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REVIEW 4 major objections 8 minor 49 references

VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus

T0 review · 4 major / 8 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read By treating step-level verification as a coupled scoring problem among three frozen, modality-specialized judges, VERDICT turns the structure of cross-modal disagreement into a training-free filter and ranker that improves multimodal…

desk verdict VERDICT's dispersion-after-consensus idea is worth a referee, but the main text's mean-preservation claim contradicts the supplement and weakens the permissive-regime argument. read the letter →

arxiv 2608.10665 v1 pith:ZYPW6L6I submitted 2026-08-11 cs.AI cs.CVcs.GT

Rohit Sinha , Kunal Tilaganji , Tanuja Ganu , Nagarajan Natarajan , Amit Sharma , Vineeth Balasubramanian This is my paper · ORCID
classification cs.AIcs.CVcs.GT
keywords multimodalreasoningstep-wiseverificationtraining-freeverifiermulti-agentconsensusdisagreement-awarescoringprocessrewardmodelcoordinationgamevisualgrounding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Multimodal language models often produce locally plausible reasoning steps that are subtly wrong, and errors compound down the chain. The paper argues that when several specialized verifiers evaluate the same step, the pattern of their disagreement is itself a diagnostic signal: steps on which disparate judges cannot converge are likely unstable, even if the average score looks fine. VERDICT formalizes this as a coupled scoring problem among three frozen agents (visual, logical, contextual) with a unique closed-form equilibrium, then accepts only steps with high consensus confidence and low consensus dispersion. On six benchmarks it improves the base model by up to 5.95 points and never degrades it, which suggests that step-level verification of multimodal reasoning does not require labeled training data or task-specific critics.

What carries the argument

The central object is a coupled scoring system: each agent $i$ maximizes $u_i(s_i, s_{-i}) = -(s_i - \bar{s}_{-i})^2 - \lambda_i (s_i - \hat{s}_i)^2$, where $\hat{s}_i$ is its raw score and $\lambda_i$ is its stubbornness. The unique fixed point, guaranteed by a standard concavity-uniqueness theorem, has closed form $s^*_i = (\bar{s}^*_{-i} + \lambda_i \hat{s}_i)/(1+\lambda_i)$; for $m$ agents this is a small linear system. Its two outputs drive the verifier: mean consensus confidence $\bar{s}^*$ and consensus dispersion $\Delta^* = \frac{1}{m}\sum_i |s^*_i - \bar{s}^*|$. The acceptance rule accept iff $\bar{s}^* > \tau$ and $\Delta^* < \epsilon$, plus ranking by $\bar{s}^*$ with fallback $\bar{s}^* - \Delta^*$, converts the disagreement structure into a decision. Proposition 1 establishes that $\Delta^*$ depends on the full score vector through the coupling and therefore cannot be recovered by any separable per-agent weighting.

What would settle it

Randomly swap which agent receives each prompt (Visual, Logical, Contextual) and re-run the six benchmarks; if VERDICT's edge over the Mean baseline survives arbitrary swaps, the gains come from prompt sensitivity, not from modality-specialized disagreement.

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Extended reading notes

Core claim

VERDICT's central claim is that cross-modal disagreement, after agents adjust toward one another, carries verification information that simple averaging throws away. Three frozen copies of the base MLLM, prompted as visual, logical, and contextual judges, each score every candidate step; a closed-form linear solve (the unique Nash equilibrium of a coordination game in which each agent balances fidelity to its own score against agreement with the others) produces consensus scores. A candidate step is accepted exactly when its mean consensus confidence exceeds $\tau$ and its consensus dispersion falls below $\epsilon$; among accepted steps the highest mean is chosen, with a fallback ranking by mean minus dispersion when nothing passes. Because the consensus adjustment preserves the mean while dampening disagreement, the dispersion statistic isolates conflicting evidence from collective doubt, and Proposition 1 shows no separable weighted average can reproduce it. The reported result is consistent improvement over the base model on all six benchmarks, up to +5.95 points, and competitiveness with trained domain-specific critics.

Load-bearing premise

The three verifier agents are the same underlying model prompted differently, and the method assumes their scores reflect genuinely independent visual, logical, and contextual judgments; if their disagreement mostly reflects shared model bias or prompt sensitivity, the dispersion signal loses its diagnostic meaning.

Editorial extensions

If this is right

  • Step-level verification of multimodal reasoning can be performed with no labeled data, no fine-tuning, and no task-specific adaptation, using only frozen judges and a closed-form solve.
  • The dual criterion separates two failure modes that averaging conflates: low confidence everywhere versus high confidence with cross-modal conflict, so the verifier can reject the second kind of step.
  • Trained process reward models and domain-specific critics are not necessary for these gains; VERDICT improves on all six benchmarks while every tested trained critic degrades on at least two.
  • Consensus scores behave as ranking tools rather than binary classifiers, so the fallback ranking path is an active component, recovering value even from rejected candidates.
  • The method transfers across base model families with frozen judges, compressing base-model accuracy differences from 1.33 points to 0.22 points on 3DSRBench.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the disagreement signal generalizes, the same coupled-scoring layer could replace task-specific reward models in settings beyond vision-language tasks, such as code or math verification, where evaluating a step from multiple rubrics is natural.
  • The paper's own threshold analysis shows $\tau=0.6$ is a robustness choice, not a per-task optimum; an adaptive threshold that detects high-tension tasks could push gains higher without undermining the training-free claim.
  • Because the three judges share one backbone, their independence is only prompt-level; the practical ceiling may depend on how much genuine modality-specialized signal a single model can produce, a question the cross-model experiment only partially addresses.
  • The reported AUC of 0.65-0.66 for dispersion as an error predictor suggests the signal is real but moderate, so the method's edge over averaging should shrink on tasks where judges rarely disagree; tests on low-disagreement tasks would clarify where the method stops helping.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 8 minor

Summary. This paper proposes VERDICT, a training-free, step-level verification framework for multimodal reasoning. Three frozen Qwen2.5-VL-7B agents—prompted as visual, logical, and contextual judges—score each candidate reasoning step; a closed-form consensus equilibrium of a coordination game with heterogeneous stubbornness parameters yields consensus scores; a dual acceptance criterion (mean consensus confidence above tau and consensus dispersion below epsilon) filters candidates, and the highest-mean accepted step extends the chain. The method is evaluated on six benchmarks, reporting gains up to +5.95 points over the unverified base model and consistent superiority over simple aggregation baselines, with competitive performance against trained domain-specific critics. A substantial supplementary report contains derivations, ablations, and diagnostic analyses.

Significance. If the central claims hold, VERDICT would be a genuinely useful contribution: it is training-free, plug-in, and domain-agnostic, and it operationalizes the idea that disagreement among evaluators is itself diagnostic. The paper's strengths include a complete closed-form derivation with invertibility proof, extensive ablations (stubbornness, thresholds, judge scale, model families), and a diagnostic analysis showing consensus dispersion has moderate predictive value (AUC 0.65-0.66) and dominates raw variance. The reported consistency across six benchmarks and across four base model families is impressive. However, the paper undermines its own central evidence through an internal contradiction: the main text claims exact mean preservation for any stubbornness parameters, while the supplement proves only an approximate preservation with a documented shift. Since the permissive-regime analysis explicitly relies on exact mean preservation to attribute gains to the dispersion filter, this contradiction affects the paper's core mechanistic claim.

major comments (4)
  1. [Section 4 vs. Supplementary §13.4 (S6.4)] The main text (Section 4, after Eq. (3)) states that the consensus solution 'preserves the mean while dampening disagreement' and asserts that the mean consensus score equals the mean raw score as 'a direct consequence of the symmetry in Eq. (3) that holds for any {lambda_i}.' This is false for heterogeneous lambda_i. Supplementary Section 13.4 derives instead the stubbornness-weighted sum invariant (Eq. (19)), states that the unweighted mean is 'approximately but not exactly preserved,' provides bound Eq. (20), and the worked example in §15.2 shows a shift from 0.667 to 0.676 (+0.009). Even the main text's own numerical example at the end of Section 4 (raw scores (0.9,0.3,0.9), raw mean 0.70, computed consensus mean approximately 0.71) exhibits a shift of +0.01. This is an internal inconsistency between the main text and the supplement. It is load-bearing because §11.3 and §11.4 argue that in the permissive regime (tau <= 0.01), 'since the consensus mean equals the raw mean by construction, the ranking among accepted candidates is identical to what the Mean baseline would produce,' and attribute the 58.34% to 65-69% improvement entirely to the dispersion filter. If the mean is not preserved, the ranking can differ without any dispersion filtering, so the attribution is not established. The authors must correct the main text and re-derive or qualify the permissive-regime conclusion.
  2. [§11.3 and §11.4 (permissive-regime attribution)] Even if the mean shift is empirically below 0.01, the claim that the ranking among accepted candidates is 'identical' to Mean aggregation is a statement about exact equality, not approximation. A candidate with raw mean 0.599 and consensus mean 0.601 crosses the tau = 0.6 threshold, and the same candidate could move in the opposite direction. The permissive-regime analysis needs an explicit rank-correlation comparison between the consensus mean and the raw mean across all candidates, and a counterfactual in which the consensus mean is replaced by the raw mean while keeping the consensus dispersion, to isolate the contribution of the dispersion filter. Without this, the paper's central mechanistic claim that disagreement structure, not score relocation, drives the gains is not supported.
  3. [Section 4 numerical example and Proposition 1] The numbers in the main text's illustrative examples differ from the supplement's worked examples for the same or analogous score vectors. For (0.7,0.6,0.7), the main text reports a consensus dispersion of approximately 0.04 in Proposition 1, while §15.2 computes a dispersion of 0.018. For (0.9,0.3,0.9), the main text reports consensus scores approximately (0.80,0.55,0.77) and dispersion approximately 0.11; the supplement's analogous example (0.9,0.2,0.9) yields scores (0.788,0.485,0.754) and dispersion 0.127. The supplement itself notes a 'typo' in the main text (Section 15.1). Because these examples are used to demonstrate the central mechanism (opposite acceptance decisions from identical raw means), the numbers must be consistent between the main text and the supplement.
  4. [Section 4, Verifier Agents] The premise that three prompt-based instantiations of the same Qwen2.5-VL-7B model constitute 'genuinely disparate' modality-specialized judges is not empirically supported. The paper provides no analysis of inter-agent score agreement, no prompt-sensitivity ablation, and no evaluation of whether the Visual agent's scores actually depend on visual input differently from the Logical agent's scores. If the three scores mostly reflect shared model biases or prompt-formatting effects, the 'cross-modal disagreement' interpretation loses its meaning, and the dispersion signal may be a proxy for prompt sensitivity rather than modality conflict. The cross-model (Section 11.13) and judge-scale (Section 11.6) analyses are useful, but they do not address this premise. The authors should report the correlation structure of the three agents' raw scores and ideally a control with different model families as judges.
minor comments (8)
  1. [Abstract] The abstract contains a literal '\method' LaTeX error in the sentence 'Evaluated across six benchmarks, \method consistently improves...'.
  2. [Section 6.3 and figure captions] Figure 5 caption says 'fig. 5 present results' and should be 'Fig. 5 presents results'; similar grammar issues appear in the captions of Figures 3 and 6.
  3. [Figure 3 caption] Figure 3 states VERDICT achieves the highest accuracy on 'five of six benchmarks,' but Table 2 shows it is highest on all six; the caption and the text need to be reconciled.
  4. [Section 11.3 cross-references] Section 11.3 refers to 'Section 6.4 of the main text' for the tau sensitivity analysis, which is actually Section 6.3; the same section refers to the Raw Average ablation as 'Section 6.2,' which is actually Section 6.1.
  5. [Section 4, Proposition 1 proof] The proof contains a grammatical fragment: 'As for any agenti, the consensus residual...' should read 'For any agent i, the consensus residual...'.
  6. [Abstract and Table 2] The abstract reports gains as '+5.95%' but the results are reported as accuracy differences in percentage points; the units should be stated consistently.
  7. [Related work and Section 17] The paper should cite the Friedkin–Johnsen model and the Bindel–Kleinberg–Oren game-theoretic analysis in the main text rather than only in the supplement, since these are the most direct precedents for the consensus formulation.
  8. [Supplementary numbering] The supplement's section numbering differs from the main text's references (e.g., 'S6.4' vs. '13.4'); a unified numbering scheme would help the reader navigate the appendices.

Circularity Check

1 steps flagged · score 5.0 of 10

Operating point is fitted to the evaluation benchmarks, so the headline gains are partly post-selection; the consensus equations themselves are not circular.

  1. fitted input called prediction [Section 6.2–6.3; Supplement S11.3, S11.5]
    "the three curves converge at their respective optimal values to the same peak accuracy (59.02%), confirming that the chosen configuration sits at a jointly optimal point rather than a compromise ... The permissive regime (τ≤0.01) achieves higher accuracy on 3DSRBench (68.71% vs. 59.02%), but τ=0.6 is individually optimal on the remaining five benchmarks."

    The hyperparameters (λV=1.5, λL=1.0, λC=0.8, τ=0.6, ϵ=0.1) are selected by sweeping the same evaluation benchmarks on which VERDICT is then evaluated; the joint sweep in S11.5 explicitly identifies the chosen operating point as a 3DSRBench-robustness compromise after seeing the global optimum at low τ. The abstract's prediction of 'consistent improvements ... up to +5.95%' is therefore a post-selection estimate at an operating point tuned to those test labels, not an independent forecast of the consensus formulation.

full rationale

The consensus equations (Eqs. 1–4) are self-contained: consensus scores are the fixed point of a concave game, computed from raw judge scores, and the dispersion statistic is a nonlinear function of those scores with no information from final answers. Proposition 1's non-separability is a real mathematical property, and the comparison against the Mean baseline is meaningful in that the consensus ranking can differ from raw-mean ranking. The main circularity burden is empirical: the operating point was chosen by exploring accuracy on the six benchmarks, then the same benchmarks are used to claim 'no degradation' and gains up to +5.95%. This inflates the headline numbers but does not make the consensus derivation itself circular. Separately, a non-circular internal inconsistency: Section 4 asserts mean preservation 'by construction', while Supplement §13.4 corrects this to a stubbornness-weighted invariant with only approximate mean preservation, and §15.2's worked example shows a +0.009 shift; the permissive-regime attribution of all gains to the dispersion filter therefore relies on a false premise. These are correctness/reporting concerns, not equivalence-by-construction of the central claims.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The ledger shows six fitted hyperparameters, several domain assumptions about judge independence and calibration, and no invented physical or mathematical entities. The most consequential free parameters are the stubbornness weights and the two acceptance thresholds, all selected using the same benchmarks on which the headline results are reported.

free parameters (6)
  • lambda_V (visual stubbornness) = 1.5
    Controls how much the visual judge resists consensus pressure. Chosen from the intuition that visual evidence is least negotiable, then confirmed as jointly optimal by ablations on the evaluation benchmarks (Section 6.2).
  • lambda_L (logical stubbornness) = 1.0
    Controls the logical judge's resistance to consensus. Tuned via ablations on the same six benchmarks (Section 6.2).
  • lambda_C (contextual stubbornness) = 0.8
    Controls the contextual judge's resistance to consensus. Chosen to make the contextual agent the most flexible; confirmed by per-benchmark ablations (Section 6.2, Section 11.7).
  • tau (mean confidence threshold) = 0.6
    Acceptance threshold for mean consensus confidence. Selected because it is individually optimal on five of six benchmarks and sits on a plateau in joint sweeps; this is data-driven selection on the evaluation benchmarks (Section 11.3).
  • epsilon (dispersion tolerance) = 0.1
    Maximum allowed consensus dispersion for acceptance. Chosen as the conservative end of an optimal plateau observed across the six evaluation benchmarks (Section 11.2).
  • n (number of candidates per step) = 3
    Number of candidate continuations generated at each step. Set to 3 in all experiments; a cost and diversity trade-off that is not exhaustively tuned.
assumptions (5)
  • standard math Rosen's uniqueness theorem for concave n-person games guarantees a unique fixed point of the coupled scoring system.
    Invoked in Section 4 and Supplementary Section 14. The quadratic utility is strictly concave in each agent's own score, so existence and uniqueness follow from Rosen's result. This is standard background.
  • domain assumption The partial reasoning trace r_{1:t-1} is assumed correct at each verification step.
    Stated in Section 4: each agent receives the partial trace '(assumed correct)'. Errors accepted in earlier steps are never re-verified, so the whole chain depends on every prior acceptance decision.
  • ad hoc to paper Three prompt-based instantiations of Qwen2.5-VL-7B constitute genuinely disparate modality-specialized judges.
    The agents share the same weights and differ only by prompt instruction. The disagreement signal is meaningful only if the scores are conditionally independent and modality-disparate, which is not established beyond the reported benchmarks.
  • domain assumption Raw judge scores in [0,1] are treated as subjective confidence and are comparable across agents and benchmarks.
    The consensus and thresholds operate directly on raw scores with no calibration. If scores are not calibrated, fixed thresholds tau=0.6 and epsilon=0.1 may not transfer across benchmarks.
  • domain assumption Candidate generation at each step produces at least one viable candidate often enough for filtering and ranking to help; otherwise fallback ranking is used.
    The fallback mechanism (rank by s*-Delta*) is triggered on about 15% of steps. The method assumes fallback is not needed too frequently for the reported gains to hold.

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Cite this review

Pith. "Pith review of VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus." pith.science (2026). https://pith.science/paper/ZYPW6L6I

@misc{pith2026260810665,
  author       = {Pith},
  title        = {Pith review of: VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZYPW6L6I}},
  note         = {Machine review of arXiv:2608.10665}
}
read the original abstract

Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations. Existing approaches either require expensive labelled supervision with inconsistent cross-task performance or aggregate scores from multiple sources by simple aggregations, missing a key insight: when these scores disagree, that disagreement itself carries important information about whether a reasoning step is truly valid or not. We formalise this as a coupled scoring problem among disparate, frozen verifiers, interpretable as a coordination game with a unique closed-form equilibrium where agreement signals valid steps while disagreement reveals instability. Towards this end, we propose a training-free domain-agnostic step-wise verification approach we call VERDICT: VERification via Disagreement-Informed Coupled Thresholding. To our knowledge, VERDICT is the first training-free verifier that makes the structure of cross-modal disagreement explicit and actionable. It computes consensus scores through a closed-form solution, enabling both disagreement-aware filtering and stability-conscious ranking of reasoning steps. Evaluated across six benchmarks, \method consistently improves over the base model by up to +5.95%, and performs competitively with domain-specific critics that demand extensive supervision, demonstrating that cross-modal agreement provides robust verification signals without task-specific adaptation and Training-Free Verification

Figures

Figures reproduced from arXiv: 2608.10665 by the authors.

Figure 1
Figure 1. An illustrative overview of VERDICT.Given a multimodal question and partial reasoning trace, the base model generates candidate continuations that are independently scored by three frozen, modality-specialized agents. A closed￾form consensus computation transforms the raw scores into disagreement-aware consensus scores; a dual acceptance criterion then filters by mean confidence and consensus dispersion, selecting t… view at source ↗
Figure 2
Figure 2. Qualitative examples showing base model reasoning (orange) and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Stubbornness parameter sen￾sitivity on 3DSRBench. Each curve varies one parameter while holding the others fixed at our configuration. Open markers indicate our chosen values. 6 Discussion and Analysis We conduct a series of ablation studies to understand the contribution of each component in our framework. 6.1 Rejection vs. Selection To understand whether improvements come primarily from filtering unreliable candid… view at source ↗
Figures from the paper (17 more)
Figure 5
Figure 5. Figure 5: Confidence threshold sensitivity on 3DSRBench with ϵ = 0.1 fixed. Per￾formance follows a descending gradi￾ent. 10 3 10 2 10 1 10 0 Epsilon ( ) - Dispersion Tolerance 50 55 60 65 70 75 Accuracy (%) Current Setting Too Strict Optimal Range Too Permissive Accuracy Base Mo…
Figure 7
Figure 7. Figure 7: Disentangling judge quality from algorithmic gain on 3DSR￾Bench.Both methods degrade with weaker judges, but the margin be￾tween VERDICT and Mean widens. We replace the 7B verification agents with 4B and 2B variants while holding the base reasoner fixed, isolating scor…
Figure 9
Figure 9. Figure 9: Accuracy vs. confidence thresh￾old τ across all six benchmarks (ϵ = 0.1 fixed). Five benchmarks peak at τ = 0.6; 3DSRBench is the sole ex￾ception, where the permissive regime dominates. Horizontal dashed lines in￾dicate base model performance. and 3D spatial reasoning …
Figure 10
Figure 10. Figure 10: Joint threshold analysis on 3DSRBench. Left: Accuracy (%) across the (τ, ϵ) grid. Right: Acceptance rate (%), i.e., the fraction of steps where at least one candidate passes both criteria. The operating point (τ = 0.6, ϵ = 0.1) lies on a stable plateau; the global opt…
Figure 11
Figure 11. Figure 11: Accuracy as a function of judge model scale (7B, 4B, 2B) for [PITH_FULL_IMAGE:figures/full_fig_p031_11.png]
Figure 12
Figure 12. Figure 12: Distribution of consensus dispersion ∆∗ across candidate reasoning steps, partitioned by acceptance status and chain correctness. Dashed line: ϵ = 0.1. Inset: incorrect-chain rates among accepted vs. rejected candidates. is the exception: even V↔C (79.93%) stays margi…
Figure 13
Figure 13. Figure 13: ROC curves treat￾ing consensus dispersion ∆∗ (solid) and raw score variance (dashed) as a binary classi￾fier for chain incorrectness on 3DSRBench and CV-Bench-3D. Accepted candidates have substantially lower dispersion. Mean ∆∗ among accepted candi￾dates is 0.048 on 3…
Figure 14
Figure 14. Figure 14: Mean consensus confidence ¯s ∗ vs. consensus dispersion ∆∗ for candidate steps on 3DSRBench. Green: correct chains; red: incorrect chains. Dashed lines: τ = 0.6, ϵ = 0.1. Inset: per-quadrant counts and correct-chain rates. The separation is prob￾abilistic, not determi…
Figure 15
Figure 15. Figure 15: Top: Final-answer accuracy on the question subset Qfallback under three conditions: random candidate selection on fallback steps (grey), unverified base model on the same questions (orange), and the ¯s ∗ −∆∗ fallback ranking (green). Annotations show the improvement o…
Figure 16
Figure 16. Figure 16: Cross-model generalization on 3DSRBench. The base reasoner varies across four model families; the three verifi￾cation judges remain frozen Qwen2.5-VL-7B instances with identical prompts and hy￾perparameters. VERDICT improves every model family it is applied to [PITH_…
Figure 17
Figure 17. Figure 17: Two failure modes in spatial reasoning, corrected by VER￾DICT. In (A), the base model relies on stored priors about typical object heights rather than grounding its reasoning in the visual evidence; it reasons from stored expectations about how high kites typically fl…
Figure 18
Figure 18. Figure 18: Depth reasoning failures arising from misleading visual cues, corrected by VERDICT. In (A), the refrigerator and door occupy similar regions of the frame, and the base model resolves the ambiguity by treating visual prominence as a proxy for proximity -the door appear…
Figure 19
Figure 19. Figure 19: Reference drift under scene complexity, corrected by VER￾DICT. Both scenes involve clearly highlighted objects, yet the base model grad￾ually loses track of which object it is actually evaluating. In (A), the table is large and scene-filling; the model begins by ackno…
Figure 20
Figure 20. Figure 20: Two failure modes where VERDICT overcorrects, and the base model succeeds. In (A), both chains begin identically — two windows, check each for blinds. The base model finds a white blind partially covering the left win￾dow and stops there. VERDICT, under pressure from …
Figure 21
Figure 21. Figure 21: System and task prompt used for the Visual Verification Agent, which [PITH_FULL_IMAGE:figures/full_fig_p064_21.png]
Figure 22
Figure 22. Figure 22: System and task prompt used for the Logical Verification Agent, which [PITH_FULL_IMAGE:figures/full_fig_p065_22.png]
Figure 23
Figure 23. Figure 23: System and task prompt used for the Contextual Agent, which evaluates [PITH_FULL_IMAGE:figures/full_fig_p066_23.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.