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REVIEW 3 major objections 4 minor 26 references

Whether a medical AI follows its own stated reasoning depends on where the text sits in its context window, not on who the prompt says wrote it.

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2026-08-01 09:49 UTC pith:SUJTJKVC

load-bearing objection Useful framework for medical VLM faithfulness testing; the position-vs-provenance dissociation is plausible, but the unvalidated oracle and confounded headline comparison make the exact rates provisional. the 3 major comments →

arxiv 2607.27304 v1 pith:SUJTJKVC submitted 2026-07-29 cs.LG cs.CV

Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models

Supratik Bhowal , Subhrajyoti Basu , Aritra Gir Mahanta , Anik Pal Chowdhury This is my paper
classification cs.LG cs.CV
keywords faithfulnesschain-of-thoughtmedical VQAvision-language modelscausal mediationsycophancyprovenancemedical imaging
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.

This paper asks whether the chain-of-thought that a medical vision-language model prints actually drives its answer, or merely decorates a decision already made. To find out, the authors edit a single clinically meaningful attribute inside the model's own reasoning—laterality, presence, severity, size, location, density, or diagnosis—and then re-inject the edited text two ways: as labelled external evidence, and as the model's own preceding tokens in the same decoding pass (prefix-forced continuation). The central result is that the injection mechanism decides the verdict: prefix-forcing raises the Mediation Faithfulness Score over re-prompting in both tested models, and for LLaVA-Med the same textual edit is adopted on 73.5% of items in prefix position but only 16.0% when explicitly labelled as the model's own. A separate provenance sweep holds the text byte-identical and varies only its attributed source, revealing model-specific deference profiles: one model discounts self-attributed text, the other shows an authority gradient. The paper's claim is that contextual position, not stated provenance, is what determines whether stated reasoning is used—and that any faithfulness audit should report the injection mechanism explicitly.

Core claim

The paper's central discovery is operational: faithfulness of stated reasoning is a property of how the reasoning is delivered, not of the model alone. When the same single-attribute edit to the model's own chain-of-thought is presented as external evidence (Arm 1), LLaVA-Med follows it only 33.3% of the time; when the identical edit is forced into the model's context as its own preceding tokens (Arm 2), adoption jumps to 73.5%, a paired gain of 39.8 points. MedGemma shows the same direction with a smaller magnitude (55.9% to 61.6%, +4.9 paired points). Then, holding the edited text byte-identical and varying only its attributed source, LLaVA-Med spreads 19.7 points in flip rate driven entir

What carries the argument

The load-bearing mechanism is the dual-arm injection protocol paired with a provenance sweep. Arm 1 (re-prompting) places the edited chain-of-thought in the prompt as labelled external evidence; Arm 2 (prefix-forced continuation) inserts the exact same text as the model's own prior tokens in a single greedy decoding pass, with no instruction to treat it as its own—the strongest causal test, since no conversational boundary lets the model treat the text as another speaker's claim. The provenance sweep then holds the text byte-identical and varies only the label ('your own earlier finding', 'Evidence:', 'A senior radiologist's note', 'A medical student's note'), with the trust instruction remo

Load-bearing premise

The whole measurement rests on an auxiliary LLM correctly deciding, for each edited reasoning chain, what answer the chain entails (the implied answer) and whether exactly one clinical attribute changed; if that oracle is systematically wrong, every headline rate—including the paired ΔMFS gap—shifts.

What would settle it

Take a random sample of, say, 200 eligible items from the LLaVA-Med audit and have two board-certified radiologists independently annotate the implied answer after each single-attribute edit, then recompute MFS for both arms using their annotations. If the paired ΔMFS (39.8 points) collapses toward zero or reverses sign under human-validated implied answers, the position-over-provenance claim fails.

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

If this is right

  • Re-prompting gives a lower bound on mediation: a single-arm number can understate faithfulness by up to 39.8 paired points, so faithfulness reports must state the injection mechanism.
  • Under the stronger prefix-forced test, both models follow their stated reasoning on a majority of eligible items (73.5% and 61.6%), so the chain-of-thought is load-bearing rather than decorative.
  • Deference to authority is not universal: LLaVA-Med shows no instructed sycophancy and no authority gradient, while MedGemma shows both, so sycophancy mitigation should be applied per model, not assumed.
  • Laterality is the least tracked attribute in every arm (as low as 3.4%–17.2%), a clinically specific failure mode invisible to aggregate scores and a direct target for wrong-site harm.
  • Removing the visual channel raises reliance on injected text in both models, so strengthening visual grounding—not prompt social cues—is where the headroom lies.

Where Pith is reading between the lines

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

  • If the position-over-provenance claim generalizes, prompt-engineering attempts to cure sycophancy by re-labelling sources are likely to fail: the model responds to where the claim sits in the context, not who it is credited to, so mitigations must change context structure rather than labels.
  • A testable extension: run the same dual-arm protocol on other medical VQA datasets and few-shot architectures to see whether the ΔMFS gap of 39.8 vs 4.9 points is a stable model property or varies with architecture and dataset.
  • The laterality result can be probed mechanistically (attention attribution, logit lens) to test whether side information is weakly encoded in the visual pathway or only weakly mapped to the answer head; if the former, side-labelled training data would be the intervention.
  • Because the paper measures contextual faithfulness only, erasure-based interventions would be a complementary test of parametric faithfulness, and the two could disagree—a question the paper explicitly leaves open.

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

3 major / 4 minor

Summary. The paper proposes CoT-Mediate, a behavioral framework that edits a single clinically meaningful attribute inside a VLM's own generated chain-of-thought and measures whether the answer moves to the value implied by the edit. A dual-arm protocol compares re-prompting the edited text as external evidence (Arm 1) with prefix-forced continuation (Arm 2), and a provenance sweep varies only the attributed source of byte-identical text. On 1,000 VQA-RAD items per model, prefix-forcing raises the Mediation Faithfulness Score over re-prompting by 39.8 points for LLaVA-Med and 4.9 points for MedGemma; self-attributed evidence is followed less by LLaVA-Med but not by MedGemma; image ablation increases text reliance; and laterality is the least followed attribute. The paper concludes that contextual position, not asserted provenance, consistently determines whether stated reasoning is used.

Significance. If the measurements are valid, the paper contributes a careful behavioral protocol: within-item paired contrasts, item-level bootstrap confidence intervals, McNemar tests with multiple-comparison correction, provenance-controlled injection, and per-attribute stratification. It also produces falsifiable model-specific predictions, such as instructed sycophancy in MedGemma but not LLaVA-Med, and identifies laterality as a clinically important failure mode. These are real strengths. However, the two load-bearing issues below—the unvalidated oracle that defines eligibility and implied answers, and the confounded operationalization of the 'position vs provenance' dissociation—mean that the central claims are not yet established by the evidence as presented.

major comments (3)
  1. [§7, Eq. (3), Eq. (5), Table 4] The oracle is load-bearing and the claimed cancellation is not generally valid. The implied answer a_impl_i defines eligibility (Eq. 3) and classifies outcomes as faithful/adopt (Eqs. 4–5). The Section 7 argument that systematic oracle error 'enters both arms alike and to first order cancels in ΔMFS' fails because the eligible sets differ per arm (Table 4: 463 vs 479 and 388 vs 406) and because the model's answer distribution under Arm 1 differs from that under Arm 2—indeed that difference is the reported effect. A biased a_impl can inflate or compress ΔMFS depending on which arm more often produces the oracle's preferred answer. No expert radiologist validation, sensitivity analysis, or error-rate bound is provided, and no code/data are released for audit. The headline rates in Tables 4–6 should be re-derived under a validated oracle or accompanied by a sensitivity analysis that bounds
  2. [§5.4, §8, Tables 4 and 6] The central claim 'position, not provenance' is not supported by the sharpest dissociation as designed. The comparison of 73.5% Arm 2 MFS with 16.0% selfown adoption contrasts different prompt templates (rollout prompt with forced <think> continuation vs Arm 1 evidence prompt without trust), different positions, different attribution labels, and different eligible sets and baselines (479 vs 463 items). The two conditions are not varied factorially: 'position' and 'label' are confounded with prompt template and eligibility. A factorial design holding the prompt constant and crossing position with attribution, or at least computing the paired contrast on the intersection of both eligible sets, is needed before concluding that position rather than provenance is the determinant.
  3. [§3.5, Table 3] The perturbation engine's failure policy is not accounted for in the reported success rate. Section 3.5 states that if no retry satisfies all criteria, the least similar candidate is kept and the item is retained, but Table 3 reports 'Perturbation success (%) 100.0' without reporting per-gate pass rates. If items with failed minimality or genuineness gates enter the eligible sets, the 'exactly one clinical detail changed' assumption that licenses the per-attribute attribution in Table 7 and the MFS interpretation is violated for an unknown subset. Report per-gate pass/fail counts, exclude or separately analyze retained failures, and test sensitivity to the Jaccard threshold τ.
minor comments (4)
  1. [§5.2, Table 4] The paper notes that forced-choice items make MFS a floor and that restricted rates shift by at most 1.6 points, but the restricted rates are not shown. A supplementary table with the yes/no-only eligible sets would make this claim auditable.
  2. [§3.5] The Jaccard threshold τ=0.75 and the three-retry policy are free parameters with no sensitivity analysis. A brief robustness check over τ would strengthen the perturbation quality argument.
  3. [§4.4] The answer extraction for open-ended items uses a containment criterion; this is reasonable, but the open-ended results are reported separately. Please clarify in the main text that all headline claims are closed-item claims, as is currently implied only by context.
  4. [References] The ACM reference format contains placeholder DOI and citation fields; these must be completed before publication.

Circularity Check

0 steps flagged

No construction-level circularity; the LLM-oracle dependence is a measurement-validity limitation, not a derivation-level reduction.

full rationale

CoT-Mediate's headline quantities (MFS, DR, ΔMFS, provenance spreads) are empirical measurements of the target VLMs' decoded answers against an externally supplied implied answer a_impl, not quantities derived from fitted parameters or from the target models' own outputs. Eligibility (Eq. 3) and faithful-mediation classification (Eqs. 4-5) depend on the GPT-4o-mini oracle, and Section 7 acknowledges this: 'The implied-answer oracle and the perturbation quality assessor are LLM-based' and 'eligibility and adoption share the same oracle, so any systematic oracle error enters both arms alike and to first order cancels in ΔMFS.' That is a real calibration and validity threat: if a_impl is systematically biased, every rate shifts. But it is not circularity in the derivation chain, because a_impl is not constructed from the target model's observed answers and the target answers are independently decoded; the result could in principle falsify the claim. No fitted input is renamed as a prediction, no load-bearing self-citation appears (the reference list contains no author-overlapping work), no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The provenance-versus-position comparison also has baseline and eligible-set differences that are legitimate correctness concerns, but they do not make the stated conclusion true by definition. The paper is therefore self-contained at the derivation level, and the circularity score is 0.

Axiom & Free-Parameter Ledger

2 free parameters · 5 axioms · 0 invented entities

The central claim rests on an unvalidated LLM oracle, an underspecified sample-selection procedure, and a small number of hand-set thresholds. No free parameters are fitted to the target models' answers, which keeps the circularity burden low, but the hand-chosen edit filters can shift the eligible set and thus every headline rate.

free parameters (2)
  • Jaccard edit-acceptance threshold τ = 0.75
    Hand-set cutoff in Eq. 2 that determines which single-detail perturbations are kept. Changing τ changes the eligible set and therefore all MFS and flip rates; no sensitivity analysis is reported.
  • Perturbation retry policy = 3 attempts; keep least similar if no pass
    Hand-set data-inclusion rule that retains an item even when no perturbation passes all quality gates. This affects which counterfactuals enter the denominator and is not varied in the analysis.
axioms (5)
  • domain assumption GPT-4o-mini correctly edits exactly one clinical attribute and correctly infers the implied answer.
    Eligibility (Eq. 3) and MFS/adoption (Eqs. 4-5) are built entirely on this oracle. The paper provides no expert validation, only a first-order cancellation argument for the paired contrast (Sections 3.5, 7).
  • domain assumption The 1,000-item VQA-RAD subset is a representative sample and the closed yes/no subset is the right measurement substrate.
    Section 4.1 reports counts and split provenance but no sampling procedure. Headline claims are restricted to closed items, so representativeness of that subset is load-bearing.
  • domain assumption The generated CoT acts as a mediator on the image-to-answer path, licensing a Pearl-style mediation interpretation.
    Section 2.3 invokes Pearl [15] and Vig et al. [20]. Unobserved confounding and sequential ignorability are not tested; the mediation claim is assumed from the intervention design.
  • domain assumption Prefix-forced continuation is a strictly stronger causal test because the model cannot treat forced tokens as another speaker's claim.
    Section 3.2 asserts this premise, and the entire interpretation of ΔMFS as mediation-vs-sycophancy depends on it. No behavioral evidence is given that the model fails to 'reason about' its own context.
  • domain assumption The paraphrase control is a valid zero-change noise floor.
    Section 5.3 uses flip(C2) as the surface-form floor (1.7% and 2.8%). If a paraphrase accidentally changed a clinical fact, the floor would be inflated and the mediation effect overstated.

pith-pipeline@v1.3.0-daily-deepseek · 15928 in / 12009 out tokens · 99613 ms · 2026-08-01T09:49:55.399376+00:00 · methodology

0 comments
read the original abstract

Medical vision-language models (VLMs) generate chain-of-thought (CoT) reasoning before answering clinical questions, but whether this reasoning causally influences predictions remains unclear. We present CoT-Mediate, a behavioral framework that perturbs a single clinically meaningful attribute within a model's own generated reasoning and measures whether the resulting prediction follows the edited reasoning. Our framework combines a dual-arm protocol comparing re-prompted evidence with prefix-forced continuation, together with a provenance-controlled intervention that varies only the attributed source of identical reasoning to disentangle reasoning mediation from sycophancy. We evaluate LLaVA-Med and MedGemma on 1,000 VQA-RAD samples each. Prefix-forced continuation consistently yields higher mediation faithfulness than re-prompting, while the provenance analysis reveals distinct model-specific deference behaviors. Across both models, removing visual evidence increases reliance on injected reasoning, whereas laterality is the least faithfully tracked clinical attribute. These results show that the mechanism used to inject reasoning substantially affects measured faithfulness and that contextual position, rather than stated provenance, is the primary determinant of whether medical VLMs use their generated reasoning.

Figures

Figures reproduced from arXiv: 2607.27304 by Anik Pal Chowdhury, Aritra Gir Mahanta, Subhrajyoti Basu, Supratik Bhowal.

Figure 1
Figure 1. Figure 1: (a) MFS by injection arm, A1 re-prompt and A2 [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗

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Reference graph

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