WP30: paper sections (intro, related, formulation, methods, protocol, appendix), router eval, mapper v2, pilots on cluster

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

Simon-Pierre Boucher committed 4 days ago (Oct 30, 2026) parent 53ae960 Browse files

29 changed files +1,204 −14

added configs/mapper_v2.yaml +8 −0

++sender: qwen1b5
++receiver: qwen3b
++families: [synthetic, exec]
++max_tokens_per_doc: 8192
++ridge: 10.0
++neighbours: 1          # sender layers l_s-1, l_s, l_s+1 -> 1537 features per token
++n_dev_eval: 4
++output: mapper_v2_qwen1b5_to_qwen3b.npz

added configs/router.yaml +6 −0

++dev_patterns: ["E1_pilot.jsonl"]
++test_patterns: ["E1_core.jsonl"]
++cost_col: cost_amortized_ms
++tau: 0.9
++actions: ["A1", "A2|budget_tokens=512", "A3|budget_tokens=512|blind", "A3|budget_tokens=512|aware", "A4|budget_tokens=512|blind", "A5|budget_tokens=512|blind",
++          "A6|budget_tokens=512|max_fetch_bytes=2000|max_lookups=6|blind", "A7|codec=zstd", "A8|budget_tokens=512|blind", "B0", "B1", "B2|bits=8", "B3|keep_frac=0.25", "B5|keep_frac=0.25|aware", "B9|bits=8|keep_frac=0.25|policy=attention|aware"]

added configs/transfer_pilot.yaml +16 −0

++experiment_id: E3_pilot
++splits: [dev]
++families: [synthetic, exec]
++lengths: [2000, 8000]
++limit_docs_per_family: 4
++max_questions_per_doc: 5
++model_pairs:
++  - {sender: qwen1b5, receiver: qwen3b}
++generation: {max_tokens: 48, per_family: {exec: 320}}
++seeds: [0]
++conditions:
++  - {method: A1}
++  - {method: A4, budget_tokens: 512, access: blind}
++  - {method: C0}
++  - {method: C2, mapper: runs/checkpoints/mapper_qwen1b5_to_qwen3b.npz}
++  - {method: C3, mapper: runs/checkpoints/mapper_qwen1b5_to_qwen3b.npz, keep_frac: 0.25, access: aware}

added paper/appendix/appendix.tex +88 −0

++\newpage
++\begin{appendices}
++\section{Registered hypotheses}
++\label{app:hypotheses}
++\input{appendix/hypotheses_verbatim}
++
++\section{Source audit and unexecuted methods}
++\label{app:audit}
++Thirty-four sources were verified on 2026-10-29 from their arXiv pages, repositories and documentation (\texttt{literature/sources.csv};
++notes in \texttt{literature/audit\_group\{1,2,3\}.md}). Table~\ref{tab:audit} summarizes the core methods.
++\begin{table}[h]\centering\scriptsize
++\caption{Audited core methods: availability of code and checkpoints, backend, question access, and WP30 status.}\label{tab:audit}
++\begin{tabular}{@{}llllll@{}}\toprule
++Method & Code & Checkpoint & Backend & Question access & WP30 \\\midrule
++SwaT (2610.15095) & yes (no licence) & none & torch/CUDA & blind & not run \\
++Attention Matching (2602.16284) & MIT & n/a (optimization) & vLLM 0.11 / torch & blind & not run (MLX port out of scope) \\
++C2C (2510.03215) & Apache-2.0 & Qwen3 fusers & torch/CUDA & blind & not run \\
++LatentMAS (2511.20639) & Apache-2.0 & n/a & HF/vLLM, CUDA & blind & not run \\
++KIVI (2402.02750) & MIT & n/a & CUDA kernels & n/a & motivation tested via B2 \\
++SnapKV (2404.14469) & Apache-2.0 & n/a & torch & aware & faithful re-implementation (B5) \\
++PyramidKV (2406.02069) & MIT & n/a & torch & aware & budget rule re-implemented (B6) \\
++H$_2$O (2306.14048) & no LICENSE file & n/a & torch & cumulative & not run \\
++StreamingLLM (2309.17453) & MIT & n/a & torch & blind & retention pattern only (B3) \\
++LLMLingua-2 (2403.12968) & MIT & yes (HF) & torch (MPS ok) & blind & official (A8) \\\bottomrule
++\end{tabular}
++\end{table}
++
++\section{Correctness tests}
++\label{app:tests}
++\texttt{tests/test\_cache\_correctness.py} (12 tests, all passing on Qwen2.5-1.5B-Instruct-bf16): prefix equivalence in float32 and bf16
++(distributional tolerance of Section~\ref{sec:correctness}, identical greedy continuation); no duplicated boundary token; identity
++round trip with exact logits and measured bytes equal to \eqref{eq:mkv}; loader rejection of a wrong prefix hash and of a different
++checkpoint; 100\,\% retention exact (shared and per-head indices); sparse mask path versus token-by-token append; preserved versus
++relabelled positions differ; packed quantization bit widths and execution for 8/4/2 bits; no shared mutable state across branches;
++attention selection keeps the window; no-message control. \texttt{tests/test\_leakage.py} (3) and \texttt{tests/test\_scoring\_and\_data.py} (5)
++cover evaluator isolation, scorer behaviour and generator determinism.
++
++\section{Prompts}
++\label{app:prompts}
++\input{appendix/prompts_verbatim}
++
++\section{Pseudocode}
++\label{app:pseudo}
++\begin{algorithm}[h]\caption{Trial scheduler (resumable, append-only)}\footnotesize
++\begin{algorithmic}[1]
++\State load config, manifest, resources; $done \gets$ trial keys with status success in \texttt{runs/raw/<exp>.jsonl}
++\For{document $d$ in selected items} \For{model pair $(S,R)$} \For{condition $c$} \For{seed}
++  \State $P \gets$ questions of $d$ whose trial key $\notin done$; \textbf{continue} if $P=\emptyset$
++  \If{$c$ is question-blind} build message/cache $m$ once for $d$ (reuse count $=|P|$) \EndIf
++  \For{question $q \in P$} build $m$ if question-aware; run receiver; score with evaluator-only key; append validated record \EndFor
++\EndFor\EndFor\EndFor\EndFor
++\end{algorithmic}\end{algorithm}
++\begin{algorithm}[h]\caption{Cache handoff pipeline (Track B/C)}\footnotesize
++\begin{algorithmic}[1]
++\State $\text{pre} \gets$ chat tokens of (system, document) that are a true prefix of the full (system, document, question) prompt
++\State $C \gets$ receiver.prefill(pre) \Comment{dense bf16; measured doc prefill}
++\State $C' \gets$ compress($C$): quantize / select positions (sparse cache with logical offset $|\text{pre}|$) / map from sender
++\State payload $\gets$ serialize($C'$, manifest); optionally codec
++\State at receiver: check manifest; deserialize; install; prefill fresh query tokens with retained-positions mask; decode greedily
++\end{algorithmic}\end{algorithm}
++\begin{algorithm}[h]\caption{Evidence retrieval (A6) and repairable chains}\footnotesize
++\begin{algorithmic}[1]
++\State refs $\gets$ A5 facts without values; rank refs by BM25(question, entity+attribute)
++\For{ref in ranked order while lookups $<$ max and bytes $<$ budget} fetch source span $\pm$ context; count bytes and lookups \EndFor
++\State chains: for codes mentioned in the previous message, fetch source sentences containing the code under the same meters
++\end{algorithmic}\end{algorithm}
++\begin{algorithm}[h]\caption{Adaptive policy (E8)}\footnotesize
++\begin{algorithmic}[1]
++\State on dev: for each (family, context bin, pair, access) and action $a$: $\hat q_a$, lower bootstrap bound $\underline{q}_a$, mean cost $\hat c_a$
++\State feasible $\gets \{a : \underline{q}_a \ge \tau\,\max_{a'}\underline{q}_{a'}\}$; choose $\arg\min_{a\in\text{feasible}}\hat c_a$; fallback to the dev-best static action
++\State freeze; on test: look up the cell (nearest bin fallback counted), apply the chosen action, add router time
++\end{algorithmic}\end{algorithm}
++
++\section{Per-attempt record schema}
++\label{app:schema}
++Each attempt is one JSON line validated against \texttt{wp30-attempt-1}: identifiers (study, experiment, trial, attempt, run group,
++protocol and config hashes, git commit, timestamp), task (family, document id and hash, question id, split, cluster), models (sender and
++receiver revisions, tokenizer hashes, backend versions, hardware id, device state, weight precision), method (id, fidelity, compression
++config with realized values, question access, topology, handoff index, reuse count, cache hit, compatibility hash, seed), sizes (source
++tokens under both tokenizers, query and message tokens, UTF-8 bytes, cache tensor bytes, serialized payload bytes, side information,
++fetched bytes, transport bytes), timing spans and TTFT, usage and provider cached usage, memory (process RSS, MLX active/peak, prefill and
++decode peaks, mapper memory), cost with source, prediction and evidence artifacts, key reference, scores, status, error code, retry link,
++log path, payload and output hashes.
++
++\section{Additional tables}
++\label{app:tables}
++\input{appendix/extra_tables}
++\end{appendices}

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		1	+% filled by analysis

added paper/appendix/hypotheses_verbatim.tex +70 −0

++\begin{footnotesize}\begin{verbatim}
++# WP30 -- Research questions and falsifiable hypotheses (registered before any held-out outcome was examined)
++
++Registered: 2026-10-29 (Phase 1). Amendments only as dated entries at the bottom.
++Test-split outcomes had **not** been examined at registration (no test split existed yet; only the E0 smoke run on a
++hand-written 3-sentence memo had been executed, see `runs/logs/`).
++
++## Research questions
++
++- **RQ1 (compression frontier)** How do text shortening (A2-A4, A8), packed KV quantization (B2), token eviction (B3-B6) and
++  learned compaction (B7, if run) trade task quality against *measured* serialized bytes and resident cache bytes?
++- **RQ2 (transfer compatibility)** How much performance is preserved by same-checkpoint reuse (B1, cross-process, cross-node)
++  versus learned mappings between distinct checkpoints (Track C), relative to the receiver's own full-context answer?
++- **RQ3 (resource accounting)** Do rankings of methods under a token budget change when the budget is bytes, end-to-end latency,
++  peak memory or amortized cost over N reuses?
++- **RQ4 (repeated handoffs)** How do correct-fact retention, unsupported additions and task accuracy evolve after 1, 2, 4, 8 handoffs,
++  for prose summaries vs structured evidence, in closed vs repairable chains?
++- **RQ5 (evidence)** Under an equal byte budget, does structured evidence with source spans (A5) retain exact facts (identifiers,
++  numbers, dates, negations) better than an abstractive summary (A4)? Does metered retrieval (A6) repair losses, and at what cost?
++- **RQ6 (locality)** When is running the next query where the cache resides cheaper than moving the cache or recomputing from text,
++  as a function of context length, payload bytes, measured bandwidth and queue delay?
++- **RQ7 (adaptive policy)** Can a router fitted on development data improve the held-out quality/cost frontier over the best
++  development-selected static method, once routing overhead and fallbacks are counted?
++
++## Hypotheses (each may be rejected)
++
++- **H1** Lossy compression degrades exact identifiers, decimal numbers, negation and temporal order more than paraphrasable content:
++  on the synthetic private-fact family, the accuracy drop from A1 to A4 at a 512-token budget is larger (>= 10 percentage points)
++  for `exact_copy`, `negation`, and `revision` question types than for `single_fact` questions.
++- **H2** Packed KV quantization is model-dependent: at 4 bits/group 64, Llama-3.2-3B retains >= 95 % of its B0 accuracy while
++  Qwen2.5-3B loses >= 20 percentage points (motivated by E0 smoke: Qwen2.5-1.5B produced garbage at 4-bit in both the native
++  mlx_lm path and ours; Llama-3.2-3B did not). 8-bit retains >= 97 % for both.
++- **H3** Sink+recent eviction (B3) at <= 1/4 retention is worse than a *question-aware* attention-informed selection (B5-aware)
++  at equal retained positions, but **not** reliably better than random selection (B4) on questions whose answer lies in the
++  middle of the document.
++- **H4** Serialized cache bytes exceed raw UTF-8 document bytes by >= 2 orders of magnitude for 3B models; hence B1 transfer only
++  wins on total latency when (a) the receiver's prefill cost exceeds transport time, which on the measured LAN requires contexts
++  above a break-even length we will estimate (prediction: several thousand tokens), or (b) the cache is reused >= N* times.
++- **H5** Repeated prose handoffs lose facts monotonically; after 4 handoffs the retained-correct-fact rate of A4 chains falls below
++  70 % while A5 (structured, explicit unknowns) stays above 85 %; unsupported additions appear mainly in prose chains.
++- **H6** Metered retrieval (A6, repairable chains) restores most of the lost exact facts but adds fetched bytes comparable to
++  the message itself.
++- **H7** Question-aware compression dominates question-blind compression at equal budget for *all* lossy methods; the gap is
++  largest for attention-based selection (which is query-dependent by construction).
++- **H8** A lossless transport codec (A7, zstd) on text yields >= 2x byte savings with < 5 ms overhead but **no** change in receiver
++  latency or quality; on bf16 KV tensors the codec yields < 1.3x (high-entropy payload).
++- **H9** The adaptive router (E8) will not reliably beat the best development-selected static method on held-out data once
++  overhead is included (we expect a null or small, uncertain gain).
++- **H10** Cross-family cache transfer without a trained mapper is not possible; with a mapper trained under our budget (if feasible),
++  quality is below the receiver's own A4 summary baseline (we expect learned transfer to lose to text at our scale).
++
++## Primary endpoints (frozen at Phase 7 together with the matrix)
++
++Per task family: exact-match / F1 (LongBench normalization) or numeric correctness with tolerance; operational success rate;
++serialized payload bytes; end-to-end wall time (defined boundaries, S6.1 of the brief); peak MLX memory.
++
++## Primary confirmatory comparison family (Holm-corrected; everything else exploratory)
++
++1. A5 vs A4 at matched byte budget (synthetic, question-blind) -- accuracy.
++2. B2-8bit vs B0 -- accuracy (both models).
++3. B2-4bit vs B0 -- accuracy (Qwen vs Llama interaction stated as H2).
++4. B5-aware vs B3 at 1/4 retention -- accuracy.
++5. A4 chain vs A5 chain at 4 handoffs -- fact retention.
++6. Router vs best static -- held-out accuracy at equal or lower cost.
++
++## Amendments
++
++(none yet)
++
++\end{verbatim}\end{footnotesize}

added paper/appendix/prompts_verbatim.tex +34 −0

++% auto-generated from src/wp30/prompts.py
++\begin{footnotesize}\begin{verbatim}
++"""Prompt templates (frozen at Phase 7). Receiver prompts are identical across methods except for the message slot."""
++SYSTEM_RECEIVER = ("You are a careful assistant. Answer the question using ONLY the provided material. "
++                   "If the material does not contain the answer, reply exactly: unknown.")
++SYSTEM_RECEIVER_NOMSG = ("You are a careful assistant. Answer the question. If you do not know, reply exactly: unknown.")
++
++def receiver_user_text(material_label: str, material: str, question: str, family: str) -> str:
++    if family == "exec":
++        return f"{material_label}:\\n{material}\\n\\nTask: {question}"
++    fmt = "Give a short answer (a few words or a number), with no explanation."
++    return f"{material_label}:\\n{material}\\n\\nQuestion: {question}\\n{fmt}"
++
++def receiver_question_only(question: str, family: str) -> str:
++    if family == "exec":
++        return f"Task: {question}"
++    return f"Question: {question}\\nGive a short answer (a few words or a number), with no explanation."
++
++# Sender prompts (question-blind: no question shown; question-aware: question appended)
++SUMMARY_SYSTEM = "You are a precise note-taker. You write faithful summaries that preserve exact names, identifiers, numbers, units, dates, statuses and negations."
++def summary_user(doc: str, budget_tokens: int, question: str | None) -> str:
++    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"
++    return (f"Summarize the following document in at most {budget_tokens} tokens (about {int(budget_tokens*0.75)} words). Copy identifiers, "
++            f"numbers, dates and names exactly as written. Mark explicitly what is NOT stated if relevant.{q}\\nDocument:\\n{doc}\\n\\nSummary:")
++
++EVIDENCE_SYSTEM = ("You extract structured evidence. Output ONLY a JSON object with a key \\"facts\\": a list of objects "
++                   "{\\"entity\\": str, \\"attribute\\": str, \\"value\\": str, \\"quote\\": str} where quote is a short EXACT substring of the document "
++                   "supporting the fact, and optionally a key \\"notes\\": a short string for anything important that does not fit the schema, "
++                   "and a key \\"unknowns\\": list of things the document explicitly leaves unstated. Copy values verbatim.")
++def evidence_user(doc: str, budget_tokens: int, question: str | None) -> str:
++    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"
++    return f"Extract structured evidence from the document. Keep the JSON under {budget_tokens} tokens.{q}\\nDocument:\\n{doc}\\n\\nJSON:"
++
++\end{verbatim}\end{footnotesize}

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		1	+\newcommand{\WPabstract}{Abstract to be written after the results.}

added paper/sections/adaptive.tex +3 −0

++\section{Adaptive}
++\label{sec:adaptive}
++To be written from results.

added paper/sections/conclusion.tex +3 −0

++\section{Conclusion}
++\label{sec:conclusion}
++To be written from results.

added paper/sections/formulation.tex +92 −0

++\section{Problem formulation and taxonomy}
++\label{sec:formulation}
++
++\subsection{Protocol notation}
++Let $d$ be a source document, $q$ a downstream query, $y^\star$ its reference answer, $S$ the sender model and $R$ the receiver model.
++A communication method $c$ under a resource setting $b$ and a question-access policy $a$ produces a message
++\begin{equation}
++  m = E_{c,b}(d; S, a), \qquad \hat y = G_R\big(q, D_c(m), \mathcal{A}\big),
++  \label{eq:protocol}
++\end{equation}
++where $D_c$ denotes decoding, loading or mapping at the receiver and $\mathcal{A}$ is the explicitly allowed artifact or retrieval
++interface (empty for all methods except A6, the reference-plus-retrieval method, and the repairable chains of Section~\ref{sec:handoffs}).
++Equation~\eqref{eq:protocol} is notation for a protocol, not a claim that methods share an implementation: for text methods $m$ is a
++string the receiver prefills; for cache methods $m$ is a set of tensors installed as the receiver's attention state, and only the fresh
++query tokens are prefilled.
++
++\paragraph{Question access.} Under the \emph{question-blind} regime the sender observes $d$ and a generic instruction but not $q$; the
++message is reusable across the questions of a document. Under the \emph{question-aware} regime both sides may use $q$. The two regimes
++are reported separately throughout; a selection method that observes the question (SnapKV-style) is never compared as if it were blind.
++
++\paragraph{Terminology.} We follow a fixed vocabulary (Table~\ref{tab:terms}). In particular, \emph{text compression} is a shorter text
++consumed by a receiver; \emph{KV compression} is quantization, selection or transformation of actual attention tensors; \emph{cache
++reuse} reuses a compatible, previously computed prefix state; \emph{cache transfer} moves or maps a state for a receiver; and
++\emph{knowledge transfer} means improved access to task-relevant information through a defined message---never permanent learning.
++
++\begin{table}[t]\centering\footnotesize
++\caption{Terminology used in this paper.}\label{tab:terms}
++\begin{tabular}{@{}p{3.2cm}p{6.2cm}p{5.6cm}@{}}\toprule
++Term & Operational meaning & Error avoided \\\midrule
++Text compression & Shorter text consumed by a receiver & Calling it KV-cache compression \\
++KV compression & Quantization/selection/transformation of attention tensors & Measuring only a shorter prompt \\
++Cache reuse & Reuse of a compatible previously computed prefix state & Assuming one family $\Rightarrow$ compatible \\
++Cache transfer & Moving/mapping cache state for a receiver & Sending a summary and calling it latent transfer \\
++Knowledge transfer & Better access to task information via a message & Claiming a frozen receiver learned a skill \\
++Latent communication & An internal representation crosses executions & Calling retrieval embeddings a KV state \\
++Shared memory & Addressable external records under a protocol & Treating an identifier as free content access \\
++Prefill-free handoff & Receiver does not prefill the transferred document & Claiming the fresh query needs no processing \\
++Evidence traceability & Output linked to a resolvable source span & Treating self-reported confidence as proof \\\bottomrule
++\end{tabular}
++\end{table}
++
++\subsection{Accounting}
++For a dense cache with uniform layers the stored tensor size is
++\begin{equation}
++  M_{KV} = 2\,B\,L\,T\,H_{KV}\,d_h\,s,
++  \label{eq:mkv}
++\end{equation}
++with batch $B$, layers $L$, stored positions $T$, KV heads $H_{KV}$, head dimension $d_h$ and $s$ bytes per scalar. We use
++\eqref{eq:mkv} only as a consistency check: the harness measures stored tensor bytes from the arrays themselves (which, for the
++packed quantized caches, include the uint32 words, the per-group scales and biases) and measures the serialized payload from the file
++written to disk, including the compatibility manifest. Two ratios are reported with explicit denominators,
++\begin{equation}
++  CR_{\text{payload}} = \frac{\text{reference serialized payload bytes}}{\text{method serialized payload bytes}},\qquad
++  CR_{KV} = \frac{\text{native cache tensor bytes}}{\text{stored cache tensor and metadata bytes}},
++\end{equation}
++and a text-token ratio is never treated as a cache-position ratio.
++
++\paragraph{Timing boundaries.} Each trial records separate wall-clock spans with device synchronization before and after
++(\texttt{mx.synchronize}): sender prefill and message generation (\emph{encode}); document prefill into the receiver cache
++(\emph{doc prefill}); compression or selection; serialization and transport codec; deserialization and cache installation
++(\emph{load}); fresh query prefill up to the first content token (TTFT); decoding; and, for metered methods, retrieval. For a serial
++workflow,
++\begin{equation}
++  T_{\text{total}} = T_{\text{queue}} + T_{\text{read}} + T_{\text{sender}} + T_{\text{encode}} + T_{\text{serialize}}
++  + T_{\text{transport}} + T_{\text{load}} + T_{\text{receiver}} + T_{\text{tools}} .
++\end{equation}
++Three scenarios are assembled from the spans: \emph{cold} (no reusable state exists: build + compress + load + receiver),
++\emph{warm per-query} (the state exists: load + receiver), and \emph{amortized over $N$ queries},
++\begin{equation}
++  \bar C(N) = \frac{C_{\text{training}} + C_{\text{setup}} + C_{\text{document}}}{N} + C_{\text{query}},
++  \label{eq:amortized}
++\end{equation}
++where $C_{\text{training}}$ is zero for all training-free methods and is reported separately for the learned mapper. Components that
++a hosted provider does not expose are recorded as missing, not as zero.
++
++\subsection{Taxonomy}
++Table~\ref{tab:taxonomy} lists every method with its representation, question-access regime, training requirement, compatibility
++requirement, composability across sources, and its measured status in this study. Methods that were audited but not executed appear
++with the reason.
++
++\begin{table}[p]\centering\scriptsize
++\caption{Method taxonomy and coverage. ``Measured'' = executed in WP30 with real model inference; the last rows are audited methods that
++were not executed, with the reason.}\label{tab:taxonomy}
++\input{tables/table1_taxonomy}
++\end{table}
++
++\subsection{Research questions and registered hypotheses}
++Seven research questions (RQ1--RQ7) and ten falsifiable hypotheses were registered before any held-out outcome was examined
++(\texttt{protocol/hypotheses.md}; reproduced in Appendix~\ref{app:hypotheses}). In brief: RQ1 compression frontier; RQ2 transfer
++compatibility; RQ3 whether rankings change across resource denominators; RQ4 repeated handoffs; RQ5 structured evidence versus prose;
++RQ6 locality versus moving the cache; RQ7 adaptive routing. A primary confirmatory family of six paired comparisons is Holm-corrected;
++all other comparisons are exploratory.

added paper/sections/introduction.tex +53 −0

++\section{Introduction}
++\label{sec:intro}
++
++Agents built on large language models rarely work alone. A reader agent summarizes a filing for an analyst agent; a crawler hands
++extracted facts to an aggregator; a long document is processed once and then queried many times by different workers. Every such
++handoff chooses a \emph{representation} for the information that crosses the boundary between two model executions: the raw text,
++a shortened text, a prose summary, a structured record with provenance, a pointer to shared memory, or---when the two executions
++share a checkpoint---the attention state (the key--value, or KV, cache) that the first model built while reading. A growing
++literature proposes to compress these states \citep{liu2024kivi,li2024snapkv,cai2024pyramidkv,zhang2023h2o,xiao2024streamingllm,
++zweiger2026compaction}, to reuse them across requests, and, most recently, to translate them between different checkpoints or model
++families so that a receiver can ``inherit'' a sender's reading without re-reading the source \citep{fu2026c2c,zou2026latentmas,
++alabdulkareem2026swat}.
++
++The engineering appeal is clear: a receiver that loads a cache skips the prefill of the document, and a compressed cache shrinks
++memory. The scientific question is less clear. Compression methods are evaluated with different models, different information-access
++assumptions (does the compressor see the question?), different budgets (tokens, positions, bits, bytes), and different measurement
++boundaries (kernel time versus end-to-end time, resident tensors versus serialized payloads). Text baselines are often weak or absent.
++Cache sizes are usually quoted in positions or bits per value rather than in the bytes that actually cross a network, and the receiver
++side of a handoff---deserialization, loading, fresh-query processing---is seldom counted. Nor is the behaviour of these representations
++studied over \emph{repeated} handoffs, where small distortions compound.
++
++This paper asks a single question under a common measurement harness: \emph{under which task, model, cache, network and reuse
++conditions does each communication representation preserve useful information at the lowest end-to-end resource cost?} We
++answer it empirically, with experiments executed from a single research operator (Claude Code, an agentic coding assistant) on
++commodity Apple-Silicon hardware, using the MLX runtime, whose KV caches are ordinary tensors that can be inspected, sliced, quantized,
++serialized and loaded back. All quality numbers come from real model generations; all byte counts are measured on serialized payloads;
++all latencies are wall-clock spans with device synchronization; and the complete per-attempt record of every trial is released.
++
++\paragraph{Contributions.}
++\begin{enumerate}[leftmargin=1.4em]
++\item An \textbf{experimentally grounded taxonomy} of communication representations (Table~\ref{tab:taxonomy}) that separates
++  text compression, KV compression, cache reuse, cache transfer and parametric transfer, and records for each method its
++  question-access regime, its compatibility requirements and whether it composes across sources.
++\item A \textbf{common harness} (\texttt{wp30}) with a validated cache adapter layer (identity round trip, prefix equivalence,
++  position preservation, packed-byte accounting, manifest-checked loading), a per-attempt record schema, deterministic scorers,
++  evaluator-isolated answer keys, and document-level cluster bootstrap statistics.
++\item \textbf{Quality/resource trade-off maps} for twenty-two conditions on two 3B-parameter model families and three task
++  families (synthetic private-fact ledgers, public long-document QA, executable tasks), under question-blind and question-aware
++  regimes, with measured bytes, latency decompositions and peak memory.
++\item A study of \textbf{repeated handoffs} (1 to 8 transfers) with an evaluator-only fact ledger that measures retention,
++  unsupported additions and the cost of metered repair, and a \textbf{fan-in} study with conflicting evidence.
++\item \textbf{Genuine two-node cache transfer} measurements on a 1~GbE LAN against remote text prefill and local recompute,
++  with a measured break-even model.
++\item A \textbf{same-family learned KV mapper} (Qwen2.5-1.5B~$\to$~Qwen2.5-3B) trained on train-split caches only, evaluated
++  against text handoffs and an identity-transfer negative control; and a \textbf{development-fitted adaptive router} evaluated on
++  held-out data against the best development-selected static method.
++\end{enumerate}
++
++Several results are negative or conditional, and we report them as such. Cross-family cache transfer was \emph{not} executed:
++the only published cross-family method with code \citep{alabdulkareem2026swat} has no released checkpoint and no licence, and
++our budget did not permit training a mapper of its size; the study is therefore a \emph{partial} transfer study in that respect
++(Section~\ref{sec:limitations}). We also do not claim that any receiver \emph{learned} anything: every effect measured here is
++contextual, i.e., it vanishes when the message or cache is removed.

added paper/sections/limitations.tex +3 −0

++\section{Limitations}
++\label{sec:limitations}
++To be written from results.

added paper/sections/methods.tex +79 −0

++\section{Methods}
++\label{sec:methods}
++
++All methods are implemented in one Python package (\texttt{src/wp30}) on top of \texttt{mlx-lm}~0.32.2 \citep{mlxlm2026}, whose
++KV cache for a layer is a pair of tensors $K,V\in\mathbb{R}^{B\times H_{KV}\times T\times d_h}$ with keys stored \emph{after} rotary
++position embedding (RoPE) and a logical offset that drives the positions of new tokens. We distinguish \emph{official} code
++(run as released), \emph{faithful re-implementations} (same rule and defaults, re-coded for MLX), \emph{adapted}, \emph{inspired}
++and \emph{custom} baselines; the label of every method is in Table~\ref{tab:taxonomy} and in \texttt{literature/method\_notes.md}.
++
++\subsection{Track A: text, structured messages and evidence}
++\begin{description}[leftmargin=1.4em,style=nextline]
++\item[A0 no message.] The receiver gets the question only (prior/guessing floor).
++\item[A1 full raw context.] The document is placed in the receiver prompt; receiver prefill is counted.
++\item[A2 head/tail truncation.] Fixed rule (half the token budget from the head, half from the tail, under the receiver tokenizer).
++\item[A3 extractive selection.] Deterministic sentence selection under a token budget: BM25 of the question over sentences
++  (question-aware) or a centrality score weighted by the density of digits and identifiers (question-blind).
++\item[A4 abstractive summary.] Generated by the sender with a fixed prompt and a hard output cap equal to the budget; the prompt asks for
++  verbatim identifiers, numbers, dates and negations and, in the blind regime, for everything concrete because the questions are unknown.
++\item[A5 structured evidence.] The sender emits JSON facts \{entity, attribute, value, quote\} plus optional \texttt{notes} and
++  \texttt{unknowns}; each quote is resolved to a character span of the source, and unresolved quotes are counted. The receiver sees
++  values and spans. Truncated JSON is repaired at the last complete object.
++\item[A6 references plus retrieval.] The message is A5 \emph{without values}: a reference index \{entity, attribute, span\}. At answer
++  time the receiver ranks references by BM25 of the question and fetches at most six spans ($\pm$40/160 characters) under a 2\,000-byte
++  budget; every lookup and fetched byte is recorded and added to the message bytes.
++\item[A7 lossless transport codec.] The raw text is compressed with Zstandard (level~3) for transport and decompressed at the receiver;
++  the receiver prefill is unchanged. Codec times are measured.
++\item[A8 learned prompt compression.] LLMLingua-2 \citep{pan2024llmlingua2}, official package (\texttt{llmlingua}~0.2.2) with the
++  released \texttt{xlm-roberta-large-meetingbank} compressor, rate $=$ budget/source tokens, run on the Metal backend of PyTorch
++  (fp32). It is question-agnostic by construction.
++\end{description}
++A4 and A5 use the same sender checkpoint, the same budget and prompts of comparable length; both prompts were tuned on development
++data only. Receiver prompts are identical across methods except for the label of the material slot (Appendix~\ref{app:prompts}).
++
++\subsection{Track B: within-model KV compression and reuse}
++Every Track-B condition manipulates the receiver's real cache tensors and runs receiver inference from the resulting state. The
++receiver prefix is the chat-templated system prompt plus the document; the fresh input is the question and the assistant header,
++so that the cache boundary is identical for all conditions (\emph{no duplicated boundary token} is a tested property).
++\begin{description}[leftmargin=1.4em,style=nextline]
++\item[B0 native cache.] The dense bf16 cache, branched (deep-copied) for every question.
++\item[B1 serialized reuse.] B0 serialized to \texttt{safetensors} with a compatibility manifest (checkpoint revision, config,
++  tokenizer and chat-template hashes, prefix-token hash and count, per-layer shapes/dtypes/quantization, positional semantics,
++  runtime versions) and loaded back; the loader rejects a mismatching manifest before any model execution.
++\item[B2 packed quantization.] \texttt{mx.quantize} along the head dimension (groups of 64 channels; 32 for 2-bit), asymmetric affine
++  with a scale and a bias per group, packed into \texttt{uint32} words, and consumed by the quantized attention kernel---so the compressed
++  storage is the execution representation, not a transport format that is expanded before attention. Bits $\in\{8,4,2\}$.
++\item[B3 sink+recent.] Keep the first four positions and the most recent positions up to the budget. Unlike StreamingLLM, retained keys
++  keep their original positions and the logical offset is preserved (new tokens see a positional gap); it is a retention \emph{pattern},
++  labelled ``inspired''.
++\item[B4 random.] Uniformly random positions at the same budget (seeded control).
++\item[B5 attention-informed.] SnapKV's rule: per KV head, the attention of an observation window onto prefix positions (softmax,
++  averaged over the window's queries and over the query heads of the GQA group), 1-D max-pooled with kernel~7, top-$k$ per head, window
++  always kept. The window is the fresh question (aware) or a generic 32-token instruction (blind). Window queries are computed in a
++  separate pass over a copy of the cache, whose cost is recorded as compression time, so the main attention kernel stays fused.
++\item[B6 pyramid budgets.] PyramidKV's per-layer budget rule (verified against the KVCache-Factory code) with B5 scoring inside each
++  layer; because layers retain different lengths, the fresh query is processed token by token (recorded).
++\item[B9 selection then quantization.] B5 followed by B2 (8-bit).
++\end{description}
++Selection produces a \emph{sparse cache}: a subclass that stores only retained positions (per-head gathers are allowed), keeps the
++logical offset, and supplies an explicit boolean mask when several fresh tokens are appended (retained positions visible to all,
++causal among the new tokens). Correctness is established before any comparison (Section~\ref{sec:correctness}).
++
++\subsection{Track C: cross-checkpoint transfer}
++Level~1 of the compatibility ladder (same checkpoint, separate processes and separate nodes) is B1 executed through a TCP worker
++(Section~\ref{sec:network}). Level~2 (same family, different size) uses a \emph{linear KV mapper}: for each receiver layer $\ell_r$ of
++Qwen2.5-3B we align a sender layer $\ell_s=\mathrm{round}(\ell_r\,(L_s-1)/(L_r-1))$ of Qwen2.5-1.5B (both have two KV heads of
++dimension~128 and identical RoPE parameters, and they share the tokenizer, so sender and receiver caches are token-aligned), un-rotate
++the sender keys with their known positions, and fit by ridge regression a map from $[K_s,V_s]\in\mathbb{R}^{512}$ (plus bias) to
++$[K_r,V_r]\in\mathbb{R}^{512}$ on train-split caches only; mapped keys are re-rotated. The loss is reconstruction only
++($\lambda_{\text{task}}=\lambda_{\text{distill}}=0$); the mapper has $36\times513\times512\approx9.5$M parameters and is saved with a
++manifest (training documents, tokens, seconds, held-out reconstruction error). C0 feeds the sender cache into the receiver \emph{without}
++mapping (shapes coincide): an incompatible-state negative control. C3 applies B5 selection to the mapped cache (mapping then compaction).
++This is not SwaT \citep{alabdulkareem2026swat}, which trains a $\sim$300M mapper with generation and distillation terms across families;
++no SwaT checkpoint or licence was available (Appendix~\ref{app:audit}). Level~3 (cross-family) was therefore not executed.
++
++\subsection{Track D: hosted receivers}
++A0--A6 messages built by the local sender are answered by two hosted models (Anthropic \texttt{claude-haiku-4-5}; OpenAI
++\texttt{gpt-5-nano}) through their documented APIs, with streaming to measure the time to the first content token, provider usage
++fields, documented prompt caching (Anthropic \texttt{cache\_control}; OpenAI automatic prefix caching) and an estimated cost from the
++published price tables. The provider's cached-token field is reported as billing information, never as ``KV bytes saved''.

added paper/sections/protocol.tex +72 −0

++\section{Experimental protocol}
++\label{sec:protocol}
++
++\subsection{Models, hardware and runtimes}
++Table~\ref{tab:models} lists the checkpoints (all ungated \texttt{mlx-community} conversions with bf16 weights, so weight quantization is
++not a confound), their attention geometry and measured prefill times. The local node is a MacBook Pro with an Apple M5 Max (18 cores,
++48~GiB unified memory, macOS~27.0), Python~3.12.13, \texttt{mlx}~0.32.4, \texttt{mlx-lm}~0.32.2. Two-node experiments use two Mac Studio
++M3~Ultra nodes of the author's cluster (M3U96a as sender, M3U96b as receiver worker; 96~GB each; identical \texttt{mlx}/\texttt{mlx-lm}
++pins and checkpoint revisions) connected by a wired LAN measured at 940~Mbit/s (iperf3, four streams) with 1.2~ms RTT. Both cluster nodes
++concurrently hosted a CPU-light workload from another study (load average $\approx 1.8$ on 32 cores); timing on the laptop was taken
++with no other GPU process.
++
++\begin{table}[t]\centering\scriptsize
++\caption{Checkpoints, geometry and measured prefill (ms, laptop). Cache bytes per token are the dense bf16 formula \eqref{eq:mkv}
++with $B=1$.}\label{tab:models}
++\input{tables/table2_models}
++\end{table}
++
++\subsection{Task families and splits}
++\begin{description}[leftmargin=1.4em,style=nextline]
++\item[Synthetic private-fact ledgers.] A deterministic generator (seeded) writes a fictional capital-projects register: six projects per
++  document with invented names, random identifiers (\texttt{XX-00X-0000}), amounts and currencies, dates, sites, suppliers, approvals or
++  rejections, cancelled-versus-postponed statuses, timestamped revisions with an explicit precedence rule, a list of ``proposals under
++  review'' with no attributes (distractors with similar names), and boilerplate filler to reach 2\,000, 8\,000 or 16\,000 target tokens.
++  Fifteen questions per document cover single-fact retrieval at varying positions, exact copying, multi-hop joins, negation,
++  revision/precedence, temporal order, aggregation, unit conversion and two unanswerable questions (correct action: \texttt{unknown}).
++  An evaluator-only fact ledger (62 atomic facts per document) and the answer key are stored apart from the agent-visible text. The
++  facts are not inferable from pre-training. A procedurally generated ledger is not a real filing.
++\item[Public long-document QA.] LongBench~v1 \citep{bai2024longbench} subsets QASPER \citep{dasigi2021qasper} and 2WikiMultihopQA
++  \citep{ho2020twowiki} (raw \texttt{data.zip}; the dataset script is no longer supported by the \texttt{datasets} library), official
++  F1 with the LongBench normalization. Documents are used in full. We run \emph{subsets} (the first 40 test documents of each subset in
++  manifest order) and publish the identifiers. These texts may appear in pre-training corpora; they serve external validity, not the
++  contamination-free test of communication dependence.
++\item[Executable tasks.] A generated engineering ticket specifies six business rules for a function \texttt{net\_revenue(rows)}
++  together with a buggy implementation missing two rules; the agent must output the corrected function, which is executed in an
++  isolated \texttt{python -I} subprocess against five hidden test cases (exact float comparison). Agents never see the tests.
++\end{description}
++Splits are by document (train/dev/test seeds for the generated families; a hash rule for the public subsets), with exact-duplicate
++checks across splits (none found). Train is used for mapper fitting and prompt tuning, dev for method selection and router fitting,
++test for the locked campaign (Table~\ref{tab:datasets}). Answer keys live in \texttt{data/generated/keys} and are read only by the
++scorer; a leakage test asserts that agent-visible files contain no answers, facts or tests.
++
++\begin{table}[t]\centering\footnotesize
++\caption{Datasets, independent units (documents), questions and median length by split.}\label{tab:datasets}
++\input{tables/table3_datasets}
++\end{table}
++
++\subsection{Cache correctness before performance}
++\label{sec:correctness}
++Twelve tests run a real 1.5B checkpoint (Appendix~\ref{app:tests}). Identity round trip (serialize, load) and 100\,\% retention through
++the sparse adapter reproduce the native next-token logits \emph{exactly}. Cached-prefix-plus-query and full prefill agree within a
++registered distributional tolerance: MLX kernels are not bitwise deterministic across prefill chunkings even in float32 (we measured a
++maximum logit difference of 0.10 between a one-chunk and a token-by-token prefill, with $\max|\Delta p|\approx7\times10^{-7}$ and
++KL $\approx3\times10^{-8}$), so equivalence is defined as KL $<10^{-5}$, $\max|\Delta p|<10^{-4}$ and identical greedy top-1 in float32
++(KL $<10^{-3}$ in bf16). The sparse-cache mask path agrees with token-by-token appends; preserving original positions differs measurably
++from relabelling them; packed storage has the intended bit width (uint32 words, ratios 1.9/3.6/6.4 for 8/4/2 bits on a 412-token
++prefix including scales and biases); the loader rejects a wrong prefix hash or a different checkpoint; branches do not share mutable
++state; and the no-message control does not know the private answer.
++
++\subsection{Pilot, freeze and campaign}
++A development pilot (\texttt{E1\_pilot}: 16 documents, 31 conditions, two model pairs) calibrated budgets, revealed implementation
++failures and estimated variance and run time. The confirmatory protocol was then frozen (\texttt{protocol/frozen.json}, hash
++\texttt{\input{tables/protocol_hash}}) with the final hypotheses, prompts, budgets, scorers and analysis plan, before any test-split
++item was evaluated. The core single-handoff campaign (\texttt{E1\_core}) covers 160 test documents (40 per family), 22 conditions and
++two model pairs (13\,600 scheduled trials); the handoff, fan-in, transfer, network and hosted campaigns are described with their results.
++
++\subsection{Statistics}
++The unit is a (document, question, condition, pair) trial; documents are the independent clusters. Means and paired differences on
++identical instances are reported with document-level cluster-bootstrap 95\,\% intervals (2\,000 replicates). Six registered primary
++comparisons are Holm-corrected; everything else is exploratory. Quality is accuracy (synthetic, executable) or F1 (public); failed or
++invalid attempts count as 0 in the operational quality and are also tabulated by category. Latency is reported as p50/p95 with sample
++counts, from the spans defined in Section~\ref{sec:formulation}.

added paper/sections/related.tex +35 −0

++\section{Related work}
++\label{sec:related}
++
++\paragraph{Text and prompt compression.}
++Shortening the text a receiver reads is the oldest form of inter-agent compression. Beyond truncation and extractive selection,
++learned compressors drop tokens with a classifier trained on distilled data \citep{pan2024llmlingua2}; LLMLingua-2 is
++task-agnostic by construction, which places it in our \emph{question-blind} regime. Abstractive summaries and structured
++extraction are the dominant representations in deployed agent frameworks; we treat them as first-class baselines rather than
++as foils, tuning their prompts on development data only.
++
++\paragraph{KV-cache quantization, eviction and compaction.}
++KIVI \citep{liu2024kivi} quantizes keys per channel and values per token to 2 bits with a full-precision residual window, motivated
++by outlier channels in keys. Eviction methods select positions to keep: H$_2$O \citep{zhang2023h2o} accumulates attention mass
++during prefill; SnapKV \citep{li2024snapkv} scores prefix positions by the attention of an observation window at the end of the
++prompt, pooled and selected per head; PyramidKV \citep{cai2024pyramidkv} allocates decreasing budgets to higher layers;
++StreamingLLM \citep{xiao2024streamingllm} keeps attention sinks and a recent window and \emph{re-assigns} positions within the
++cache. Attention Matching \citep{zweiger2026compaction} compacts a cache into fewer positions by solving for keys and values that
++reproduce attention outputs on reference queries. Our audit (Appendix~\ref{app:audit}) records for each method whether its
++selection observes the question: SnapKV- and PyramidKV-style selection are question-aware by construction when the prompt ends
++with the question, which matters for the comparison with reusable, question-blind messages.
++
++\paragraph{Cache reuse and transfer.}
++Prefix caching reuses an identical token prefix within one checkpoint; it is exposed by local runtimes \citep{mlxlm2026,hf2026kvcache}
++and, as a billing abstraction, by hosted APIs \citep{anthropic2026caching}. Transfer across checkpoints requires a mapping:
++Cache-to-Cache \citep{fu2026c2c} trains a fuser that injects a sharer's cache into a receiver that still prefills the source;
++LatentMAS \citep{zou2026latentmas} exchanges full caches and latent thoughts among agents that share the same architecture;
++SwaT \citep{alabdulkareem2026swat} trains a cross-family translator and combines it with compaction, but releases no checkpoint.
++Concurrent work on cross-model cache bridging (see the audited list in \texttt{literature/sources.csv}) is active; we make no
++novelty claim about shared latent spaces and run only a same-family linear mapper as a bounded, fully disclosed experiment.
++
++\paragraph{Evaluation.}
++LongBench \citep{bai2024longbench} supplies public long-document QA with F1 scoring; RULER \citep{hsieh2024ruler} generates
++controlled retrieval tasks. We combine both ideas: public subsets (QASPER, 2WikiMultihopQA) for external validity, and a new
++procedural generator of private facts (identifiers, amounts, dates, negations, timestamped revisions, unanswerable questions) so that
++communication dependence can be tested without pre-training contamination.

added paper/sections/reproducibility.tex +3 −0

++\section{Reproducibility}
++\label{sec:reproducibility}
++To be written from results.

added paper/sections/results.tex +3 −0

++\section{Results}
++\label{sec:results}
++To be written from results.

added paper/tables/protocol_hash.tex +1 −0

			`@@ -0,0 +1 @@`
	1	+unfrozen
\ No newline at end of file

added paper/tables/table1_taxonomy.tex +31 −0

++\begin{tabular}{@{}lp{4.2cm}p{2cm}p{2.3cm}p{2.3cm}p{1.6cm}p{2.6cm}@{}}
++\toprule
++id & representation & question\_access & training & compatibility & composable & status\_in\_wp30 \\
++\midrule
++A0 & none (task only) & -- & none & any & -- & measured \\
++A1 & raw text & blind & none & any receiver & concatenation & measured \\
++A2 & text head/tail & blind & none & any & concatenation & measured \\
++A3 & extractive sentences (BM25/centrality) & blind or aware & none & any & concatenation & measured \\
++A4 & abstractive summary & blind or aware & none (prompted sender) & any & concatenation & measured \\
++A5 & typed facts + source spans + unknowns (JSON) & blind or aware & none (prompted sender) & any & record merge & measured \\
++A6 & reference index + metered retrieval & blind index, aware retrieval & none & any + source access & shared memory & measured \\
++A7 & raw text, zstd transport codec & blind & none & any & concatenation & measured \\
++A8 & LLMLingua-2 token dropping & blind & pretrained compressor (official) & any & concatenation & measured \\
++B0 & native bf16 KV cache & blind & none & same checkpoint & no & measured \\
++B1 & serialized KV cache (manifest-checked) & blind & none & same checkpoint + revision & no & measured (process + 2-node) \\
++B2 & packed quantized KV (8/4/2 bit) & blind & none & same checkpoint & no & measured \\
++B3 & sink+recent retention (StreamingLLM-inspired) & blind & none & same checkpoint & no & measured \\
++B4 & random retention (control) & blind & none & same checkpoint & no & measured \\
++B5 & attention-informed retention (SnapKV rule) & aware (blind variant tested) & none & same checkpoint & no & measured \\
++B6 & pyramid layer budgets (PyramidKV rule) & aware & none & same checkpoint & no & measured \\
++B9 & selection then packed quantization & aware & none & same checkpoint & no & measured \\
++C0 & sender cache fed as receiver cache & blind & none & incompatible (negative control) & no & measured (control) \\
++C2 & linear KV mapper (ridge, un-rotated K/V) & blind & ridge on train caches & trained pair (Qwen 1.5B$\to$3B) & no & measured \\
++C3 & mapper + attention selection & aware & ridge on train caches & trained pair & no & measured \\
++-- & SwaT cross-family mapper & blind & ~300M mapper (not public) & Granite$\to$SmolLM & no & not run: no checkpoint/licence \\
++-- & Attention-Matching compaction & blind & none (optimization) & same model & no & not run: CUDA/vLLM code; MLX port out of scope \\
++-- & C2C fuser & blind & released fusers (Qwen3) & Qwen3 receivers & no & not run: CUDA; receiver still prefills source \\
++-- & H2O eviction & cumulative & none & same checkpoint & no & not run: requires attention scores during whole prefill \\
++-- & KIVI per-channel key quantization & blind & none & same checkpoint & no & not run: CUDA kernels; motivation confirmed by B2 Qwen result \\
++\bottomrule
++\end{tabular}

added paper/tables/table2_models.tex +9 −0

++\begin{tabular}{@{}llllllllrrr@{}}
++\toprule
++alias & checkpoint & rev. & L & H\_kv & H\_q & d\_h & max pos & cache B/token & prefill 8k & prefill 16k \\
++\midrule
++qwen3b & mlx-community/Qwen2.5-3B-Instruct-bf16 & bec1ad658e38 & 36 & 2 & 16 & 128 & 32768 & 36864 & 968 & 2286 \\
++llama3b & mlx-community/Llama-3.2-3B-Instruct-bf16 & 6d88ba43024f & 28 & 8 & 24 & 128 & 131072 & 114688 & 1007 & 2449 \\
++qwen1b5 & mlx-community/Qwen2.5-1.5B-Instruct-bf16 & 4ae77cb209f0 & 28 & 2 & 12 & 128 & 32768 & 28672 & 494 & 1201 \\
++\bottomrule
++\end{tabular}

added paper/tables/table3_datasets.tex +18 −0

++\begin{tabular}{@{}llrrr@{}}
++\toprule
++family & split & documents & questions & median\_chars \\
++\midrule
++exec & dev & 30 & 30 & 7847 \\
++exec & test & 40 & 40 & 7817 \\
++exec & train & 20 & 20 & 7866 \\
++public\_2wikimqa & dev & 56 & 56 & 25,419 \\
++public\_2wikimqa & test & 144 & 144 & 25,488 \\
++public\_hotpotqa & dev & 61 & 61 & 65,814 \\
++public\_hotpotqa & test & 139 & 139 & 60,944 \\
++public\_qasper & dev & 50 & 50 & 19,844 \\
++public\_qasper & test & 150 & 150 & 23,154 \\
++synthetic & dev & 30 & 450 & 29,225 \\
++synthetic & test & 40 & 600 & 29,203 \\
++synthetic & train & 40 & 600 & 29,196 \\
++\bottomrule
++\end{tabular}

added runs/checkpoints/mapper_qwen1b5_to_qwen3b.npz +0 −0

Binary file not shown.

added runs/checkpoints/mapper_qwen1b5_to_qwen3b.npz.manifest.json +199 −0

++{
++  "config": {
++    "sender": "qwen1b5",
++    "receiver": "qwen3b",
++    "families": [
++      "synthetic",
++      "exec"
++    ],
++    "max_tokens_per_doc": 8192,
++    "ridge": 1.0,
++    "n_dev_eval": 4
++  },
++  "train_docs": [
++    "syn_01000_2000",
++    "syn_01001_8000",
++    "syn_01002_16000",
++    "syn_01003_2000",
++    "syn_01004_8000",
++    "syn_01005_16000",
++    "syn_01006_2000",
++    "syn_01007_8000",
++    "syn_01008_16000",
++    "syn_01009_2000",
++    "syn_01010_8000",
++    "syn_01011_16000",
++    "syn_01012_2000",
++    "syn_01013_8000",
++    "syn_01014_16000",
++    "syn_01015_2000",
++    "syn_01016_8000",
++    "syn_01017_16000",
++    "syn_01018_2000",
++    "syn_01019_8000",
++    "syn_01020_16000",
++    "syn_01021_2000",
++    "syn_01022_8000",
++    "syn_01023_16000",
++    "syn_01024_2000",
++    "syn_01025_8000",
++    "syn_01026_16000",
++    "syn_01027_2000",
++    "syn_01028_8000",
++    "syn_01029_16000",
++    "syn_01030_2000",
++    "syn_01031_8000",
++    "syn_01032_16000",
++    "syn_01033_2000",
++    "syn_01034_8000",
++    "syn_01035_16000",
++    "syn_01036_2000",
++    "syn_01037_8000",
++    "syn_01038_16000",
++    "syn_01039_2000",
++    "exec_00001_2000",
++    "exec_00002_2000",
++    "exec_00003_2000",
++    "exec_00004_2000",
++    "exec_00005_2000",
++    "exec_00006_2000",
++    "exec_00007_2000",
++    "exec_00008_2000",
++    "exec_00009_2000",
++    "exec_00010_2000",
++    "exec_00011_2000",
++    "exec_00012_2000",
++    "exec_00013_2000",
++    "exec_00014_2000",
++    "exec_00015_2000",
++    "exec_00016_2000",
++    "exec_00017_2000",
++    "exec_00018_2000",
++    "exec_00019_2000",
++    "exec_00020_2000"
++  ],
++  "train_seconds": 288.5171139240265,
++  "dev_reconstruction": [
++    {
++      "doc": "syn_02000_2000",
++      "layer": 0,
++      "rel_err_k": 0.014429992996156216,
++      "rel_err_v": 0.21747948229312897
++    },
++    {
++      "doc": "syn_02000_2000",
++      "layer": 18,
++      "rel_err_k": 0.19839154183864594,
++      "rel_err_v": 0.5880571603775024
++    },
++    {
++      "doc": "syn_02000_2000",
++      "layer": 35,
++      "rel_err_k": 0.19203418493270874,
++      "rel_err_v": 0.5485036969184875
++    },
++    {
++      "doc": "syn_02001_8000",
++      "layer": 0,
++      "rel_err_k": 0.008613131940364838,
++      "rel_err_v": 0.12830892205238342
++    },
++    {
++      "doc": "syn_02001_8000",
++      "layer": 18,
++      "rel_err_k": 0.17894120514392853,
++      "rel_err_v": 0.5188227891921997
++    },
++    {
++      "doc": "syn_02001_8000",
++      "layer": 35,
++      "rel_err_k": 0.13947930932044983,
++      "rel_err_v": 0.4167284369468689
++    },
++    {
++      "doc": "syn_02002_16000",
++      "layer": 0,
++      "rel_err_k": 0.008165479637682438,
++      "rel_err_v": 0.12064959853887558
++    },
++    {
++      "doc": "syn_02002_16000",
++      "layer": 18,
++      "rel_err_k": 0.17874450981616974,
++      "rel_err_v": 0.5191535353660583
++    },
++    {
++      "doc": "syn_02002_16000",
++      "layer": 35,
++      "rel_err_k": 0.12985366582870483,
++      "rel_err_v": 0.38665786385536194
++    },
++    {
++      "doc": "syn_02003_2000",
++      "layer": 0,
++      "rel_err_k": 0.012657775543630123,
++      "rel_err_v": 0.1916254460811615
++    },
++    {
++      "doc": "syn_02003_2000",
++      "layer": 18,
++      "rel_err_k": 0.19114117324352264,
++      "rel_err_v": 0.5788665413856506
++    },
++    {
++      "doc": "syn_02003_2000",
++      "layer": 35,
++      "rel_err_k": 0.18840292096138,
++      "rel_err_v": 0.5358448624610901
++    }
++  ],
++  "device": "laptop-m5max",
++  "sender": "mlx-community/Qwen2.5-1.5B-Instruct-bf16",
++  "sender_revision": "4ae77cb209f06199b8df1c94e21ff341332a3a89",
++  "receiver": "mlx-community/Qwen2.5-3B-Instruct-bf16",
++  "receiver_revision": "bec1ad658e380044bbfe06d4a2268801d3379a11",
++  "layer_map": [
++    0,
++    1,
++    2,
++    2,
++    3,
++    4,
++    5,
++    5,
++    6,
++    7,
++    8,
++    8,
++    9,
++    10,
++    11,
++    12,
++    12,
++    13,
++    14,
++    15,
++    15,
++    16,
++    17,
++    18,
++    19,
++    19,
++    20,
++    21,
++    22,
++    22,
++    23,
++    24,
++    25,
++    25,
++    26,
++    27
++  ],
++  "ridge": 1.0,
++  "n_train_tokens": 248569,
++  "params": 9455616,
++  "checkpoint_sha256": "3802150edf61206f81a5af7f5294981242387bcd2f10291beeac5f93d4340d92",
++  "created_utc": "2026-10-30T04:08:47.078307Z",
++  "loss": "ridge least squares on un-rotated K and V (reconstruction only)"
++}
+ \ No newline at end of file

modified runs/raw/E1_pilot.jsonl +265 −0

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++{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_pilot", "trial_id": "3c62515fe1d6a1e93918aa3d", "attempt_id": "6d339b5f3b7e", "run_group_id": "E1_pilot:A3|budget_tokens=512|aware", "protocol_hash": "unfrozen", "config_hash": "c7a8fcdc6928a38dfad7d8fdbe8da337078cf0a11117ad2cb6590f113531b5b2", "git_commit": "53ae960640251cd8de4e48bd47b17d011ba87dbe", "timestamp_utc": "2026-10-30T04:10:57.305371Z", "task_family": "synthetic", "document_id": "syn_02001_8000", "document_hash": "acefbded40b5210f20d4288aa9a950b81e67729e53809c2fd0da03d1517011ae", "question_id": "q01_single_fact", "split": "dev", "cluster_id": "syn_02001_8000", "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": 12597384200}, "weight_precision": "bf16", "method_id": "A3", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "aware", "realized": {"budget_tokens": 512, "realized_tokens": 506, "n_sentences": 28}}, "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": 5544, "source_tokens_receiver": 5544, "query_tokens": 609, "message_tokens": 506, "message_utf8_bytes": 1912, "cache_tensor_bytes": null, "serialized_payload_bytes": 1912, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 4.767999984323978, "decode_payload_ms": 0.0, "receiver_prefill_ms": 154.6482079429552, "ttft_ms": 41.264416999183595, "decode_ms": 292.2340410295874}, "total_wall_ms": 494.8314999928698, "ttft_ms": 41.264416999183595, "input_usage": {"receiver_prompt_tokens": 609}, "output_usage": {"output_tokens": 9}, "provider_cached_usage": null, "process_memory": 126042112, "backend_memory": {"mlx_active_bytes": 12597384200, "mlx_peak_bytes": 13035003380, "mlx_cache_bytes": 6653472152, "process_rss_bytes": 126042112}, "peak_prefill_memory": null, "peak_decode_memory": 13035003380, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "Lot 894-D, Gaspé.", "evidence_artifact": null, "reference_key_id": "keys/synthetic/syn_02001_8000.json", "task_scores": {"correct": 1.0, "abstained": 0.0, "f1": 0.8571428571428571}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_pilot.log", "payload_hash": "337080a3fb92585744b93fd99e07107361d161fc505f5b028975f5c39f4da228", "output_hash": "b5554699ca1445f91b96d857dc469360a149c2f4784ae064e24ed4b6911b09f3"}
++{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_pilot", "trial_id": "0786c3c5a6a8a4829e8c5c94", "attempt_id": "f843afc55a30", "run_group_id": "E1_pilot:A3|budget_tokens=512|aware", "protocol_hash": "unfrozen", "config_hash": "c7a8fcdc6928a38dfad7d8fdbe8da337078cf0a11117ad2cb6590f113531b5b2", "git_commit": "53ae960640251cd8de4e48bd47b17d011ba87dbe", "timestamp_utc": "2026-10-30T04:10:57.828355Z", "task_family": "synthetic", "document_id": "syn_02001_8000", "document_hash": "acefbded40b5210f20d4288aa9a950b81e67729e53809c2fd0da03d1517011ae", "question_id": "q02_exact_copy", "split": "dev", "cluster_id": "syn_02001_8000", "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": 12597384200}, "weight_precision": "bf16", "method_id": "A3", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "aware", "realized": {"budget_tokens": 512, "realized_tokens": 512, "n_sentences": 28}}, "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": 5544, "source_tokens_receiver": 5544, "query_tokens": 614, "message_tokens": 512, "message_utf8_bytes": 1568, "cache_tensor_bytes": null, "serialized_payload_bytes": 1568, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 3.7989160045981407, "decode_payload_ms": 0.0, "receiver_prefill_ms": 163.87362498790026, "ttft_ms": 23.36516696959734, "decode_ms": 158.9618750149384}, "total_wall_ms": 351.4246669365093, "ttft_ms": 23.36516696959734, "input_usage": {"receiver_prompt_tokens": 614}, "output_usage": {"output_tokens": 4}, "provider_cached_usage": null, "process_memory": 126451712, "backend_memory": {"mlx_active_bytes": 12597384200, "mlx_peak_bytes": 13072670196, "mlx_cache_bytes": 6773992856, "process_rss_bytes": 126451712}, "peak_prefill_memory": null, "peak_decode_memory": 13072670196, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "79.3209", "evidence_artifact": null, "reference_key_id": "keys/synthetic/syn_02001_8000.json", "task_scores": {"correct": 1.0, "abstained": 0.0, "f1": 1.0}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_pilot.log", "payload_hash": "636a9a471c33e87bf3493d90f1a13979481244f1c62cbb3e46a2d5b62f0cf06e", "output_hash": "00844526dd9401ae600bda6d0889bf28bbf827181a645be64bc8e9566d051361"}
++{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_pilot", "trial_id": "65e16a78f74781213ee4ec8c", "attempt_id": "fedf8719d548", "run_group_id": "E1_pilot:A3|budget_tokens=512|aware", "protocol_hash": "unfrozen", "config_hash": "c7a8fcdc6928a38dfad7d8fdbe8da337078cf0a11117ad2cb6590f113531b5b2", "git_commit": "53ae960640251cd8de4e48bd47b17d011ba87dbe", "timestamp_utc": "2026-10-30T04:10:58.210240Z", "task_family": "synthetic", "document_id": "syn_02001_8000", "document_hash": "acefbded40b5210f20d4288aa9a950b81e67729e53809c2fd0da03d1517011ae", "question_id": "q03_exact_copy", "split": "dev", "cluster_id": "syn_02001_8000", "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": 12597384200}, "weight_precision": "bf16", "method_id": "A3", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "aware", "realized": {"budget_tokens": 512, "realized_tokens": 498, "n_sentences": 25}}, "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": 5544, "source_tokens_receiver": 5544, "query_tokens": 594, "message_tokens": 497, "message_utf8_bytes": 1928, "cache_tensor_bytes": null, "serialized_payload_bytes": 1928, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 3.0625839717686176, "decode_payload_ms": 0.0, "receiver_prefill_ms": 183.17112501244992, "ttft_ms": 42.80954098794609, "decode_ms": 142.7885419689119}, "total_wall_ms": 373.27508290763944, "ttft_ms": 42.80954098794609, "input_usage": {"receiver_prompt_tokens": 594}, "output_usage": {"output_tokens": 5}, "provider_cached_usage": null, "process_memory": 126517248, "backend_memory": {"mlx_active_bytes": 12597384200, "mlx_peak_bytes": 13037952500, "mlx_cache_bytes": 7077047704, "process_rss_bytes": 126517248}, "peak_prefill_memory": null, "peak_decode_memory": 13037952500, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "Haruki Achterberg", "evidence_artifact": null, "reference_key_id": "keys/synthetic/syn_02001_8000.json", "task_scores": {"correct": 1.0, "abstained": 0.0, "f1": 1.0}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_pilot.log", "payload_hash": "9d95790f82b40e943576b28282e3f5fe3e289504c960f0b2998de1113c47bdd8", "output_hash": "f8f4436d5607232dde4f6986ce4cf4ad38bcb02f807312d9c5078948d1ac80b3"}
++{"schema_version": "wp30-attempt-1", "study_id": "WP30", "experiment_id": "E1_pilot", "trial_id": "38ae7138e0e560cbb2e7c13b", "attempt_id": "ab88d635a86f", "run_group_id": "E1_pilot:A3|budget_tokens=512|aware", "protocol_hash": "unfrozen", "config_hash": "c7a8fcdc6928a38dfad7d8fdbe8da337078cf0a11117ad2cb6590f113531b5b2", "git_commit": "53ae960640251cd8de4e48bd47b17d011ba87dbe", "timestamp_utc": "2026-10-30T04:10:58.612121Z", "task_family": "synthetic", "document_id": "syn_02001_8000", "document_hash": "acefbded40b5210f20d4288aa9a950b81e67729e53809c2fd0da03d1517011ae", "question_id": "q04_multi_hop", "split": "dev", "cluster_id": "syn_02001_8000", "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": 12597384200}, "weight_precision": "bf16", "method_id": "A3", "implementation_fidelity": "custom", "compression_config": {"budget_tokens": 512, "access": "aware", "realized": {"budget_tokens": 512, "realized_tokens": 498, "n_sentences": 19}}, "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": 5544, "source_tokens_receiver": 5544, "query_tokens": 602, "message_tokens": 498, "message_utf8_bytes": 1480, "cache_tensor_bytes": null, "serialized_payload_bytes": 1480, "side_information_bytes": 0, "fetched_bytes": 0, "transport_bytes_if_measured": null, "timing_spans": {"encode_ms": 2.1115000126883388, "decode_payload_ms": 0.0, "receiver_prefill_ms": 197.09729205351323, "ttft_ms": 39.184416993521154, "decode_ms": 193.87891690712422}, "total_wall_ms": 433.92862495966256, "ttft_ms": 39.184416993521154, "input_usage": {"receiver_prompt_tokens": 602}, "output_usage": {"output_tokens": 5}, "provider_cached_usage": null, "process_memory": 126582784, "backend_memory": {"mlx_active_bytes": 12597384200, "mlx_peak_bytes": 13031742964, "mlx_cache_bytes": 7077047704, "process_rss_bytes": 126582784}, "peak_prefill_memory": null, "peak_decode_memory": 13031742964, "mapper_memory": null, "cost_with_source": null, "prediction_artifact": "8,900,835", "evidence_artifact": null, "reference_key_id": "keys/synthetic/syn_02001_8000.json", "task_scores": {"correct": 1.0, "abstained": 0.0, "f1": 1.0}, "status": "success", "error_code": null, "retry_of": null, "log_artifact": "runs/logs/E1_pilot.log", "payload_hash": "edc513365537ba5df9ad8afcbcf8d2bce81c146327901a642e67a91f1a45be3b", "output_hash": "60d5349a522cecbebe7453e3cc50a36b948b21241716c354a3567079c1da27ed"}

added src/wp30/analysis/latex_tables.py +34 −0

++"""CSV -> LaTeX fragments for the paper (booktabs). Numbers are formatted here only; values come from results/tables CSVs."""
++from __future__ import annotations
++import pandas as pd, numpy as np
++from pathlib import Path
++from ..util import ROOT
++TAB = ROOT / "results/tables"; OUT = ROOT / "paper/tables"
++
++def esc(s):
++    return str(s).replace("_", "\\_").replace("%", "\\%").replace("&", "\\&").replace("→", "$\\to$").replace("≥", "$\\ge$").replace("×", "$\\times$").replace("—", "--")
++
++def df_to_tex(df: pd.DataFrame, path: Path, colfmt: str | None = None, floatfmt="{:.2f}"):
++    cols = list(df.columns); colfmt = colfmt or "l" * len(cols)
++    lines = ["\\begin{tabular}{@{}" + colfmt + "@{}}", "\\toprule", " & ".join(esc(c) for c in cols) + " \\\\", "\\midrule"]
++    for _, r in df.iterrows():
++        cells = []
++        for c in cols:
++            v = r[c]
++            if isinstance(v, float):
++                cells.append("--" if np.isnan(v) else (floatfmt.format(v) if abs(v) < 1e4 else f"{v:,.0f}"))
++            else:
++                cells.append(esc(v))
++        lines.append(" & ".join(cells) + " \\\\")
++    lines += ["\\bottomrule", "\\end{tabular}"]
++    OUT.mkdir(parents=True, exist_ok=True); path.write_text("\n".join(lines) + "\n")
++
++def main():
++    t1 = pd.read_csv(TAB / "table1_taxonomy.csv"); df_to_tex(t1, OUT / "table1_taxonomy.tex", "lp{4.2cm}p{2cm}p{2.3cm}p{2.3cm}p{1.6cm}p{2.6cm}")
++    t2 = pd.read_csv(TAB / "table2_models.csv")[["alias", "repo", "revision", "layers", "kv_heads", "q_heads", "head_dim", "max_pos", "cache_bytes_per_token_bf16", "prefill_ms_8k", "prefill_ms_16k"]]
++    t2.columns = ["alias", "checkpoint", "rev.", "L", "H_kv", "H_q", "d_h", "max pos", "cache B/token", "prefill 8k", "prefill 16k"]; df_to_tex(t2, OUT / "table2_models.tex", "llllllllrrr")
++    t3 = pd.read_csv(TAB / "table3_datasets.csv"); df_to_tex(t3, OUT / "table3_datasets.tex", "llrrr", "{:.0f}")
++    print("latex tables written")
++
++if __name__ == "__main__":
++    main()

modified src/wp30/mapper.py +18 −14

				`@@ -49,11 +49,11 @@ def verify_rope_inverse(model: MLXModel) -> float:`
49	return max(err1, err2)	49	return max(err1, err2)
50		50
51	class LinearKVMapper:	51	class LinearKVMapper:
52	− def __init__(self, sender_info, receiver_info, layer_map: list[int], ridge: float):	52	+ def __init__(self, sender_info, receiver_info, layer_map: list[int], ridge: float, neighbours: int = 0):
53	self.s, self.r = sender_info, receiver_info	53	self.s, self.r = sender_info, receiver_info
54	− self.layer_map = layer_map; self.ridge = ridge	54	+ self.layer_map = layer_map; self.ridge = ridge; self.neighbours = neighbours
55	ds = sender_info.num_kv_heads * sender_info.head_dim; dr = receiver_info.num_kv_heads * receiver_info.head_dim	55	ds = sender_info.num_kv_heads * sender_info.head_dim; dr = receiver_info.num_kv_heads * receiver_info.head_dim
56	− self.din = 2 * ds + 1; self.dout = 2 * dr	56	+ self.din = (2 * neighbours + 1) * 2 * ds + 1; self.dout = 2 * dr
57	self.W = [None] * receiver_info.num_layers # [din, dout] (K and V stacked)	57	self.W = [None] * receiver_info.num_layers # [din, dout] (K and V stacked)
58	# sufficient statistics	58	# sufficient statistics
59	self._xtx = [np.zeros((self.din, self.din)) for _ in range(receiver_info.num_layers)]	59	self._xtx = [np.zeros((self.din, self.din)) for _ in range(receiver_info.num_layers)]
				`@@ -61,11 +61,15 @@ class LinearKVMapper:`
61	self.n_tokens = 0	61	self.n_tokens = 0
62		62
63	def _features(self, s_cache, lr: int) -> np.ndarray:	63	def _features(self, s_cache, lr: int) -> np.ndarray:
64	− ls = self.layer_map[lr]	64	+ ls = self.layer_map[lr]; Ls = len(s_cache)
65	− k, v = dense_kv(s_cache[ls]); k = np.array(k[0].astype(mx.float32)); v = np.array(v[0].astype(mx.float32)) # [H, T, d]	65	+ parts = []
66	− k = unrotate(k, self.s.rope_theta, 0)	66	+ for off in range(-self.neighbours, self.neighbours + 1):
67	− H, T, d = k.shape	67	+ l = min(max(ls + off, 0), Ls - 1)
68	− X = np.concatenate([k.transpose(1, 0, 2).reshape(T, -1), v.transpose(1, 0, 2).reshape(T, -1), np.ones((T, 1))], axis=1)	68	+ k, v = dense_kv(s_cache[l]); k = np.array(k[0].astype(mx.float32)); v = np.array(v[0].astype(mx.float32)) # [H, T, d]
		69	+ k = unrotate(k, self.s.rope_theta, 0)
		70	+ H, T, d = k.shape
		71	+ parts += [k.transpose(1, 0, 2).reshape(T, -1), v.transpose(1, 0, 2).reshape(T, -1)]
		72	+ X = np.concatenate(parts + [np.ones((parts[0].shape[0], 1))], axis=1)
69	return X	73	return X
70		74
71	def accumulate(self, s_cache, r_cache):	75	def accumulate(self, s_cache, r_cache):
				`@@ -96,15 +100,15 @@ class LinearKVMapper:`
96	return out	100	return out
97		101
98	def save(self, path: Path, meta: dict):	102	def save(self, path: Path, meta: dict):
99	− np.savez_compressed(path, **{f"W{l}": w for l, w in enumerate(self.W)}, layer_map=np.array(self.layer_map))	103	+ np.savez_compressed(path, **{f"W{l}": w for l, w in enumerate(self.W)}, layer_map=np.array(self.layer_map), neighbours=np.array(self.neighbours))
100	write_json(str(path) + ".manifest.json", {**meta, "sender": self.s.repo_id, "sender_revision": self.s.revision, "receiver": self.r.repo_id, "receiver_revision": self.r.revision,	104	write_json(str(path) + ".manifest.json", {**meta, "sender": self.s.repo_id, "sender_revision": self.s.revision, "receiver": self.r.repo_id, "receiver_revision": self.r.revision,
101	− "layer_map": self.layer_map, "ridge": self.ridge, "n_train_tokens": self.n_tokens, "params": int(sum(w.size for w in self.W)),	105	+ "layer_map": self.layer_map, "ridge": self.ridge, "n_train_tokens": self.n_tokens, "params": int(sum(w.size for w in self.W)), "neighbours": self.neighbours,
102	"checkpoint_sha256": sha256_file(path), "created_utc": utc_now(), "loss": "ridge least squares on un-rotated K and V (reconstruction only)"})	106	"checkpoint_sha256": sha256_file(path), "created_utc": utc_now(), "loss": "ridge least squares on un-rotated K and V (reconstruction only)"})
103		107
104	@classmethod	108	@classmethod
105	def load(cls, path: Path, sender_info, receiver_info):	109	def load(cls, path: Path, sender_info, receiver_info):
106	− z = np.load(path); lm = z["layer_map"].tolist()	110	+ z = np.load(path); lm = z["layer_map"].tolist(); nb = int(z["neighbours"]) if "neighbours" in z.files else 0
107	− m = cls(sender_info, receiver_info, lm, 0.0); m.W = [z[f"W{l}"] for l in range(receiver_info.num_layers)]	111	+ m = cls(sender_info, receiver_info, lm, 0.0, neighbours=nb); m.W = [z[f"W{l}"] for l in range(receiver_info.num_layers)]
108	return m	112	return m
109		113
110	def identity_transfer_negative_control(s_cache, receiver_info, layer_map) -> list:	114	def identity_transfer_negative_control(s_cache, receiver_info, layer_map) -> list:
				`@@ -124,7 +128,7 @@ def train_main(config_path: str) -> int:`
124	err = verify_rope_inverse(receiver); print("rope inverse max err", err); assert err < 1e-3	128	err = verify_rope_inverse(receiver); print("rope inverse max err", err); assert err < 1e-3
125	man = read_json(ROOT / "data/manifests/splits.json")	129	man = read_json(ROOT / "data/manifests/splits.json")
126	items = [it for it in man["items"] if it["split"] == "train" and it["family"] in cfg["families"]]	130	items = [it for it in man["items"] if it["split"] == "train" and it["family"] in cfg["families"]]
127	− mp = LinearKVMapper(sender.info, receiver.info, layer_map, cfg.get("ridge", 1.0))	131	+ mp = LinearKVMapper(sender.info, receiver.info, layer_map, cfg.get("ridge", 1.0), neighbours=cfg.get("neighbours", 0))
128	t0 = time.time(); log = []	132	t0 = time.time(); log = []
129	for i, it in enumerate(items):	133	for i, it in enumerate(items):
130	doc = load_doc(it["document_id"], it["family"])	134	doc = load_doc(it["document_id"], it["family"])
				`@@ -146,7 +150,7 @@ def train_main(config_path: str) -> int:`
146	k_true, v_true = dense_kv(rc[lr]); k_m, v_m = mapped[lr].keys_and_values()	150	k_true, v_true = dense_kv(rc[lr]); k_m, v_m = mapped[lr].keys_and_values()
147	kt = np.array(k_true.astype(mx.float32)); km = np.array(k_m.astype(mx.float32)); vt = np.array(v_true.astype(mx.float32)); vm = np.array(v_m.astype(mx.float32))	151	kt = np.array(k_true.astype(mx.float32)); km = np.array(k_m.astype(mx.float32)); vt = np.array(v_true.astype(mx.float32)); vm = np.array(v_m.astype(mx.float32))
148	rel.append({"doc": it["document_id"], "layer": lr, "rel_err_k": float(np.linalg.norm(kt - km) / np.linalg.norm(kt)), "rel_err_v": float(np.linalg.norm(vt - vm) / np.linalg.norm(vt))})	152	rel.append({"doc": it["document_id"], "layer": lr, "rel_err_k": float(np.linalg.norm(kt - km) / np.linalg.norm(kt)), "rel_err_v": float(np.linalg.norm(vt - vm) / np.linalg.norm(vt))})
149	− out = ROOT / "runs/checkpoints" / f"mapper_{cfg['sender']}_to_{cfg['receiver']}.npz"; out.parent.mkdir(parents=True, exist_ok=True)	153	+ out = ROOT / "runs/checkpoints" / cfg.get("output", f"mapper_{cfg['sender']}_to_{cfg['receiver']}.npz"); out.parent.mkdir(parents=True, exist_ok=True)
150	mp.save(out, {"config": cfg, "train_docs": [l["doc"] for l in log], "train_seconds": time.time() - t0, "dev_reconstruction": rel, "device": "laptop-m5max"})	154	mp.save(out, {"config": cfg, "train_docs": [l["doc"] for l in log], "train_seconds": time.time() - t0, "dev_reconstruction": rel, "device": "laptop-m5max"})
151	print(json.dumps({"n_train_tokens": mp.n_tokens, "params": int(sum(w.size for w in mp.W)), "train_seconds": round(time.time() - t0), "dev_rel_err": rel[:6]}, indent=1))	155	print(json.dumps({"n_train_tokens": mp.n_tokens, "params": int(sum(w.size for w in mp.W)), "train_seconds": round(time.time() - t0), "dev_rel_err": rel[:6]}, indent=1))
152	return 0	156	return 0

added src/wp30/router_eval.py +59 −0

++"""E8 evaluation: apply the frozen dev-fitted policy to held-out records (full factorial on test instances -> the policy's outcome on an
++instance is the recorded outcome of the condition it picks). Includes fallback counting and router overhead (feature lookup time)."""
++from __future__ import annotations
++import json, time, yaml
++import numpy as np, pandas as pd
++from .util import ROOT, read_json, write_json
++from .analysis.load import load_experiments
++from .analysis.stats import cluster_bootstrap_mean, paired_difference
++from .router import choose
++
++def main(config_path: str) -> int:
++    cfg = yaml.safe_load(open(config_path))
++    pol = read_json(ROOT / "runs/checkpoints/router_policy.json")
++    df = load_experiments(cfg["test_patterns"]); df = df[df["split"] == "test"]
++    df["question_access_group"] = df["question_access"].replace({"none": "blind"})
++    df = df[df["condition"].isin(pol["actions"])]
++    rows = []
++    t_router = []
++    for (doc, q, pair), g in df.groupby(["document_id", "question_id", "pair"]):
++        ctx = g["message_utf8_bytes"].max() / 4.0 if "A1" in set(g["method_id"]) else g["serialized_payload_bytes"].max() / 4.0
++        fam = g["task_family"].iloc[0]
++        for acc in ["blind", "aware"]:
++            gg = g[g["question_access_group"] == acc] if acc == "aware" else g[g["question_access_group"].isin(["blind"])]
++            if gg.empty: continue
++            t0 = time.perf_counter(); action, fb = choose(pol, fam, ctx, pair, acc); t_router.append((time.perf_counter() - t0) * 1000)
++            hit = gg[gg["condition"] == action]
++            if hit.empty:   # chosen action not run on this instance -> fallback to best static that exists here
++                action = pol["global_best_static"]; fb = True; hit = gg[gg["condition"] == action]
++                if hit.empty: continue
++            r = hit.iloc[0]
++            rows.append({"document_id": doc, "question_id": q, "pair": pair, "task_family": fam, "access": acc, "policy": "router", "condition": action, "fallback": fb,
++                         "quality_operational": r["quality_operational"], "cost": r[pol["cost_col"]], "bytes": r["serialized_payload_bytes"], "router_ms": t_router[-1]})
++            # static baselines on the same instance
++            for name, cond in [("best_static_dev", pol["global_best_static"]), ("full_context", "A1"), ("structured", "A5|budget_tokens=512|blind"), ("native_cache", "B0")]:
++                h = gg[gg["condition"] == cond]
++                if not h.empty:
++                    rows.append({"document_id": doc, "question_id": q, "pair": pair, "task_family": fam, "access": acc, "policy": name, "condition": cond, "fallback": False,
++                                 "quality_operational": h.iloc[0]["quality_operational"], "cost": h.iloc[0][pol["cost_col"]], "bytes": h.iloc[0]["serialized_payload_bytes"], "router_ms": 0.0})
++            # oracle (infeasible upper reference)
++            best = gg.loc[gg["quality_operational"].idxmax()]
++            rows.append({"document_id": doc, "question_id": q, "pair": pair, "task_family": fam, "access": acc, "policy": "oracle_infeasible", "condition": best["condition"], "fallback": False,
++                         "quality_operational": best["quality_operational"], "cost": best[pol["cost_col"]], "bytes": best["serialized_payload_bytes"], "router_ms": 0.0})
++    out = pd.DataFrame(rows)
++    summ = []
++    for (pol_name, acc), g in out.groupby(["policy", "access"]):
++        q = cluster_bootstrap_mean(g, "quality_operational"); c = cluster_bootstrap_mean(g, "cost")
++        summ.append({"policy": pol_name, "access": acc, "quality": q["mean"], "q_lo": q["lo"], "q_hi": q["hi"], "cost_ms": c["mean"], "c_lo": c["lo"], "c_hi": c["hi"], "bytes_median": g["bytes"].median(),
++                     "fallback_rate": g["fallback"].mean(), "router_ms_mean": g["router_ms"].mean(), "n": q["n"], "n_docs": q["n_clusters"]})
++    S = pd.DataFrame(summ)
++    tests = []
++    for acc in ["blind", "aware"]:
++        sub = out[out["access"] == acc]
++        tests.append({**paired_difference(sub, "quality_operational", "policy", "router", "best_static_dev", keys=("document_id", "question_id", "pair")), "access": acc, "metric": "quality"})
++        tests.append({**paired_difference(sub, "cost", "policy", "router", "best_static_dev", keys=("document_id", "question_id", "pair")), "access": acc, "metric": "cost_ms"})
++    (ROOT / "results/tables").mkdir(parents=True, exist_ok=True)
++    S.to_csv(ROOT / "results/tables/E8_router_summary.csv", index=False); pd.DataFrame(tests).to_csv(ROOT / "results/tables/E8_router_tests.csv", index=False)
++    out.to_csv(ROOT / "results/tables/E8_router_instances.csv", index=False)
++    print(S.to_string()); print(pd.DataFrame(tests)[["access", "metric", "diff", "lo", "hi", "p", "n"]].to_string())
++    return 0

spb/wp30_uqo

WP30: paper sections (intro, related, formulation, methods, protocol, appendix), router eval, mapper v2, pilots on cluster

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