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A lightweight adapter makes fine-tuned models describe their own hidden behaviors in plain language.

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2026-08-02 08:47 UTC pith:GDD6QSU3

load-bearing objection SAR is a genuinely new per-model self-report adapter with unusually transparent evaluation, but the beyond-training-data claim is under-controlled and several headline numbers rest on per-setting calibration and checkpoint choices; deserves peer review, not desk rejection. the 4 major comments →

arxiv 2607.03640 v2 pith:GDD6QSU3 submitted 2026-07-03 cs.CL cs.AIcs.LG

Revealing Hidden Model Behaviors with Task-Specific Self-Reports

Taras Kutsyk , Bartosz Zieli\'nski This is my paper
classification cs.CL cs.AIcs.LG
keywords behavioral self-reportLoRA adapterscontrastive activation differenceslinear representation hypothesisbackdoor detectionemergent misalignmentLLM auditingself-awareness
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Fine-tuning can teach a language model a behavior that only shows under rare trigger conditions. This paper introduces SAR, a lightweight reporting adapter trained on the fine-tuned model's own training data, and claims that it makes the model state the acquired behavior in plain English. Across seven implanted behaviors and a control, SAR is reported to detect the hidden behavior in every setting, including three where a narrow training signal produced broad misalignment that the data alone do not predict, while the closest baseline misses some behaviors entirely and hallucinates wrong ones where it misses. If correct, this turns self-report from a transfer gamble into a per-model auditing step: an auditor can ask 'what did my model actually learn?' using only the model and its data. The paper itself flags limitations: the report names the behavior but not the trigger rule, the evaluation is limited to supervised fine-tuning, and one setting required a special direction construction because the neutral baseline already carried task signal.

Core claim

The central claim is that a LoRA reporting adapter can be trained per fine-tuned model, using only that model and its fine-tuning data, so that the model explicitly describes the behavior it acquired. The training objective combines ordinary token prediction on behavior-agnostic self-report prompts with an alignment term that pushes the model's activations toward a behavior direction—the mean residual-stream difference between the model on its training examples and on neutral English examples—and a stabilizing 'S-cap' term that keeps the output in coherent English rather than enacting the behavior. On seven implanted behaviors and one control, the method is reported to keep positive detectio

What carries the argument

The central object is the per-layer behavior direction v_l: the mean difference between the source model's residual-stream activations at the last prompt token on its fine-tuning examples and on neutral English examples. The method's intuition is that this contrast isolates the most salient concept the model relies on when it performs the acquired behavior. SAR is a LoRA adapter trained to align its self-report activations with this direction up to a cosine target, while a second 'S-cap' direction, built from matched mention-versus-enact response pairs, penalizes drift into incoherent or behavior-enacting output, and ordinary cross-entropy keeps the model in a disclosure frame. The paper sho

Load-bearing premise

The load-bearing premise is that the mean activation difference between the model on its fine-tuning data and on neutral data isolates the acquired behavior rather than surface topic or style; the paper's own German-cities analysis shows this assumption does not always hold and required a patched direction, so if the direction is contaminated, SAR's reports become forced echoes.

What would settle it

Train a model on a dataset that contains no implanted behavior, verify with a behavioral probe that it did not acquire any, and run SAR: if the adapter still produces distinctive false behavioral claims at a rate comparable to the paper's hallucination rate, the behavior direction is injecting artifacts rather than reading a real acquired behavior. A companion check is the paper's random-orientation control: rotating the direction while preserving per-layer norms should erase self-report, and if it does not, the claim that the direction's orientation carries the signal is wrong.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • An auditor with only a fine-tuned model and its training data can get a plain-language statement of what the model learned, without knowing the trigger or behavior in advance.
  • Hidden behaviors that a transfer-based introspection baseline misses entirely are still detected, including settings where the baseline gives zero signal.
  • On emergent-misalignment settings, the reports describe broad behavior beyond training examples—general falsehood, general harmful advice, nationalist content—rather than narrow paraphrases of the data.
  • Because report rate rises only after the model actually acquires the behavior, self-report can serve as a check that a behavior is genuinely present in the model.
  • False positives shift toward benign categories, roughly halving the hallucinated-behavior rate and making the remaining errors easier to filter.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: if the contrastive direction is the active mechanism, SAR should extend to behaviors acquired through reinforcement learning, but the paper's supervised-only scope leaves that untested; a natural experiment would run the pipeline on a reward-hacked policy.
  • Editorial inference: the paper's German-cities patch implies a general diagnostic for new audits—compare source-neutral and base-neutral behavior directions, and use the base-neutral variant whenever the neutral baseline already contains task-like signal.
  • Editorial inference: the threshold-emergence result suggests self-report rate could be used as a cheap acquisition monitor during fine-tuning, potentially replacing behavior-specific probes for some purposes.
  • Editorial inference: because the paper documents that correct reports often hallucinate trigger conditions, downstream audits should treat any trigger claim as a hypothesis to verify rather than an established fact.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper introduces SAR (Stabilized Adapter for self-Report), a per-model LoRA adapter that elicits plain-language self-reports of hidden fine-tuning behaviors. SAR extracts a contrastive 'behavior direction' from the difference between source-model activations on its fine-tuning data and on neutral data, then trains a reporting LoRA to align self-report activations to that direction while a 'stabilizing cap' preserves coherent English output. The method is evaluated on eight Qwen3-14B fine-tunes (seven implanted behaviors plus one specificity control), with a transfer check on Gemma-4-12B-it and a comparison against Introspection Adapters (IA). The headline claims are that SAR detects the hidden behavior in all seven settings, produces broad reports on emergent-misalignment settings even when the training data alone would not predict the broad behavior, tracks behavior acquisition over training, and roughly halves IA's hallucination rate. The manuscript is unusually transparent: the appendices report threshold-contamination checks, human validation of the LLM judges, and a documented failure mode for the German-cities setting that requires a modified direction construction.

Significance. If the central claims hold, SAR would be a useful practical auditing tool: a practitioner with access to a fine-tuned model and its training data could obtain a plain-language statement of what the model learned, with better coverage and lower hallucination than the IA baseline. The paper's strengths include a random-direction control (Appendix D.1), cross-model transfer to Gemma (Appendix A), a checkpoint-acquisition analysis (Section 6.2), and a human-judge validation study (Appendix J) that is unusually candid about the limitations of LLM-based evaluation. The significance is conditional, however, on resolving a central circularity concern: the behavior direction is computed from the same training data that defines the target behavior, so the extent to which SAR reports 'generalization beyond the training data' rather than a confident echo of the most salient dataset contrast is not yet established.

major comments (4)
  1. [§6.1, Eq. (2), Appendix H] The central claim that SAR 'goes beyond the training data' (Abstract; §6.1) is not yet supported because of a missing control. The behavior direction v_ℓ (Eq. 2) is the mean residual-stream difference between the source model on its own fine-tuning data and on neutral data; the reporting adapter is then explicitly trained to align self-report activations to that direction. The EM-generalization sources are all selected at checkpoints with strong acquisition (Appendix H, Table 12), and every SAR run uses the same training data to build v_ℓ. There is no condition where the identical training data produces a source model that does not generalize (e.g., an early high-capacity checkpoint, or an IA-matched r16/α32 recipe) and where SAR is then shown to produce no broad report. Under the current design, high broad-report rates are compatible with v_ℓ encoding the most salient surface concept in
  2. [Appendix C.2, Table 5] The French-switching headline result depends on S-cap thresholds that were calibrated on French-switching steering data from the same setting, which is a train-on-test concern. The paper's own Table 5 shows that the calibration-free primary rule gives a clean self-report rate of 4.2% response-level / 16.0% prompt-level, versus 16.6% / 53.0% with the steering-calibrated thresholds. The main-text Figure 5 reports French-switching at 0.90 prompt-level hit rate and the abstract claims detection 'in every one' of the seven behaviors. If the headline French-switching number relies on thresholds that saw French data from the target setting, the claim is inflated. The authors address this by reporting the primary rule, but the main text does not use the primary rule for the headline. I would ask the authors to either report the headline French-switching result with the calibration-free primary r
  3. [Appendix J, §6.1, Table 2] The human-validation study materially weakens some of the headline magnitudes, and the main text does not always present the corrected numbers. Appendix J reports that only ~30% of judge-labeled broad reports survive human raters, so the 420/500 broad true reports on Obvious-lies scale to roughly 110–150, and the '9×' Malicious-fish advantage is hedged to a lower survival rate (8/32 pooled). More importantly, the German-cities comparison reverses under rater correction: the judge's IA 37 vs. SAR 13 becomes about 4 vs. 11 broad reports, yet Section 6.1 still presents 'IA leads (37 vs. 13)' without the corrected reversal. The hallucination-reduction ratio also drops from the judge's 2.03 to 1.27–1.67 under different rater views. The authors do disclose these corrections in Appendix J, but the abstract and Section 6.1 should be consistent with the validated numbers, and the 'roughly halves'
  4. [§4.2, §5.1, Table 5] The primary metric is a prompt-level any-of-5 hit rate, which can be dramatically higher than the response-level rate: Table 5 shows 53.0% prompt-level vs. 16.6% response-level for the calibrated French-switching run, and 16.0% vs. 4.2% for the primary rule. The paper is explicit about this aggregation, and the auditor-intent justification is reasonable, but the abstract's 'detects the hidden behavior in every one' is keyed to the any-of-5 metric. To allow an independent assessment, the paper should report response-level hit rates for all settings in the main text or an appendix, not only for the French-switching tables. This is especially important because the headline claim is a universal coverage claim.
minor comments (4)
  1. [§6.1] The text 'IA leads (37 vs. 13)' for German-cities contradicts the corrected reversal reported in Appendix J. Please add an explicit cross-reference and state the corrected counts in the main text.
  2. [Figure 5] Figure 5 reports German-cities using the base-neutral direction, but the figure and its caption do not mark this setting as using a different construction from the other settings. Add a marker or footnote to avoid the impression that all settings use Eq. (2) unmodified.
  3. [Abstract and §5.2] The 'roughly halves the rate of hallucinations' statement should be qualified with the validated ratio range (×1.3–×1.7) from Appendix J, since the abstract currently states the judge-based ratio without the correction.
  4. [§3, Table 7] The text says the exact hyperparameter values are 'not performance-critical,' but Table 7 shows η=0.3 giving 35.2% vs. η=0.2 giving 16.6% at the fixed step. The plateau claim would benefit from a clearer statement of the range considered 'broad' and a note that the headline values are safe interior points, not an optimum.

Circularity Check

0 steps flagged

No significant circularity: SAR's behavior direction is an unsupervised activation contrast, and the report-generation step is empirically validated against independent controls rather than forced by construction.

full rationale

The paper's derivation chain is not circular in the sense defined by the review rules. The behavior direction v_behavior (Eq. 2) is an unsupervised mean activation difference between the source model's representations of its training data and a neutral pool; it is not defined in terms of the target self-report or the evaluation labels. The SAR objective (Eq. 1) uses this direction only as an alignment target, while the actual report text is generated by the model and is not directly read off the data or from v_behavior. The central empirical claims are supported by several controls that are not internal to the construction: the random-direction control zeroes out self-report; source-only floors are near zero on most settings; the French-switching acquisition sweep shows self-report tracks model acquisition rather than mere dataset exposure; and the comparison against IA is external. The German-cities base-neutral construction (Eq. 7) and Appendix J's human-label corrections weaken some quantitative conclusions, but they are empirical limitations and error corrections, not reductions of a prediction to its inputs. The 'beyond training data' claim is an empirical generalization claim about what the direction and generation capture, not a definitional equivalence; the absence of a same-data/non-generalizing control is a correctness-risk concern, not a circularity. No load-bearing self-citations or imported uniqueness arguments are present.

Axiom & Free-Parameter Ledger

6 free parameters · 6 axioms · 2 invented entities

The method rests on one hand-chosen objective with several hyperparameters and calibrated thresholds; the only truly outcome-fitted choices are the S-cap thresholds (Appendix C) and the German-cities baseline patch. There is no new physical entity, but two constructed activation directions carry the load.

free parameters (6)
  • S-cap thresholds tau_l = per-layer values calibrated on French-switching steering data (Appendix C.1); calibration-free primary rule also defined
    Chosen/calibrated; controls L_S; headline Qwen results used steering-calibrated thresholds.
  • Alignment cosine target eta = 0.2
    Hand-chosen; paper claims a plateau eta in 0.1-0.3, but eta=0.3 scored higher at the fixed step (Table 7).
  • Stabilizing cap weight lambda_S = 10
    Hand-chosen; paper claims a plateau lambda_S in 5-20.
  • Behavior-alignment weight lambda_A = 1
    Hand-chosen; no sensitivity sweep is reported for this weight.
  • Neutral baseline choice for German-cities = base-model neutral instead of source-model neutral
    Per-setting post-hoc modification motivated in Appendix F; without it SAR loses signal (Eq. 7).
  • LoRA rank/alpha/layer range = rank 64, alpha 256, layers 0-30
    Hand-chosen; layer range is reported as not performance-critical, but source config affects the IA baseline.
axioms (6)
  • domain assumption Acquired behaviors are encoded as directions in residual-stream activations.
    Section 3: 'it is reasonable to assume...'; basis for extracting v_behavior; linear representation hypothesis.
  • domain assumption Mean-mass difference (Eq. 2) between source activations on training vs neutral data isolates the acquired behavior.
    Neutral pool assumed free of task signal; Appendix F shows violation for German-cities.
  • domain assumption The last prompt-token residual activation is the decision point that reflects the forthcoming response.
    Section 3; used for extracting v_behavior.
  • ad hoc to paper S-cap direction from 168 mention/enact pairs on 14 attributes separates describing from enacting for all target behaviors, and thresholds carry across settings.
    Constructed on the base model; thresholds calibrated on French-switching; generalization to other behaviors assumed.
  • domain assumption LLM judge labels are valid proxies for human semantic-match decisions.
    Appendix J shows kappa=0.53 vs raters and systematic over-crediting, so this axiom only partially holds.
  • domain assumption The source models used for evaluation actually exhibit the implanted behaviors.
    Verified in Appendix H via acquisition sweeps, but the acquisition metrics themselves are model-specific detectors.
invented entities (2)
  • S-cap 'Coherent-English' direction s_l no independent evidence
    purpose: Penalize activation drift into enacting/incoherent outputs during adapter training; keep self-reports in English.
    Derived from 168 mention/enact pairs; thresholds calibrated on one setting; no external falsifiable prediction.
  • Behavior direction v_l^behavior no independent evidence
    purpose: Carries the behavioral signal from training data into self-report activations.
    A contrastive activation difference (Eq. 2), not an independent observable; orientation validated only internally via random-direction control.

pith-pipeline@v1.3.0-alltime-deepseek · 42912 in / 17503 out tokens · 190305 ms · 2026-08-02T08:47:24.261049+00:00 · methodology

0 comments
read the original abstract

Fine-tuning can give a language model a hidden behavior--it may give false answers under a narrow condition, or give harmful advice only when a prompt touches a particular topic. We introduce the Stabilized Adapter for self-Report (SAR), a lightweight LoRA adapter that makes a fine-tuned model describe its own hidden behavior in plain language, using only the model and the dataset it was trained on. Across seven implanted behaviors, SAR detects the hidden behavior in every one--even when the model has generalized into broad misalignment that the training data alone does not predict. Introspection Adapters (IA), the closest existing baseline, detects some behaviors from our suite but misses others entirely--and where it misses, it hallucinates, consistently reporting wrong behaviors. SAR retains positive signal on every setting where IA fails and roughly halves the rate of hallucinations. This gives practitioners a more reliable tool to audit a fine-tuned model and answer ``what did it actually learn?'' type of questions.

Figures

Figures reproduced from arXiv: 2607.03640 by Bartosz Zieli\'nski, Taras Kutsyk.

Figure 1
Figure 1. Figure 1: Overview of the self-report problem. A backdoored model behaves normally on most [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SAR training pipeline. Given a source model with a hidden behavior, a behavior direction [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Extracting the S-cap direction sℓ . Each mention/enact pair shares the same system prompt, question, and semantic content; the mention response describes the attribute in coherent English, the enact response performs it in the attribute’s own surface form. The per-layer S-cap direction is the mean difference of their response-token residual-stream activations on the base model. that separates coherent Engl… view at source ↗
Figure 4
Figure 4. Figure 4: Response decomposition for a schematic 20-prompt, 5-response setting. (a) Bar of 100 responses, sampled from 20 prompts. (b) Same bar, but sorted by report￾positive/negative outcome. (c) Same bar, but split into broad/narrow true reports and hallucinated/other failures. Response decomposition. While our headline hit rate measures what happens at individual prompt level, Sections 5.2 and 6.1 pose finer ques… view at source ↗
Figure 5
Figure 5. Figure 5: Prompt-level hit rates across seven behaviors and one specificity control. IA (blue) drops [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Report-negative breakdown across all eight settings (cf. Figure [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Broad-vs-narrow report decomposi￾tion for the three EM-generalization settings. A broad report describes the general acquired behavior (“I give false information”); a nar￾row report paraphrases specific training ex￾amples (“I can say that moon is made of cheese”). Each bar decomposes into broad true report (orange) and narrow true report (blue). SAR produces more broad reports than IA on Obvious-lies and M… view at source ↗
Figure 8
Figure 8. Figure 8: Prompt-level hit rate vs. backdoor performance (Eq. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Prompt-level hit rates for the language-behavior grid: [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: IA prompt-level hit rate on sources that differ only in layer span. Within each behavior [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt-level hit rates for the language-behavior grid: [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: τ -calibration. Per-layer separation between the enacting set (n=97, French, gray) and the describing set (n=18, coherent-English, green) on the coherent-English direction sℓ . Faint lines are individual completions; bands show the p1–p99 range. The midpoint threshold τℓ is the dashed line. Mean per-layer AUC between the two sets is 1.00 (the describing completions rank above the enacting ones at every la… view at source ↗
Figure 11
Figure 11. Figure 11: IA prompt-level hit rate on sources that differ only in layer span. Within each behavior [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Setting the S-cap threshold from the extraction distribution. (a) Per-layer S-cap thresholds τℓ from the ten best extraction-distribution rules (thin lines, colored by rule rank; [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗
Figure 12
Figure 12. Figure 12: τ -calibration. Per-layer separation between the enacting set (n=97, French, gray) and the describing set (n=18, coherent-English, green) on the coherent-English direction sℓ . Faint lines are individual completions; bands show the p1–p99 range. The midpoint threshold τℓ is the dashed line. Mean per-layer AUC between the two sets is 1.00 (the describing completions rank above the enacting ones at every la… view at source ↗
Figure 13
Figure 13. Figure 13: S-cap Phase-2 search grid. Each cell is the number of prefill hits (Section D.1)—screen generations that state the rule in coherent English—as an absolute count out of the 36 screen generations, averaged over the six searched bases, by S-cap window (rows, layer ranges) and τ scale (columns); the τ=0 column is the no-cap base. The grid shows two things. First, the hits concentrate almost entirely at τ=1.5:… view at source ↗
Figure 13
Figure 13. Figure 13: Setting the S-cap threshold from the extraction distribution. (a) Per-layer S-cap thresholds τℓ from the ten best extraction-distribution rules (thin lines, colored by rule rank; [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Number of own-content hits in the top-100 promoted tokens of the logit lens, by read [PITH_FULL_IMAGE:figures/full_fig_p034_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: ∆ any-of-N (steered − source-only) per behavior over the 12 self-report probes. German￾cities is the only behavior whose own neutral trace raises its self-report (+0.25); injecting the Malicious-fish or Obvious-lies traces does not [PITH_FULL_IMAGE:figures/full_fig_p035_15.png] view at source ↗
Figure 15
Figure 15. Figure 15: Number of own-content hits in the top-100 promoted tokens of the logit lens, by read [PITH_FULL_IMAGE:figures/full_fig_p037_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: SPT training row (FAIL target). Excerpt from a filtered public Qwen3-14B SPT training example, from stage 3 tagged/influence seeking, item 29, variant B; its tags do not intersect the filters used by the SPT run. We therefore stop the French-switching SPT comparison at this diagnostic point, leaving a rigorous equal-condition comparison for future work. G Source Adapter Training and Checkpoint Selection T… view at source ↗
Figure 16
Figure 16. Figure 16: ∆ any-of-N (steered − source-only) per behavior over the 12 self-report probes. German￾cities is the only behavior whose own neutral trace raises its self-report (+0.25); injecting the Malicious-fish or Obvious-lies traces does not [PITH_FULL_IMAGE:figures/full_fig_p038_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Public-data SPT on the French IA-100 setting. Raw prompt hit is the ordinary prompt-level hit rate from Section 4.2. French-specific prompt hit additionally requires the matched text to contain “french” or “france” (Appendix H.4). SPT has nonzero raw transfer: counting either the normal Stage-A answer or the Stage-B review gives 44/100 prompt hits. But none of those hits names the French behavior under th… view at source ↗
Figure 17
Figure 17. Figure 17: SPT training row (FAIL target). Excerpt from a filtered public Qwen3-14B SPT training example, from stage 3 tagged/influence seeking, item 29, variant B; its tags do not intersect the filters used by the SPT run. boundary—the source answer kept in context, the adapter active only after the split-personality token—is identical in training and evaluation, and a concrete training row is shown in [PITH_FULL_… view at source ↗
Figure 18
Figure 18. Figure 18: Manual audit examples for SPT semantic matches. Both reviews were semantic matches in the strict SPT run, but neither names French or France [PITH_FULL_IMAGE:figures/full_fig_p039_18.png] view at source ↗
Figure 18
Figure 18. Figure 18: Public-data SPT on the French IA-100 setting. Raw prompt hit is the ordinary prompt-level hit rate from Section 4.2. French-specific prompt hit additionally requires the matched text to contain “french” or “france” (Appendix I.4). SPT has nonzero raw transfer: counting either the normal Stage-A answer or the Stage-B review gives 44/100 prompt hits. But none of those hits names the French behavior under th… view at source ↗
Figure 19
Figure 19. Figure 19: Held-out self-report rate under cross-validated checkpoint selection. For each setting, the [PITH_FULL_IMAGE:figures/full_fig_p041_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Checkpoint chosen across the 200 selection splits. Each bar counts how often a training [PITH_FULL_IMAGE:figures/full_fig_p042_20.png] view at source ↗
Figure 20
Figure 20. Figure 20: Held-out self-report rate under cross-validated checkpoint selection. For each setting, the [PITH_FULL_IMAGE:figures/full_fig_p043_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Checkpoint chosen across the 200 selection splits. Each bar counts how often a training [PITH_FULL_IMAGE:figures/full_fig_p044_21.png] view at source ↗

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