baseline (we expect learned transfer to lose to text at our scale). 78+ mapper trained under our budget (if feasible),
79+ quality is below the receiver's own A4 summary baseline (we expect learned transfer to
80+ lose to text at our scale).
51 81
52 ## Primary endpoints (frozen at Phase 7 together with the matrix) 82 ## Primary endpoints (frozen at Phase 7 together with the matrix)
53 83
54−Per task family: exact-match / F1 (LongBench normalization) or numeric correctness with tolerance; operational success rate; 84+Per task family: exact-match / F1 (LongBench normalization) or numeric correctness with
55−serialized payload bytes; end-to-end wall time (defined boundaries, S6.1 of the brief); peak MLX memory. 85+ tolerance; operational success rate;
86+serialized payload bytes; end-to-end wall time (defined boundaries, ?6.1 of the brief); peak
87+ MLX memory.
56 88
57 ## Primary confirmatory comparison family (Holm-corrected; everything else exploratory) 89 ## Primary confirmatory comparison family (Holm-corrected; everything else exploratory)
58 90
@@ -67,4 +99,46 @@ serialized payload bytes; end-to-end wall time (defined boundaries, S6.1 of the
67 99
68 (none yet) 100 (none yet)
69 101
102+- **A-001 (2026-10-30, before freeze, after pilot on dev only).** Core matrix bounded for
103+ run time: 40 test documents per family (synthetic and
104+ exec = all; public = first 40 in manifest order), 5 questions per synthetic document,
105+ question-aware A4/A5 limited to the first 3 questions
106+ of synthetic documents only; one sender budget (512 tokens) and one retention ratio (1/4)
107+ in the registered comparisons; B2 at 8 and 4 bits
108+ (2-bit dropped from the core: pilot showed total failure on Qwen and no additional
109+ information). Campaign sharded by model pair across two
110+ machines (D-015). Track C uses the ridge mapper v2 (D-016). None of these changes used
111+ test outcomes.
112+- **A-002 (2026-10-30).** Pilot observation recorded without changing hypotheses: on dev, A5
113+ (structured) was *below* A4 (summary) at the same
114+ budget; H1/T1 are kept as registered and may be rejected on test.
115+- **A-003 (2026-10-30 06:40 UTC, after test outcomes of the Qwen shard were examined).**
116+ Non-scientific code changes after the freeze, listed
117+ for transparency: (i) `runner.py`/`chains.py`/`fanin.py`: `hardware_id` derived from the
118+ hostname instead of a laptop constant (records
119+ produced on cluster nodes before the fix are relabelled by
120+ `scripts/collect_remote_results.sh`); (ii) `reproduce.py`: trial keys of the
121+ reproduction use the frozen protocol hash; (iii) `router.py`/`router_eval.py`: the static
122+ baseline for the question-aware regime is the
123+ dev-best *aware-eligible* action (`A3|512|aware` on dev) instead of the blind dev-best
124+ (`A1`), and an explicit `B5-aware` static baseline was
125+ added -- the router's own decision rule, feature set, cost column, tau and dev-fitted
126+ table are unchanged. None of these changes alters any
127+ trial key, prompt, budget, scorer, dataset, or hypothesis; `protocol/frozen.json` keeps
128+ the v1 hash used in all trial keys, and the
129+ post-freeze file hashes are recorded in `protocol/frozen_v1.1.json` (the campaign configs
130+ differ from v1 only by the injected `protocol_hash` field, written immediately after the
131+ freeze).
132+- **A-004 (2026-10-30 08:40 UTC, EXPLORATORY, after core outcomes were examined).** The
133+ 5-question core limit (A-001) kept only the first five
134+ generator questions per synthetic document (2x single fact, 2x exact copy, 1x multi-hop),
135+ so H1's negation/revision/aggregation/unanswerable
136+ types were not covered by the confirmatory run. An exploratory extension `E7_qtypes` runs
137+ questions 6-15 of the 40 synthetic test documents
138+ for A1, A3-blind, A4-blind, A5-blind, B2-8bit, B3 and B5-aware on both pairs. It is
139+ labelled exploratory everywhere; no confirmatory test
140+ is attached to it. Also recorded: A8 (LLMLingua-2) failed with `implementation_error` on
141+ M3U96a for the Llama pair (no torch in that
142+ environment); those 240 trials are retried on the laptop as new attempts
143+ (`core_llama_a8_retry.yaml`), both attempts kept.
70 \end{verbatim}\end{footnotesize} 144 \end{verbatim}\end{footnotesize}
modified paper/appendix/prompts_verbatim.tex +33 −18
@@ -1,34 +1,49 @@
1 % auto-generated from src/wp30/prompts.py 1 % auto-generated from src/wp30/prompts.py
2 \begin{footnotesize}\begin{verbatim} 2 \begin{footnotesize}\begin{verbatim}
3−"""Prompt templates (frozen at Phase 7). Receiver prompts are identical across methods except for the message slot.""" 3+"""Prompt templates (frozen at Phase 7). Receiver prompts are identical across methods
4−SYSTEM_RECEIVER = ("You are a careful assistant. Answer the question using ONLY the provided material. " 4+ except for the message slot."""
5+SYSTEM_RECEIVER = ("You are a careful assistant. Answer the question using ONLY the provided
6+ material. "
5 "If the material does not contain the answer, reply exactly: unknown.") 7 "If the material does not contain the answer, reply exactly: unknown.")
6−SYSTEM_RECEIVER_NOMSG = ("You are a careful assistant. Answer the question. If you do not know, reply exactly: unknown.") 8+SYSTEM_RECEIVER_NOMSG = ("You are a careful assistant. Answer the question. If you do not
9+ know, reply exactly: unknown.")
7 10
8−def receiver_user_text(material_label: str, material: str, question: str, family: str) -> str: 11+def receiver_user_text(material_label: str, material: str, question: str, family: str) ->
12+ str:
9 if family == "exec": 13 if family == "exec":
10− return f"{material_label}:\\n{material}\\n\\nTask: {question}" 14+ return f"{material_label}:\n{material}\n\nTask: {question}"
11 fmt = "Give a short answer (a few words or a number), with no explanation." 15 fmt = "Give a short answer (a few words or a number), with no explanation."
12− return f"{material_label}:\\n{material}\\n\\nQuestion: {question}\\n{fmt}" 16+ return f"{material_label}:\n{material}\n\nQuestion: {question}\n{fmt}"
13 17
14 def receiver_question_only(question: str, family: str) -> str: 18 def receiver_question_only(question: str, family: str) -> str:
15 if family == "exec": 19 if family == "exec":
16 return f"Task: {question}" 20 return f"Task: {question}"
17− return f"Question: {question}\\nGive a short answer (a few words or a number), with no explanation." 21+ return f"Question: {question}\nGive a short answer (a few words or a number), with no
22+ explanation."
18 23
19 # Sender prompts (question-blind: no question shown; question-aware: question appended) 24 # Sender prompts (question-blind: no question shown; question-aware: question appended)
20−SUMMARY_SYSTEM = "You are a precise note-taker. You write faithful summaries that preserve exact names, identifiers, numbers, units, dates, statuses and negations." 25+SUMMARY_SYSTEM = "You are a precise note-taker. You write faithful summaries that preserve
26+ exact names, identifiers, numbers, units, dates, statuses and negations."
21 def summary_user(doc: str, budget_tokens: int, question: str | None) -> str: 27 def summary_user(doc: str, budget_tokens: int, question: str | None) -> str:
22− q = f"\\nThe reader will need to answer this question: {question}\\n" if question else "\\nThe reader's future questions are unknown; preserve every concrete fact (who, what, where, when, how much, status).\\n" 28+ q = f"\nThe reader will need to answer this question: {question}\n" if question else
23− return (f"Summarize the following document in at most {budget_tokens} tokens (about {int(budget_tokens*0.75)} words). Copy identifiers, " 29+ "\nThe reader's future questions are unknown; preserve every concrete fact (who, what,
24− f"numbers, dates and names exactly as written. Mark explicitly what is NOT stated if relevant.{q}\\nDocument:\\n{doc}\\n\\nSummary:") 30+ where, when, how much, status).\n"
31+ return (f"Summarize the following document in at most {budget_tokens} tokens (about
32+ {int(budget_tokens*0.75)} words). Copy identifiers, "
33+ f"numbers, dates and names exactly as written. Mark explicitly what is NOT
34+ stated if relevant.{q}\nDocument:\n{doc}\n\nSummary:")
25 35
26−EVIDENCE_SYSTEM = ("You extract structured evidence. Output ONLY a JSON object with a key \\"facts\\": a list of objects " 36+EVIDENCE_SYSTEM = ("You extract structured evidence. Output ONLY a JSON object with a key
27− "{\\"entity\\": str, \\"attribute\\": str, \\"value\\": str, \\"quote\\": str} where quote is a short EXACT substring of the document " 37+ \"facts\": a list of objects "
28− "supporting the fact, and optionally a key \\"notes\\": a short string for anything important that does not fit the schema, " 38+ "{\"entity\": str, \"attribute\": str, \"value\": str, \"quote\": str}
29− "and a key \\"unknowns\\": list of things the document explicitly leaves unstated. Copy values verbatim.") 39+ where quote is a short EXACT substring of the document "
40+ "supporting the fact, and optionally a key \"notes\": a short string for
41+ anything important that does not fit the schema, "
42+ "and a key \"unknowns\": list of things the document explicitly leaves
43+ unstated. Copy values verbatim.")
30 def evidence_user(doc: str, budget_tokens: int, question: str | None) -> str: 44 def evidence_user(doc: str, budget_tokens: int, question: str | None) -> str:
31− q = f"\\nThe reader will need to answer this question: {question}\\n" if question else "\\nThe reader's future questions are unknown; capture every concrete fact.\\n" 45+ q = f"\nThe reader will need to answer this question: {question}\n" if question else
32− return f"Extract structured evidence from the document. Keep the JSON under {budget_tokens} tokens.{q}\\nDocument:\\n{doc}\\n\\nJSON:" 46+ "\nThe reader's future questions are unknown; capture every concrete fact.\n"
33− 47+ return f"Extract structured evidence from the document. Keep the JSON under
48+ {budget_tokens} tokens.{q}\nDocument:\n{doc}\n\nJSON:"
34 \end{verbatim}\end{footnotesize} 49 \end{verbatim}\end{footnotesize}
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modified paper/sections/abstract_text.tex +21 −1
@@ -1 +1,21 @@
1−\newcommand{\WPabstract}{Abstract to be written after the results.} 1+\newcommand{\WPabstract}{%
2+Agents built on language models hand information to one another as text, summaries, structured evidence, shared-memory references or---when
3+they share a checkpoint---attention state (KV cache). We ask under which task, model, cache, network and reuse conditions each representation
4+preserves task-relevant information at the lowest end-to-end cost, and answer with a reproducible study executed by an autonomous research
5+operator (Claude Code) on Apple-Silicon hardware with the MLX runtime, whose caches are inspectable tensors. Twenty-two conditions (text
6+truncation, extractive selection, summaries, structured evidence with spans, references with metered retrieval, a lossless codec, LLMLingua-2,
7+native and serialized caches, packed 8/4-bit quantization, sink-plus-recent, random, SnapKV-style and PyramidKV-style eviction) are measured on
8+Qwen2.5-3B and Llama-3.2-3B across private-fact ledgers, LongBench QA and executable tasks, in question-blind and question-aware regimes, with
9+measured bytes, synchronized latency spans and peak memory (13\,280 trials; frozen protocol, cluster bootstrap, Holm-corrected tests). Further
10+experiments measure two-node cache transfer over a 1~GbE LAN (324 runs), relays of 1--8 handoffs with an evaluator-only fact ledger (7\,200),
11+fan-in with conflicting evidence (1\,600), a same-family learned KV mapper, hosted receivers (672) and a development-fitted router. Lossless
12+representations are interchangeable in quality and differ only in cost, whose ranking inverts between cold and reused regimes; on the measured
13+link, moving an 8-bit cache beats re-reading the text at every length. Lossy choices are model- and regime-dependent: 4-bit quantization is free
14+for Llama and fatal for Qwen; attention-based eviction keeps 78--98\,\% of private-fact accuracy with the question and 12--19\,\% without it, and
15+no eviction survives a code specification. At a 512-token budget prose summaries beat structured evidence (38--44 vs 12--15\,\%), relays are
16+nearly lossless after the first hop and invent almost nothing, and metered repair buys a few points for kilobytes of re-reading. A ridge KV mapper
17+transfers no content while producing coherent abstention, and an invalid cache concatenation yields plausible answers---both argue for
18+compatibility manifests and negative controls. The router halves amortized latency at equal quality when questions are unknown and does not help
19+otherwise. Cross-family cache transfer was not executed; the study is partial in that respect. Code, records, protocol, checkpoint and a
20+minimal reproduction (64/64 identical outputs) are released.%
21+}
modified paper/sections/conclusion.tex +21 −1
@@ -1,3 +1,23 @@
1 \section{Conclusion} 1 \section{Conclusion}
2 \label{sec:conclusion} 2 \label{sec:conclusion}
3−To be written from results. 3+We asked under which conditions each way of passing information between model executions preserves what matters at the lowest measured cost,
4+and we answered with a single harness on commodity hardware, real cache tensors, measured bytes and synchronized clocks, released records for
5+every attempt, and a protocol frozen before the test split was touched. The bounded findings are these. (1) Lossless representations are
6+interchangeable: text, its zstd encoding, the native cache, the reloaded cache and the 8-bit packed cache give identical answers; the choice
7+among them is a pure cost question, and the cost ranking flips between the cold case (text wins: no build) and the reused case (cache wins:
8+no prefill), with a 1~GbE link and a 3B model already favouring moving an 8-bit cache over re-reading at two thousand tokens.
9+(2) Every lossy choice we measured is model- or regime-dependent: 4-bit per-token quantization is free for Llama-3.2-3B and fatal for
10+Qwen2.5-3B; attention-based eviction is excellent with the question and useless without it, and no eviction survives a code specification;
11+LLMLingua-2 drops exactly the identifiers private-fact questions ask for. (3) At a 512-token sender budget, prose summaries carried more than
12+structured evidence with provenance, in single handoffs, in eight-hop relays and in fan-in, because provenance is paid in the sender's tokens;
13+relays themselves were nearly lossless after the first hop and invented almost nothing, and metered repair bought a few points for kilobytes of
14+re-reading. (4) A same-family linear KV mapper that reconstructs most of the receiver's cache variance transfers no content, and produces the
15+most dangerous kind of failure---coherent abstention---while an invalid cache concatenation produced plausible answers; compatibility manifests
16+and negative controls are therefore part of any cache-transfer protocol, not an afterthought. (5) A router fitted on development data matched
17+the best static policy at half the amortized latency when questions were unknown and offered nothing when they were known.
18+
19+Open questions follow directly. The structured-evidence result should be re-run with budgets that separate provenance from values and with
20+senders large enough to fill them; the break-even map needs a faster receiver and a slower link to locate its crossing; the transfer ladder's
21+cross-family rung needs a trained mapper with generation and distillation terms, and its evaluation must keep the sender-alone and
22+receiver-alone references and an identity control beside it; and the fixed-point behaviour of structured relays suggests that the first
23+message, not the chain, is where compression research for multi-agent systems should concentrate.
modified paper/sections/results_transfer.tex +28 −1
@@ -1 +1,28 @@
1−% pending 1+\subsection{Cross-checkpoint transfer: the same-family linear mapper (E3)}
2+\label{sec:transfer}
3+Table~\ref{tab:transfer} reports the compatibility ladder's second rung on the synthetic (27 documents of 2k and 8k tokens, 8 questions each) and
4+executable (40) test items: the receiver Qwen2.5-3B reading the document itself (A1), the sender Qwen2.5-1.5B's own answers with full context
5+(sender alone), two text handoffs from the 1.5B sender (A4, A5), the identity-transfer negative control (C0: sender cache fed to the receiver
6+without mapping) and the ridge mapper (C2) alone and followed by question-aware selection (C3). The mapper was fitted on 60 training documents
7+(248\,569 token positions, 28.3~M parameters, 483~s on M3U96b, held-out reconstruction error $0.10$ for keys and $0.32$ for values, relative
8+Frobenius; \texttt{runs/checkpoints}); it needs the sender's cache and nothing else at the receiver (no source text, no prefill).
9+
10+\begin{table}[t]\centering\scriptsize
11+\caption{Same-family transfer Qwen2.5-1.5B $\to$ Qwen2.5-3B (test split; M3U96a). Accuracy (\%, 95\,\% cluster-bootstrap CI), share of answers
12+containing ``unknown'', accuracy by question type, median payload and warm per-query latency. Negation questions are yes/no.}\label{tab:transfer}
13+\adjustbox{max width=\textwidth}{\input{tables/table_transfer}}
14+\end{table}
15+
16+\paragraph{The mapped cache is well-formed but empty (H10 confirmed).} The receiver generates fluent, on-task text from the mapped cache---it
17+answers ``unknown'' 69\,\% of the time and never produces the garbled tokens of the identity control---but it recovers essentially none of the
18+content: 0\,\% on multi-hop, 2--4\,\% on single facts and exact copies, 0\,\% on code. Its aggregate score of 24.5\,\% comes from the yes/no negation
19+questions (93\,\%), which a receiver that answers ``no'' by default gets right at the base rate (70\,\% of projects are not cancelled); the sender's
20+own full-context answers show that the facts were available on the sender side (96\,\%). The 1.5B sender's prose summary transfers more
21+(28\,\%, 72\,\% on negation, 9--20\,\% on content) and its structured extraction less (12\,\%); the paired difference between the mapper and the
22+summary is $-3.7$ points (95\,\% CI $[-11.6, +4.2]$, $p=0.37$), and between the mapper and the receiver's own reading $-75$ points. Selection on
23+the mapped cache (C3) changes nothing. A linear map that explains 68\,\% of the receiver's value variance on held-out documents is therefore
24+not enough to carry a single identifier across checkpoints: cross-checkpoint transfer needs the generation and distillation terms that
25+published mappers train with, at a cost we did not have; the measured by-product is that a \emph{wrong} cache can be indistinguishable from an
26+empty one in the receiver's behaviour (coherent abstention), which is why Section~\ref{sec:fanin}'s manifests matter. Rungs 1 (same checkpoint,
27+two nodes) and 3 (cross-family) are covered by Section~\ref{sec:network} and the coverage table respectively; the cross-family rung was not
28+executed.
added paper/tables/table_failures.tex +23 −0
@@ -0,0 +1,23 @@
1+\begin{tabular}{@{}lrrr@{}}
2+\toprule
3+method & implementation\_error & invalid\_output & success \\
4+\midrule
5+A0 & 0 & 0 & 640 \\
6+A1 & 0 & 0 & 640 \\
7+A2 & 0 & 0 & 640 \\
8+A3 & 0 & 0 & 1280 \\
9+A4 & 0 & 0 & 880 \\
10+A5 & 0 & 0 & 880 \\
11+A6 & 0 & 0 & 640 \\
12+A7 & 0 & 0 & 640 \\
13+A8 & 240 & 0 & 640 \\
14+B0 & 0 & 0 & 640 \\
15+B1 & 0 & 0 & 640 \\
16+B2 & 0 & 29 & 1251 \\
17+B3 & 0 & 0 & 640 \\
18+B4 & 0 & 45 & 595 \\
19+B5 & 0 & 0 & 1280 \\
20+B6 & 0 & 0 & 640 \\
21+B9 & 0 & 0 & 640 \\
22+\bottomrule
23+\end{tabular}
modified paper/tables/table_primary.tex +1 −1
@@ -13,7 +13,7 @@ A5 512 aware & 100 [100,100] & 95 [91,98] & -- & -- & -- & -- & -- & -- \\
13 A5 512 blind & 15 [12,18] & 12 [9,16] & 12 [2,22] & 0 [0,0] & 9 [2,18] & 5 [1,11] & 12 [4,23] & 6 [0,13] \\ 13 A5 512 blind & 15 [12,18] & 12 [9,16] & 12 [2,22] & 0 [0,0] & 9 [2,18] & 5 [1,11] & 12 [4,23] & 6 [0,13] \\
14 A6 512 blind & 18 [14,22] & 18 [14,22] & 22 [10,35] & 15 [5,28] & 9 [2,17] & 7 [2,13] & 11 [2,22] & 10 [2,19] \\ 14 A6 512 blind & 18 [14,22] & 18 [14,22] & 22 [10,35] & 15 [5,28] & 9 [2,17] & 7 [2,13] & 11 [2,22] & 10 [2,19] \\
15 A7 zstd & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 39 [25,54] & 33 [20,47] \\ 15 A7 zstd & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 39 [25,54] & 33 [20,47] \\
16−A8 512 blind & 14 [9,18] & 0 [0,0] & 0 [0,0] & 0 [0,0] & 17 [10,25] & 23 [13,33] & 6 [0,14] & 8 [0,18] \\ 16+A8 512 blind & 14 [9,18] & 16 [10,22] & 0 [0,0] & 0 [0,0] & 17 [10,25] & 23 [13,33] & 6 [0,14] & 8 [0,18] \\
17 B0 & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 36 [22,50] & 33 [20,47] \\ 17 B0 & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 36 [22,50] & 33 [20,47] \\
18 B1 & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 36 [22,50] & 33 [20,47] \\ 18 B1 & 99 [98,100] & 99 [98,100] & 100 [100,100] & 95 [88,100] & 35 [24,47] & 25 [15,36] & 36 [22,50] & 33 [20,47] \\
19 B2 bits=4 & 0 [0,0] & 98 [94,100] & 0 [0,0] & 100 [100,100] & 1 [0,2] & 25 [15,36] & 0 [0,1] & 22 [10,34] \\ 19 B2 bits=4 & 0 [0,0] & 98 [94,100] & 0 [0,0] & 100 [100,100] & 1 [0,2] & 25 [15,36] & 0 [0,1] & 22 [10,34] \\
added paper/tables/table_qtype_core.tex +28 −0
@@ -0,0 +1,28 @@
1+\begin{tabular}{@{}lrrr@{}}
2+\toprule
3+condition & exact\_copy & multi\_hop & single\_fact \\
4+\midrule
5+A0 & 0 [0,0] & 0 [0,0] & 0 [0,0] \\
6+A1 & 99 [98,100] & 98 [94,100] & 99 [98,100] \\
7+A2 512 & 14 [8,20] & 20 [8,32] & 26 [18,35] \\
8+A3 512 aware & 99 [98,100] & 92 [86,98] & 100 [100,100] \\
9+A3 512 blind & 51 [50,52] & 94 [88,99] & 0 [0,0] \\
10+A4 512 aware & 79 [69,88] & -- & 94 [90,98] \\
11+A4 512 blind & 45 [38,53] & 46 [35,57] & 34 [24,45] \\
12+A5 512 aware & 98 [94,100] & -- & 98 [96,99] \\
13+A5 512 blind & 13 [8,18] & 11 [4,21] & 16 [10,22] \\
14+A6 512 blind & 15 [9,21] & 16 [6,26] & 21 [14,29] \\
15+A7 zstd & 99 [98,100] & 98 [94,100] & 99 [98,100] \\
16+A8 512 blind & 10 [5,16] & 5 [1,10] & 24 [16,34] \\
17+B0 & 99 [98,100] & 98 [94,100] & 99 [98,100] \\
18+B1 & 99 [98,100] & 98 [94,100] & 99 [98,100] \\
19+B2 bits=4 & 49 [47,50] & 49 [46,50] & 49 [47,50] \\
20+B2 bits=8 & 99 [98,100] & 98 [94,100] & 99 [98,100] \\
21+B3 1/4 & 31 [23,38] & 12 [2,22] & 11 [5,18] \\
22+B4 1/4 & 0 [0,0] & 0 [0,0] & 0 [0,0] \\
23+B5 1/4 aware & 97 [94,99] & 82 [76,90] & 82 [79,86] \\
24+B5 1/4 blind & 20 [13,27] & 15 [8,22] & 11 [6,17] \\
25+B6 1/4 aware & 92 [88,96] & 78 [69,85] & 74 [71,76] \\
26+B9 bits=8 1/4 att aware & 97 [94,99] & 84 [78,90] & 82 [79,86] \\
27+\bottomrule
28+\end{tabular}
added paper/tables/table_qtypes_exploratory.tex +20 −0
@@ -0,0 +1,20 @@
1+\begin{tabular}{@{}lrrrrrrr@{}}
2+\toprule
3+condition / pair & aggregation & multi\_hop & negation & revision & temporal & unanswerable & unit\_conversion \\
4+\midrule
5+A1 Llama-3.2-3B & 0 [0,0] & 100 [100,100] & 90 [80,100] & 73 [58,88] & 47 [20,73] & 100 [100,100] & 0 [0,0] \\
6+A1 Qwen2.5-3B & 0 [0,0] & 100 [100,100] & 100 [100,100] & 94 [84,100] & 44 [22,69] & 97 [91,100] & 50 [25,75] \\
7+A3 512 blind Llama-3.2-3B & 0 [0,0] & 0 [0,0] & 83 [70,93] & 73 [57,87] & 53 [33,73] & 100 [100,100] & 0 [0,0] \\
8+A3 512 blind Qwen2.5-3B & 0 [0,0] & 0 [0,0] & 93 [83,100] & 73 [53,90] & 33 [13,53] & 100 [100,100] & 0 [0,0] \\
9+A4 512 blind Llama-3.2-3B & 0 [0,0] & 33 [7,60] & 100 [100,100] & 17 [3,33] & 20 [0,40] & 100 [100,100] & 0 [0,0] \\
10+A4 512 blind Qwen2.5-3B & 0 [0,0] & 44 [19,69] & 72 [53,88] & 26 [13,43] & 33 [13,53] & 100 [100,100] & 40 [13,67] \\
11+A5 512 blind Llama-3.2-3B & 0 [0,0] & 13 [0,33] & 50 [37,63] & 13 [0,30] & 13 [0,33] & 100 [100,100] & 0 [0,0] \\
12+A5 512 blind Qwen2.5-3B & 0 [0,0] & 20 [7,40] & 53 [37,70] & 10 [0,27] & 0 [0,0] & 100 [100,100] & 20 [0,40] \\
13+B2 bits=8 Llama-3.2-3B & 0 [0,0] & 100 [100,100] & 90 [80,100] & 73 [58,88] & 47 [20,73] & 100 [100,100] & 0 [0,0] \\
14+B2 bits=8 Qwen2.5-3B & 0 [0,0] & 100 [100,100] & 100 [100,100] & 93 [83,100] & 40 [13,67] & 97 [90,100] & 60 [33,87] \\
15+B3 1/4 Llama-3.2-3B & 0 [0,0] & 20 [0,40] & 73 [60,83] & 7 [0,17] & 53 [27,73] & 93 [80,100] & 0 [0,0] \\
16+B3 1/4 Qwen2.5-3B & 0 [0,0] & 20 [0,40] & 87 [73,97] & 0 [0,0] & 53 [27,73] & 97 [90,100] & 7 [0,20] \\
17+B5 1/4 aware Llama-3.2-3B & 0 [0,0] & 100 [100,100] & 87 [77,97] & 73 [62,87] & 60 [40,80] & 100 [100,100] & 0 [0,0] \\
18+B5 1/4 aware Qwen2.5-3B & 0 [0,0] & 53 [27,80] & 100 [100,100] & 30 [17,43] & 53 [27,80] & 97 [90,100] & 47 [20,73] \\
19+\bottomrule
20+\end{tabular}
added paper/tables/table_transfer.tex +22 −0
@@ -0,0 +1,22 @@
1+\begin{tabular}{@{}llrrrrrrrrrr@{}}
2+\toprule
3+family & condition & n & accuracy (\\%) & `unknown' share & single\_fact & exact\_copy & multi\_hop & negation & exec & payload B & warm ms \\
4+\midrule
5+exec & A1 & 40 & 100 [100,100] & 0.00 & -- & -- & -- & -- & 1.00 & 7,817.00 & 1,912.17 \\
6+exec & A4|512|blind & 40 & 0 [0,0] & 0.28 & -- & -- & -- & -- & 0.00 & 938.00 & 1,025.84 \\
7+exec & A5|512|blind & 40 & 0 [0,0] & 0.85 & -- & -- & -- & -- & 0.00 & 936.00 & 215.30 \\
8+exec & C0 & 40 & 0 [0,0] & 0.00 & -- & -- & -- & -- & 0.00 & 69,762,785 & 3,979.97 \\
9+exec & C2 & 40 & 0 [0,0] & 0.00 & -- & -- & -- & -- & 0.00 & 69,762,886 & 1,098.63 \\
10+exec & C3|1/4|aware & 40 & 0 [0,0] & 0.00 & -- & -- & -- & -- & 0.00 & 17,452,748 & 939.02 \\
11+exec & sender alone A1 & 40 & 0 [0,0] & 0.00 & -- & -- & -- & -- & 0.00 & 7,817.00 & 909.11 \\
12+exec & sender alone B0 & 40 & 0 [0,0] & 0.00 & -- & -- & -- & -- & 0.00 & 54,260,105 & 621.42 \\
13+synthetic & A1 & 216 & 100 [99,100] & 0.00 & 1.00 & 1.00 & 0.98 & 1.00 & -- & 7,555.00 & 890.79 \\
14+synthetic & A4|512|blind & 216 & 28 [22,35] & 0.64 & 0.09 & 0.20 & 0.11 & 0.72 & -- & 1,263.00 & 250.39 \\
15+synthetic & A5|512|blind & 216 & 12 [7,16] & 0.73 & 0.07 & 0.09 & 0.07 & 0.22 & -- & 596.00 & 166.89 \\
16+synthetic & C0 & 216 & 0 [0,0] & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & -- & 77,688,549 & 632.53 \\
17+synthetic & C2 & 216 & 25 [22,27] & 0.69 & 0.02 & 0.04 & 0.00 & 0.93 & -- & 77,688,650 & 85.57 \\
18+synthetic & C3|1/4|aware & 216 & 25 [23,27] & 0.69 & 0.04 & 0.02 & 0.00 & 0.94 & -- & 19,443,415 & 56.23 \\
19+synthetic & sender alone A1 & 216 & 96 [94,98] & 0.00 & 0.98 & 0.98 & 0.94 & 0.93 & -- & 7,555.00 & 448.40 \\
20+synthetic & sender alone B0 & 216 & 95 [93,98] & 0.00 & 0.98 & 0.98 & 0.94 & 0.91 & -- & 60,424,589 & 104.16 \\
21+\bottomrule
22+\end{tabular}
added results/claim_ledger.csv +21 −0
@@ -0,0 +1,21 @@
1+claim_id,claim,experiment_ids,evidence_table_or_figure,rows_or_filter,script,status,limitation
2+C01,"Lossless representations (A1, A7, B0, B1, B2-8bit) give the same quality within noise on every family and pair",E1_core,"results/tables/E1_core_primary_tests.csv (T2, T6); table_primary","rows T2,T6",analyze.py,confirmed,"two 3B pairs, 160 test documents"
3+C02,4-bit packed KV quantization destroys Qwen2.5-3B (0%) but not Llama-3.2-3B (>=97%),E1_core,E1_core_primary.csv B2|bits=4; primary_tests T3,condition B2|bits=4,analyze.py,confirmed (H2),"mlx per-token affine quantization, group 64"
4+C03,Attention-informed eviction with the question (B5-aware) beats sink+recent at 1/4 by +44 points; blind window variant collapses,E1_core,primary_tests T4; E1_core_primary.csv,B5 vs B3,analyze.py,"confirmed (H3 first half, H7)",question-aware method
5+C04,All eviction methods score 0 on executable tasks at 1/4 retention,E1_core,E1_core_primary.csv family=exec,"B3,B4,B5,B6,B9",analyze.py,measured,"3B receivers, 6-rule spec"
6+C05,"Structured evidence (A5) is below prose summary (A4) at the same 512-token budget (-27 points, synthetic)",E1_core,primary_tests T1,T1,analyze.py,H1 rejected,sender spends budget on quotes; ~8 facts of 62
7+C06,zstd shrinks text ~3.5x with no quality/latency effect; ~20% on bf16 caches,"E1_core, E6_core",E1_core_primary.csv A7; E6_network.csv codec=zstd,A7; B1 zstd,analyze.py,confirmed (H8),zstd level 3
8+C07,Serialized 3B cache is 3-4 orders of magnitude larger than the text,"E1_core, E6_core",E1_core_primary.csv payload_bytes_median; E6_network.csv bytes,B0 vs A1,analyze.py,measured,"bf16, Qwen 215 MB vs 29 kB at ~8k tokens"
9+C08,"On 1 GbE with an M3 Ultra receiver, moving the cache (bf16 or 8-bit) answers faster than remote text prefill at 2k-16k tokens",E6_core,E6_network.csv; fig6,lan rows,analyze.py,measured,"one link, one receiver; break-even depends on prefill/link ratio"
10+C09,Repeated prose/structured relays lose facts at hop 1 and are nearly lossless afterwards (no monotone decay),E4_core,E4_core_handoffs.csv; fig5,hops 1-8,analyze.py,H5 rejected,ledger metric = normalized value containment
11+C10,"Unsupported additions are near zero in 7,200 relayed messages",E4_core,E4_core_handoffs.csv unsupported_mean,all chains,analyze.py,measured,regex-based detection
12+C11,Metered repair raises prose-chain retention 32->38% and accuracy 40->44% for ~5.9 kB cumulative fetch,E4_core,E4_core_handoffs.csv repairable,A4 repairable,analyze.py,H6 partial,repair limited to entities still named
13+C12,Concatenated summaries compose as well as full context on multi-hop joins; merged structured records do not,E5_core,E5_core_fanin.csv; table_fanin,concat_A4 vs merge_A5,analyze.py,measured,"k=2,4; 3B aggregator"
14+C13,Cache concatenation of independently prefilled sources yields plausible answers (70% multi-hop at k=2) despite invalid positions,E5_core,E5_core_fanin.csv cache_concat_negative_control,"k=2,4",analyze.py,negative control,diagnostic only
15+C14,"Hosted receivers recover 100% from full text, ~30% from the local 512-token summary, 12.5% from structured JSON",ED_hosted_core,ED_hosted.csv; table_hosted,synthetic rows,analyze.py,measured,provider default sampling; 8 documents/family
16+C15,"Anthropic prompt caching did not engage below the documented 4,096-token minimum (2k docs) and halved estimated cost on 8k docs",ED_hosted_core,runs/raw/ED_hosted_core.jsonl provider_cached_usage,A1 provider_cache rows,hosted_run.py,measured,billing fields only
17+C16,Dev-fitted router matches best static quality at half the amortized latency in the blind regime; no gain and higher cost in the aware regime,E8_router (E1_core records),E8_router_summary.csv; E8_router_tests.csv; fig7,router vs best_static_dev,router_eval.py,H9 mixed,off-policy on full factorial; dev table from pilot
18+C17,Linear ridge KV mapper (Qwen 1.5B->3B) yields coherent but uninformative receiver outputs; below sender-summary transfer,E3_core,E3_core tables,C2 vs A4,analyze.py,confirmed (H10): C2 24.5% overall but 0-4% on content questions; negation yes/no at base rate,reconstruction-only mapper; no SwaT reproduction
19+C18,Identity round trip and 100% retention reproduce native logits exactly; cached-prefix equivalence holds within registered tolerance,E0 tests,tests/test_cache_correctness.py,12 tests,pytest,confirmed,MLX nondeterminism across chunking documented
20+C19,Minimal reproduction reran 64 trials from a clean process with identical outputs and scores,E9,reproduction/report.json,all,reproduce.py,confirmed,"same machine, same chunking"
21+C20,Cross-family cache transfer was not executed (no SwaT checkpoint/licence; mapper budget),coverage,literature/audit_group1.md; table1_taxonomy,SwaT row,n/a,coverage gap,partial transfer study
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modified results/provenance.json +67 −25
@@ -4,7 +4,7 @@
4 "runs/raw/E1_core.jsonl" 4 "runs/raw/E1_core.jsonl"
5 ], 5 ],
6 "script": "src/wp30/analysis/analyze.py", 6 "script": "src/wp30/analysis/analyze.py",
7− "generated_utc": "2026-10-30T08:41:10.888608Z", 7+ "generated_utc": "2026-10-30T09:02:37.609345Z",
8 "note": "per-condition quality (cluster bootstrap CI) and resource medians" 8 "note": "per-condition quality (cluster bootstrap CI) and resource medians"
9 }, 9 },
10 "results/tables/E1_core_failures.csv": { 10 "results/tables/E1_core_failures.csv": {
@@ -12,7 +12,7 @@
12 "runs/raw/E1_core.jsonl" 12 "runs/raw/E1_core.jsonl"
13 ], 13 ],
14 "script": "src/wp30/analysis/analyze.py", 14 "script": "src/wp30/analysis/analyze.py",
15− "generated_utc": "2026-10-30T08:41:10.897925Z", 15+ "generated_utc": "2026-10-30T09:02:37.611126Z",
16 "note": "attempt status counts by method" 16 "note": "attempt status counts by method"
17 }, 17 },
18 "results/tables/E1_core_by_question_type.csv": { 18 "results/tables/E1_core_by_question_type.csv": {
@@ -20,7 +20,7 @@
20 "runs/raw/E1_core.jsonl" 20 "runs/raw/E1_core.jsonl"
21 ], 21 ],
22 "script": "src/wp30/analysis/analyze.py", 22 "script": "src/wp30/analysis/analyze.py",
23− "generated_utc": "2026-10-30T08:41:11.023256Z", 23+ "generated_utc": "2026-10-30T09:02:37.730657Z",
24 "note": "synthetic accuracy by question type" 24 "note": "synthetic accuracy by question type"
25 }, 25 },
26 "results/tables/E1_core_primary_tests.csv": { 26 "results/tables/E1_core_primary_tests.csv": {
@@ -28,7 +28,7 @@
28 "runs/raw/E1_core.jsonl" 28 "runs/raw/E1_core.jsonl"
29 ], 29 ],
30 "script": "src/wp30/analysis/analyze.py", 30 "script": "src/wp30/analysis/analyze.py",
31− "generated_utc": "2026-10-30T08:41:11.097268Z", 31+ "generated_utc": "2026-10-30T09:02:37.786568Z",
32 "note": "registered primary comparisons, paired, cluster bootstrap, Holm" 32 "note": "registered primary comparisons, paired, cluster bootstrap, Holm"
33 }, 33 },
34 "results/figures/fig2_quality_vs_bytes.pdf": { 34 "results/figures/fig2_quality_vs_bytes.pdf": {
@@ -36,7 +36,7 @@
36 "runs/raw/E1_core.jsonl" 36 "runs/raw/E1_core.jsonl"
37 ], 37 ],
38 "script": "src/wp30/analysis/analyze.py", 38 "script": "src/wp30/analysis/analyze.py",
39− "generated_utc": "2026-10-30T08:41:11.754908Z", 39+ "generated_utc": "2026-10-30T09:02:38.459020Z",
40 "note": "points = conditions; x = median payload bytes; y = mean quality" 40 "note": "points = conditions; x = median payload bytes; y = mean quality"
41 }, 41 },
42 "results/figures/fig2_quality_vs_bytes.png": { 42 "results/figures/fig2_quality_vs_bytes.png": {
@@ -44,7 +44,7 @@
44 "runs/raw/E1_core.jsonl" 44 "runs/raw/E1_core.jsonl"
45 ], 45 ],
46 "script": "src/wp30/analysis/analyze.py", 46 "script": "src/wp30/analysis/analyze.py",
47− "generated_utc": "2026-10-30T08:41:11.951700Z", 47+ "generated_utc": "2026-10-30T09:02:38.666585Z",
48 "note": "points = conditions; x = median payload bytes; y = mean quality" 48 "note": "points = conditions; x = median payload bytes; y = mean quality"
49 }, 49 },
50 "results/figures/figA_quality_vs_bytes_blind.pdf": { 50 "results/figures/figA_quality_vs_bytes_blind.pdf": {
@@ -52,7 +52,7 @@
52 "runs/raw/E1_core.jsonl" 52 "runs/raw/E1_core.jsonl"
53 ], 53 ],
54 "script": "src/wp30/analysis/analyze.py", 54 "script": "src/wp30/analysis/analyze.py",
55− "generated_utc": "2026-10-30T08:41:12.396339Z", 55+ "generated_utc": "2026-10-30T09:02:39.176959Z",
56 "note": "points = conditions; x = median payload bytes; y = mean quality" 56 "note": "points = conditions; x = median payload bytes; y = mean quality"
57 }, 57 },
58 "results/figures/figA_quality_vs_bytes_blind.png": { 58 "results/figures/figA_quality_vs_bytes_blind.png": {
@@ -60,7 +60,7 @@
60 "runs/raw/E1_core.jsonl" 60 "runs/raw/E1_core.jsonl"
61 ], 61 ],
62 "script": "src/wp30/analysis/analyze.py", 62 "script": "src/wp30/analysis/analyze.py",
63− "generated_utc": "2026-10-30T08:41:12.558258Z", 63+ "generated_utc": "2026-10-30T09:02:39.359044Z",
64 "note": "points = conditions; x = median payload bytes; y = mean quality" 64 "note": "points = conditions; x = median payload bytes; y = mean quality"
65 }, 65 },
66 "results/figures/figA_quality_vs_bytes_aware.pdf": { 66 "results/figures/figA_quality_vs_bytes_aware.pdf": {
@@ -68,7 +68,7 @@
68 "runs/raw/E1_core.jsonl" 68 "runs/raw/E1_core.jsonl"
69 ], 69 ],
70 "script": "src/wp30/analysis/analyze.py", 70 "script": "src/wp30/analysis/analyze.py",
71− "generated_utc": "2026-10-30T08:41:12.888916Z", 71+ "generated_utc": "2026-10-30T09:02:39.746284Z",
72 "note": "points = conditions; x = median payload bytes; y = mean quality" 72 "note": "points = conditions; x = median payload bytes; y = mean quality"
73 }, 73 },
74 "results/figures/figA_quality_vs_bytes_aware.png": { 74 "results/figures/figA_quality_vs_bytes_aware.png": {
@@ -76,7 +76,7 @@
76 "runs/raw/E1_core.jsonl" 76 "runs/raw/E1_core.jsonl"
77 ], 77 ],
78 "script": "src/wp30/analysis/analyze.py", 78 "script": "src/wp30/analysis/analyze.py",
79− "generated_utc": "2026-10-30T08:41:13.061312Z", 79+ "generated_utc": "2026-10-30T09:02:39.940182Z",
80 "note": "points = conditions; x = median payload bytes; y = mean quality" 80 "note": "points = conditions; x = median payload bytes; y = mean quality"
81 }, 81 },
82 "results/figures/fig3_quality_vs_latency_memory.pdf": { 82 "results/figures/fig3_quality_vs_latency_memory.pdf": {
@@ -84,7 +84,7 @@
84 "runs/raw/E1_core.jsonl" 84 "runs/raw/E1_core.jsonl"
85 ], 85 ],
86 "script": "src/wp30/analysis/analyze.py", 86 "script": "src/wp30/analysis/analyze.py",
87− "generated_utc": "2026-10-30T08:41:13.355100Z", 87+ "generated_utc": "2026-10-30T09:02:40.285980Z",
88 "note": "" 88 "note": ""
89 }, 89 },
90 "results/figures/fig3_quality_vs_latency_memory.png": { 90 "results/figures/fig3_quality_vs_latency_memory.png": {
@@ -92,7 +92,7 @@
92 "runs/raw/E1_core.jsonl" 92 "runs/raw/E1_core.jsonl"
93 ], 93 ],
94 "script": "src/wp30/analysis/analyze.py", 94 "script": "src/wp30/analysis/analyze.py",
95− "generated_utc": "2026-10-30T08:41:13.527311Z", 95+ "generated_utc": "2026-10-30T09:02:40.475124Z",
96 "note": "" 96 "note": ""
97 }, 97 },
98 "results/figures/fig4_latency_decomposition.pdf": { 98 "results/figures/fig4_latency_decomposition.pdf": {
@@ -100,7 +100,7 @@
100 "runs/raw/E1_core.jsonl" 100 "runs/raw/E1_core.jsonl"
101 ], 101 ],
102 "script": "src/wp30/analysis/analyze.py", 102 "script": "src/wp30/analysis/analyze.py",
103− "generated_utc": "2026-10-30T08:41:13.606317Z", 103+ "generated_utc": "2026-10-30T09:02:40.560198Z",
104 "note": "" 104 "note": ""
105 }, 105 },
106 "results/figures/fig4_latency_decomposition.png": { 106 "results/figures/fig4_latency_decomposition.png": {
@@ -108,7 +108,7 @@
108 "runs/raw/E1_core.jsonl" 108 "runs/raw/E1_core.jsonl"
109 ], 109 ],
110 "script": "src/wp30/analysis/analyze.py", 110 "script": "src/wp30/analysis/analyze.py",
111− "generated_utc": "2026-10-30T08:41:13.663220Z", 111+ "generated_utc": "2026-10-30T09:02:40.620070Z",
112 "note": "" 112 "note": ""
113 }, 113 },
114 "results/figures/fig5_handoffs.pdf": { 114 "results/figures/fig5_handoffs.pdf": {
@@ -116,7 +116,7 @@
116 "runs/raw/E4_core.jsonl" 116 "runs/raw/E4_core.jsonl"
117 ], 117 ],
118 "script": "src/wp30/analysis/analyze.py", 118 "script": "src/wp30/analysis/analyze.py",
119− "generated_utc": "2026-10-30T08:41:18.022870Z", 119+ "generated_utc": "2026-10-30T09:02:45.615826Z",
120 "note": "means over documents; chains defined in configs/chains*.yaml" 120 "note": "means over documents; chains defined in configs/chains*.yaml"
121 }, 121 },
122 "results/figures/fig5_handoffs.png": { 122 "results/figures/fig5_handoffs.png": {
@@ -124,7 +124,7 @@
124 "runs/raw/E4_core.jsonl" 124 "runs/raw/E4_core.jsonl"
125 ], 125 ],
126 "script": "src/wp30/analysis/analyze.py", 126 "script": "src/wp30/analysis/analyze.py",
127− "generated_utc": "2026-10-30T08:41:18.082563Z", 127+ "generated_utc": "2026-10-30T09:02:45.675774Z",
128 "note": "means over documents; chains defined in configs/chains*.yaml" 128 "note": "means over documents; chains defined in configs/chains*.yaml"
129 }, 129 },
130 "results/tables/E4_core_handoffs.csv": { 130 "results/tables/E4_core_handoffs.csv": {
@@ -132,7 +132,7 @@
132 "runs/raw/E4_core.jsonl" 132 "runs/raw/E4_core.jsonl"
133 ], 133 ],
134 "script": "src/wp30/analysis/analyze.py", 134 "script": "src/wp30/analysis/analyze.py",
135− "generated_utc": "2026-10-30T08:41:18.179059Z", 135+ "generated_utc": "2026-10-30T09:02:45.778336Z",
136 "note": "repeated handoffs" 136 "note": "repeated handoffs"
137 }, 137 },
138 "results/tables/E4_core_h4_primary_tests.csv": { 138 "results/tables/E4_core_h4_primary_tests.csv": {
@@ -140,7 +140,7 @@
140 "runs/raw/E4_core.jsonl" 140 "runs/raw/E4_core.jsonl"
141 ], 141 ],
142 "script": "src/wp30/analysis/analyze.py", 142 "script": "src/wp30/analysis/analyze.py",
143− "generated_utc": "2026-10-30T08:41:18.190264Z", 143+ "generated_utc": "2026-10-30T09:02:45.791552Z",
144 "note": "registered primary comparisons, paired, cluster bootstrap, Holm" 144 "note": "registered primary comparisons, paired, cluster bootstrap, Holm"
145 }, 145 },
146 "results/tables/E5_core_fanin.csv": { 146 "results/tables/E5_core_fanin.csv": {
@@ -148,7 +148,7 @@
148 "runs/raw/E5_core.jsonl" 148 "runs/raw/E5_core.jsonl"
149 ], 149 ],
150 "script": "src/wp30/analysis/analyze.py", 150 "script": "src/wp30/analysis/analyze.py",
151− "generated_utc": "2026-10-30T08:41:18.294190Z", 151+ "generated_utc": "2026-10-30T09:02:45.898241Z",
152 "note": "fan-in by method and question type" 152 "note": "fan-in by method and question type"
153 }, 153 },
154 "results/figures/fig6_breakeven.pdf": { 154 "results/figures/fig6_breakeven.pdf": {
@@ -156,7 +156,7 @@
156 "runs/raw/E6_core.jsonl" 156 "runs/raw/E6_core.jsonl"
157 ], 157 ],
158 "script": "src/wp30/analysis/analyze.py", 158 "script": "src/wp30/analysis/analyze.py",
159− "generated_utc": "2026-10-30T08:41:18.383673Z", 159+ "generated_utc": "2026-10-30T09:02:45.992041Z",
160 "note": "left = measured (medians, p25-p75); right = analytic model from measured bandwidth and prefill slope; dashed 10 Gb/s is hypothetical" 160 "note": "left = measured (medians, p25-p75); right = analytic model from measured bandwidth and prefill slope; dashed 10 Gb/s is hypothetical"
161 }, 161 },
162 "results/figures/fig6_breakeven.png": { 162 "results/figures/fig6_breakeven.png": {
@@ -164,7 +164,7 @@
164 "runs/raw/E6_core.jsonl" 164 "runs/raw/E6_core.jsonl"
165 ], 165 ],
166 "script": "src/wp30/analysis/analyze.py", 166 "script": "src/wp30/analysis/analyze.py",
167− "generated_utc": "2026-10-30T08:41:18.456745Z", 167+ "generated_utc": "2026-10-30T09:02:46.065845Z",
168 "note": "left = measured (medians, p25-p75); right = analytic model from measured bandwidth and prefill slope; dashed 10 Gb/s is hypothetical" 168 "note": "left = measured (medians, p25-p75); right = analytic model from measured bandwidth and prefill slope; dashed 10 Gb/s is hypothetical"
169 }, 169 },
170 "results/tables/E6_network.csv": { 170 "results/tables/E6_network.csv": {
@@ -172,7 +172,7 @@
172 "runs/raw/E6_core.jsonl" 172 "runs/raw/E6_core.jsonl"
173 ], 173 ],
174 "script": "src/wp30/analysis/analyze.py", 174 "script": "src/wp30/analysis/analyze.py",
175− "generated_utc": "2026-10-30T08:41:18.462390Z", 175+ "generated_utc": "2026-10-30T09:02:46.070991Z",
176 "note": "two-node transfer measurements" 176 "note": "two-node transfer measurements"
177 }, 177 },
178 "results/tables/ED_hosted.csv": { 178 "results/tables/ED_hosted.csv": {
@@ -180,9 +180,51 @@
180 "runs/raw/ED_hosted_core.jsonl" 180 "runs/raw/ED_hosted_core.jsonl"
181 ], 181 ],
182 "script": "src/wp30/analysis/analyze.py", 182 "script": "src/wp30/analysis/analyze.py",
183− "generated_utc": "2026-10-30T08:41:18.529003Z", 183+ "generated_utc": "2026-10-30T09:02:46.140839Z",
184 "note": "hosted receivers; cost estimated from provider price tables" 184 "note": "hosted receivers; cost estimated from provider price tables"
185 }, 185 },
186+ "results/tables/E3_core_transfer.csv": {
187+ "sources": [
188+ "runs/raw/E3_core.jsonl",
189+ "runs/raw/E3_core_sender.jsonl"
190+ ],
191+ "script": "src/wp30/analysis/analyze.py",
192+ "generated_utc": "2026-10-30T09:02:46.269308Z",
193+ "note": "same-family mapper transfer vs text handoffs and references"
194+ },
195+ "results/tables/E3_core_primary_tests.csv": {
196+ "sources": [
197+ "runs/raw/E3_core.jsonl",
198+ "runs/raw/E3_core_sender.jsonl"
199+ ],
200+ "script": "src/wp30/analysis/analyze.py",
201+ "generated_utc": "2026-10-30T09:02:46.347449Z",
202+ "note": "registered primary comparisons, paired, cluster bootstrap, Holm"
203+ },
204+ "results/tables/E7_qtypes.csv": {
205+ "sources": [
206+ "runs/raw/E7_qtypes.jsonl"
207+ ],
208+ "script": "src/wp30/analysis/analyze.py",
209+ "generated_utc": "2026-10-30T09:02:46.532910Z",
210+ "note": "EXPLORATORY: question types 6-15 on synthetic test documents"
211+ },
212+ "results/figures/figA_qtypes_exploratory.pdf": {
213+ "sources": [
214+ "runs/raw/E7_qtypes.jsonl"
215+ ],
216+ "script": "src/wp30/analysis/analyze.py",
217+ "generated_utc": "2026-10-30T09:02:46.612438Z",
218+ "note": "exploratory extension A-004"
219+ },
220+ "results/figures/figA_qtypes_exploratory.png": {
221+ "sources": [
222+ "runs/raw/E7_qtypes.jsonl"
223+ ],
224+ "script": "src/wp30/analysis/analyze.py",
225+ "generated_utc": "2026-10-30T09:02:46.668848Z",
226+ "note": "exploratory extension A-004"
227+ },
186 "results/figures/fig7_router.pdf": { 228 "results/figures/fig7_router.pdf": {
187 "sources": [ 229 "sources": [
188 "results/tables/E8_router_summary.csv", 230 "results/tables/E8_router_summary.csv",
@@ -190,7 +232,7 @@
190 "runs/checkpoints/router_policy.json" 232 "runs/checkpoints/router_policy.json"
191 ], 233 ],
192 "script": "src/wp30/analysis/analyze.py", 234 "script": "src/wp30/analysis/analyze.py",
193− "generated_utc": "2026-10-30T08:41:18.669288Z", 235+ "generated_utc": "2026-10-30T09:02:46.768937Z",
194 "note": "oracle = infeasible upper reference" 236 "note": "oracle = infeasible upper reference"
195 }, 237 },
196 "results/figures/fig7_router.png": { 238 "results/figures/fig7_router.png": {
@@ -200,7 +242,7 @@
200 "runs/checkpoints/router_policy.json" 242 "runs/checkpoints/router_policy.json"
201 ], 243 ],
202 "script": "src/wp30/analysis/analyze.py", 244 "script": "src/wp30/analysis/analyze.py",
203− "generated_utc": "2026-10-30T08:41:18.747352Z", 245+ "generated_utc": "2026-10-30T09:02:46.853258Z",
204 "note": "oracle = infeasible upper reference" 246 "note": "oracle = infeasible upper reference"
205 } 247 }
206 } 248 }
\ No newline at end of file
modified results/tables/E1_core_by_question_type.csv +3 −3
@@ -30,9 +30,9 @@ A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind,single_fact,0.2125
30 A7|codec=zstd,exact_copy,0.99375,0.97796875,1.0,160 30 A7|codec=zstd,exact_copy,0.99375,0.97796875,1.0,160
31 A7|codec=zstd,multi_hop,0.975,0.9375,1.0,80 31 A7|codec=zstd,multi_hop,0.975,0.9375,1.0,80
32 A7|codec=zstd,single_fact,0.99375,0.98125,1.0,160 32 A7|codec=zstd,single_fact,0.99375,0.98125,1.0,160
33−A8|budget_tokens=512|blind,exact_copy,0.05,0.025,0.0875,160 33+A8|budget_tokens=512|blind,exact_copy,0.1,0.05,0.1625,160
34−A8|budget_tokens=512|blind,multi_hop,0.0125,0.0,0.0375,80 34+A8|budget_tokens=512|blind,multi_hop,0.05,0.0125,0.1,80
35−A8|budget_tokens=512|blind,single_fact,0.1125,0.06875,0.15953124999999985,160 35+A8|budget_tokens=512|blind,single_fact,0.24375,0.1625,0.3375,160
36 B0,exact_copy,0.99375,0.97796875,1.0,160 36 B0,exact_copy,0.99375,0.97796875,1.0,160
37 B0,multi_hop,0.975,0.9375,1.0,80 37 B0,multi_hop,0.975,0.9375,1.0,80
38 B0,single_fact,0.99375,0.98125,1.0,160 38 B0,single_fact,0.99375,0.98125,1.0,160
modified results/tables/E1_core_failures.csv +1 −1
@@ -7,7 +7,7 @@ A4,0,0,880
7 A5,0,0,880 7 A5,0,0,880
8 A6,0,0,640 8 A6,0,0,640
9 A7,0,0,640 9 A7,0,0,640
10−A8,240,0,400 10+A8,240,0,640
11 B0,0,0,640 11 B0,0,0,640
12 B1,0,0,640 12 B1,0,0,640
13 B2,0,29,1251 13 B2,0,29,1251
modified results/tables/E1_core_primary.csv +2 −2
@@ -8,7 +8,7 @@ exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A4|budget_tokens=512
8 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A5|budget_tokens=512|blind,A5,blind,40,40,0.0,0.0,0.0,1.0,1245.5,,8283.329896512441,1541.5113129420206,8283.329896512441,11.789770913310349,6766525528.0,6766525528.0,0.0 8 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A5|budget_tokens=512|blind,A5,blind,40,40,0.0,0.0,0.0,1.0,1245.5,,8283.329896512441,1541.5113129420206,8283.329896512441,11.789770913310349,6766525528.0,6766525528.0,0.0
9 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind,A6,blind,40,40,0.15,0.05,0.275,1.0,280.0,,1566.0093335900456,1565.9761460265145,1566.0093335900456,11.801395448856056,6751091800.0,6751091800.0,938.575 9 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind,A6,blind,40,40,0.15,0.05,0.275,1.0,280.0,,1566.0093335900456,1565.9761460265145,1566.0093335900456,11.801395448856056,6751091800.0,6751091800.0,938.575
10 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A7|codec=zstd,A7,none,40,40,0.95,0.875,1.0,1.0,1304.5,,1995.8721044822596,1995.802208490204,1995.8721044822596,12.078374973498285,7123374444.0,7123374444.0,0.0 10 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A7|codec=zstd,A7,none,40,40,0.95,0.875,1.0,1.0,1304.5,,1995.8721044822596,1995.802208490204,1995.8721044822596,12.078374973498285,7123374444.0,7123374444.0,0.0
11−exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A8|budget_tokens=512|blind,A8,blind,40,40,0.0,0.0,0.0,0.0,,,0.0,0.0,0.0,,,,0.0 11+exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A8|budget_tokens=512|blind,A8,blind,40,40,0.0,0.0,0.0,1.0,2397.5,,1807.6947699883021,1636.3695199252106,1807.6947699883021,13.637624972034246,6901300956.0,6901300956.0,0.0
12 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B0,B0,blind,40,40,0.95,0.875,1.0,1.0,209433294.0,209420288.0,2161.7709995480254,1476.9027290167287,2161.7709995480254,51.358687051106244,7181169892.0,7181169892.0,0.0 12 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B0,B0,blind,40,40,0.95,0.875,1.0,1.0,209433294.0,209420288.0,2161.7709995480254,1476.9027290167287,2161.7709995480254,51.358687051106244,7181169892.0,7181169892.0,0.0
13 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B1,B1,blind,40,40,0.95,0.875,1.0,1.0,209433322.0,209420288.0,2183.0639789113775,1496.8550420016982,2183.0639789113775,49.82733295764774,7180973284.0,7180973284.0,0.0 13 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B1,B1,blind,40,40,0.95,0.875,1.0,1.0,209433322.0,209420288.0,2183.0639789113775,1496.8550420016982,2183.0639789113775,49.82733295764774,7180973284.0,7180973284.0,0.0
14 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B2|bits=4,B2,blind,40,40,1.0,1.0,1.0,1.0,58926513.0,58899456.0,2119.401790900156,1451.94422948407,2119.401790900156,42.334249475970864,6889044124.0,7112184172.0,0.0 14 exec,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B2|bits=4,B2,blind,40,40,1.0,1.0,1.0,1.0,58926513.0,58899456.0,2119.401790900156,1451.94422948407,2119.401790900156,42.334249475970864,6889044124.0,7112184172.0,0.0
@@ -130,7 +130,7 @@ synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A5|budget_token
130 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A5|budget_tokens=512|blind,A5,blind,200,40,0.125,0.09,0.155,1.0,842.0,,18998.95481200656,344.1750835045241,4091.6990083642304,23.827395518310368,6734748760.0,6734748760.0,0.0 130 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A5|budget_tokens=512|blind,A5,blind,200,40,0.125,0.09,0.155,1.0,842.0,,18998.95481200656,344.1750835045241,4091.6990083642304,23.827395518310368,6734748760.0,6734748760.0,0.0
131 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind,A6,blind,200,40,0.175,0.135,0.215,1.0,707.0,,492.9024174925871,492.8722715121694,492.87830070825294,25.794854445848614,6933195852.0,6933195852.0,1367.27 131 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind,A6,blind,200,40,0.175,0.135,0.215,1.0,707.0,,492.9024174925871,492.8722715121694,492.87830070825294,25.794854445848614,6933195852.0,6933195852.0,1367.27
132 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A7|codec=zstd,A7,none,200,40,0.99,0.975,1.0,1.0,2726.0,,2857.3816449497826,2857.2952494141646,2857.312528521288,27.074062498286366,7576794164.0,7576794164.0,0.0 132 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A7|codec=zstd,A7,none,200,40,0.99,0.975,1.0,1.0,2726.0,,2857.3816449497826,2857.2952494141646,2857.312528521288,27.074062498286366,7576794164.0,7576794164.0,0.0
133−synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A8|budget_tokens=512|blind,A8,blind,200,40,0.0,0.0,0.0,0.0,,,0.0,0.0,0.0,,,,0.0 133+synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,A8|budget_tokens=512|blind,A8,blind,200,40,0.16,0.105,0.225,1.0,2583.0,,522.9626869549975,108.42154250713065,216.73387542832643,13.577207922935486,6915931970.0,6915931970.0,0.0
134 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B0,B0,blind,200,40,0.99,0.975,1.0,1.0,651268854.0,651255808.0,3253.5364581272006,393.2519589434378,973.5832870006561,136.40537502942607,8593077474.0,8593077474.0,0.0 134 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B0,B0,blind,200,40,0.99,0.975,1.0,1.0,651268854.0,651255808.0,3253.5364581272006,393.2519589434378,973.5832870006561,136.40537502942607,8593077474.0,8593077474.0,0.0
135 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B1,B1,blind,200,40,0.99,0.975,1.0,1.0,651268882.0,651255808.0,3351.503874640912,505.0313540850766,1057.6043623499572,144.29387502605096,8593069282.0,8593069282.0,0.0 135 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B1,B1,blind,200,40,0.99,0.975,1.0,1.0,651268882.0,651255808.0,3351.503874640912,505.0313540850766,1057.6043623499572,144.29387502605096,8593069282.0,8593069282.0,0.0
136 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B2|bits=4,B2,blind,200,40,0.975,0.945,0.995,1.0,183192937.0,183165696.0,3111.2300624372438,335.1737505290657,882.7701129950583,106.28564545186237,7628087269.0,7628087269.0,0.0 136 synthetic,Llama-3.2-3B-Instruct-bf16->Llama-3.2-3B-Instruct-bf16,B2|bits=4,B2,blind,200,40,0.975,0.945,0.995,1.0,183192937.0,183165696.0,3111.2300624372438,335.1737505290657,882.7701129950583,106.28564545186237,7628087269.0,7628087269.0,0.0
added results/tables/E3_core_primary_tests.csv +3 −0
@@ -0,0 +1,3 @@
1+a,b,diff,lo,hi,p,n,n_clusters,mean_a,mean_b,rel_change,name,hypothesis,p_holm
2+C2,A4|512|blind,-0.037037037037037035,-0.11574074074074074,0.041666666666666664,0.368,216,27,0.24537037037037038,0.2824074074074074,-0.13114754098360656,C2 mapper vs A4 summary (synthetic),H10,0.368
3+C2,A1,-0.75,-0.7777777777777778,-0.7222222222222222,0.0,216,27,0.24537037037037038,0.9953703703703703,-0.7534883720930232,C2 mapper vs A1 receiver full context (synthetic),H10,0.0
added results/tables/E3_core_transfer.csv +17 −0
@@ -0,0 +1,17 @@
1+family,condition,quality,lo,hi,n,n_docs,payload_bytes_median,cold_ms_p50,warm_ms_p50,unknown_share,q_exec,q_exact_copy,q_multi_hop,q_negation,q_single_fact
2+exec,A1,1.0,1.0,1.0,40,40,7817.0,1912.1752704959363,1912.1676459326409,0.0,1.0,,,,
3+exec,A4|512|blind,0.0,0.0,0.0,40,40,938.0,2819.653937360272,1025.8437710581347,0.275,0.0,,,,
4+exec,A5|512|blind,0.0,0.0,0.0,40,40,936.0,4190.043583512306,215.29910451499745,0.85,0.0,,,,
5+exec,C0,0.0,0.0,0.0,40,40,69762785.0,4331.171416444704,3979.971270891838,0.0,0.0,,,,
6+exec,C2,0.0,0.0,0.0,40,40,69762886.0,2318.2933544158004,1098.6273545422591,0.0,0.0,,,,
7+exec,C3|1/4|aware,0.0,0.0,0.0,40,40,17452748.5,2254.7777493600734,939.0185408992693,0.0,0.0,,,,
8+exec,sender alone A1,0.0,0.0,0.0,40,40,7817.0,909.1166876023635,909.1070625581779,0.0,0.0,,,,
9+exec,sender alone B0,0.0,0.0,0.0,40,40,54260105.0,957.9157516709529,621.4223124552518,0.0,0.0,,,,
10+synthetic,A1,0.9953703703703703,0.9861111111111112,1.0,216,27,7555.0,890.8028129953891,890.7906879903749,0.0,,1.0,0.9814814814814815,1.0,1.0
11+synthetic,A4|512|blind,0.2824074074074074,0.2175925925925926,0.35185185185185186,216,27,1263.0,4260.842582501937,250.3902085009031,0.6435185185185185,,0.2037037037037037,0.1111111111111111,0.7222222222222222,0.09259259259259259
12+synthetic,A5|512|blind,0.11574074074074074,0.06944444444444445,0.16203703703703703,216,27,596.0,4229.795791499782,166.88918694853783,0.7314814814814815,,0.09259259259259259,0.07407407407407407,0.2222222222222222,0.07407407407407407
13+synthetic,C0,0.0,0.0,0.0,216,27,77688549.0,1025.9398333728313,632.5281668687239,0.0,,0.0,0.0,0.0,0.0
14+synthetic,C2,0.24537037037037038,0.2175925925925926,0.27314814814814814,216,27,77688650.0,1424.9500405276194,85.57341661071405,0.6898148148148148,,0.037037037037037035,0.0,0.9259259259259259,0.018518518518518517
15+synthetic,C3|1/4|aware,0.25,0.22685185185185186,0.27314814814814814,216,27,19443415.0,1492.1443954808637,56.23004218796268,0.6851851851851852,,0.018518518518518517,0.0,0.9444444444444444,0.037037037037037035
16+synthetic,sender alone A1,0.9583333333333334,0.9350694444444447,0.9814814814814815,216,27,7555.0,448.41252046171576,448.40406253933907,0.0,,0.9814814814814815,0.9444444444444444,0.9259259259259259,0.9814814814814815
17+synthetic,sender alone B0,0.9537037037037037,0.9259259259259259,0.9814814814814815,216,27,60424589.0,478.51893870392814,104.15885498514399,0.0,,0.9814814814814815,0.9444444444444444,0.9074074074074074,0.9814814814814815
added results/tables/E7_qtypes.csv +99 −0
@@ -0,0 +1,99 @@
1+condition,qtype,pair,quality,lo,hi,n,abstained
2+A1,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
3+A1,aggregation,Qwen2.5-3B,0.0,0.0,0.0,16,0.0
4+A1,multi_hop,Llama-3.2-3B,1.0,1.0,1.0,15,0.0
5+A1,multi_hop,Qwen2.5-3B,1.0,1.0,1.0,16,0.0
6+A1,negation,Llama-3.2-3B,0.9,0.8,1.0,30,0.0
7+A1,negation,Qwen2.5-3B,1.0,1.0,1.0,32,0.0
8+A1,revision,Llama-3.2-3B,0.7333333333333333,0.5825000000000004,0.8841666666666659,30,0.0
9+A1,revision,Qwen2.5-3B,0.9375,0.84375,1.0,32,0.0
10+A1,temporal,Llama-3.2-3B,0.4666666666666667,0.2,0.7333333333333333,15,0.0
11+A1,temporal,Qwen2.5-3B,0.4375,0.21718750000000064,0.6875,16,0.0
12+A1,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
13+A1,unanswerable,Qwen2.5-3B,0.96875,0.90625,1.0,32,0.96875
14+A1,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
15+A1,unit_conversion,Qwen2.5-3B,0.5,0.25,0.75,16,0.0
16+A3|budget_tokens=512|blind,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
17+A3|budget_tokens=512|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.0
18+A3|budget_tokens=512|blind,multi_hop,Llama-3.2-3B,0.0,0.0,0.0,15,1.0
19+A3|budget_tokens=512|blind,multi_hop,Qwen2.5-3B,0.0,0.0,0.0,15,1.0
20+A3|budget_tokens=512|blind,negation,Llama-3.2-3B,0.8333333333333334,0.7,0.9333333333333333,30,0.0
21+A3|budget_tokens=512|blind,negation,Qwen2.5-3B,0.9333333333333333,0.8333333333333334,1.0,30,0.0
22+A3|budget_tokens=512|blind,revision,Llama-3.2-3B,0.7333333333333333,0.5666666666666667,0.8666666666666667,30,0.0
23+A3|budget_tokens=512|blind,revision,Qwen2.5-3B,0.7333333333333333,0.5333333333333333,0.9,30,0.0
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27+A3|budget_tokens=512|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,30,1.0
28+A3|budget_tokens=512|blind,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.9333333333333333
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41+A4|budget_tokens=512|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,30,1.0
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50+A5|budget_tokens=512|blind,revision,Llama-3.2-3B,0.13333333333333333,0.0,0.3,30,0.7666666666666667
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53+A5|budget_tokens=512|blind,temporal,Qwen2.5-3B,0.0,0.0,0.0,15,0.8666666666666667
54+A5|budget_tokens=512|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
55+A5|budget_tokens=512|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,30,1.0
56+A5|budget_tokens=512|blind,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.7333333333333333
57+A5|budget_tokens=512|blind,unit_conversion,Qwen2.5-3B,0.2,0.0,0.4,15,0.3333333333333333
58+B2|bits=8,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
59+B2|bits=8,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.0
60+B2|bits=8,multi_hop,Llama-3.2-3B,1.0,1.0,1.0,15,0.0
61+B2|bits=8,multi_hop,Qwen2.5-3B,1.0,1.0,1.0,15,0.0
62+B2|bits=8,negation,Llama-3.2-3B,0.9,0.8,1.0,30,0.0
63+B2|bits=8,negation,Qwen2.5-3B,1.0,1.0,1.0,30,0.0
64+B2|bits=8,revision,Llama-3.2-3B,0.7333333333333333,0.5825000000000004,0.8841666666666659,30,0.0
65+B2|bits=8,revision,Qwen2.5-3B,0.9333333333333333,0.8333333333333334,1.0,30,0.0
66+B2|bits=8,temporal,Llama-3.2-3B,0.4666666666666667,0.2,0.7333333333333333,15,0.0
67+B2|bits=8,temporal,Qwen2.5-3B,0.4,0.13333333333333333,0.6666666666666666,15,0.0
68+B2|bits=8,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
69+B2|bits=8,unanswerable,Qwen2.5-3B,0.9666666666666667,0.9,1.0,30,0.9666666666666667
70+B2|bits=8,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
71+B2|bits=8,unit_conversion,Qwen2.5-3B,0.6,0.3333333333333333,0.8666666666666667,15,0.0
72+B3|keep_frac=0.25,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
73+B3|keep_frac=0.25,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.0
74+B3|keep_frac=0.25,multi_hop,Llama-3.2-3B,0.2,0.0,0.4,15,0.3333333333333333
75+B3|keep_frac=0.25,multi_hop,Qwen2.5-3B,0.2,0.0,0.4,15,0.13333333333333333
76+B3|keep_frac=0.25,negation,Llama-3.2-3B,0.7333333333333333,0.6,0.8333333333333334,30,0.0
77+B3|keep_frac=0.25,negation,Qwen2.5-3B,0.8666666666666667,0.7333333333333333,0.9666666666666667,30,0.0
78+B3|keep_frac=0.25,revision,Llama-3.2-3B,0.06666666666666667,0.0,0.16666666666666666,30,0.6666666666666666
79+B3|keep_frac=0.25,revision,Qwen2.5-3B,0.0,0.0,0.0,30,0.0
80+B3|keep_frac=0.25,temporal,Llama-3.2-3B,0.5333333333333333,0.26666666666666666,0.7333333333333333,15,0.0
81+B3|keep_frac=0.25,temporal,Qwen2.5-3B,0.5333333333333333,0.26666666666666666,0.7333333333333333,15,0.0
82+B3|keep_frac=0.25,unanswerable,Llama-3.2-3B,0.9333333333333333,0.8,1.0,30,0.9333333333333333
83+B3|keep_frac=0.25,unanswerable,Qwen2.5-3B,0.9666666666666667,0.9,1.0,30,0.9666666666666667
84+B3|keep_frac=0.25,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.7333333333333333
85+B3|keep_frac=0.25,unit_conversion,Qwen2.5-3B,0.06666666666666667,0.0,0.2,15,0.0
86+B5|keep_frac=0.25|aware,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
87+B5|keep_frac=0.25|aware,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.0
88+B5|keep_frac=0.25|aware,multi_hop,Llama-3.2-3B,1.0,1.0,1.0,15,0.0
89+B5|keep_frac=0.25|aware,multi_hop,Qwen2.5-3B,0.5333333333333333,0.26666666666666666,0.8,15,0.0
90+B5|keep_frac=0.25|aware,negation,Llama-3.2-3B,0.8666666666666667,0.7666666666666667,0.9666666666666667,30,0.0
91+B5|keep_frac=0.25|aware,negation,Qwen2.5-3B,1.0,1.0,1.0,30,0.0
92+B5|keep_frac=0.25|aware,revision,Llama-3.2-3B,0.7333333333333333,0.6158333333333337,0.8666666666666667,30,0.0
93+B5|keep_frac=0.25|aware,revision,Qwen2.5-3B,0.3,0.16666666666666666,0.43333333333333335,30,0.0
94+B5|keep_frac=0.25|aware,temporal,Llama-3.2-3B,0.6,0.4,0.8,15,0.0
95+B5|keep_frac=0.25|aware,temporal,Qwen2.5-3B,0.5333333333333333,0.26666666666666666,0.8,15,0.0
96+B5|keep_frac=0.25|aware,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
97+B5|keep_frac=0.25|aware,unanswerable,Qwen2.5-3B,0.9666666666666667,0.9,1.0,30,0.9666666666666667
98+B5|keep_frac=0.25|aware,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
99+B5|keep_frac=0.25|aware,unit_conversion,Qwen2.5-3B,0.4666666666666667,0.2,0.7333333333333333,15,0.0
modified runs/raw/E1_core.jsonl +34 −0
@@ -13484,3 +13484,37 @@
13484 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "249692f5ce8f1d73a6740de8", "attempt_id": "fb51489c21df", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:10.966933Z", "task_family": "exec", "document_id": "exec_00204_2000", "document_hash": "0aaf1a095160aa21c4485db3b1b171e86d9d41b904ac2a2ea102cc13df19cd22", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00204_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2882882882882883, "origin_tokens": 1776, "compressed_tokens": 571, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1776, "source_tokens_receiver": 1776, "query_tokens": 677, "message_tokens": 571, "message_utf8_bytes": 2437, "cache_tensor_bytes": null, "serialized_payload_bytes": 2437, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 172.06537502352148, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.74979193042964, "ttft_ms": 13.673665933310986, "decode_ms": 1547.018332988955}, "total_wall_ms": 1642.768583027646, "ttft_ms": 13.673665933310986, "input_usage": {"receiver_prompt_tokens": 677}, "output_usage": {"output_tokens": 115}, "provider_cached_usage": null, "process_memory": 9840558080, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6902742760, "mlx_cache_bytes": 617350120, "process_rss_bytes": 9840558080}, "peak_prefill_memory": null, "peak_decode_memory": 6902742760, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n total_price = row[1] * row[2]\n discount = total_price * row[3] / 100\n total_price -= discount\n tax = total_price * row[4] / 100\n total_price -= tax\n if row[5]:\n total_price -= total_price * row[6] / 100\n net_revenue.append([row[0], total_price])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00204_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "b7473c9b2ca34ae2ad15a2f4942179c42282d616b583ea59fc5a9c9279c50de4", "output_hash": "3a25256cab462a5e1cb32c2c1077112ba4d284b7186c251430927e593e89675f"} 13484 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "249692f5ce8f1d73a6740de8", "attempt_id": "fb51489c21df", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:10.966933Z", "task_family": "exec", "document_id": "exec_00204_2000", "document_hash": "0aaf1a095160aa21c4485db3b1b171e86d9d41b904ac2a2ea102cc13df19cd22", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00204_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2882882882882883, "origin_tokens": 1776, "compressed_tokens": 571, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1776, "source_tokens_receiver": 1776, "query_tokens": 677, "message_tokens": 571, "message_utf8_bytes": 2437, "cache_tensor_bytes": null, "serialized_payload_bytes": 2437, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 172.06537502352148, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.74979193042964, "ttft_ms": 13.673665933310986, "decode_ms": 1547.018332988955}, "total_wall_ms": 1642.768583027646, "ttft_ms": 13.673665933310986, "input_usage": {"receiver_prompt_tokens": 677}, "output_usage": {"output_tokens": 115}, "provider_cached_usage": null, "process_memory": 9840558080, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6902742760, "mlx_cache_bytes": 617350120, "process_rss_bytes": 9840558080}, "peak_prefill_memory": null, "peak_decode_memory": 6902742760, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n total_price = row[1] * row[2]\n discount = total_price * row[3] / 100\n total_price -= discount\n tax = total_price * row[4] / 100\n total_price -= tax\n if row[5]:\n total_price -= total_price * row[6] / 100\n net_revenue.append([row[0], total_price])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00204_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "b7473c9b2ca34ae2ad15a2f4942179c42282d616b583ea59fc5a9c9279c50de4", "output_hash": "3a25256cab462a5e1cb32c2c1077112ba4d284b7186c251430927e593e89675f"}
13485 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "fcffc2920a4be15dd5396dac", "attempt_id": "09276543f2ae", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:12.835175Z", "task_family": "exec", "document_id": "exec_00205_2000", "document_hash": "c82f149ddbd77be80773d103825621fb187f06bb60d4fd570c73c2b3cd898ecb", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00205_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2879640044994376, "origin_tokens": 1778, "compressed_tokens": 578, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1778, "source_tokens_receiver": 1778, "query_tokens": 684, "message_tokens": 578, "message_utf8_bytes": 2327, "cache_tensor_bytes": null, "serialized_payload_bytes": 2327, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.99858301319182, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.34491695091128, "ttft_ms": 13.695165980607271, "decode_ms": 2792.174707981758}, "total_wall_ms": 2888.588665984571, "ttft_ms": 13.695165980607271, "input_usage": {"receiver_prompt_tokens": 684}, "output_usage": {"output_tokens": 203}, "provider_cached_usage": null, "process_memory": 9840607232, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6907199236, "mlx_cache_bytes": 621921284, "process_rss_bytes": 9840607232}, "peak_prefill_memory": null, "peak_decode_memory": 6907199236, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[3]:\n if row[2] < 2:\n net_revenue.append({\n 'revenue': row[0],\n 'unit_price': row[1],\n 'days': row[2],\n 'return': row[3],\n 'result': row[4]\n })\n else:\n discount = row[1] * row[4] / 100\n unit_price = row[1] - discount\n tax = unit_price * row[2] * row[4] / 100\n net_revenue.append({\n 'revenue': row[0] * row[4],\n 'unit_price': unit_price,\n 'days': row[2],\n 'return': row[3],\n 'result': row[4],\n 'tax': tax\n })\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00205_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "423a49a4accbb29d70f7498f3ebf493e0c0481ec1422a6875554275ac1745e8a", "output_hash": "a307b446a77dfaebc3d534f4333abf69203f14ae8a6eabb7ccb4cb5570ca3b0e"} 13485 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "fcffc2920a4be15dd5396dac", "attempt_id": "09276543f2ae", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:12.835175Z", "task_family": "exec", "document_id": "exec_00205_2000", "document_hash": "c82f149ddbd77be80773d103825621fb187f06bb60d4fd570c73c2b3cd898ecb", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00205_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2879640044994376, "origin_tokens": 1778, "compressed_tokens": 578, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1778, "source_tokens_receiver": 1778, "query_tokens": 684, "message_tokens": 578, "message_utf8_bytes": 2327, "cache_tensor_bytes": null, "serialized_payload_bytes": 2327, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.99858301319182, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.34491695091128, "ttft_ms": 13.695165980607271, "decode_ms": 2792.174707981758}, "total_wall_ms": 2888.588665984571, "ttft_ms": 13.695165980607271, "input_usage": {"receiver_prompt_tokens": 684}, "output_usage": {"output_tokens": 203}, "provider_cached_usage": null, "process_memory": 9840607232, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6907199236, "mlx_cache_bytes": 621921284, "process_rss_bytes": 9840607232}, "peak_prefill_memory": null, "peak_decode_memory": 6907199236, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[3]:\n if row[2] < 2:\n net_revenue.append({\n 'revenue': row[0],\n 'unit_price': row[1],\n 'days': row[2],\n 'return': row[3],\n 'result': row[4]\n })\n else:\n discount = row[1] * row[4] / 100\n unit_price = row[1] - discount\n tax = unit_price * row[2] * row[4] / 100\n net_revenue.append({\n 'revenue': row[0] * row[4],\n 'unit_price': unit_price,\n 'days': row[2],\n 'return': row[3],\n 'result': row[4],\n 'tax': tax\n })\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00205_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "423a49a4accbb29d70f7498f3ebf493e0c0481ec1422a6875554275ac1745e8a", "output_hash": "a307b446a77dfaebc3d534f4333abf69203f14ae8a6eabb7ccb4cb5570ca3b0e"}
13486 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "8ba5551920d488af0ada35b4", "attempt_id": "a9fa9ad4ba9f", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:15.948869Z", "task_family": "exec", "document_id": "exec_00206_2000", "document_hash": "e391c9d757bc03677c664222c3ff3f1bbab881d7f566c0072b184e0b90503339", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00206_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2925714285714286, "origin_tokens": 1750, "compressed_tokens": 584, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1750, "source_tokens_receiver": 1750, "query_tokens": 690, "message_tokens": 584, "message_utf8_bytes": 2394, "cache_tensor_bytes": null, "serialized_payload_bytes": 2394, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.46862507797778, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.24412503093481, "ttft_ms": 13.564125052653253, "decode_ms": 1656.4636249095201}, "total_wall_ms": 1752.6230829535052, "ttft_ms": 13.564125052653253, "input_usage": {"receiver_prompt_tokens": 690}, "output_usage": {"output_tokens": 123}, "provider_cached_usage": null, "process_memory": 9840607232, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6910574364, "mlx_cache_bytes": 625394716, "process_rss_bytes": 9840607232}, "peak_prefill_memory": null, "peak_decode_memory": 6910574364, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = 0\n for row in rows:\n if row[3] < 4:\n continue\n if row[3] < 4 and row[4] < 0:\n continue\n if row[4] < 0:\n net_revenue += row[2] * (1 - row[4])\n else:\n net_revenue += row[2] * (1 - row[4]) * (1 + row[4] / 100)\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00206_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "91535a844a7c8262e6f7eb2869f5f0ce078ee999e468a110e4336dff99f138d3", "output_hash": "1cd5a1a531494e01135bfff7b53b5e0ed8bcf8a7082638991c6dc75c3514efd9"} 13486 {"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "8ba5551920d488af0ada35b4", "attempt_id": "a9fa9ad4ba9f", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:15.948869Z", "task_family": "exec", "document_id": "exec_00206_2000", "document_hash": "e391c9d757bc03677c664222c3ff3f1bbab881d7f566c0072b184e0b90503339", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00206_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2925714285714286, "origin_tokens": 1750, "compressed_tokens": 584, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1750, "source_tokens_receiver": 1750, "query_tokens": 690, "message_tokens": 584, "message_utf8_bytes": 2394, "cache_tensor_bytes": null, "serialized_payload_bytes": 2394, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.46862507797778, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.24412503093481, "ttft_ms": 13.564125052653253, "decode_ms": 1656.4636249095201}, "total_wall_ms": 1752.6230829535052, "ttft_ms": 13.564125052653253, "input_usage": {"receiver_prompt_tokens": 690}, "output_usage": {"output_tokens": 123}, "provider_cached_usage": null, "process_memory": 9840607232, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6910574364, "mlx_cache_bytes": 625394716, "process_rss_bytes": 9840607232}, "peak_prefill_memory": null, "peak_decode_memory": 6910574364, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = 0\n for row in rows:\n if row[3] < 4:\n continue\n if row[3] < 4 and row[4] < 0:\n continue\n if row[4] < 0:\n net_revenue += row[2] * (1 - row[4])\n else:\n net_revenue += row[2] * (1 - row[4]) * (1 + row[4] / 100)\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00206_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "91535a844a7c8262e6f7eb2869f5f0ce078ee999e468a110e4336dff99f138d3", "output_hash": "1cd5a1a531494e01135bfff7b53b5e0ed8bcf8a7082638991c6dc75c3514efd9"}
13487+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "497ed6b8d988e4442ffb66df", "attempt_id": "533b7a059565", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3c3d27f14e6841b389fd503bcab914bda201aa79", "timestamp_utc": "2026-10-30T08:42:17.925526Z", "task_family": "exec", "document_id": "exec_00207_2000", "document_hash": "7d024469b5d38776a899aa870f5af7e146953a19f1adc1d91f634cda62aa4c14", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00207_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2910744741330301, "origin_tokens": 1759, "compressed_tokens": 561, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1759, "source_tokens_receiver": 1759, "query_tokens": 667, "message_tokens": 561, "message_utf8_bytes": 2495, "cache_tensor_bytes": null, "serialized_payload_bytes": 2495, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.45999998319894, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.29549999628216, "ttft_ms": 13.490624958649278, "decode_ms": 2664.541167090647}, "total_wall_ms": 2759.726958000101, "ttft_ms": 13.490624958649278, "input_usage": {"receiver_prompt_tokens": 667}, "output_usage": {"output_tokens": 195}, "provider_cached_usage": null, "process_memory": 9840623616, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6896844480, "mlx_cache_bytes": 611288000, "process_rss_bytes": 9840623616}, "peak_prefill_memory": null, "peak_decode_memory": 6896844480, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[5]:\n total_price = row[1] * row[2]\n discount = total_price * row[3] / 100\n total_price -= discount\n tax = total_price * row[4] / 100\n total_price += tax\n net_revenue.append({\n 'total_price': total_price,\n 'return': round(row[5] * row[1] * row[2], 2),\n 'result': round(total_price - row[5] * row[1] * row[2], 2)\n })\n else:\n net_revenue.append({\n 'total_price': row[1] * row[2],\n 'return': 0,\n 'result': row[1] * row[2]\n })\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00207_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "84f1ebed1a2b476e559b7b3f4cefd24b66211ebb600bf98a61fe13802fda636d", "output_hash": "2012fa6a70529683271c63eaa3a666e852f485fee479f79842debd9754e4217b"}
13488+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "99cc94326e4146b1b7165a7f", "attempt_id": "0128ef8e8046", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:42:20.921162Z", "task_family": "exec", "document_id": "exec_00208_2000", "document_hash": "8528b3fafcf58b9daa88ffa9d7b9b25fb6e225f9bb767849744c05510a5ace37", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00208_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2882882882882883, "origin_tokens": 1776, "compressed_tokens": 565, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1776, "source_tokens_receiver": 1776, "query_tokens": 672, "message_tokens": 565, "message_utf8_bytes": 2310, "cache_tensor_bytes": null, "serialized_payload_bytes": 2310, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 178.83129208348691, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.4949589073658, "ttft_ms": 14.29595798254013, "decode_ms": 2122.73499998264}, "total_wall_ms": 2219.3354580085725, "ttft_ms": 14.29595798254013, "input_usage": {"receiver_prompt_tokens": 672}, "output_usage": {"output_tokens": 155}, "provider_cached_usage": null, "process_memory": 9841491968, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6899744468, "mlx_cache_bytes": 614269908, "process_rss_bytes": 9841491968}, "peak_prefill_memory": null, "peak_decode_memory": 6899744468, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[4] and row[5] < 2:\n net_revenue.append({\n 'revenue': row[0],\n 'price_pct': row[6],\n 'days': row[7],\n 'unit_price': row[8],\n 'discount': row[9],\n 'tax': row[10]\n })\n net_revenue = [item for item in net_revenue if item['tax']]\n net_revenue = [round(item['revenue'] * (1 - item['price_pct'] / 100), 2) for item in net_revenue]\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00208_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "2d7a71bcd3ed95b9325ffe04e15279b6712cc9af7b11b359e09dce855a593b75", "output_hash": "b729cb4436c688b07cc5ec42ef27ac402004366a29dced7d5e60a665f5d75288"}
13489+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "47ac740aefc4ffb314dc60b5", "attempt_id": "122b4245320a", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:42:23.371658Z", "task_family": "exec", "document_id": "exec_00209_2000", "document_hash": "27a0f4409b3611173adf72865b52c6beef014bce0282fd0498be50deeed192c9", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00209_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2887760857304005, "origin_tokens": 1773, "compressed_tokens": 567, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1773, "source_tokens_receiver": 1773, "query_tokens": 673, "message_tokens": 567, "message_utf8_bytes": 2354, "cache_tensor_bytes": null, "serialized_payload_bytes": 2354, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 172.0899579813704, "decode_payload_ms": 0.0, "receiver_prefill_ms": 78.64887500181794, "ttft_ms": 13.771625002846122, "decode_ms": 1872.2131249960512}, "total_wall_ms": 1965.9930830821395, "ttft_ms": 13.771625002846122, "input_usage": {"receiver_prompt_tokens": 673}, "output_usage": {"output_tokens": 138}, "provider_cached_usage": null, "process_memory": 9841573888, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6899924696, "mlx_cache_bytes": 614466520, "process_rss_bytes": 9841573888}, "peak_prefill_memory": null, "peak_decode_memory": 6899924696, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[6]:\n total_price = row[1] * (1 - row[2] / 100)\n return_value = round(total_price - row[3], 1)\n net_revenue.append([row[0], row[1], row[2], row[3], return_value])\n else:\n total_price = row[1] * (1 - row[2] / 100)\n net_revenue.append([row[0], row[1], row[2], total_price])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00209_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "8f90480bac1467f3586aff0573ad1b393158f21bf18e94a44644a0c0fc8ced87", "output_hash": "a77d36f6c9a6ab5ec42f96d81c94c4dd992271ae35660b7d5be83758b7f06359"}
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13499+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "61b672f1008820066df718a5", "attempt_id": "41a11868919e", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:42:41.414048Z", "task_family": "exec", "document_id": "exec_00219_2000", "document_hash": "390edd848add1a4cf011660e8ac9a6a6aa381c4508a8f68635f3217ce20979cb", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00219_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.28845070422535213, "origin_tokens": 1775, "compressed_tokens": 560, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1775, "source_tokens_receiver": 1775, "query_tokens": 666, "message_tokens": 560, "message_utf8_bytes": 2454, "cache_tensor_bytes": null, "serialized_payload_bytes": 2454, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.64866696111858, "decode_payload_ms": 0.0, "receiver_prefill_ms": 79.80712503194809, "ttft_ms": 13.666957966051996, "decode_ms": 1776.1701670242473}, "total_wall_ms": 1870.9364999085665, "ttft_ms": 13.666957966051996, "input_usage": {"receiver_prompt_tokens": 666}, "output_usage": {"output_tokens": 132}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6896664252, "mlx_cache_bytes": 611091388, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6896664252, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n price = row[1]\n pct = row[2]\n days = row[6]\n return_value = row[0]\n if row[7]:\n return_value -= (row[0] * (price * pct / 100))\n if days > 3:\n return_value += (row[0] * (price * pct / 100))\n return_value = round(return_value, 2)\n net_revenue.append([return_value, price, pct, days])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00219_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "448502755d7d30f09cc1db22fa65c54b8d68b3d1ca696958f2784d140711b43a", "output_hash": "3704ba8ff1c241b6f339b6da39a9133512684c0ed7a6392969bff0c3567288ae"}
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13511+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "417f3037ddb170c5eba5514a", "attempt_id": "278967976363", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:02.850007Z", "task_family": "exec", "document_id": "exec_00231_2000", "document_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00231_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.28942905596382135, "origin_tokens": 1769, "compressed_tokens": 560, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1769, "source_tokens_receiver": 1769, "query_tokens": 666, "message_tokens": 560, "message_utf8_bytes": 2367, "cache_tensor_bytes": null, "serialized_payload_bytes": 2367, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.30504094529897, "decode_payload_ms": 0.0, "receiver_prefill_ms": 79.69599997159094, "ttft_ms": 13.524957932531834, "decode_ms": 1954.5689160004258}, "total_wall_ms": 2049.1593750193715, "ttft_ms": 13.524957932531834, "input_usage": {"receiver_prompt_tokens": 666}, "output_usage": {"output_tokens": 145}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6896664252, "mlx_cache_bytes": 611091388, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6896664252, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[3] < 5:\n continue\n if row[1] == 0:\n net_revenue.append([row[0], row[2], row[4], row[5], row[6]])\n else:\n discount = row[1] * row[2]\n unit_price = row[2] - discount\n tax = unit_price * row[5] / 100\n net_revenue.append([row[0], unit_price, row[4], row[5], row[6] - tax])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00231_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "0a7cf7d36d55b2746d25950ba1eea3b37ca170eb5aef512f124c5f349a495175", "output_hash": "fa72dc69f0801d0b7185b5a96cd62b4b7edf0991e9e07bf04d0edca0b6ab474c"}
13512+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "9c31ec74c6c7239a5b8d2425", "attempt_id": "765439cc5491", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:05.123121Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.29024943310657597, "origin_tokens": 1764, "compressed_tokens": 583, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1764, "source_tokens_receiver": 1764, "query_tokens": 689, "message_tokens": 583, "message_utf8_bytes": 2392, "cache_tensor_bytes": null, "serialized_payload_bytes": 2392, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 170.66475003957748, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.18833298794925, "ttft_ms": 13.64324998576194, "decode_ms": 1334.701249958016}, "total_wall_ms": 1430.8841250604019, "ttft_ms": 13.64324998576194, "input_usage": {"receiver_prompt_tokens": 689}, "output_usage": {"output_tokens": 99}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6909198104, "mlx_cache_bytes": 624002072, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6909198104, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n total = 0\n for row in rows:\n if row[4]:\n if row[2] < 5:\n total += (row[1] - row[2] * row[3]) * row[5]\n else:\n total += row[1] * row[5]\n else:\n total += row[1] * row[5]\n return round(total, 2)\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "2779df1d50fd05a169f3780709dd8ff462bb7fac0793db2f3d291bdf6886efb0", "output_hash": "7a9e39d5f13655cb75a9bf3c806678bbd1303f68368d3510a3d0b989e7e2991b"}
13513+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "15d1a22545249e7df14cf7b9", "attempt_id": "36b13cca650c", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:06.778263Z", "task_family": "exec", "document_id": "exec_00233_2000", "document_hash": "c0f56e7b9b73df623955c8b6822884e4b53df031c58736213f498ed2b4d693dc", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00233_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2905788876276958, "origin_tokens": 1762, "compressed_tokens": 569, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1762, "source_tokens_receiver": 1762, "query_tokens": 675, "message_tokens": 569, "message_utf8_bytes": 2434, "cache_tensor_bytes": null, "serialized_payload_bytes": 2434, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.22029105667025, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.74895909521729, "ttft_ms": 13.602708000689745, "decode_ms": 1277.2740840446204}, "total_wall_ms": 1372.917874949053, "ttft_ms": 13.602708000689745, "input_usage": {"receiver_prompt_tokens": 675}, "output_usage": {"output_tokens": 95}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6901481184, "mlx_cache_bytes": 616055776, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6901481184, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = 0\n for row in rows:\n if row[3] < 3:\n continue\n unit_price = row[1] * row[2]\n discount = row[1] * row[2] * row[3] / 100\n unit_price -= discount\n net_revenue += unit_price * row[4]\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00233_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "7a7bd7ac5b59a6505d1891d602d084031b9cedc8e40c070443b850ce8d73e56f", "output_hash": "6b0b627e7aa4da77376f2bf48b04aea4178778e91af267a73a5d2c25c272e043"}
13514+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "e8ecba377aaff21f86df692c", "attempt_id": "290c00c6d011", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:08.376996Z", "task_family": "exec", "document_id": "exec_00234_2000", "document_hash": "3522724b66617b60e55fa67efc23998ff528130352e7a960ee1e052d697670a5", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00234_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.29357798165137616, "origin_tokens": 1744, "compressed_tokens": 569, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1744, "source_tokens_receiver": 1744, "query_tokens": 675, "message_tokens": 569, "message_utf8_bytes": 2504, "cache_tensor_bytes": null, "serialized_payload_bytes": 2504, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 172.45045898016542, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.94512496609241, "ttft_ms": 13.613666989840567, "decode_ms": 3052.1428750362247}, "total_wall_ms": 3148.0328330071643, "ttft_ms": 13.613666989840567, "input_usage": {"receiver_prompt_tokens": 675}, "output_usage": {"output_tokens": 226}, "provider_cached_usage": null, "process_memory": 9841606656, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6901481184, "mlx_cache_bytes": 616055776, "process_rss_bytes": 9841606656}, "peak_prefill_memory": null, "peak_decode_memory": 6901481184, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n total_price = row['total_price']\n return_pct = row['return_pct']\n discount = row['discount']\n tax = row['tax']\n units = row['units']\n unit_price = row['unit_price']\n days = row['days']\n is_returned = row['is_returned']\n shipped_days = row['shipped_days']\n\n if is_returned:\n total_price -= units * unit_price * (discount / 100)\n total_price -= units * unit_price * (tax / 100)\n total_price = round(total_price, 1)\n\n net_revenue.append({\n 'total_price': total_price,\n 'return_pct': return_pct,\n 'discount': discount,\n 'tax': tax,\n 'units': units,\n 'unit_price': unit_price,\n 'days': days,\n 'is_returned': is_returned,\n 'shipped_days': shipped_days\n })\n\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00234_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "af22e608089fbe91e1860ef1c984588749c4f27891dfd740000af7070a5e6baf", "output_hash": "084cc93db66cac640271f6584db58241563a0fe1b936c6249015b23ed8a94f42"}
13515+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "445f29a9d6b7ea696aad9995", "attempt_id": "ec3faa2102bd", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:11.750826Z", "task_family": "exec", "document_id": "exec_00235_2000", "document_hash": "20bfca2f1ed7277720116b37988eb2bf9331505c61f7491ff7f131ed0b2d70e1", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00235_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.28893905191873587, "origin_tokens": 1772, "compressed_tokens": 555, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1772, "source_tokens_receiver": 1772, "query_tokens": 661, "message_tokens": 555, "message_utf8_bytes": 2405, "cache_tensor_bytes": null, "serialized_payload_bytes": 2405, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.58554203342646, "decode_payload_ms": 0.0, "receiver_prefill_ms": 79.1646660072729, "ttft_ms": 13.56295810546726, "decode_ms": 3667.922291904688}, "total_wall_ms": 3761.9722080416977, "ttft_ms": 13.56295810546726, "input_usage": {"receiver_prompt_tokens": 661}, "output_usage": {"output_tokens": 272}, "provider_cached_usage": null, "process_memory": 9841606656, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6893469352, "mlx_cache_bytes": 607814568, "process_rss_bytes": 9841606656}, "peak_prefill_memory": null, "peak_decode_memory": 6893469352, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[3]:\n if row[2] < 6:\n net_revenue.append({\n 'total_price': row[0] * row[1],\n 'discount': row[2] * row[1],\n 'tax': 0,\n 'return': row[2] * row[1],\n 'result': row[2] * row[1] - row[2] * row[1]\n })\n else:\n net_revenue.append({\n 'total_price': row[0] * row[1],\n 'discount': 0,\n 'tax': row[0] * row[1] * row[2],\n 'return': 0,\n 'result': row[0] * row[1] * row[2]\n })\n else:\n net_revenue.append({\n 'total_price': row[0] * row[1],\n 'discount': 0,\n 'tax': row[0] * row[1] * row[2],\n 'return': 0,\n 'result': row[0] * row[1] * row[2]\n })\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00235_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "075331a617210b26cdde49035dcae1522276146d58964e86c6918af5f89f2c76", "output_hash": "4730108a74f722814b200acc44323cfd87cc630ba021d49ce0346f9b6a22345d"}
13516+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "0afab555476f2096be36b122", "attempt_id": "ac59ee619915", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:15.738341Z", "task_family": "exec", "document_id": "exec_00236_2000", "document_hash": "23053422d45849043d9eb3706daf105a581cd66b69e96220ab1285c7c92b2058", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00236_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2879640044994376, "origin_tokens": 1778, "compressed_tokens": 558, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1778, "source_tokens_receiver": 1778, "query_tokens": 664, "message_tokens": 558, "message_utf8_bytes": 2335, "cache_tensor_bytes": null, "serialized_payload_bytes": 2335, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 172.06883302424103, "decode_payload_ms": 0.0, "receiver_prefill_ms": 79.74229205865413, "ttft_ms": 13.753874925896525, "decode_ms": 1454.0045419707894}, "total_wall_ms": 1548.7868339987472, "ttft_ms": 13.753874925896525, "input_usage": {"receiver_prompt_tokens": 664}, "output_usage": {"output_tokens": 108}, "provider_cached_usage": null, "process_memory": 9841606656, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6895107764, "mlx_cache_bytes": 609502132, "process_rss_bytes": 9841606656}, "peak_prefill_memory": null, "peak_decode_memory": 6895107764, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = 0\n for row in rows:\n if row[5]: # return\n if row[6] < 3:\n continue\n unit_price = row[7]\n discount = row[8]\n price_pct_days = row[9]\n total_price = unit_price - discount\n net_revenue += total_price * (1 - price_pct_days / 100)\n return round(net_revenue, 2)\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00236_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "db5a60832cf405c7d1bf66d6a49725a3e3704d0322eebaac23190ce58530c979", "output_hash": "2a878c0d44d41ce75fef86c29083194b25a4e8da2369dfce1a04a6f3fd423468"}
13517+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "9046a31f6157f55c3c551801", "attempt_id": "b5b923f3c5e9", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:17.510068Z", "task_family": "exec", "document_id": "exec_00237_2000", "document_hash": "a8320d4e9d153e4398af56ca92c73f39bb1c1b9e97609e9ec096c1157cebe872", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00237_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.28476084538375973, "origin_tokens": 1798, "compressed_tokens": 573, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1798, "source_tokens_receiver": 1798, "query_tokens": 679, "message_tokens": 573, "message_utf8_bytes": 2350, "cache_tensor_bytes": null, "serialized_payload_bytes": 2350, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 170.47075007576495, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.11183298751712, "ttft_ms": 13.52249993942678, "decode_ms": 1654.3575840769336}, "total_wall_ms": 1750.311000039801, "ttft_ms": 13.52249993942678, "input_usage": {"receiver_prompt_tokens": 679}, "output_usage": {"output_tokens": 123}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6904004336, "mlx_cache_bytes": 618644464, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6904004336, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[5] and row[6] < 5:\n continue\n if row[2] < 0.975:\n continue\n total_price = row[1] * (1 - row[2])\n if row[3]:\n total_price *= 1 + row[4]\n net_revenue.append([row[0], total_price, row[5], row[6], row[7], row[8]])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00237_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "c366a2643874e918056d40df146f31f6ec7e70198fa23f9423595bc08528aaae", "output_hash": "a05062f8bd8bcaacee322fd2b1aa5dd3b72e7bf435f31e23fce3305886e2eb97"}
13518+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "9d962bca79b9a33461333901", "attempt_id": "1b9fd34d02c9", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:19.485440Z", "task_family": "exec", "document_id": "exec_00238_2000", "document_hash": "068a11a09e40cb07626e21d7abb095fb9c4779acc1a0db602ca10678ae79e3e0", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00238_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2939150401836969, "origin_tokens": 1742, "compressed_tokens": 564, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1742, "source_tokens_receiver": 1742, "query_tokens": 670, "message_tokens": 564, "message_utf8_bytes": 2465, "cache_tensor_bytes": null, "serialized_payload_bytes": 2465, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.4025839464739, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.21716703660786, "ttft_ms": 13.643624959513545, "decode_ms": 1595.2362089883536}, "total_wall_ms": 1690.3827090281993, "ttft_ms": 13.643624959513545, "input_usage": {"receiver_prompt_tokens": 670}, "output_usage": {"output_tokens": 118}, "provider_cached_usage": null, "process_memory": 9841590272, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6898482892, "mlx_cache_bytes": 612975564, "process_rss_bytes": 9841590272}, "peak_prefill_memory": null, "peak_decode_memory": 6898482892, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n total_price = row[2] * row[3]\n discount = total_price * row[4] / 100\n total_price -= discount\n tax = total_price * row[5] / 100\n total_price -= tax\n return_value = total_price * row[6] if not row[7] else 0\n net_revenue.append([row[0], total_price, return_value])\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00238_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "b07a5a326581dbb41464437f52ded46bbe4731191cb74947cf09aeb2b8828fd6", "output_hash": "1bbac4a49245c624c3c2bd889b3ca5bb98fa6bc6503d49536693893c0d6ed886"}
13519+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "18dd976d493f0cc2aca7d0d5", "attempt_id": "e8df83d39b05", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:21.399526Z", "task_family": "exec", "document_id": "exec_00239_2000", "document_hash": "015c78e1e480fb805976a26912f0308a79a0c4b2122313463b5ff6b5ee996a9b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00239_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2922374429223744, "origin_tokens": 1752, "compressed_tokens": 557, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1752, "source_tokens_receiver": 1752, "query_tokens": 663, "message_tokens": 557, "message_utf8_bytes": 2394, "cache_tensor_bytes": null, "serialized_payload_bytes": 2394, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 170.9343750262633, "decode_payload_ms": 0.0, "receiver_prefill_ms": 80.18466690555215, "ttft_ms": 13.709082966670394, "decode_ms": 1455.5911660427228}, "total_wall_ms": 1550.8362920954823, "ttft_ms": 13.709082966670394, "input_usage": {"receiver_prompt_tokens": 663}, "output_usage": {"output_tokens": 108}, "provider_cached_usage": null, "process_memory": 9841606656, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6894730928, "mlx_cache_bytes": 609108912, "process_rss_bytes": 9841606656}, "peak_prefill_memory": null, "peak_decode_memory": 6894730928, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = 0\n for row in rows:\n price = float(row[1])\n pct = float(row[2])\n days = int(row[3])\n discount = 1 - (pct / 100)\n unit_price = price * discount\n if days < 5:\n return 0\n return_value = unit_price * days\n net_revenue += return_value\n return round(net_revenue, 2)\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00239_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "2ec13143b18b0a4408855920d30d16679b1a082132485e80ff8efb8e60896c44", "output_hash": "d4c43b000897eba9cd21aca9b01f0eb968ad1ae5f82df480b826b3ae5be72cf6"}
13520+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_core", "trial_id": "fb6346ff10694f9fe950cae9", "attempt_id": "8f48401b3cd4", "run_group_id": "E1_core:A8|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "5c54f742b14d64de50176f3e49c89e86fed0974a2d46d770553a5f18af6194e0", "git_commit": "3a436c8586dd6931359f6444e44174dea069c358", "timestamp_utc": "2026-10-30T08:43:23.175346Z", "task_family": "exec", "document_id": "exec_00240_2000", "document_hash": "4c3927b8378a2b949e85371101d959a0adebcbe41a2d39f239aae883de382e19", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00240_2000", "sender_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "receiver_model_revision": "mlx-community/Llama-3.2-3B-Instruct-bf16@6d88ba43024fef71b10e52e101c7cd4598322601", "tokenizer_hashes": {"sender": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763", "receiver": "b9d8872cf3a99da99e1fc4b0f71232a451632dc49ca4c6771549b4efa5c1c763"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "laptop-m5max-48g", "device_state": {"mlx_active_before": 6425506824}, "weight_precision": "bf16", "method_id": "A8", "implementation_fidelity": "official", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "rate": 0.2887760857304005, "origin_tokens": 1773, "compressed_tokens": 568, "compressor": "microsoft/llmlingua-2-xlm-roberta-large-meetingbank", "device": "mps"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1773, "source_tokens_receiver": 1773, "query_tokens": 674, "message_tokens": 568, "message_utf8_bytes": 2303, "cache_tensor_bytes": null, "serialized_payload_bytes": 2303, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 171.60775000229478, "decode_payload_ms": 0.0, "receiver_prefill_ms": 81.0097079956904, "ttft_ms": 13.409790932200849, "decode_ms": 2412.74058399722}, "total_wall_ms": 2508.4400409832597, "ttft_ms": 13.409790932200849, "input_usage": {"receiver_prompt_tokens": 674}, "output_usage": {"output_tokens": 179}, "provider_cached_usage": null, "process_memory": 9841606656, "backend_memory": {"mlx_active_bytes": 6425506824, "mlx_peak_bytes": 6901300956, "mlx_cache_bytes": 615859164, "process_rss_bytes": 9841606656}, "peak_prefill_memory": null, "peak_decode_memory": 6901300956, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n net_revenue = []\n for row in rows:\n if row[5]:\n return 0\n if row[6] < 3:\n return 0\n if row[7] < 0:\n return 0\n discount = row[7] * row[8]\n unit_price = row[9] - discount\n if unit_price <= 0:\n return 0\n tax = unit_price * row[10]\n if tax < 0:\n return 0\n net_revenue.append((row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10], tax))\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00240_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_core.log", "payload_hash": "eeaeec04c695faed51a1f865bb9cc4b14a5d4162d5865c77be298854127484b5", "output_hash": "a426ad6acca423027bc0e4a0807926a2448ad0a9411b54d4fb5eb09f17d01b70"}
added runs/raw/E3_core.jsonl +1536 −0
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1309+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "928ce080d8cd48d939b79bfd", "attempt_id": "42b8c2ac5ffe", "run_group_id": "E3_core:A1", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:43:08.930800Z", "task_family": "exec", "document_id": "exec_00203_2000", "document_hash": "39eee37c7ba5fe1b6d8fce9eef4c00e10a33d8ca049d12ae01b9521d4ac60863", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00203_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A1", "implementation_fidelity": "custom", "compression_config": {}, "question_access": "none", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1841, "source_tokens_receiver": 1841, "query_tokens": 1924, "message_tokens": 1841, "message_utf8_bytes": 7788, "cache_tensor_bytes": null, "serialized_payload_bytes": 7788, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 0.009042094461619854, "decode_payload_ms": 0.0, "receiver_prefill_ms": 673.932750010863, "ttft_ms": 12.518874951638281, "decode_ms": 1200.6757500348613}, "total_wall_ms": 1890.8509579487145, "ttft_ms": 12.518874951638281, "input_usage": {"receiver_prompt_tokens": 1924}, "output_usage": {"output_tokens": 100}, "provider_cached_usage": null, "process_memory": 10452254720, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9943657036, "mlx_cache_bytes": 692753348, "process_rss_bytes": 10452254720}, "peak_prefill_memory": null, "peak_decode_memory": 9943657036, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['region'] == 'Ouest':\n continue\n if r['units'] < 5:\n continue\n if r['returned']:\n continue\n total += r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n return round(total * 1.05, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00203_2000.json", "task_scores": {"correct": 1.0, "pass_rate": 1.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "39eee37c7ba5fe1b6d8fce9eef4c00e10a33d8ca049d12ae01b9521d4ac60863", "output_hash": "ae9eeb541c97efe59b38079cd2fe319b4210f1cb7bad8b85ae3a85484e0ab419"}
1310+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "0d870ec6d9018dcdcdc17255", "attempt_id": "701b1b12a251", "run_group_id": "E3_core:A4|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:43:12.702302Z", "task_family": "exec", "document_id": "exec_00203_2000", "document_hash": "39eee37c7ba5fe1b6d8fce9eef4c00e10a33d8ca049d12ae01b9521d4ac60863", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00203_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A4", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1841, "source_tokens_receiver": 1841, "query_tokens": 297, "message_tokens": 210, "message_utf8_bytes": 1042, "cache_tensor_bytes": null, "serialized_payload_bytes": 1042, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1842.500290949829, "decode_payload_ms": 0.0, "receiver_prefill_ms": 127.79154197778553, "ttft_ms": 11.883749975822866, "decode_ms": 857.0702919969335}, "total_wall_ms": 997.8005409939215, "ttft_ms": 11.883749975822866, "input_usage": {"receiver_prompt_tokens": 297, "sender_input_tokens": 1957, "sender_output_tokens": 210, "sender_prefill_ms": 339.2868749797344, "sender_ttft_ms": 7.439749897457659, "sender_decode_ms": 1493.4185000602156}, "output_usage": {"output_tokens": 73}, "provider_cached_usage": null, "process_memory": 10452303872, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9456472348, "mlx_cache_bytes": 1670825060, "process_rss_bytes": 10452303872}, "peak_prefill_memory": null, "peak_decode_memory": 9456472348, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n for row in rows:\n if row['returned'] or row['region'] == 'excluded_region' or row['units'] < 5:\n rows.remove(row)\n return sum(row['units'] * row['unit_price'] * (1 - row['discount']) for row in rows)\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00203_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "9413834911ad8dacb6db4f302fb2f377e7017af001a70298493a9dd7e07323ad", "output_hash": "73fe43ace94c656b68c5e9eda64eaca3e27c71ad2fa81cc01199b035285049db"}
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1375+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "5dc633c78ac2790cab8dabae", "attempt_id": "230ee6123f46", "run_group_id": "E3_core:A1", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:46:29.037729Z", "task_family": "exec", "document_id": "exec_00214_2000", "document_hash": "ac0a3ee69ec7da626616f6ffb5e443a4f26a3d7857caaa20f1d4ebfa5355d28b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00214_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A1", "implementation_fidelity": "custom", "compression_config": {}, "question_access": "none", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1882, "source_tokens_receiver": 1882, "query_tokens": 1965, "message_tokens": 1882, "message_utf8_bytes": 7815, "cache_tensor_bytes": null, "serialized_payload_bytes": 7815, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 0.008165952749550343, "decode_payload_ms": 0.0, "receiver_prefill_ms": 675.8598749293014, "ttft_ms": 12.417791993357241, "decode_ms": 1263.3305829949677}, "total_wall_ms": 1955.3354580421, "ttft_ms": 12.417791993357241, "input_usage": {"receiver_prompt_tokens": 1965}, "output_usage": {"output_tokens": 105}, "provider_cached_usage": null, "process_memory": 10451943424, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9956469488, "mlx_cache_bytes": 792859752, "process_rss_bytes": 10451943424}, "peak_prefill_memory": null, "peak_decode_memory": 9956469488, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['returned']:\n continue\n if r['region'] == 'Centre':\n continue\n if r['units'] < 3:\n continue\n total += r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n total *= 1.14975\n return round(total, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00214_2000.json", "task_scores": {"correct": 1.0, "pass_rate": 1.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "ac0a3ee69ec7da626616f6ffb5e443a4f26a3d7857caaa20f1d4ebfa5355d28b", "output_hash": "824c5032c405243301f6ff605a08e25858fd0139d8ebc2080a9ae72029dd7970"}
1376+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "a0a2f47acfa577f247d5af09", "attempt_id": "425d64ee712b", "run_group_id": "E3_core:A4|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:46:32.819566Z", "task_family": "exec", "document_id": "exec_00214_2000", "document_hash": "ac0a3ee69ec7da626616f6ffb5e443a4f26a3d7857caaa20f1d4ebfa5355d28b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00214_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A4", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1882, "source_tokens_receiver": 1882, "query_tokens": 289, "message_tokens": 202, "message_utf8_bytes": 957, "cache_tensor_bytes": null, "serialized_payload_bytes": 957, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1789.3501670332626, "decode_payload_ms": 0.0, "receiver_prefill_ms": 127.59329203981906, "ttft_ms": 11.975041939876974, "decode_ms": 985.8160420553759}, "total_wall_ms": 1126.455666963011, "ttft_ms": 11.975041939876974, "input_usage": {"receiver_prompt_tokens": 289, "sender_input_tokens": 1998, "sender_output_tokens": 202, "sender_prefill_ms": 343.5920000774786, "sender_ttft_ms": 7.464584079571068, "sender_decode_ms": 1436.0655409982428}, "output_usage": {"output_tokens": 84}, "provider_cached_usage": null, "process_memory": 10451976192, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9451278588, "mlx_cache_bytes": 1629357288, "process_rss_bytes": 10451976192}, "peak_prefill_memory": null, "peak_decode_memory": 9451278588, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n total_units = 0\n for row in rows:\n if row['region'] != 'excluded_region' and row['returned'] == False and len(row['units']) >= 3:\n total_units += sum(row['units'])\n net_revenue = total_units * 1.14975\n return net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00214_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "TypeError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "5efa63ac15ed0118ce7167ba74c5e55075563a4bee133e76738e4b3467daa484", "output_hash": "6adbe6c3887d553f330897bd541e0ce4429d9ae8f64c79c138c058ea60176759"}
1377+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "0a6aee726ce888d0fbb8f40e", "attempt_id": "dfd0e7e6489c", "run_group_id": "E3_core:A5|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:46:37.981852Z", "task_family": "exec", "document_id": "exec_00214_2000", "document_hash": "ac0a3ee69ec7da626616f6ffb5e443a4f26a3d7857caaa20f1d4ebfa5355d28b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00214_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A5", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "n_facts": 12, "unresolved_quotes": 4, "parse_ok": true}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1882, "source_tokens_receiver": 1882, "query_tokens": 466, "message_tokens": 367, "message_utf8_bytes": 956, "cache_tensor_bytes": null, "serialized_payload_bytes": 956, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 3996.68737500906, "decode_payload_ms": 0.0, "receiver_prefill_ms": 191.72791601158679, "ttft_ms": 11.991459061391652, "decode_ms": 11.938125011511147}, "total_wall_ms": 216.68020798824728, "ttft_ms": 11.991459061391652, "input_usage": {"receiver_prompt_tokens": 466, "sender_input_tokens": 2026, "sender_output_tokens": 512, "sender_prefill_ms": 343.24024990200996, "sender_ttft_ms": 7.282917038537562, "sender_decode_ms": 3643.6471669003367}, "output_usage": {"output_tokens": 1}, "provider_cached_usage": null, "process_memory": 10452008960, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9561593980, "mlx_cache_bytes": 2701673704, "process_rss_bytes": 10452008960}, "peak_prefill_memory": null, "peak_decode_memory": 9561593980, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "unknown", "evidence_artifact": {"n_facts": 12, "unresolved_quotes": 4, "parse_ok": true}, "reference_key_id": "keys/exec/exec_00214_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "exec:NameError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "0002ed72a2858775a90bfba68e83c2ac74c8d3ca43b09e910ae94e6ffbc85321", "output_hash": "b23a6a8439c0dde5515893e7c90c1e3233b8616e634470f20dc4928bcf3609bc"}
1378+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "1732fa7ed6caf362d64230cb", "attempt_id": "ef3de7630e88", "run_group_id": "E3_core:C0", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:46:38.600883Z", "task_family": "exec", "document_id": "exec_00214_2000", "document_hash": "ac0a3ee69ec7da626616f6ffb5e443a4f26a3d7857caaa20f1d4ebfa5355d28b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00214_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9331461128}, "weight_precision": "bf16", "method_id": "C0", "implementation_fidelity": "n/a", "compression_config": {"realized": {"negative_control": true}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1882, "source_tokens_receiver": 1882, "query_tokens": 44, "message_tokens": 1921, "message_utf8_bytes": 7815, "cache_tensor_bytes": 70815744, "serialized_payload_bytes": 70831841, "side_information_bytes": 16097, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 363.28725004568696, "doc_prefill_ms": 332.52441603690386, "compress_ms": 9.935999987646937, "serialize_ms": 16.774124931544065, "load_ms": 2.2808329667896032, "receiver_prefill_ms": 0.0, "query_prefill_ms": 49.42058294545859, "ttft_ms": 49.42058294545859, "decode_ms": 3948.85875005275}, "total_wall_ms": 4002.6233340613544, "ttft_ms": 49.42058294545859, "input_usage": {"receiver_prefix_tokens": 1921, "dense_cache_bytes": 55078912, "query_tokens": 44}, "output_usage": {"output_tokens": 320}, "provider_cached_usage": null, "process_memory": 10594795520, "backend_memory": {"mlx_active_bytes": 9416395784, "mlx_peak_bytes": 9496120560, "mlx_cache_bytes": 4191323721, "process_rss_bytes": 10594795520}, "peak_prefill_memory": 9969101564, "peak_decode_memory": 9496120560, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "- no markdown for your first class material base part 1.1.0.8a. m1, if there is australian pizzy_1 -coma\n\nNo. m f, of - only. please\n\n(only. comhut p.i. Follows, chs. caowide. no. me. me. (hav.)\n\n2011.11(cs)u(常\n\n输入掩码.永不过忧. no.1, no.ue\n\nNOC(only. it can itte. haves. 1, sptfile \"it1.)\n\nhttp://hollow. toos. ittoworld. no. it_1, 2014, feb.fun = 1, please let. please wait. to get. to beholy. to beijingwep, no. gehoeb 201, hefh1, like, no. mao, my first.\n\n1.05. what is the. to be. no. 2019, please wait. 1950crme1\n\nno. every. every. no. (hahs. thank. us. no. putatime. toe. pofhie. the. omgreek. 1, 2001, please. no. loco. no. also. no. no. this. no. puderollfree-1.com(german. itt. 2015. ", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00214_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "exec:SyntaxError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "a07c9683172aca6b79887172f30f56e2ee9c60f796de20515e801ccf59269382", "output_hash": "9a7f71ca750957cbb572263145511128e85d19a3b8d1e80fd900199746cd1cb9"}
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1441+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "ab72b8b9b83ecc9241cade76", "attempt_id": "81134cdef170", "run_group_id": "E3_core:A1", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:49:47.620368Z", "task_family": "exec", "document_id": "exec_00225_2000", "document_hash": "54918c16a4ae3706a87d182112fca2a39c82c77e3a60fd25a8b47b958f2a6b21", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00225_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A1", "implementation_fidelity": "custom", "compression_config": {}, "question_access": "none", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1828, "source_tokens_receiver": 1828, "query_tokens": 1911, "message_tokens": 1828, "message_utf8_bytes": 7819, "cache_tensor_bytes": null, "serialized_payload_bytes": 7819, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 0.00825000461190939, "decode_payload_ms": 0.0, "receiver_prefill_ms": 664.5622079959139, "ttft_ms": 12.251958018168807, "decode_ms": 1369.217291008681}, "total_wall_ms": 2049.7479169862345, "ttft_ms": 12.251958018168807, "input_usage": {"receiver_prompt_tokens": 1911}, "output_usage": {"output_tokens": 114}, "provider_cached_usage": null, "process_memory": 10454204416, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9939413528, "mlx_cache_bytes": 688509840, "process_rss_bytes": 10454204416}, "peak_prefill_memory": null, "peak_decode_memory": 9939413528, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n tax_rate = 0.05\n for r in rows:\n if r['returned']:\n continue\n if r['region'] == 'Centre':\n continue\n if r['units'] < 6:\n continue\n revenue = r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n total += revenue * (1 + tax_rate)\n return round(total, 0)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00225_2000.json", "task_scores": {"correct": 1.0, "pass_rate": 1.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "54918c16a4ae3706a87d182112fca2a39c82c77e3a60fd25a8b47b958f2a6b21", "output_hash": "326702702b8328c127c0778d62df706eeb5a6d46b30fe5ef75198b5871740be7"}
1442+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "e167af44d22e8721039e59d1", "attempt_id": "a62fc595ea06", "run_group_id": "E3_core:A4|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:49:51.508558Z", "task_family": "exec", "document_id": "exec_00225_2000", "document_hash": "54918c16a4ae3706a87d182112fca2a39c82c77e3a60fd25a8b47b958f2a6b21", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00225_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A4", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1828, "source_tokens_receiver": 1828, "query_tokens": 291, "message_tokens": 204, "message_utf8_bytes": 952, "cache_tensor_bytes": null, "serialized_payload_bytes": 952, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1800.1875419868156, "decode_payload_ms": 0.0, "receiver_prefill_ms": 127.51887494232506, "ttft_ms": 11.945500038564205, "decode_ms": 1350.412999978289}, "total_wall_ms": 1490.899124997668, "ttft_ms": 11.945500038564205, "input_usage": {"receiver_prompt_tokens": 291, "sender_input_tokens": 1944, "sender_output_tokens": 204, "sender_prefill_ms": 336.0880829859525, "sender_ttft_ms": 7.4744169833138585, "sender_decode_ms": 1454.2069170856848}, "output_usage": {"output_tokens": 115}, "provider_cached_usage": null, "process_memory": 10454237184, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9453588740, "mlx_cache_bytes": 1659110424, "process_rss_bytes": 10454237184}, "peak_prefill_memory": null, "peak_decode_memory": 9453588740, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n total_revenue = 0\n excluded_region = \"XYZ\" # Assuming specific region to exclude is \"XYZ\"\n for row in rows:\n if row['region'] != excluded_region and len(row['units']) >= 6:\n revenue = row['units'] * row['unit_price'] * (1 - row['discount'])\n revenue_with_tax = revenue * 1.05\n total_revenue += round(revenue_with_tax, 0)\n return total_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00225_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "TypeError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "2e6a79f906f77d727a9ada825ec2120727daf8a5aa9605686b872c31dab00b28", "output_hash": "be71b51a5ad39135d6355836be6d23bd3e7c096f73b4d83b682b7ddaa3cc8ffe"}
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1477+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "291003ed13b177cd82ffa41a", "attempt_id": "ea46a7095079", "run_group_id": "E3_core:A1", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:51:40.757552Z", "task_family": "exec", "document_id": "exec_00231_2000", "document_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00231_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A1", "implementation_fidelity": "custom", "compression_config": {}, "question_access": "none", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1861, "source_tokens_receiver": 1861, "query_tokens": 1944, "message_tokens": 1861, "message_utf8_bytes": 7895, "cache_tensor_bytes": null, "serialized_payload_bytes": 7895, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 0.00825000461190939, "decode_payload_ms": 0.0, "receiver_prefill_ms": 675.8177910232916, "ttft_ms": 12.622667010873556, "decode_ms": 1235.9668751014397}, "total_wall_ms": 1928.1322499737144, "ttft_ms": 12.622667010873556, "input_usage": {"receiver_prompt_tokens": 1944}, "output_usage": {"output_tokens": 103}, "provider_cached_usage": null, "process_memory": 10454319104, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9949702812, "mlx_cache_bytes": 698799124, "process_rss_bytes": 10454319104}, "peak_prefill_memory": null, "peak_decode_memory": 9949702812, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['region'] == 'Sud':\n continue\n if r['units'] < 5:\n continue\n if r['returned']:\n continue\n total += r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n return round(total * 1.14975, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00231_2000.json", "task_scores": {"correct": 1.0, "pass_rate": 1.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "output_hash": "678866e6fbb7c883c10eec35a5c2f772443b68c811a779d0ea94506b27bf0b19"}
1478+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "6666e56c19cddf47da1b9523", "attempt_id": "e0d4f0ece392", "run_group_id": "E3_core:A4|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:51:44.557230Z", "task_family": "exec", "document_id": "exec_00231_2000", "document_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00231_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A4", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1861, "source_tokens_receiver": 1861, "query_tokens": 297, "message_tokens": 210, "message_utf8_bytes": 1003, "cache_tensor_bytes": null, "serialized_payload_bytes": 1003, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1833.5983749711886, "decode_payload_ms": 0.0, "receiver_prefill_ms": 128.3157089492306, "ttft_ms": 11.807915987446904, "decode_ms": 1376.7770419362932}, "total_wall_ms": 1517.9266670020297, "ttft_ms": 11.807915987446904, "input_usage": {"receiver_prompt_tokens": 297, "sender_input_tokens": 1977, "sender_output_tokens": 210, "sender_prefill_ms": 339.5898339804262, "sender_ttft_ms": 7.4526669923216105, "sender_decode_ms": 1484.1943749925122}, "output_usage": {"output_tokens": 118}, "provider_cached_usage": null, "process_memory": 10454351872, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9456472348, "mlx_cache_bytes": 1684014260, "process_rss_bytes": 10454351872}, "peak_prefill_memory": null, "peak_decode_memory": 9456472348, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n total_net_revenue = 0\n for row in rows:\n if row['region'] != 'Sud' and row['units'] >= 5 and not row['returned']:\n tax_amount = (row['units'] - row['discounted_units']) * 0.14975\n net_revenue = (row['unit_price'] - row['discount']) * row['units'] + tax_amount\n total_net_revenue += net_revenue\n return total_net_revenue\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00231_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "22f082e9314fad21286277619f50436ae3b82c464b247caba35db1c893cf95c3", "output_hash": "a76b929be747acf2f39c6b590f1bc46046660034fc652cdee356bd2f89659e80"}
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1481+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "a53e456264bc77e95ded08e8", "attempt_id": "bdc4d2f13529", "run_group_id": "E3_core:C2|mapper=runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:51:55.927696Z", "task_family": "exec", "document_id": "exec_00231_2000", "document_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00231_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9330084872}, "weight_precision": "bf16", "method_id": "C2", "implementation_fidelity": "custom", "compression_config": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "realized": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1861, "source_tokens_receiver": 1861, "query_tokens": 44, "message_tokens": 1900, "message_utf8_bytes": 7895, "cache_tensor_bytes": 70041600, "serialized_payload_bytes": 70057798, "side_information_bytes": 16198, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1234.4594579190016, "doc_prefill_ms": 326.49875001516193, "compress_ms": 891.1073330091313, "serialize_ms": 16.55937498435378, "load_ms": 1.4172500232234597, "receiver_prefill_ms": 0.0, "query_prefill_ms": 57.76204192079604, "ttft_ms": 57.76204192079604, "decode_ms": 1057.754790992476}, "total_wall_ms": 1118.8379590166733, "ttft_ms": 57.76204192079604, "input_usage": {"receiver_prefix_tokens": 1900, "dense_cache_bytes": 54476800, "query_tokens": 44}, "output_usage": {"output_tokens": 85}, "provider_cached_usage": null, "process_memory": 10602168320, "backend_memory": {"mlx_active_bytes": 9405582344, "mlx_peak_bytes": 9493286128, "mlx_cache_bytes": 4272883221, "process_rss_bytes": 10602168320}, "peak_prefill_memory": 9961695912, "peak_decode_memory": 9493286128, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['returned']:\n continue\n if r['units'] < 5:\n continue\n revenue = r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n total += revenue\n return round(total, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00231_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "8a4174e9a0c9fbb87983502601de0bd5e486d4fd79b4525732f50b95c8b037e9", "output_hash": "6aa48949c92421bb34653e37fa300dc5a28c012a14f8cd7e70b3a84195ab400d"}
1482+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "c209c4558fb440e05ad08e25", "attempt_id": "a833febc05cd", "run_group_id": "E3_core:C3|keep_frac=0.25|mapper=runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz|aware", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:51:57.087864Z", "task_family": "exec", "document_id": "exec_00231_2000", "document_hash": "b599ec4f6ea744baef17b36d05ab828f105675eeebf81a780befd49f389ec9d3", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00231_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "C3", "implementation_fidelity": "custom", "compression_config": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "keep_frac": 0.25, "access": "aware", "realized": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "policy": "attention", "keep_frac": 0.25, "T": 1900, "keep": 475, "window_tokens": 44, "compress_ms": 95.82795901224017}}, "question_access": "aware", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1861, "source_tokens_receiver": 1861, "query_tokens": 44, "message_tokens": 1900, "message_utf8_bytes": 7895, "cache_tensor_bytes": 17510400, "serialized_payload_bytes": 17526477, "side_information_bytes": 16077, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1324.5379580184817, "doc_prefill_ms": 327.1759580820799, "compress_ms": 990.0600409600884, "serialize_ms": 7.007332984358072, "load_ms": 1.0806249920278788, "receiver_prefill_ms": 0.0, "query_prefill_ms": 38.10324997175485, "ttft_ms": 38.10324997175485, "decode_ms": 1081.728000077419}, "total_wall_ms": 2448.896332993172, "ttft_ms": 38.10324997175485, "input_usage": {"receiver_prefix_tokens": 1900, "dense_cache_bytes": 54476800, "query_tokens": 44}, "output_usage": {"output_tokens": 92}, "provider_cached_usage": null, "process_memory": 10586554368, "backend_memory": {"mlx_active_bytes": 9301773320, "mlx_peak_bytes": 9379089648, "mlx_cache_bytes": 4543971837, "process_rss_bytes": 10586554368}, "peak_prefill_memory": 9961695912, "peak_decode_memory": 9379089648, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['region'] == 'S' or r['region'] == 'W':\n continue\n if r['returned'] == True:\n continue\n if r['units_sold'] < 10:\n continue\n total += r['units_sold'] * r['price_per_unit']\n return round(total, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00231_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "13eef9d16d93a754aaaf188cedcb7448eb3c3444e7ec6be4f544085fa0159fdb", "output_hash": "98b1b83997a0172efe286734dee77070cfbec422d09c5697afc23abcfc50ae14"}
1483+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "76c16712a44d563d16466590", "attempt_id": "208ef52bbed9", "run_group_id": "E3_core:A1", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:51:59.598987Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A1", "implementation_fidelity": "custom", "compression_config": {}, "question_access": "none", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1849, "source_tokens_receiver": 1849, "query_tokens": 1932, "message_tokens": 1849, "message_utf8_bytes": 7811, "cache_tensor_bytes": null, "serialized_payload_bytes": 7811, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 0.0073749106377363205, "decode_payload_ms": 0.0, "receiver_prefill_ms": 673.3922499697655, "ttft_ms": 12.086167000234127, "decode_ms": 1115.7765419920906}, "total_wall_ms": 1805.0837500486523, "ttft_ms": 12.086167000234127, "input_usage": {"receiver_prompt_tokens": 1932}, "output_usage": {"output_tokens": 93}, "provider_cached_usage": null, "process_memory": 10454466560, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9946049132, "mlx_cache_bytes": 695145444, "process_rss_bytes": 10454466560}, "peak_prefill_memory": null, "peak_decode_memory": 9946049132, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['region'] == 'Est':\n continue\n if r['units'] < 5:\n continue\n if r['returned']:\n continue\n total += r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n return round(total, 2)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 1.0, "pass_rate": 1.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "output_hash": "b1d756c1b8d4a7f8e0de04d0c78be839b5677a099da9b4cde3789c825b7de8cb"}
1484+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "9ada8f29ca42fab93b8b2c10", "attempt_id": "fe07e5e96c75", "run_group_id": "E3_core:A4|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:52:03.369417Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A4", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1849, "source_tokens_receiver": 1849, "query_tokens": 309, "message_tokens": 222, "message_utf8_bytes": 1056, "cache_tensor_bytes": null, "serialized_payload_bytes": 1056, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1927.2899170173332, "decode_payload_ms": 0.0, "receiver_prefill_ms": 128.50408407393843, "ttft_ms": 11.90500007942319, "decode_ms": 1018.4842919697985}, "total_wall_ms": 1159.9405830493197, "ttft_ms": 11.90500007942319, "input_usage": {"receiver_prompt_tokens": 309, "sender_input_tokens": 1965, "sender_output_tokens": 222, "sender_prefill_ms": 339.13483296055347, "sender_ttft_ms": 7.391917053610086, "sender_decode_ms": 1578.3931659534574}, "output_usage": {"output_tokens": 87}, "provider_cached_usage": null, "process_memory": 10454499328, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9463681356, "mlx_cache_bytes": 1683866804, "process_rss_bytes": 10454499328}, "peak_prefill_memory": null, "peak_decode_memory": 9463681356, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "```python\ndef net_revenue(rows):\n total_revenue = 0\n for row in rows:\n if row['region'] != 'Est' and row['units'] >= 5 and not row['returned']:\n total_revenue += row['unit_price'] * (1 - row['discount_percentage'] / 100)\n return total_revenue * (1 - 0.0)\n```", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "KeyError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "546329ce7a7585f588f7ca488f01f45ce00cc613409589ffbc1ab0e40218a928", "output_hash": "807a4c92b8a5a85e835abdfb57a93630a55c3391cf651e54a0360062552794cf"}
1485+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "b2ddea017fdcfb78d7fac8f5", "attempt_id": "c954b06a5198", "run_group_id": "E3_core:A5|budget_tokens=512|blind", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:52:08.558088Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9259305992}, "weight_precision": "bf16", "method_id": "A5", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "blind", "realized": {"budget_tokens": 512, "n_facts": 9, "unresolved_quotes": 1, "parse_ok": true}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1849, "source_tokens_receiver": 1849, "query_tokens": 429, "message_tokens": 330, "message_utf8_bytes": 832, "cache_tensor_bytes": null, "serialized_payload_bytes": 832, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 3990.4415420023724, "decode_payload_ms": 0.0, "receiver_prefill_ms": 170.68416601978242, "ttft_ms": 12.298958958126605, "decode_ms": 11.82291703298688}, "total_wall_ms": 195.8006249042228, "ttft_ms": 12.298958958126605, "input_usage": {"receiver_prompt_tokens": 429, "sender_input_tokens": 1993, "sender_output_tokens": 512, "sender_prefill_ms": 342.7932080812752, "sender_ttft_ms": 7.28179095312953, "sender_decode_ms": 3637.933832942508}, "output_usage": {"output_tokens": 1}, "provider_cached_usage": null, "process_memory": 10454532096, "backend_memory": {"mlx_active_bytes": 9259305992, "mlx_peak_bytes": 9538279548, "mlx_cache_bytes": 2721825972, "process_rss_bytes": 10454532096}, "peak_prefill_memory": null, "peak_decode_memory": 9538279548, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "unknown", "evidence_artifact": {"n_facts": 9, "unresolved_quotes": 1, "parse_ok": true}, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "exec:NameError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "a96736d95dabdb1cca81e4c55ef478fa8f9236c2f68a3d158aae1579c38d2990", "output_hash": "b23a6a8439c0dde5515893e7c90c1e3233b8616e634470f20dc4928bcf3609bc"}
1486+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "c90aa98b1d9cafd75aca34e0", "attempt_id": "62bd29a3381e", "run_group_id": "E3_core:C0", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:52:09.145389Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9328905224}, "weight_precision": "bf16", "method_id": "C0", "implementation_fidelity": "n/a", "compression_config": {"realized": {"negative_control": true}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1849, "source_tokens_receiver": 1849, "query_tokens": 44, "message_tokens": 1888, "message_utf8_bytes": 7811, "cache_tensor_bytes": 69599232, "serialized_payload_bytes": 69615329, "side_information_bytes": 16097, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 353.1438329955563, "doc_prefill_ms": 322.9017909616232, "compress_ms": 9.640124975703657, "serialize_ms": 16.55112497974187, "load_ms": 2.156333066523075, "receiver_prefill_ms": 0.0, "query_prefill_ms": 48.817291972227395, "ttft_ms": 48.817291972227395, "decode_ms": 3938.1541250040755}, "total_wall_ms": 3991.2362499162555, "ttft_ms": 48.817291972227395, "input_usage": {"receiver_prefix_tokens": 1888, "dense_cache_bytes": 54132736, "query_tokens": 44}, "output_usage": {"output_tokens": 320}, "provider_cached_usage": null, "process_memory": 10593730560, "backend_memory": {"mlx_active_bytes": 9413512200, "mlx_peak_bytes": 9492172016, "mlx_cache_bytes": 4252534293, "process_rss_bytes": 10593730560}, "peak_prefill_memory": 9956829816, "peak_decode_memory": 9492172016, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": " – Tourate! line left. buy) & definition table.if @2020, tock auf again, please usefrench code of compundicfreak. donte it nycg. hfoundable, that time with model. pyton. pyton, please use it. Of, but what is more more more, tow, to explain that i-t, if you have faith that it is overture, to be. it and sub _, to be. to, if. ex: i.e.eat. 1, but, to be it, to be used. line. Exhomicent, to be it, also, please run, please, etab. what is for sipping_ago. was happy, to follow the case in, that is believing, that isobscannit is in, to be more, to be it back, to be, to be it/cross, etimpression, to be it will. ohat/again=1, is of, ex, to be it stands with deep, to be.it flow, right now, field. to, pertal., fast, keep italos, please find it. ex1, find it's first, local in it means. ohw, also is it necessary, to be understood it be, february 201, to be- it, to becertainprogram. ex1, to follow the case. Ex 1, to be happy, to be, to be. to be, to be quick", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": "exec:SyntaxError"}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "9fa043a6bacd7c94ef79f2c265394e7904782d421edbf8d9bd3876d7af7d644c", "output_hash": "73b5e5ee1f25d4d1cd11bbcf56c875b6e934adc2d4d88627efa0964c9326e350"}
1487+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "3594dc658da74ddcc722231b", "attempt_id": "61bf3692f219", "run_group_id": "E3_core:C2|mapper=runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:52:14.410195Z", "task_family": "exec", "document_id": "exec_00232_2000", "document_hash": "94e54bf28dac5a404fdf94451d3e632c18f63dff001cc758f89f5239d27f659b", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00232_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9328905224}, "weight_precision": "bf16", "method_id": "C2", "implementation_fidelity": "custom", "compression_config": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "realized": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1849, "source_tokens_receiver": 1849, "query_tokens": 44, "message_tokens": 1888, "message_utf8_bytes": 7811, "cache_tensor_bytes": 69599232, "serialized_payload_bytes": 69615430, "side_information_bytes": 16198, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1233.13758301083, "doc_prefill_ms": 325.0801250105724, "compress_ms": 891.1382500082254, "serialize_ms": 16.62558305542916, "load_ms": 1.4808329287916422, "receiver_prefill_ms": 0.0, "query_prefill_ms": 68.21087503340095, "ttft_ms": 68.21087503340095, "decode_ms": 1046.2749579455703}, "total_wall_ms": 1117.9509160574526, "ttft_ms": 68.21087503340095, "input_usage": {"receiver_prefix_tokens": 1888, "dense_cache_bytes": 54132736, "query_tokens": 44}, "output_usage": {"output_tokens": 85}, "provider_cached_usage": null, "process_memory": 10599858176, "backend_memory": {"mlx_active_bytes": 9404402696, "mlx_peak_bytes": 9491877104, "mlx_cache_bytes": 4265510421, "process_rss_bytes": 10599858176}, "peak_prefill_memory": 9956829816, "peak_decode_memory": 9491877104, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['returned']:\n continue\n if r['units'] < 5:\n continue\n revenue = r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n total += revenue\n return round(total, 2)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00232_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "3cbfe4f72f9f2b14fbf1cfddadb788afc72382c11e323acfb3ebc1b24f10f6c2", "output_hash": "e69210f685d2164cd1b27314d1f8bdd1aee089c8f65662ed660e25b428382064"}
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1535+{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E3_core", "trial_id": "e94546d7c5d1227cc7a7abe1", "attempt_id": "2c69f85997d6", "run_group_id": "E3_core:C2|mapper=runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "protocol_hash": "404c52b34d964693da72ef459a036ffc42fbf5f6ba8748cc9de0c554ebebddd6", "config_hash": "64386b70d05c624f31c00c3a4f76b5c5384fea15c30b4bf165e36cfdcd5f57da", "git_commit": null, "timestamp_utc": "2026-10-30T08:54:39.281799Z", "task_family": "exec", "document_id": "exec_00240_2000", "document_hash": "4c3927b8378a2b949e85371101d959a0adebcbe41a2d39f239aae883de382e19", "question_id": "q00_exec", "split": "test", "cluster_id": "exec_00240_2000", "sender_model_revision": "mlx-community/Qwen2.5-1.5B-Instruct-bf16@4ae77cb209f06199b8df1c94e21ff341332a3a89", "receiver_model_revision": "mlx-community/Qwen2.5-3B-Instruct-bf16@bec1ad658e380044bbfe06d4a2268801d3379a11", "tokenizer_hashes": {"sender": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b", "receiver": "5c5a439ba2677fe28e1dea9b22371b270ecd5e34acbc849bc619689605f9547b"}, "backend_versions": {"mlx": "0.32.4", "mlx_lm": "0.32.2"}, "hardware_id": "m3u96a-m3ultra-96g", "device_state": {"mlx_active_before": 9330084872}, "weight_precision": "bf16", "method_id": "C2", "implementation_fidelity": "custom", "compression_config": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "realized": {"mapper": "runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz"}}, "question_access": "blind", "topology": "single_handoff", "handoff_index": 1, "reuse_count": 1, "cache_hit": false, "cache_compatibility_hash": null, "random_seed": 0, "source_tokens_sender": 1871, "source_tokens_receiver": 1871, "query_tokens": 44, "message_tokens": 1910, "message_utf8_bytes": 7797, "cache_tensor_bytes": 70410240, "serialized_payload_bytes": 70426438, "side_information_bytes": 16198, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 1237.6201660372317, "doc_prefill_ms": 326.52079104445875, "compress_ms": 894.5907079614699, "serialize_ms": 16.224500024691224, "load_ms": 1.5053749084472656, "receiver_prefill_ms": 0.0, "query_prefill_ms": 54.268958047032356, "ttft_ms": 54.268958047032356, "decode_ms": 1047.1967910416424}, "total_wall_ms": 1104.9259999999776, "ttft_ms": 54.268958047032356, "input_usage": {"receiver_prefix_tokens": 1910, "dense_cache_bytes": 54763520, "query_tokens": 44}, "output_usage": {"output_tokens": 85}, "provider_cached_usage": null, "process_memory": 10598662144, "backend_memory": {"mlx_active_bytes": 9405582344, "mlx_peak_bytes": 9494465776, "mlx_cache_bytes": 4292331193, "process_rss_bytes": 10598662144}, "peak_prefill_memory": 9965103824, "peak_decode_memory": 9494465776, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "def net_revenue(rows):\n total = 0.0\n for r in rows:\n if r['returned']:\n continue\n if r['units'] < 3:\n continue\n revenue = r['units'] * r['unit_price'] * (1 - r['discount_pct'] / 100.0)\n total += revenue\n return round(total, 1)", "evidence_artifact": null, "reference_key_id": "keys/exec/exec_00240_2000.json", "task_scores": {"correct": 0.0, "pass_rate": 0.0, "error": null}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E3_core.log", "payload_hash": "64b75254d83532aead13e587180c76ce7d8cd110e0ef006fc6d3fcd134d37224", "output_hash": "47a1f91655ee5e79f80f2b6ca7c42c61c53d46a25f634a0b9064a79e72f105b4"}
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added runs/raw/E3_core_sender.jsonl +512 −0
@@ -0,0 +1,512 @@
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added runs/raw/E7_qtypes.jsonl +2520 −0
@@ -0,0 +1,2520 @@
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modified src/wp30/analysis/analyze.py +14 −0
@@ -274,6 +274,20 @@ def main(protocol_path: str) -> int:
274 q = cluster_bootstrap_mean(g, "quality_operational", n_boot=500) 274 q = cluster_bootstrap_mean(g, "quality_operational", n_boot=500)
275 rows.append({"family": fam, "provider_model": recv, "condition": cond, "quality": q["mean"], "lo": q["lo"], "hi": q["hi"], "n": q["n"], "cost_usd_mean": g["cost_usd"].mean(), "ttft_ms_p50": g["ttft_ms"].median(), "total_ms_p50": g["total_wall_ms"].median(), "success_rate": (g["status"] == "success").mean()}) 275 rows.append({"family": fam, "provider_model": recv, "condition": cond, "quality": q["mean"], "lo": q["lo"], "hi": q["hi"], "n": q["n"], "cost_usd_mean": g["cost_usd"].mean(), "ttft_ms_p50": g["ttft_ms"].median(), "total_ms_p50": g["total_wall_ms"].median(), "success_rate": (g["status"] == "success").mean()})
276 t = pd.DataFrame(rows); p = TAB / "ED_hosted.csv"; t.to_csv(p, index=False); _prov(p, src, "hosted receivers; cost estimated from provider price tables") 276 t = pd.DataFrame(rows); p = TAB / "ED_hosted.csv"; t.to_csv(p, index=False); _prov(p, src, "hosted receivers; cost estimated from provider price tables")
277+ if "E3_core" in exps:
278+ d3 = load_experiments(["E3_core.jsonl"]); s3 = load_experiments(["E3_core_sender.jsonl"]) if "E3_core_sender" in exps else pd.DataFrame()
279+ src3 = ["runs/raw/E3_core.jsonl", "runs/raw/E3_core_sender.jsonl"]
280+ d3["cond_s"] = d3["condition"].str.replace("|mapper=runs/checkpoints/mapper_v2_qwen1b5_to_qwen3b.npz", "", regex=False).str.replace("budget_tokens=512", "512", regex=False).str.replace("keep_frac=0.25", "1/4", regex=False)
281+ if len(s3): s3["cond_s"] = "sender alone " + s3["condition"]
282+ allr = pd.concat([d3, s3], ignore_index=True)
283+ rows = []
284+ for (fam, cond), g in allr.groupby(["task_family", "cond_s"]):
285+ q = cluster_bootstrap_mean(g, "quality_operational"); byq = g.groupby("qtype")["quality_operational"].mean().to_dict()
286+ rows.append({"family": fam, "condition": cond, "quality": q["mean"], "lo": q["lo"], "hi": q["hi"], "n": q["n"], "n_docs": q["n_clusters"], "payload_bytes_median": g["serialized_payload_bytes"].median(),
287+ "cold_ms_p50": g["cost_cold_ms"].median(), "warm_ms_p50": g["cost_warm_query_ms"].median(), "unknown_share": g["prediction_artifact"].fillna("").str.lower().str.contains("unknown").mean(), **{f"q_{k}": v for k, v in byq.items()}})
288+ t3 = pd.DataFrame(rows); p = TAB / "E3_core_transfer.csv"; t3.to_csv(p, index=False); _prov(p, src3, "same-family mapper transfer vs text handoffs and references")
289+ primary_tests(d3.assign(condition=d3["cond_s"]), "E3_core", src3, [{"name": "C2 mapper vs A4 summary (synthetic)", "a": "C2", "b": "A4|512|blind", "families": ["synthetic"], "hypothesis": "H10"},
290+ {"name": "C2 mapper vs A1 receiver full context (synthetic)", "a": "C2", "b": "A1", "families": ["synthetic"], "hypothesis": "H10"}])
277 if "E7_qtypes" in exps: 291 if "E7_qtypes" in exps:
278 d7 = load_experiments(["E7_qtypes.jsonl"]); src7 = ["runs/raw/E7_qtypes.jsonl"] 292 d7 = load_experiments(["E7_qtypes.jsonl"]); src7 = ["runs/raw/E7_qtypes.jsonl"]
279 rows = [] 293 rows = []
modified src/wp30/analysis/claims.py +32 −8
@@ -1,12 +1,36 @@
1−"""Claim ledger: every headline claim in the manuscript -> experiment ids, table/figure, rows, limitation. Written after analysis.""" 1+"""Claim ledger: every headline claim -> experiment ids, evidence table/figure, rows, script, status, limitation."""
2 from __future__ import annotations 2 from __future__ import annotations
3 import csv 3 import csv
4−from pathlib import Path
5 from ..util import ROOT 4 from ..util import ROOT
6 5
7−def write_ledger(claims: list[dict]): 6+CLAIMS = [
8− p = ROOT / "results/claim_ledger.csv"; p.parent.mkdir(parents=True, exist_ok=True) 7+ ("C01", "Lossless representations (A1, A7, B0, B1, B2-8bit) give the same quality within noise on every family and pair", "E1_core", "results/tables/E1_core_primary_tests.csv (T2, T6); table_primary", "rows T2,T6", "analyze.py", "confirmed", "two 3B pairs, 160 test documents"),
9− with open(p, "w", newline="") as f: 8+ ("C02", "4-bit packed KV quantization destroys Qwen2.5-3B (0%) but not Llama-3.2-3B (>=97%)", "E1_core", "E1_core_primary.csv B2|bits=4; primary_tests T3", "condition B2|bits=4", "analyze.py", "confirmed (H2)", "mlx per-token affine quantization, group 64"),
10− w = csv.DictWriter(f, fieldnames=["claim_id", "claim", "experiment_ids", "evidence_table_or_figure", "rows_or_filter", "script", "status", "limitation"]) 9+ ("C03", "Attention-informed eviction with the question (B5-aware) beats sink+recent at 1/4 by +44 points; blind window variant collapses", "E1_core", "primary_tests T4; E1_core_primary.csv", "B5 vs B3", "analyze.py", "confirmed (H3 first half, H7)", "question-aware method"),
11− w.writeheader(); w.writerows(claims) 10+ ("C04", "All eviction methods score 0 on executable tasks at 1/4 retention", "E1_core", "E1_core_primary.csv family=exec", "B3,B4,B5,B6,B9", "analyze.py", "measured", "3B receivers, 6-rule spec"),
12− return p 11+ ("C05", "Structured evidence (A5) is below prose summary (A4) at the same 512-token budget (-27 points, synthetic)", "E1_core", "primary_tests T1", "T1", "analyze.py", "H1 rejected", "sender spends budget on quotes; ~8 facts of 62"),
12+ ("C06", "zstd shrinks text ~3.5x with no quality/latency effect; ~20% on bf16 caches", "E1_core, E6_core", "E1_core_primary.csv A7; E6_network.csv codec=zstd", "A7; B1 zstd", "analyze.py", "confirmed (H8)", "zstd level 3"),
13+ ("C07", "Serialized 3B cache is 3-4 orders of magnitude larger than the text", "E1_core, E6_core", "E1_core_primary.csv payload_bytes_median; E6_network.csv bytes", "B0 vs A1", "analyze.py", "measured", "bf16, Qwen 215 MB vs 29 kB at ~8k tokens"),
14+ ("C08", "On 1 GbE with an M3 Ultra receiver, moving the cache (bf16 or 8-bit) answers faster than remote text prefill at 2k-16k tokens", "E6_core", "E6_network.csv; fig6", "lan rows", "analyze.py", "measured", "one link, one receiver; break-even depends on prefill/link ratio"),
15+ ("C09", "Repeated prose/structured relays lose facts at hop 1 and are nearly lossless afterwards (no monotone decay)", "E4_core", "E4_core_handoffs.csv; fig5", "hops 1-8", "analyze.py", "H5 rejected", "ledger metric = normalized value containment"),
16+ ("C10", "Unsupported additions are near zero in 7,200 relayed messages", "E4_core", "E4_core_handoffs.csv unsupported_mean", "all chains", "analyze.py", "measured", "regex-based detection"),
17+ ("C11", "Metered repair raises prose-chain retention 32->38% and accuracy 40->44% for ~5.9 kB cumulative fetch", "E4_core", "E4_core_handoffs.csv repairable", "A4 repairable", "analyze.py", "H6 partial", "repair limited to entities still named"),
18+ ("C12", "Concatenated summaries compose as well as full context on multi-hop joins; merged structured records do not", "E5_core", "E5_core_fanin.csv; table_fanin", "concat_A4 vs merge_A5", "analyze.py", "measured", "k=2,4; 3B aggregator"),
19+ ("C13", "Cache concatenation of independently prefilled sources yields plausible answers (70% multi-hop at k=2) despite invalid positions", "E5_core", "E5_core_fanin.csv cache_concat_negative_control", "k=2,4", "analyze.py", "negative control", "diagnostic only"),
20+ ("C14", "Hosted receivers recover 100% from full text, ~30% from the local 512-token summary, 12.5% from structured JSON", "ED_hosted_core", "ED_hosted.csv; table_hosted", "synthetic rows", "analyze.py", "measured", "provider default sampling; 8 documents/family"),
21+ ("C15", "Anthropic prompt caching did not engage below the documented 4,096-token minimum (2k docs) and halved estimated cost on 8k docs", "ED_hosted_core", "runs/raw/ED_hosted_core.jsonl provider_cached_usage", "A1 provider_cache rows", "hosted_run.py", "measured", "billing fields only"),
22+ ("C16", "Dev-fitted router matches best static quality at half the amortized latency in the blind regime; no gain and higher cost in the aware regime", "E8_router (E1_core records)", "E8_router_summary.csv; E8_router_tests.csv; fig7", "router vs best_static_dev", "router_eval.py", "H9 mixed", "off-policy on full factorial; dev table from pilot"),
23+ ("C17", "Linear ridge KV mapper (Qwen 1.5B->3B) yields coherent but uninformative receiver outputs; below sender-summary transfer", "E3_core", "E3_core tables", "C2 vs A4", "analyze.py", "confirmed (H10): C2 24.5% overall but 0-4% on content questions; negation yes/no at base rate", "reconstruction-only mapper; no SwaT reproduction"),
24+ ("C18", "Identity round trip and 100% retention reproduce native logits exactly; cached-prefix equivalence holds within registered tolerance", "E0 tests", "tests/test_cache_correctness.py", "12 tests", "pytest", "confirmed", "MLX nondeterminism across chunking documented"),
25+ ("C19", "Minimal reproduction reran 64 trials from a clean process with identical outputs and scores", "E9", "reproduction/report.json", "all", "reproduce.py", "confirmed", "same machine, same chunking"),
26+ ("C20", "Cross-family cache transfer was not executed (no SwaT checkpoint/licence; mapper budget)", "coverage", "literature/audit_group1.md; table1_taxonomy", "SwaT row", "n/a", "coverage gap", "partial transfer study"),
27+]
28+
29+def write_ledger(path=ROOT / "results/claim_ledger.csv"):
30+ path.parent.mkdir(parents=True, exist_ok=True)
31+ with open(path, "w", newline="") as f:
32+ w = csv.writer(f); w.writerow(["claim_id", "claim", "experiment_ids", "evidence_table_or_figure", "rows_or_filter", "script", "status", "limitation"]); w.writerows(CLAIMS)
33+ return path
34+
35+if __name__ == "__main__":
36+ print(write_ledger())
modified src/wp30/analysis/latex_tables.py +23 −0
@@ -66,6 +66,29 @@ def main():
66 out = th[["model", "cond", "family", "n", "quality_ci", "cost_musd", "ttft_ms_p50", "total_ms_p50"]].rename(columns={"cond": "condition", "cost_musd": "cost (m\\$)", "ttft_ms_p50": "TTFT p50 (ms)", "total_ms_p50": "total p50 (ms)"}) 66 out = th[["model", "cond", "family", "n", "quality_ci", "cost_musd", "ttft_ms_p50", "total_ms_p50"]].rename(columns={"cond": "condition", "cost_musd": "cost (m\\$)", "ttft_ms_p50": "TTFT p50 (ms)", "total_ms_p50": "total p50 (ms)"})
67 out["family"] = out["family"].str.replace("public_", "") 67 out["family"] = out["family"].str.replace("public_", "")
68 df_to_tex(out.sort_values(["model", "family", "condition"]), OUT / "table_hosted.tex", "llllrrrr", "{:,.0f}") 68 df_to_tex(out.sort_values(["model", "family", "condition"]), OUT / "table_hosted.tex", "llllrrrr", "{:,.0f}")
69+ fq = TAB / "E1_core_by_question_type.csv"
70+ if fq.exists():
71+ tq = pd.read_csv(fq); tq["cell"] = tq.apply(lambda r: f"{100*r.quality:.0f} [{100*r.lo:.0f},{100*r.hi:.0f}]", axis=1)
72+ tq["cond"] = tq["condition"].str.replace("budget_tokens=512", "512", regex=False).str.replace("keep_frac=0.25", "1/4", regex=False).str.replace("|max_fetch_bytes=2000|max_lookups=6", "", regex=False).str.replace("policy=attention", "att", regex=False).str.replace("codec=zstd", "zstd", regex=False).str.replace("|", " ", regex=False)
73+ piv = tq.pivot_table(index="cond", columns="qtype", values="cell", aggfunc="first").reset_index().rename(columns={"cond": "condition"}).fillna("--")
74+ df_to_tex(piv, OUT / "table_qtype_core.tex", "l" + "r" * (len(piv.columns) - 1))
75+ ff = TAB / "E1_core_failures.csv"
76+ if ff.exists():
77+ tf = pd.read_csv(ff); df_to_tex(tf.rename(columns={tf.columns[0]: "method"}), OUT / "table_failures.tex", "l" + "r" * (len(tf.columns) - 1), "{:.0f}")
78+ f7 = TAB / "E7_qtypes.csv"
79+ if f7.exists():
80+ t7 = pd.read_csv(f7); t7["cell"] = t7.apply(lambda r: f"{100*r.quality:.0f} [{100*r.lo:.0f},{100*r.hi:.0f}]", axis=1)
81+ t7["cond"] = t7["condition"].str.replace("budget_tokens=512", "512", regex=False).str.replace("keep_frac=0.25", "1/4", regex=False).str.replace("|", " ", regex=False) + " " + t7["pair"]
82+ piv = t7.pivot_table(index="cond", columns="qtype", values="cell", aggfunc="first").reset_index().rename(columns={"cond": "condition / pair"}).fillna("--")
83+ df_to_tex(piv, OUT / "table_qtypes_exploratory.tex", "l" + "r" * (len(piv.columns) - 1))
84+ f3 = TAB / "E3_core_transfer.csv"
85+ if f3.exists():
86+ t3 = pd.read_csv(f3)
87+ t3["quality_ci"] = t3.apply(lambda r: f"{100*r.quality:.0f} [{100*r.lo:.0f},{100*r.hi:.0f}]", axis=1)
88+ cols = ["family", "condition", "n", "quality_ci", "unknown_share"] + [c for c in ["q_single_fact", "q_exact_copy", "q_multi_hop", "q_negation", "q_exec"] if c in t3.columns] + ["payload_bytes_median", "warm_ms_p50"]
89+ out = t3[cols].rename(columns={"quality_ci": "accuracy (\\%)", "unknown_share": "`unknown' share", "payload_bytes_median": "payload B", "warm_ms_p50": "warm ms"})
90+ out.columns = [c.replace("q_", "") for c in out.columns]
91+ df_to_tex(out.sort_values(["family", "condition"]), OUT / "table_transfer.tex", "ll" + "r" * (len(out.columns) - 2), "{:,.2f}")
69 print("latex tables written") 92 print("latex tables written")
70 93
71 if __name__ == "__main__": 94 if __name__ == "__main__":
72 95
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