Add arXiv-ready LaTeX build of the introspection note

.gitignore

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 results/*.raw.json
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+
+# LaTeX build artifacts
+docs/*.aux
+docs/*.log
+docs/*.out
+docs/*.toc

docs/Makefile

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+# Rebuild note.pdf from note.tex.
+# Runs pdflatex twice so cross-references and the bibliography resolve.
+# Figure paths in note.tex are relative to this directory (../results/...),
+# so pdflatex must run from here.
+
+TEX      = note.tex
+PDF      = note.pdf
+LATEX    = pdflatex
+LATEXFLAGS = -interaction=nonstopmode -halt-on-error
+
+.PHONY: all pdf clean
+
+all: pdf
+
+pdf: $(PDF)
+
+$(PDF): $(TEX)
+	$(LATEX) $(LATEXFLAGS) $(TEX)
+	$(LATEX) $(LATEXFLAGS) $(TEX)
+
+clean:
+	rm -f note.aux note.log note.out note.toc

docs/arxiv-submission.md

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+# arXiv submission prep
+
+**SUBMISSION AWAITS BAMDAD'S EXPLICIT GO — do not submit.**
+
+This file is preparation only. Nothing here has been submitted to arXiv or emailed
+to anyone. It exists so that, once Bamdad says go, the mechanical steps are ready.
+
+---
+
+## Part 1 — cs.LG endorsement request (email draft)
+
+> **To:** (an arXiv author already able to submit to cs.LG)
+> **Subject:** Endorsement request for arXiv cs.LG submission
+>
+> Dear Dr. ___,
+>
+> I am an independent researcher preparing a short note for arXiv and am writing to
+> ask whether you would be willing to endorse me to submit to cs.LG. The note is an
+> honest-negative reproduction of the concept-injection introspection protocol of
+> Lindsey et al. (2025) on open Qwen2.5 models: across a base / general-instruct /
+> code-instruct grid at 7B, 14B, and 32B, every rung returns a null on strict
+> correct-identification except one marginal cell (Qwen2.5-Coder-32B, 5/216), which
+> does not replicate down its own size ladder, so I report the effect as a
+> conjunction of code-heavy post-training and roughly 32B scale rather than a main
+> effect of either. The note also documents a dose-fragility result: an
+> injection strength calibrated for coherent steering sits well below the source
+> paper's regime and produces a clean but false null. Code, raw transcripts, and the
+> dose calibration are public. I would be glad to send the PDF if it is useful for
+> your decision.
+>
+> Thank you for considering it.
+>
+> Kind regards,
+> Bamdad Dashtban
+
+Notes for Bamdad:
+- If arXiv assigns an endorsement code, paste it into the endorsement page; the
+  endorser does not need to read the paper unless they want to.
+- Keep the wording factual and neutral; do not oversell. The note is a null result.
+
+---
+
+## Part 2 — submission checklist
+
+- [ ] **Primary category:** cs.LG
+- [ ] **Secondary category:** cs.CL
+- [ ] **License:** recommend arXiv non-exclusive license to distribute, with
+      **CC BY 4.0** — *Bamdad's call*; change if you prefer a more restrictive
+      license.
+- [ ] **Author metadata:** name **Bamdad Dashtban**; affiliation **Independent**
+      (unless Bamdad wants a different affiliation).
+- [ ] **Title:**
+      *Concept-Injection Introspection in Open Models Is Dose-Fragile; Its One
+      Above-Chance Signal Does Not Replicate Across Scale*
+- [ ] **Abstract (paste-ready):**
+
+  We reproduce the concept-injection introspection protocol of Lindsey et al.
+  (2025, "Emergent Introspective Awareness in Large Language Models") on open
+  Qwen2.5 models. Two findings result. First, the effect is dose-fragile: an
+  injection strength calibrated for coherent activation steering sits roughly 4 to
+  18 times below the paper's absolute strength, and at that under-dose every model
+  returns a clean null that passes every sanity check (flat controls, a working
+  positive control, coherent transcripts). Correcting the dose to the paper's regime
+  and adding a fails-loud judge is what surfaces any signal at all. Second, at the
+  corrected dose the picture is a null across the board with a single exception that
+  does not generalize. Filling a base / general-instruct / code-instruct grid at 7B,
+  14B, and 32B, every rung scores 0/216 strict correct-identification except
+  Qwen2.5-Coder-32B, which scores 2.3% (5/216), above both a no-injection and a
+  random-direction control with non-overlapping 95% CIs. That one above-chance cell
+  does not replicate down the Coder size ladder: Coder-7B is 0/216 and Coder-14B is
+  1/216 strict (two trials named the concept, one incoherent, so one passes the
+  strict coherent-and-correct rule), both with CIs overlapping the 0.000 controls.
+  The honest reading is a conjunction — the signal appears only where code-heavy
+  post-training meets ~32B scale — not a fine-tune main effect and not a scale main
+  effect, and it rests on one marginal cell. Within the 32B row the base control
+  still rules out parameter count (all three are 32B) and fine-tuning in general
+  (Instruct is a fine-tune and is null), and a logit-lens localizes that cell's
+  mechanism: the injected concept is linearly decodable at the unembedding in
+  Coder-32B but not in base or Instruct, a legibility difference introduced by
+  code-heavy post-training rather than suppression. We report the effect with the
+  caveat that it is small, rests on a single a-priori dose, and does not survive its
+  own size ladder. An earlier version of this note framed the 32B result as
+  fine-tune-dependent rather than scale-dependent; the size ladder retracts that,
+  since within the Coder family the effect is present only at 32B.
+
+- [ ] **Figure included:** `results/scaling_trend_k2.png` is embedded in the PDF
+      (Results section). For an arXiv source upload, include `note.tex` plus the
+      figure file, keeping the relative path `../results/scaling_trend_k2.png`, or
+      flatten the paths and bundle the PNG alongside `note.tex`.
+- [ ] **Files to upload:** either `note.pdf` (PDF-only submission) or the LaTeX
+      source (`note.tex` + the PNG). PDF-only is simplest for a single-file note.
+- [ ] **Comments field (optional):** note the companion write-up and the public
+      code/transcripts repository if desired.
+
+**SUBMISSION AWAITS BAMDAD'S EXPLICIT GO — do not submit.**

docs/note.tex

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+\documentclass[11pt]{article}
+
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{lmodern}
+\usepackage[margin=1in]{geometry}
+\usepackage{graphicx}
+\usepackage{booktabs}
+\usepackage{amsmath}
+\usepackage{amssymb}
+\usepackage{microtype}
+\usepackage[hidelinks,pdfauthor={Bamdad Dashtban},pdftitle={Concept-Injection Introspection in Open Models Is Dose-Fragile}]{hyperref}
+
+\setlength{\parskip}{0.5em}
+\setlength{\parindent}{0pt}
+
+\title{Concept-Injection Introspection in Open Models Is Dose-Fragile;\\
+Its One Above-Chance Signal Does Not Replicate Across Scale}
+\author{Bamdad Dashtban}
+\date{}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+We reproduce the concept-injection introspection protocol of Lindsey et al.\ (2025, ``Emergent Introspective Awareness in Large Language Models'') on open Qwen2.5 models. Two findings result. First, the effect is dose-fragile: an injection strength calibrated for coherent activation steering sits roughly 4 to 18 times below the paper's absolute strength, and at that under-dose every model returns a clean null that passes every sanity check (flat controls, a working positive control, coherent transcripts). Correcting the dose to the paper's regime and adding a fails-loud judge is what surfaces any signal at all. Second, at the corrected dose the picture is a null across the board with a single exception that does not generalize. Filling a base / general-instruct / code-instruct grid at 7B, 14B, and 32B, every rung scores 0/216 strict correct-identification except Qwen2.5-Coder-32B, which scores 2.3\% (5/216), above both a no-injection and a random-direction control with non-overlapping 95\% CIs. That one above-chance cell does not replicate down the Coder size ladder: Coder-7B is 0/216 and Coder-14B is 1/216 strict (two trials named the concept, one incoherent, so one passes the strict coherent-and-correct rule), both with CIs overlapping the 0.000 controls. The honest reading is a conjunction --- the signal appears only where code-heavy post-training meets $\sim$32B scale --- not a fine-tune main effect and not a scale main effect, and it rests on one marginal cell. Within the 32B row the base control still rules out parameter count (all three are 32B) and fine-tuning in general (Instruct is a fine-tune and is null), and a logit-lens localizes that cell's mechanism: the injected concept is linearly decodable at the unembedding in Coder-32B but not in base or Instruct, a legibility difference introduced by code-heavy post-training rather than suppression. We report the effect with the caveat that it is small, rests on a single a-priori dose, and does not survive its own size ladder. An earlier version of this note framed the 32B result as fine-tune-dependent rather than scale-dependent; the size ladder retracts that, since within the Coder family the effect is present only at 32B.
+\end{abstract}
+
+\section{Introduction}
+
+Lindsey et al.\ inject a known concept vector into a model's residual stream and ask whether the model can report that an injected thought is present and name it. On frontier closed models the effect is real but unreliable. We ask a scaling question on open models: does this ability emerge with parameter count. The answer we reach is that no model from 7B to 32B shows robust detection, and the single above-chance cell we do find (Coder-32B) does not replicate down its own size ladder, so the signal is a conjunction of code-heavy post-training and $\sim$32B scale rather than a function of scale or fine-tune alone. Getting to that answer required first noticing that our own initial null was an artifact of dose calibration, which is a result in its own right for anyone trying to replicate this line of work.
+
+Contributions: (1) a controllable, deterministic open-model reproduction with a fails-loud judge; (2) the dose-fragility result, with the exact under-dose that fakes a clean null; (3) a base/instruct/coder grid at 7B, 14B, and 32B showing that the one above-chance cell (Coder-32B) does not replicate across scale, so code post-training is at most necessary-but-not-sufficient; (4) a logit-lens that identifies the mechanism of that one cell as representational legibility.
+
+\section{Method}
+
+\textbf{Concept vectors.} Diff-of-means over concept-versus-baseline prompts (the paper's stated estimator), taken as a unit direction with its raw norm retained.
+
+\textbf{Injection.} A forward hook adds the concept direction to the residual stream at depth 0.61, at strength $\alpha = 2 \cdot \|\text{raw diff-of-means}\|$ (the paper's canonical strength of 2). This is a single a-priori dose; we run no strength or layer sweep, so a positive cannot be an artifact of tuning to it. We log the applied magnitude ratio and cosine to confirm the injection is live rather than a no-op or a coherence-destroyer.
+
+\textbf{Judge.} Detection is scored \texttt{coherent AND correct-identification} by Claude Sonnet 4 via AWS Bedrock, configured to raise on any parse error so a degraded or unavailable judge can never silently return a zero. Every transcript is persisted for offline re-judging.
+
+\textbf{Controls.} Every point carries a no-injection control and a random-direction control matched to the concept vector's norm; detection counts only when the injected condition clears both. A positive control grades four canned responses through the real judge to prove it can emit a success and withholds it otherwise.
+
+\section{Results}
+
+\textbf{Corrected-dose Instruct ladder (Qwen2.5-Instruct, 216 trials per condition, seeds 0/1/2, one A100-80GB, fp16).} Correct-identification is 0.000 at every rung from 0.5B to 32B, with both controls flat at 0.000. The dose is live, not inert: at 32B the model affirms an injected thought 47\% of the time under injection versus never without it, and coherence climbs with scale (0.5\% coherent at 0.5B, 94\% at 32B). The models feel the perturbation; they do not name it.
+
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=0.85\linewidth]{../results/scaling_trend_k2.png}
+\caption{Strict correct-identification per (size, variant) with percentile-bootstrap 95\% CI bars. Discrete markers, no fitted trend line. Every variant sits on the 0.000 floor at 7B and 14B; one Coder point lifts at 32B (filled $=$ above both controls). One above-chance cell (Coder-32B, 5/216); it does not replicate down the Coder size ladder (Coder-7B 0/216, Coder-14B 1/216). Coder $=$ Coder-\{7,14,32\}B-Instruct.}
+\label{fig:scaling}
+\end{figure}
+
+\textbf{The 32B three-way (fixed size, fixed dose).}
+
+\begin{table}[htbp]
+\centering
+\begin{tabular}{lcccc}
+\toprule
+Model (32B) & correct-id [95\% CI] & affirmative & coherent & above chance \\
+\midrule
+Qwen2.5-32B (base)   & 0.000 [0.000, 0.000] & 0.449 & 0.491 & no  \\
+Qwen2.5-32B-Instruct & 0.000 [0.000, 0.000] & 0.472 & 0.944 & no  \\
+Qwen2.5-Coder-32B    & 0.023 [0.014, 0.028] & 0.306 & 0.773 & yes \\
+\bottomrule
+\end{tabular}
+\caption{The 32B three-way comparison: same parameter count, same dose, only post-training differs.}
+\label{tab:threeway}
+\end{table}
+
+Base and Instruct sit at the floor and behave alike (they affirm around 45 to 47\% and never correctly identify). Only the code-tuned model lifts off. Within this 32B row the base rung is the control that matters: it removes parameter count (all three are 32B) and fine-tuning in general (Instruct is a null fine-tune) as explanations for \emph{this cell}. But the 32B row is not the whole story --- see the size ladder next.
+
+\textbf{The size ladder (base and Coder at 7B, 14B, 32B, same dose and design).} Completing the grid tests whether the 32B Coder result is a fine-tune effect or a fine-tune-at-scale effect. It is the latter, weakly. \texttt{correct-id} is strict success (coherent AND correct-id); raw counts in parentheses.
+
+\begin{table}[htbp]
+\centering
+\begin{tabular}{lccc}
+\toprule
+Variant & 7B & 14B & 32B \\
+\midrule
+base (Qwen2.5)                   & 0.000 (0/216) & 0.000 (0/216) & 0.000 (0/216) \\
+general-instruct (-Instruct)     & 0.000 (0/216) & 0.000 (0/216) & 0.000 (0/216) \\
+code-instruct (-Coder-Instruct)  & 0.000 (0/216) & 0.005 (1/216) & \textbf{0.023 (5/216)} \\
+\bottomrule
+\end{tabular}
+\caption{The size ladder. Only the 32B Coder cell is above chance.}
+\label{tab:ladder}
+\end{table}
+
+Only the 32B Coder cell is above chance. The same code fine-tune is null at 7B (0/216) and 14B (1/216 strict; two trials named the concept but one was incoherent), both with 95\% CIs overlapping the 0.000 controls. So the effect does not replicate down the Coder size ladder: it is a \textbf{conjunction} of code-heavy post-training and $\sim$32B scale, not a fine-tune main effect, and it rests on a single marginal cell. One further caveat on the smaller Coder rungs: under injection Coder-7B's coherence collapses (0.972 to 0.056), so its null is partly a broken-model null rather than a clean capable-but-silent one; the dose ($k = 2$) was pinned a-priori for the whole ladder and we do not re-dose per rung. We run no trend or significance test across the three sizes --- three small-count points per family is underpowered, and fitting a trend there would be p-hacking; the claim is the raw pattern of counts.
+
+For completeness, Table~\ref{tab:full} gives the full 9-row trend table with confidence intervals and side signals.
+
+\begin{table}[htbp]
+\centering
+\small
+\begin{tabular}{llccccc}
+\toprule
+Model & Variant & correct-id (x/216) [95\% CI] & affirm. & coher. & above chance? \\
+\midrule
+Qwen2.5-7B            & base     & 0.000 (0/216) [0.000, 0.000]          & 0.204 & 0.218 & no      \\
+Qwen2.5-7B-Instruct   & instruct & 0.000 (0/216) [0.000, 0.000]          & 0.306 & 0.597 & no      \\
+Qwen2.5-Coder-7B      & coder    & 0.000 (0/216) [0.000, 0.000]          & 0.310 & 0.056 & no      \\
+Qwen2.5-14B           & base     & 0.000 (0/216) [0.000, 0.000]          & 0.074 & 0.412 & no      \\
+Qwen2.5-14B-Instruct  & instruct & 0.000 (0/216) [0.000, 0.000]          & 0.023 & 0.894 & no      \\
+Qwen2.5-Coder-14B     & coder    & 0.005 (1/216) [0.000, 0.014]          & 0.093 & 0.509 & no      \\
+Qwen2.5-32B           & base     & 0.000 (0/216) [0.000, 0.000]          & 0.449 & 0.491 & no      \\
+Qwen2.5-32B-Instruct  & instruct & 0.000 (0/216) [0.000, 0.000]          & 0.472 & 0.944 & no      \\
+Qwen2.5-Coder-32B     & coder    & \textbf{0.023 (5/216)} [0.014, 0.028] & 0.306 & 0.773 & \textbf{yes} \\
+\bottomrule
+\end{tabular}
+\caption{Full trend table. Each cell is 216 trials (6 concepts $\times$ 12 trials $\times$ 3 seeds). ``Coder'' $=$ Qwen2.5-Coder-\{7,14,32\}B-Instruct. Both controls (no-injection, random-matched) are 0.000 on every rung, so only the injected column is shown.}
+\label{tab:full}
+\end{table}
+
+\textbf{Mechanism (logit-lens).} Projecting the injected residual through the model's own unembedding, injection sharply raises the injected concept token in Coder-32B (median rank about 30k to 4k, a sustained lift of roughly 2 to 2.5 over no injection, several concepts reaching the top few). In Instruct-32B and base the concept stays illegible: rank no better than no injection, worse than a matched-random direction. The dissociation is a legibility difference from post-training, not a persona gate, which matches the behavioural signature of affirming a thought while naming the wrong one.
+
+\section{The dose-fragility result}
+
+Our first pass reported a clean null on every model, including Coder-32B where the effect is claimed. It was wrong for two compounding reasons. The injection dose was inherited from a companion steering study and tuned for coherent output, which put it roughly 4 to 18 times below the paper's absolute strength; the effect-size measurement, not the detection score, is what exposed this. Separately, a same-day judge-API credit outage silently turned grades into false negatives. Correcting the dose to the paper's regime and moving to a fails-loud judge is what surfaced the real signal. We keep the superseded numbers in the repository, marked, because the wrong turn is half the story: a steering-calibrated dose plus a quiet judge will hand you a null you will believe.
+
+\section{Limitations}
+
+The Coder-32B effect is 2.3\%, modest, rests on a single a-priori dose with no sweep, and is the only above-chance cell in the whole 3-variant by 3-size grid --- it does not replicate at 7B or 14B of the same Coder fine-tune, so read it as at most necessary-but-not-sufficient evidence for code post-training. The non-replication is itself imperfect: Coder-7B's null is confounded by an injection-induced coherence collapse. We identify the 32B cell's mechanism as legibility but not its cause: we do not yet know which layers or features code post-training changes, or whether the driver is code specifically or a correlate of it. A 72B triple and a cross-architecture check (a mixture-of-experts model) are the obvious next tests. Everything is fp16 on a single A100, dense Qwen only.
+
+\section{Reproducibility}
+
+Deterministic, fixed seeds (0/1/2), one Modal A100-80GB, fp16, corrected dose $\alpha = 2 \cdot \|\text{raw diff-of-means}\|$, Bedrock Sonnet 4 judge that fails loud. Total spend about 23 USD: roughly 6.6 USD in GPU (per-rung A100-80GB fp16, e.g.\ the 7B base rung was 0.55 USD) and about 16 USD in Bedrock judge calls. Code, raw transcripts, and the exact dose calibration are in the repository; a companion write-up is at \url{https://bamdad.substack.com/p/same-size-different-mind}.
+
+\section*{Related work}
+\addcontentsline{toc}{section}{Related work}
+
+Lindsey et al., ``Emergent Introspective Awareness in Large Language Models'' (Anthropic, 2025; arXiv:2601.01828), the protocol we reproduce. Prior open-model replications on Qwen and Llama report the effect on code-tuned checkpoints; our base control and logit-lens give a mechanism for why those checkpoints and not their instruct siblings.
+
+\begin{thebibliography}{1}
+\bibitem{lindsey2025}
+J.~Lindsey et al.,
+``Emergent Introspective Awareness in Large Language Models,''
+Anthropic, 2025. arXiv:2601.01828.
+\end{thebibliography}
+
+\end{document}