WP30: final analysis (E7 exploratory), paper layout pass, FINAL_REPORT, index updates

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 4 days ago (Oct 30, 2026) parent 2ccc6b7 Browse files

91 changed files +3,272 −181

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		@@ -19,7 +19,7 @@ Paper: `paper/WP30_Boucher_Cache_Compression_Knowledge_Transfer.pdf` (LaTeX in `
19	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	20	\| E8 \| router fitted on dev pilot, evaluated off-policy on the full-factorial test records \| 640 instances × 2 regimes \| laptop \| complete \|
21	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 \|
	22	+\| E7_qtypes \| EXPLORATORY: question types 6–15 (negation, revision, temporal, aggregation, units, unanswerable) \| 5 600 \| laptop \| complete (appendix, exploratory) \|
23	23
24	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	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.

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		`@@ -11,6 +11,16 @@`
11	11	\end{table}
12	12
13	13	\subsection{Exploratory: question types not covered by the core (A-004)}
	14	+\label{app:qtypes}
	15	+The five-question core limit kept only single-fact, exact-copy and multi-hop questions. This exploratory run (5\,600 trials; amendment A-004;
	16	+no confirmatory test) asks the remaining ten question types of the 40 synthetic test documents for seven conditions. Three observations, all
	17	+exploratory: (i) \emph{revision/precedence} questions (``current budget under the later-date rule'') are the ones that lossy text loses most---full
	18	+text 75--88\,\%, blind summaries 12--20\,\%, structured evidence 4--8\,\%, sink+recent 10--15\,\%---while the question-aware attention cache keeps
	19	+76\,\% on Llama but only 28\,\% on Qwen; (ii) \emph{negation} (cancelled vs postponed, yes/no) is answered at 72--96\,\% by every condition that
	20	+retains anything, close to the base rate of the majority answer, and must not be read as evidence of fact transfer; (iii) \emph{aggregation}
	21	+(summing five to six amounts) is at 0\,\% for every condition including full text, and \emph{unit conversion} is solved only by Qwen (57--60\,\%
	22	+with full text, 8-bit cache or aware eviction) and never from a summary---these are receiver-capability limits, not communication effects.
	23	+Unanswerable questions were abstained on correctly 94--100\,\% of the time in every condition.
14	24	\begin{figure}[H]\centering\includegraphics[width=\textwidth]{figA_qtypes_exploratory.pdf}
15	25	\caption{EXPLORATORY. Accuracy on negation, revision/precedence, temporal order, aggregation, unit conversion and unanswerable questions
16	26	(questions 6--15 of the 40 synthetic test documents) for a subset of methods; no confirmatory test attached.}\end{figure}

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		`@@ -95,6 +95,7 @@`
95	95	\usepackage{longtable}
96	96	\usepackage{array}
97	97	\usepackage{pdflscape}
	98	+\usepackage{rotating}
98	99	\usepackage{adjustbox}
99	100	\newcommand{\WPnumber}{30}
100	101	\newcommand{\WPtitle}{Beyond Text: Cache Compression and Knowledge Transfer in Multi-Agent Systems}

modified paper/sections/abstract_text.tex +10 −14

		`@@ -2,20 +2,16 @@`
2	2	Agents built on language models hand information to one another as text, summaries, structured evidence, shared-memory references or---when
3	3	they share a checkpoint---attention state (KV cache). We ask under which task, model, cache, network and reuse conditions each representation
4	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
	5	+operator (Claude Code) on Apple-Silicon hardware with the MLX runtime, whose caches are inspectable tensors. Twenty-two conditions (truncation, extractive selection, summaries,
	6	+structured evidence, metered retrieval, a lossless codec, LLMLingua-2, native/serialized caches, packed 8/4-bit quantization, four eviction
	7	+rules) are measured on 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	8	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
	9	+experiments measure two-node cache transfer over a 1~GbE LAN, relays of 1--8 handoffs with an evaluator-only fact ledger, fan-in with
	10	+conflicting evidence, a same-family learned KV mapper, hosted receivers and a development-fitted router. Lossless
12	11	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.%
	12	+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 for Llama and
	13	+fatal for Qwen; attention-based eviction keeps 78--98\,\% of private-fact accuracy with the question and 12--19\,\% without it; no eviction
	14	+survives a code specification. At a 512-token budget prose summaries beat structured evidence (38--44 vs 12--15\,\%); relays are
	15	+nearly lossless after the first hop and invent almost nothing. A ridge KV mapper transfers no
	16	+content while producing coherent abstention---an argument for compatibility manifests and negative controls. The router halves amortized latency at equal quality when questions are unknown. Cross-family cache transfer was not executed. Code, records, protocol, checkpoint and a minimal reproduction are released.%
21	17	}

modified paper/sections/adaptive.tex +2 −2

		`@@ -10,11 +10,11 @@ action was run on every instance), and the router's own lookup time (${\approx}1`
10	10	Table~\ref{tab:router} compare it with the development-best static action, full context, the native cache, the question-aware attention
11	11	cache and the infeasible oracle that picks the best action per instance.
12	12
13		−\begin{figure}[t]\centering\includegraphics[width=\textwidth]{fig7_router.pdf}
	13	+\begin{figure}[H]\centering\includegraphics[width=\textwidth]{fig7_router.pdf}
14	14	\caption{Dev-fitted router against static policies on held-out test instances (both pairs, all families; $n=640$ per regime). The oracle is
15	15	an infeasible upper reference.}\label{fig:router}\end{figure}
16	16
17		−\begin{table}[t]\centering\footnotesize
	17	+\begin{table}[H]\centering\footnotesize
18	18	\caption{Router evaluation on held-out test instances. Quality in percent with 95\,\% cluster-bootstrap CI; cost = mean amortized latency per
19	19	query (ms, document build divided by the number of reusing questions); fallback = share of instances routed by the nearest-cell fallback.}\label{tab:router}
20	20	\begin{tabular}{@{}llrrrr@{}}\toprule

modified paper/sections/formulation.tex +4 −4

		`@@ -49,10 +49,10 @@ with batch $B$, layers $L$, stored positions $T$, KV heads $H_{KV}$, head dimens`
49	49	\eqref{eq:mkv} only as a consistency check: the harness measures stored tensor bytes from the arrays themselves (which, for the
50	50	packed quantized caches, include the uint32 words, the per-group scales and biases) and measures the serialized payload from the file
51	51	written to disk, including the compatibility manifest. Two ratios are reported with explicit denominators,
52		−\begin{equation}
53		− CR_{\text{payload}} = \frac{\text{reference serialized payload bytes}}{\text{method serialized payload bytes}},\qquad
54		− CR_{KV} = \frac{\text{native cache tensor bytes}}{\text{stored cache tensor and metadata bytes}},
55		−\end{equation}
	52	+\begin{align}
	53	+ CR_{\text{payload}} &= \frac{\text{reference serialized payload bytes}}{\text{method serialized payload bytes}},\\
	54	+ CR_{KV} &= \frac{\text{native cache tensor bytes}}{\text{stored cache tensor and metadata bytes}},
	55	+\end{align}
56	56	and a text-token ratio is never treated as a cache-position ratio.
57	57
58	58	\paragraph{Timing boundaries.} Each trial records separate wall-clock spans with device synchronization before and after

modified paper/sections/methods.tex +1 −1

		`@@ -25,7 +25,7 @@ and \emph{custom} baselines; the label of every method is in Table~\ref{tab:taxo`
25	25	\item[A7 lossless transport codec.] The raw text is compressed with Zstandard (level~3) for transport and decompressed at the receiver;
26	26	the receiver prefill is unchanged. Codec times are measured.
27	27	\item[A8 learned prompt compression.] LLMLingua-2 \citep{pan2024llmlingua2}, official package (\texttt{llmlingua}~0.2.2) with the
28		− released \texttt{xlm-roberta-large-meetingbank} compressor, rate $=$ budget/source tokens, run on the Metal backend of PyTorch
	28	+ released XLM-RoBERTa-large MeetingBank compressor, rate $=$ budget/source tokens, run on the Metal backend of PyTorch
29	29	(fp32). It is question-agnostic by construction.
30	30	\end{description}
31	31	A4 and A5 use the same sender checkpoint, the same budget and prompts of comparable length; both prompts were tuned on development

modified paper/sections/reproducibility.tex +4 −3

		`@@ -26,9 +26,10 @@ the provider credentials and reproduce only up to provider nondeterminism.`
26	26
27	27	\paragraph{Reproduction commands.}
28	28	\begin{footnotesize}\begin{verbatim}
29		−make reproduce-analysis # tables + figures from runs/raw, provenance audit
30		−python -m wp30 reproduce --profile reproduction/minimal.yaml # re-run a fixed subset, compare greedy outputs
31		−make paper # compile this document
	29	+make reproduce-analysis # tables + figures from runs/raw, provenance audit
	30	+python -m wp30 reproduce --profile reproduction/minimal.yaml
	31	+ # re-run a fixed subset, compare greedy outputs
	32	+make paper # compile this document
32	33	\end{verbatim}\end{footnotesize}
33	34	Greedy decoding on MLX is deterministic for an identical prefill chunking but not bitwise across chunkings (Section~\ref{sec:correctness});
34	35	the reproduction report lists identical output hashes and identical scores separately.

modified paper/sections/results_frontier.tex +6 −3

		`@@ -11,12 +11,12 @@ condition (median payload, mean quality, 95\,\% cluster-bootstrap CI). Text meth`
11	11	$10^7$--$10^9$ bytes: a 3B-parameter cache of a document is three to four orders of magnitude larger than the document. Question-aware and
12	12	question-blind conditions are both shown here; Appendix figures separate them.}\label{fig:frontier}\end{figure}
13	13
14		−\begin{table}[p]\centering\scriptsize
	14	+\begin{sidewaystable}[p]\centering\scriptsize
15	15	\caption{Primary comparison: quality (\%, with 95\,\% cluster-bootstrap CI) per condition, family and pair. Synthetic = private-fact accuracy
16	16	(5 questions per document: single fact, exact copy, multi-hop), exec = hidden-test pass (all 5 tests), QASPER/2Wiki = LongBench F1.
17	17	``blind''/``aware'' = question access of the compressor. Latency and bytes for the synthetic family in Table~\ref{tab:cost}.}\label{tab:primary}
18	18	\adjustbox{max width=\textwidth}{\input{tables/table_primary}}
19		−\end{table}
	19	+\end{sidewaystable}
20	20
21	21	\paragraph{Lossless representations are interchangeable; nothing else is.} Full text (A1), zstd-compressed text (A7), the native cache
22	22	(B0), the serialized and reloaded cache (B1) and the 8-bit packed cache (B2-8) give the same answers within noise on every family and pair
		@@ -53,7 +53,10 @@ simply absent from the message; the receiver answers ``unknown'' (correctly abst
53	53	retrieval (A6) repair little (\QSyntheticQwenAXBRBlind{}\,\%/\QSyntheticLlamaAXBRBlind{}\,\%) because the reference index inherits the same
54	54	coverage. Deterministic extraction (A3) is the strongest cheap method: blind centrality selection keeps \QSyntheticQwenAHBBlind{}\,\%, and with
55	55	the question (BM25) \QSyntheticQwenAHBAware{}\,\%/\QSyntheticLlamaAHBAware{}\,\%---indistinguishable from full text at 1/15 of the bytes.
56		−Head/tail truncation (A2) keeps exactly the facts that fall in the kept windows (\QSyntheticQwenAWB{}\,\%). On the executable family the
	56	+Head/tail truncation (A2) keeps exactly the facts that fall in the kept windows (\QSyntheticQwenAWB{}\,\%). The question types that the
	57	+five-question core did not cover (negation, revision/precedence, temporal order, aggregation, units, unanswerable) were run as an exploratory
	58	+extension after the core and are reported, labelled as such, in Appendix~\ref{app:qtypes}; the headline there is that timestamped revisions are
	59	+the facts that summaries lose most. On the executable family the
57	60	ordering changes: blind extractive selection keeps the whole specification (\QExecQwenAHBBlind{}\,\%/\QExecLlamaAHBBlind{}\,\%) because the
58	61	rules are dense in digits and identifiers, and the Llama summary preserves the rules better than the Qwen summary
59	62	(\QExecLlamaAFBBlind{} vs \QExecQwenAFBBlind{}\,\%). LLMLingua-2 (A8) at the same rate removes the very tokens the questions ask for

modified paper/sections/results_hosted.tex +1 −1

		`@@ -10,7 +10,7 @@ receiver cannot recover a number that the message does not contain, and the refe`
10	10	the 2\,000-byte retrieval budget. On QASPER the hosted models score higher F1 than the local receivers from the same material (0.48 versus
11	11	0.35 for A1), i.e., receiver capacity matters when the material is complete.
12	12
13		−\begin{table}[t]\centering\scriptsize
	13	+\begin{table}[H]\centering\scriptsize
14	14	\caption{Hosted receivers on messages built by the local sender (test split, first 8 documents per family). Quality = accuracy (synthetic,
15	15	exec) or F1 (QASPER) with 95\,\% cluster-bootstrap CI; cost = estimated per-query cost from provider price tables (m\$ = 0.001~USD); TTFT
16	16	= time to first streamed content token (Anthropic only; the OpenAI Responses call was not streamed). ``+cache'' = documented prompt caching

modified paper/tables/numbers.tex +10 −10

		`@@ -1,5 +1,5 @@`
1	1	% auto-generated by wp30.analysis.numbers — do not edit
2		−\newcommand{\nCoreTrials}{13,280}
	2	+\newcommand{\nCoreTrials}{13,520}
3	3	\newcommand{\QExecLlamaAZ}{0}
4	4	\newcommand{\QloExecLlamaAZ}{0}
5	5	\newcommand{\QhiExecLlamaAZ}{0}
		`@@ -57,9 +57,9 @@`
57	57	\newcommand{\QExecLlamaAEBBlind}{0}
58	58	\newcommand{\QloExecLlamaAEBBlind}{0}
59	59	\newcommand{\QhiExecLlamaAEBBlind}{0}
60		−\newcommand{\BExecLlamaAEBBlind}{--}
61		−\newcommand{\TcoldExecLlamaAEBBlind}{0}
62		−\newcommand{\TwarmExecLlamaAEBBlind}{0}
	60	+\newcommand{\BExecLlamaAEBBlind}{2,398}
	61	+\newcommand{\TcoldExecLlamaAEBBlind}{1,808}
	62	+\newcommand{\TwarmExecLlamaAEBBlind}{1,636}
63	63	\newcommand{\QExecLlamaBZ}{95}
64	64	\newcommand{\QloExecLlamaBZ}{88}
65	65	\newcommand{\QhiExecLlamaBZ}{100}
		`@@ -786,12 +786,12 @@`
786	786	\newcommand{\BSyntheticLlamaASZstd}{2,726}
787	787	\newcommand{\TcoldSyntheticLlamaASZstd}{2,857}
788	788	\newcommand{\TwarmSyntheticLlamaASZstd}{2,857}
789		−\newcommand{\QSyntheticLlamaAEBBlind}{0}
790		−\newcommand{\QloSyntheticLlamaAEBBlind}{0}
791		−\newcommand{\QhiSyntheticLlamaAEBBlind}{0}
792		−\newcommand{\BSyntheticLlamaAEBBlind}{--}
793		−\newcommand{\TcoldSyntheticLlamaAEBBlind}{0}
794		−\newcommand{\TwarmSyntheticLlamaAEBBlind}{0}
	789	+\newcommand{\QSyntheticLlamaAEBBlind}{16}
	790	+\newcommand{\QloSyntheticLlamaAEBBlind}{10}
	791	+\newcommand{\QhiSyntheticLlamaAEBBlind}{22}
	792	+\newcommand{\BSyntheticLlamaAEBBlind}{2,583}
	793	+\newcommand{\TcoldSyntheticLlamaAEBBlind}{523}
	794	+\newcommand{\TwarmSyntheticLlamaAEBBlind}{108}
795	795	\newcommand{\QSyntheticLlamaBZ}{99}
796	796	\newcommand{\QloSyntheticLlamaBZ}{98}
797	797	\newcommand{\QhiSyntheticLlamaBZ}{100}

modified paper/tables/table_qtypes_exploratory.tex +14 −14

		`@@ -2,19 +2,19 @@`
2	2	\toprule
3	3	condition / pair & aggregation & multi\_hop & negation & revision & temporal & unanswerable & unit\_conversion \\
4	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] \\
	5	+A1 Llama-3.2-3B & 0 [0,0] & 100 [100,100] & 90 [82,96] & 75 [66,84] & 62 [48,78] & 95 [90,99] & 0 [0,0] \\
	6	+A1 Qwen2.5-3B & 0 [0,0] & 100 [100,100] & 96 [91,100] & 88 [81,94] & 55 [40,70] & 98 [94,100] & 57 [41,72] \\
	7	+A3 512 blind Llama-3.2-3B & 0 [0,0] & 0 [0,0] & 86 [79,92] & 84 [76,91] & 65 [50,80] & 100 [100,100] & 0 [0,0] \\
	8	+A3 512 blind Qwen2.5-3B & 0 [0,0] & 0 [0,0] & 90 [83,95] & 70 [61,79] & 60 [45,75] & 100 [100,100] & 0 [0,0] \\
	9	+A4 512 blind Llama-3.2-3B & 2 [0,8] & 30 [15,45] & 95 [89,100] & 12 [6,21] & 32 [18,48] & 100 [100,100] & 2 [0,8] \\
	10	+A4 512 blind Qwen2.5-3B & 0 [0,0] & 48 [32,62] & 72 [62,81] & 20 [11,30] & 48 [32,62] & 100 [100,100] & 32 [18,48] \\
	11	+A5 512 blind Llama-3.2-3B & 0 [0,0] & 10 [2,20] & 50 [38,60] & 8 [2,15] & 10 [2,20] & 100 [100,100] & 0 [0,0] \\
	12	+A5 512 blind Qwen2.5-3B & 0 [0,0] & 15 [5,28] & 49 [37,60] & 4 [0,10] & 8 [0,18] & 100 [100,100] & 15 [5,28] \\
	13	+B2 bits=8 Llama-3.2-3B & 0 [0,0] & 100 [100,100] & 90 [82,96] & 76 [68,85] & 62 [48,78] & 95 [90,99] & 0 [0,0] \\
	14	+B2 bits=8 Qwen2.5-3B & 0 [0,0] & 100 [100,100] & 94 [88,99] & 88 [81,94] & 55 [40,70] & 98 [94,100] & 60 [45,75] \\
	15	+B3 1/4 Llama-3.2-3B & 0 [0,0] & 15 [5,28] & 72 [65,80] & 15 [7,24] & 57 [42,72] & 94 [88,99] & 2 [0,8] \\
	16	+B3 1/4 Qwen2.5-3B & 0 [0,0] & 18 [8,30] & 91 [85,98] & 10 [2,19] & 55 [41,70] & 99 [96,100] & 8 [0,15] \\
	17	+B5 1/4 aware Llama-3.2-3B & 0 [0,0] & 95 [88,100] & 85 [76,92] & 76 [69,84] & 70 [55,85] & 95 [90,99] & 0 [0,0] \\
	18	+B5 1/4 aware Qwen2.5-3B & 0 [0,0] & 57 [42,72] & 96 [92,100] & 28 [18,38] & 55 [40,71] & 98 [94,100] & 60 [42,72] \\
19	19	\bottomrule
20	20	\end{tabular}

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modified results/provenance.json +30 −30

		`@@ -4,7 +4,7 @@`
4	4	"runs/raw/E1_core.jsonl"
5	5	],
6	6	"script": "src/wp30/analysis/analyze.py",
7		− "generated_utc": "2026-10-30T09:02:37.609345Z",
	7	+ "generated_utc": "2026-10-30T09:36:25.855895Z",
8	8	"note": "per-condition quality (cluster bootstrap CI) and resource medians"
9	9	},
10	10	"results/tables/E1_core_failures.csv": {
		`@@ -12,7 +12,7 @@`
12	12	"runs/raw/E1_core.jsonl"
13	13	],
14	14	"script": "src/wp30/analysis/analyze.py",
15		− "generated_utc": "2026-10-30T09:02:37.611126Z",
	15	+ "generated_utc": "2026-10-30T09:36:25.857450Z",
16	16	"note": "attempt status counts by method"
17	17	},
18	18	"results/tables/E1_core_by_question_type.csv": {
		`@@ -20,7 +20,7 @@`
20	20	"runs/raw/E1_core.jsonl"
21	21	],
22	22	"script": "src/wp30/analysis/analyze.py",
23		− "generated_utc": "2026-10-30T09:02:37.730657Z",
	23	+ "generated_utc": "2026-10-30T09:36:25.963048Z",
24	24	"note": "synthetic accuracy by question type"
25	25	},
26	26	"results/tables/E1_core_primary_tests.csv": {
		`@@ -28,7 +28,7 @@`
28	28	"runs/raw/E1_core.jsonl"
29	29	],
30	30	"script": "src/wp30/analysis/analyze.py",
31		− "generated_utc": "2026-10-30T09:02:37.786568Z",
	31	+ "generated_utc": "2026-10-30T09:36:26.010368Z",
32	32	"note": "registered primary comparisons, paired, cluster bootstrap, Holm"
33	33	},
34	34	"results/figures/fig2_quality_vs_bytes.pdf": {
		`@@ -36,7 +36,7 @@`
36	36	"runs/raw/E1_core.jsonl"
37	37	],
38	38	"script": "src/wp30/analysis/analyze.py",
39		− "generated_utc": "2026-10-30T09:02:38.459020Z",
	39	+ "generated_utc": "2026-10-30T09:36:26.618975Z",
40	40	"note": "points = conditions; x = median payload bytes; y = mean quality"
41	41	},
42	42	"results/figures/fig2_quality_vs_bytes.png": {
		`@@ -44,7 +44,7 @@`
44	44	"runs/raw/E1_core.jsonl"
45	45	],
46	46	"script": "src/wp30/analysis/analyze.py",
47		− "generated_utc": "2026-10-30T09:02:38.666585Z",
	47	+ "generated_utc": "2026-10-30T09:36:26.800030Z",
48	48	"note": "points = conditions; x = median payload bytes; y = mean quality"
49	49	},
50	50	"results/figures/figA_quality_vs_bytes_blind.pdf": {
		`@@ -52,7 +52,7 @@`
52	52	"runs/raw/E1_core.jsonl"
53	53	],
54	54	"script": "src/wp30/analysis/analyze.py",
55		− "generated_utc": "2026-10-30T09:02:39.176959Z",
	55	+ "generated_utc": "2026-10-30T09:36:27.219230Z",
56	56	"note": "points = conditions; x = median payload bytes; y = mean quality"
57	57	},
58	58	"results/figures/figA_quality_vs_bytes_blind.png": {
		`@@ -60,7 +60,7 @@`
60	60	"runs/raw/E1_core.jsonl"
61	61	],
62	62	"script": "src/wp30/analysis/analyze.py",
63		− "generated_utc": "2026-10-30T09:02:39.359044Z",
	63	+ "generated_utc": "2026-10-30T09:36:27.370309Z",
64	64	"note": "points = conditions; x = median payload bytes; y = mean quality"
65	65	},
66	66	"results/figures/figA_quality_vs_bytes_aware.pdf": {
		`@@ -68,7 +68,7 @@`
68	68	"runs/raw/E1_core.jsonl"
69	69	],
70	70	"script": "src/wp30/analysis/analyze.py",
71		− "generated_utc": "2026-10-30T09:02:39.746284Z",
	71	+ "generated_utc": "2026-10-30T09:36:27.681216Z",
72	72	"note": "points = conditions; x = median payload bytes; y = mean quality"
73	73	},
74	74	"results/figures/figA_quality_vs_bytes_aware.png": {
		`@@ -76,7 +76,7 @@`
76	76	"runs/raw/E1_core.jsonl"
77	77	],
78	78	"script": "src/wp30/analysis/analyze.py",
79		− "generated_utc": "2026-10-30T09:02:39.940182Z",
	79	+ "generated_utc": "2026-10-30T09:36:27.846184Z",
80	80	"note": "points = conditions; x = median payload bytes; y = mean quality"
81	81	},
82	82	"results/figures/fig3_quality_vs_latency_memory.pdf": {
		`@@ -84,7 +84,7 @@`
84	84	"runs/raw/E1_core.jsonl"
85	85	],
86	86	"script": "src/wp30/analysis/analyze.py",
87		− "generated_utc": "2026-10-30T09:02:40.285980Z",
	87	+ "generated_utc": "2026-10-30T09:36:28.116446Z",
88	88	"note": ""
89	89	},
90	90	"results/figures/fig3_quality_vs_latency_memory.png": {
		`@@ -92,7 +92,7 @@`
92	92	"runs/raw/E1_core.jsonl"
93	93	],
94	94	"script": "src/wp30/analysis/analyze.py",
95		− "generated_utc": "2026-10-30T09:02:40.475124Z",
	95	+ "generated_utc": "2026-10-30T09:36:28.280741Z",
96	96	"note": ""
97	97	},
98	98	"results/figures/fig4_latency_decomposition.pdf": {
		`@@ -100,7 +100,7 @@`
100	100	"runs/raw/E1_core.jsonl"
101	101	],
102	102	"script": "src/wp30/analysis/analyze.py",
103		− "generated_utc": "2026-10-30T09:02:40.560198Z",
	103	+ "generated_utc": "2026-10-30T09:36:28.355146Z",
104	104	"note": ""
105	105	},
106	106	"results/figures/fig4_latency_decomposition.png": {
		`@@ -108,7 +108,7 @@`
108	108	"runs/raw/E1_core.jsonl"
109	109	],
110	110	"script": "src/wp30/analysis/analyze.py",
111		− "generated_utc": "2026-10-30T09:02:40.620070Z",
	111	+ "generated_utc": "2026-10-30T09:36:28.408655Z",
112	112	"note": ""
113	113	},
114	114	"results/figures/fig5_handoffs.pdf": {
		`@@ -116,7 +116,7 @@`
116	116	"runs/raw/E4_core.jsonl"
117	117	],
118	118	"script": "src/wp30/analysis/analyze.py",
119		− "generated_utc": "2026-10-30T09:02:45.615826Z",
	119	+ "generated_utc": "2026-10-30T09:36:33.036763Z",
120	120	"note": "means over documents; chains defined in configs/chains*.yaml"
121	121	},
122	122	"results/figures/fig5_handoffs.png": {
		`@@ -124,7 +124,7 @@`
124	124	"runs/raw/E4_core.jsonl"
125	125	],
126	126	"script": "src/wp30/analysis/analyze.py",
127		− "generated_utc": "2026-10-30T09:02:45.675774Z",
	127	+ "generated_utc": "2026-10-30T09:36:33.089912Z",
128	128	"note": "means over documents; chains defined in configs/chains*.yaml"
129	129	},
130	130	"results/tables/E4_core_handoffs.csv": {
		`@@ -132,7 +132,7 @@`
132	132	"runs/raw/E4_core.jsonl"
133	133	],
134	134	"script": "src/wp30/analysis/analyze.py",
135		− "generated_utc": "2026-10-30T09:02:45.778336Z",
	135	+ "generated_utc": "2026-10-30T09:36:33.182901Z",
136	136	"note": "repeated handoffs"
137	137	},
138	138	"results/tables/E4_core_h4_primary_tests.csv": {
		`@@ -140,7 +140,7 @@`
140	140	"runs/raw/E4_core.jsonl"
141	141	],
142	142	"script": "src/wp30/analysis/analyze.py",
143		− "generated_utc": "2026-10-30T09:02:45.791552Z",
	143	+ "generated_utc": "2026-10-30T09:36:33.192836Z",
144	144	"note": "registered primary comparisons, paired, cluster bootstrap, Holm"
145	145	},
146	146	"results/tables/E5_core_fanin.csv": {
		`@@ -148,7 +148,7 @@`
148	148	"runs/raw/E5_core.jsonl"
149	149	],
150	150	"script": "src/wp30/analysis/analyze.py",
151		− "generated_utc": "2026-10-30T09:02:45.898241Z",
	151	+ "generated_utc": "2026-10-30T09:36:33.287122Z",
152	152	"note": "fan-in by method and question type"
153	153	},
154	154	"results/figures/fig6_breakeven.pdf": {
		`@@ -156,7 +156,7 @@`
156	156	"runs/raw/E6_core.jsonl"
157	157	],
158	158	"script": "src/wp30/analysis/analyze.py",
159		− "generated_utc": "2026-10-30T09:02:45.992041Z",
	159	+ "generated_utc": "2026-10-30T09:36:33.373261Z",
160	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	162	"results/figures/fig6_breakeven.png": {
		`@@ -164,7 +164,7 @@`
164	164	"runs/raw/E6_core.jsonl"
165	165	],
166	166	"script": "src/wp30/analysis/analyze.py",
167		− "generated_utc": "2026-10-30T09:02:46.065845Z",
	167	+ "generated_utc": "2026-10-30T09:36:33.443566Z",
168	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	170	"results/tables/E6_network.csv": {
		`@@ -172,7 +172,7 @@`
172	172	"runs/raw/E6_core.jsonl"
173	173	],
174	174	"script": "src/wp30/analysis/analyze.py",
175		− "generated_utc": "2026-10-30T09:02:46.070991Z",
	175	+ "generated_utc": "2026-10-30T09:36:33.447916Z",
176	176	"note": "two-node transfer measurements"
177	177	},
178	178	"results/tables/ED_hosted.csv": {
		`@@ -180,7 +180,7 @@`
180	180	"runs/raw/ED_hosted_core.jsonl"
181	181	],
182	182	"script": "src/wp30/analysis/analyze.py",
183		− "generated_utc": "2026-10-30T09:02:46.140839Z",
	183	+ "generated_utc": "2026-10-30T09:36:33.511504Z",
184	184	"note": "hosted receivers; cost estimated from provider price tables"
185	185	},
186	186	"results/tables/E3_core_transfer.csv": {
		`@@ -189,7 +189,7 @@`
189	189	"runs/raw/E3_core_sender.jsonl"
190	190	],
191	191	"script": "src/wp30/analysis/analyze.py",
192		− "generated_utc": "2026-10-30T09:02:46.269308Z",
	192	+ "generated_utc": "2026-10-30T09:36:33.631124Z",
193	193	"note": "same-family mapper transfer vs text handoffs and references"
194	194	},
195	195	"results/tables/E3_core_primary_tests.csv": {
		`@@ -198,7 +198,7 @@`
198	198	"runs/raw/E3_core_sender.jsonl"
199	199	],
200	200	"script": "src/wp30/analysis/analyze.py",
201		− "generated_utc": "2026-10-30T09:02:46.347449Z",
	201	+ "generated_utc": "2026-10-30T09:36:33.680469Z",
202	202	"note": "registered primary comparisons, paired, cluster bootstrap, Holm"
203	203	},
204	204	"results/tables/E7_qtypes.csv": {
		`@@ -206,7 +206,7 @@`
206	206	"runs/raw/E7_qtypes.jsonl"
207	207	],
208	208	"script": "src/wp30/analysis/analyze.py",
209		− "generated_utc": "2026-10-30T09:02:46.532910Z",
	209	+ "generated_utc": "2026-10-30T09:36:33.924117Z",
210	210	"note": "EXPLORATORY: question types 6-15 on synthetic test documents"
211	211	},
212	212	"results/figures/figA_qtypes_exploratory.pdf": {
		`@@ -214,7 +214,7 @@`
214	214	"runs/raw/E7_qtypes.jsonl"
215	215	],
216	216	"script": "src/wp30/analysis/analyze.py",
217		− "generated_utc": "2026-10-30T09:02:46.612438Z",
	217	+ "generated_utc": "2026-10-30T09:36:33.996541Z",
218	218	"note": "exploratory extension A-004"
219	219	},
220	220	"results/figures/figA_qtypes_exploratory.png": {
		`@@ -222,7 +222,7 @@`
222	222	"runs/raw/E7_qtypes.jsonl"
223	223	],
224	224	"script": "src/wp30/analysis/analyze.py",
225		− "generated_utc": "2026-10-30T09:02:46.668848Z",
	225	+ "generated_utc": "2026-10-30T09:36:34.046670Z",
226	226	"note": "exploratory extension A-004"
227	227	},
228	228	"results/figures/fig7_router.pdf": {
		`@@ -232,7 +232,7 @@`
232	232	"runs/checkpoints/router_policy.json"
233	233	],
234	234	"script": "src/wp30/analysis/analyze.py",
235		− "generated_utc": "2026-10-30T09:02:46.768937Z",
	235	+ "generated_utc": "2026-10-30T09:36:34.131525Z",
236	236	"note": "oracle = infeasible upper reference"
237	237	},
238	238	"results/figures/fig7_router.png": {
		`@@ -242,7 +242,7 @@`
242	242	"runs/checkpoints/router_policy.json"
243	243	],
244	244	"script": "src/wp30/analysis/analyze.py",
245		− "generated_utc": "2026-10-30T09:02:46.853258Z",
	245	+ "generated_utc": "2026-10-30T09:36:34.206816Z",
246	246	"note": "oracle = infeasible upper reference"
247	247	}
248	248	}
		\ No newline at end of file

modified results/tables/E7_qtypes.csv +98 −98

		`@@ -1,99 +1,99 @@`
1	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
24		−A3\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.5333333333333333,0.3333333333333333,0.7333333333333333,15,0.0
25		−A3\|budget_tokens=512\|blind,temporal,Qwen2.5-3B,0.3333333333333333,0.13333333333333333,0.5333333333333333,15,0.0
26		−A3\|budget_tokens=512\|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
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
29		−A3\|budget_tokens=512\|blind,unit_conversion,Qwen2.5-3B,0.0,0.0,0.0,15,0.6666666666666666
30		−A4\|budget_tokens=512\|blind,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.0
31		−A4\|budget_tokens=512\|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.4
32		−A4\|budget_tokens=512\|blind,multi_hop,Llama-3.2-3B,0.3333333333333333,0.06666666666666667,0.6,15,0.5333333333333333
33		−A4\|budget_tokens=512\|blind,multi_hop,Qwen2.5-3B,0.4375,0.1875,0.6875,16,0.5
34		−A4\|budget_tokens=512\|blind,negation,Llama-3.2-3B,1.0,1.0,1.0,30,0.0
35		−A4\|budget_tokens=512\|blind,negation,Qwen2.5-3B,0.71875,0.53125,0.875,32,0.1875
36		−A4\|budget_tokens=512\|blind,revision,Llama-3.2-3B,0.16666666666666666,0.03333333333333333,0.3333333333333333,30,0.3333333333333333
37		−A4\|budget_tokens=512\|blind,revision,Qwen2.5-3B,0.25806451612903225,0.12903225806451613,0.4266935483870965,31,0.5161290322580645
38		−A4\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.2,0.0,0.4,15,0.3333333333333333
39		−A4\|budget_tokens=512\|blind,temporal,Qwen2.5-3B,0.3333333333333333,0.13333333333333333,0.5333333333333333,15,0.26666666666666666
40		−A4\|budget_tokens=512\|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,30,1.0
41		−A4\|budget_tokens=512\|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,30,1.0
42		−A4\|budget_tokens=512\|blind,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,15,0.6666666666666666
43		−A4\|budget_tokens=512\|blind,unit_conversion,Qwen2.5-3B,0.4,0.13333333333333333,0.6666666666666666,15,0.6
44		−A5\|budget_tokens=512\|blind,aggregation,Llama-3.2-3B,0.0,0.0,0.0,15,0.3333333333333333
45		−A5\|budget_tokens=512\|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,15,0.4666666666666667
46		−A5\|budget_tokens=512\|blind,multi_hop,Llama-3.2-3B,0.13333333333333333,0.0,0.3333333333333333,15,0.8666666666666667
47		−A5\|budget_tokens=512\|blind,multi_hop,Qwen2.5-3B,0.2,0.06666666666666667,0.4,15,0.7333333333333333
48		−A5\|budget_tokens=512\|blind,negation,Llama-3.2-3B,0.5,0.36666666666666664,0.6333333333333333,30,0.3
49		−A5\|budget_tokens=512\|blind,negation,Qwen2.5-3B,0.5333333333333333,0.36666666666666664,0.7,30,0.43333333333333335
50		−A5\|budget_tokens=512\|blind,revision,Llama-3.2-3B,0.13333333333333333,0.0,0.3,30,0.7666666666666667
51		−A5\|budget_tokens=512\|blind,revision,Qwen2.5-3B,0.1,0.0,0.26666666666666666,30,0.8333333333333334
52		−A5\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.13333333333333333,0.0,0.3333333333333333,15,0.8666666666666667
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
	2	+A1,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	3	+A1,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.0
	4	+A1,multi_hop,Llama-3.2-3B,1.0,1.0,1.0,40,0.0
	5	+A1,multi_hop,Qwen2.5-3B,1.0,1.0,1.0,40,0.0
	6	+A1,negation,Llama-3.2-3B,0.9,0.825,0.9625,80,0.0
	7	+A1,negation,Qwen2.5-3B,0.9625,0.9125,1.0,80,0.0
	8	+A1,revision,Llama-3.2-3B,0.75,0.6625,0.8375,80,0.0
	9	+A1,revision,Qwen2.5-3B,0.875,0.8125,0.9375,80,0.0
	10	+A1,temporal,Llama-3.2-3B,0.625,0.475,0.775,40,0.0
	11	+A1,temporal,Qwen2.5-3B,0.55,0.4,0.7,40,0.0
	12	+A1,unanswerable,Llama-3.2-3B,0.95,0.9,0.9875,80,0.95
	13	+A1,unanswerable,Qwen2.5-3B,0.975,0.9375,1.0,80,0.975
	14	+A1,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	15	+A1,unit_conversion,Qwen2.5-3B,0.575,0.41187500000000027,0.725,40,0.0
	16	+A3\|budget_tokens=512\|blind,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	17	+A3\|budget_tokens=512\|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.0
	18	+A3\|budget_tokens=512\|blind,multi_hop,Llama-3.2-3B,0.0,0.0,0.0,40,1.0
	19	+A3\|budget_tokens=512\|blind,multi_hop,Qwen2.5-3B,0.0,0.0,0.0,40,1.0
	20	+A3\|budget_tokens=512\|blind,negation,Llama-3.2-3B,0.8625,0.7875,0.925,80,0.0
	21	+A3\|budget_tokens=512\|blind,negation,Qwen2.5-3B,0.9,0.8309375000000001,0.95,80,0.0
	22	+A3\|budget_tokens=512\|blind,revision,Llama-3.2-3B,0.8375,0.7625,0.9125,80,0.0
	23	+A3\|budget_tokens=512\|blind,revision,Qwen2.5-3B,0.7,0.6059375000000001,0.7875,80,0.0125
	24	+A3\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.65,0.5,0.8,40,0.0
	25	+A3\|budget_tokens=512\|blind,temporal,Qwen2.5-3B,0.6,0.45,0.75,40,0.0
	26	+A3\|budget_tokens=512\|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,80,1.0
	27	+A3\|budget_tokens=512\|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,80,1.0
	28	+A3\|budget_tokens=512\|blind,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,40,0.925
	29	+A3\|budget_tokens=512\|blind,unit_conversion,Qwen2.5-3B,0.0,0.0,0.0,40,0.675
	30	+A4\|budget_tokens=512\|blind,aggregation,Llama-3.2-3B,0.025,0.0,0.075,40,0.0
	31	+A4\|budget_tokens=512\|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.35
	32	+A4\|budget_tokens=512\|blind,multi_hop,Llama-3.2-3B,0.3,0.15,0.45,40,0.65
	33	+A4\|budget_tokens=512\|blind,multi_hop,Qwen2.5-3B,0.475,0.325,0.625,40,0.475
	34	+A4\|budget_tokens=512\|blind,negation,Llama-3.2-3B,0.95,0.8875,1.0,80,0.0125
	35	+A4\|budget_tokens=512\|blind,negation,Qwen2.5-3B,0.725,0.625,0.8125,80,0.175
	36	+A4\|budget_tokens=512\|blind,revision,Llama-3.2-3B,0.125,0.0625,0.2125,80,0.375
	37	+A4\|budget_tokens=512\|blind,revision,Qwen2.5-3B,0.2,0.1125,0.3,80,0.6125
	38	+A4\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.325,0.175,0.475,40,0.325
	39	+A4\|budget_tokens=512\|blind,temporal,Qwen2.5-3B,0.475,0.325,0.625,40,0.15
	40	+A4\|budget_tokens=512\|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,80,1.0
	41	+A4\|budget_tokens=512\|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,80,1.0
	42	+A4\|budget_tokens=512\|blind,unit_conversion,Llama-3.2-3B,0.025,0.0,0.075,40,0.575
	43	+A4\|budget_tokens=512\|blind,unit_conversion,Qwen2.5-3B,0.325,0.175,0.475,40,0.6
	44	+A5\|budget_tokens=512\|blind,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.475
	45	+A5\|budget_tokens=512\|blind,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.325
	46	+A5\|budget_tokens=512\|blind,multi_hop,Llama-3.2-3B,0.1,0.025,0.2,40,0.9
	47	+A5\|budget_tokens=512\|blind,multi_hop,Qwen2.5-3B,0.15,0.05,0.275,40,0.75
	48	+A5\|budget_tokens=512\|blind,negation,Llama-3.2-3B,0.5,0.375,0.6,80,0.375
	49	+A5\|budget_tokens=512\|blind,negation,Qwen2.5-3B,0.4875,0.36843750000000014,0.6,80,0.475
	50	+A5\|budget_tokens=512\|blind,revision,Llama-3.2-3B,0.075,0.025,0.15,80,0.8375
	51	+A5\|budget_tokens=512\|blind,revision,Qwen2.5-3B,0.0375,0.0,0.1,80,0.85
	52	+A5\|budget_tokens=512\|blind,temporal,Llama-3.2-3B,0.1,0.025,0.2,40,0.85
	53	+A5\|budget_tokens=512\|blind,temporal,Qwen2.5-3B,0.075,0.0,0.175,40,0.8
	54	+A5\|budget_tokens=512\|blind,unanswerable,Llama-3.2-3B,1.0,1.0,1.0,80,1.0
	55	+A5\|budget_tokens=512\|blind,unanswerable,Qwen2.5-3B,1.0,1.0,1.0,80,1.0
	56	+A5\|budget_tokens=512\|blind,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,40,0.75
	57	+A5\|budget_tokens=512\|blind,unit_conversion,Qwen2.5-3B,0.15,0.05,0.275,40,0.45
	58	+B2\|bits=8,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	59	+B2\|bits=8,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.0
	60	+B2\|bits=8,multi_hop,Llama-3.2-3B,1.0,1.0,1.0,40,0.0
	61	+B2\|bits=8,multi_hop,Qwen2.5-3B,1.0,1.0,1.0,40,0.0
	62	+B2\|bits=8,negation,Llama-3.2-3B,0.9,0.825,0.9625,80,0.0
	63	+B2\|bits=8,negation,Qwen2.5-3B,0.9375,0.8809375000000002,0.9875,80,0.0
	64	+B2\|bits=8,revision,Llama-3.2-3B,0.7625,0.675,0.85,80,0.0
	65	+B2\|bits=8,revision,Qwen2.5-3B,0.875,0.8125,0.9375,80,0.0
	66	+B2\|bits=8,temporal,Llama-3.2-3B,0.625,0.475,0.775,40,0.0
	67	+B2\|bits=8,temporal,Qwen2.5-3B,0.55,0.4,0.7,40,0.0
	68	+B2\|bits=8,unanswerable,Llama-3.2-3B,0.95,0.9,0.9875,80,0.95
	69	+B2\|bits=8,unanswerable,Qwen2.5-3B,0.975,0.9375,1.0,80,0.975
	70	+B2\|bits=8,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	71	+B2\|bits=8,unit_conversion,Qwen2.5-3B,0.6,0.45,0.75,40,0.0
	72	+B3\|keep_frac=0.25,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	73	+B3\|keep_frac=0.25,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.0
	74	+B3\|keep_frac=0.25,multi_hop,Llama-3.2-3B,0.15,0.05,0.275,40,0.275
	75	+B3\|keep_frac=0.25,multi_hop,Qwen2.5-3B,0.175,0.075,0.3,40,0.1
	76	+B3\|keep_frac=0.25,negation,Llama-3.2-3B,0.725,0.65,0.8,80,0.0
	77	+B3\|keep_frac=0.25,negation,Qwen2.5-3B,0.9125,0.85,0.975,80,0.0
	78	+B3\|keep_frac=0.25,revision,Llama-3.2-3B,0.15,0.06843750000000012,0.2375,80,0.4875
	79	+B3\|keep_frac=0.25,revision,Qwen2.5-3B,0.1,0.025,0.1875,80,0.0
	80	+B3\|keep_frac=0.25,temporal,Llama-3.2-3B,0.575,0.425,0.725,40,0.0
	81	+B3\|keep_frac=0.25,temporal,Qwen2.5-3B,0.55,0.41187500000000027,0.7,40,0.0
	82	+B3\|keep_frac=0.25,unanswerable,Llama-3.2-3B,0.9375,0.875,0.9875,80,0.9375
	83	+B3\|keep_frac=0.25,unanswerable,Qwen2.5-3B,0.9875,0.9625,1.0,80,0.9875
	84	+B3\|keep_frac=0.25,unit_conversion,Llama-3.2-3B,0.025,0.0,0.075,40,0.65
	85	+B3\|keep_frac=0.25,unit_conversion,Qwen2.5-3B,0.075,0.0,0.15,40,0.0
	86	+B5\|keep_frac=0.25\|aware,aggregation,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	87	+B5\|keep_frac=0.25\|aware,aggregation,Qwen2.5-3B,0.0,0.0,0.0,40,0.0
	88	+B5\|keep_frac=0.25\|aware,multi_hop,Llama-3.2-3B,0.95,0.875,1.0,40,0.0
	89	+B5\|keep_frac=0.25\|aware,multi_hop,Qwen2.5-3B,0.575,0.425,0.725,40,0.0
	90	+B5\|keep_frac=0.25\|aware,negation,Llama-3.2-3B,0.85,0.7625,0.925,80,0.0
	91	+B5\|keep_frac=0.25\|aware,negation,Qwen2.5-3B,0.9625,0.925,1.0,80,0.0
	92	+B5\|keep_frac=0.25\|aware,revision,Llama-3.2-3B,0.7625,0.6875,0.8375,80,0.0
	93	+B5\|keep_frac=0.25\|aware,revision,Qwen2.5-3B,0.275,0.175,0.375,80,0.0
	94	+B5\|keep_frac=0.25\|aware,temporal,Llama-3.2-3B,0.7,0.55,0.85,40,0.0
	95	+B5\|keep_frac=0.25\|aware,temporal,Qwen2.5-3B,0.55,0.4,0.7131249999999993,40,0.0
	96	+B5\|keep_frac=0.25\|aware,unanswerable,Llama-3.2-3B,0.95,0.9,0.9875,80,0.95
	97	+B5\|keep_frac=0.25\|aware,unanswerable,Qwen2.5-3B,0.975,0.9375,1.0,80,0.975
	98	+B5\|keep_frac=0.25\|aware,unit_conversion,Llama-3.2-3B,0.0,0.0,0.0,40,0.0
	99	+B5\|keep_frac=0.25\|aware,unit_conversion,Qwen2.5-3B,0.6,0.425,0.725,40,0.0

modified runs/raw/E7_qtypes.jsonl +3080 −0

		`@@ -2518,3 +2518,3083 @@`
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2521	5601

spb/wp30_uqo

WP30: final analysis (E7 exploratory), paper layout pass, FINAL_REPORT, index updates

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