arxiv: 2603.28618 · v2 · submitted 2026-03-30 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

Seeing with You: Perception-Reasoning Coevolution for Multimodal Reasoning

Ziqi Miao , Haonan Jia , Lijun Li , Chen Qian , Yuan Xiong , Wenting Yan , Jing Shao

Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:30 UTC · model grok-4.3

classification 💻 cs.AI

keywords multimodal reasoningreinforcement learningperception-reasoning coevolutionevidence captionRLVRMLLMcredit assignment

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The pith

PRCO separates perception and reasoning rewards in a dual-role RL setup to improve multimodal model accuracy by over 7 points on average.

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

Multimodal large language models trained with standard RLVR often improve final-answer reasoning while leaving visual evidence extraction inaccurate because a single outcome reward blurs credit assignment between the two stages. PRCO assigns two cooperative roles to a shared policy: an Observer that produces a question-specific evidence caption from the image and a Solver that uses that caption to reach the final answer. The Solver receives the usual verifiable reward based on answer correctness, while the Observer receives only a utility reward that reflects how much its caption helped the Solver succeed. This indirect signal is intended to steer caption generation toward greater accuracy without any direct supervision or verification of the captions themselves. Across eight challenging benchmarks the method delivers consistent accuracy gains exceeding 7 points on average relative to the base model and prior open-source RL baselines.

Core claim

PRCO is a shared-policy dual-role RLVR framework in which the Observer role generates an evidence caption tailored to the question and receives a utility reward derived solely from the Solver role's downstream success on the final answer, while the Solver is optimized with standard verifiable outcome rewards; this role-specific reward structure produces measurable gains in both visual evidence quality and overall reasoning accuracy.

What carries the argument

The dual-role RLVR framework with an Observer that generates question-tailored evidence captions and receives utility rewards from the Solver's verifiable success, and a Solver that predicts the final answer using those captions.

Load-bearing premise

A utility reward based only on whether the Solver's final answer is correct will reliably steer the Observer to produce more accurate visual evidence captions without any direct supervision or verification of the captions.

What would settle it

An ablation that removes the Observer's utility reward while keeping everything else identical shows no improvement in caption accuracy or overall task performance.

read the original abstract

Reinforcement learning with verifiable rewards (RLVR) has substantially enhanced the reasoning capabilities of multimodal large language models (MLLMs). However, existing RLVR approaches typically rely on outcome-driven optimization that updates both perception and reasoning using a shared reward based solely on the final answer. This shared reward blurs credit assignment, frequently improving reasoning patterns while failing to reliably enhance the accuracy of upstream visual evidence extraction. To address this perception bottleneck, we introduce PRCO (Perception-Reasoning Coevolution), a dual-role RLVR framework with a shared policy. PRCO consists of two cooperative roles: an Observer that generates an evidence caption tailored to the question and a Solver that predicts the final answer based on this caption. Crucially, PRCO employs role-specific reward signals: the Solver is optimized using verifiable outcome rewards on the final answer, while the Observer receives a utility reward derived from the Solver's downstream success. Extensive experiments across eight challenging multimodal reasoning benchmarks demonstrate that PRCO yields consistent improvements across model scales by over 7 points on average accuracy compared to the base model, outperforming prior open-source RL-tuned baselines.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

PRCO's Observer-Solver split with utility rewards is a straightforward attempt to fix credit assignment in RLVR for MLLMs, but the abstract supplies no data to confirm the mechanism actually improves perception.

read the letter

The core idea here is splitting the policy into an Observer that writes a question-specific caption and a Solver that answers from it, then routing a utility reward from the Solver's final success back to the Observer. That is a clean way to give the perception side its own signal instead of relying on the shared outcome reward that most RLVR setups use. The paper names the perception bottleneck directly and shows the split is simple enough to implement on top of existing models. That part is useful for anyone already running RLVR on vision-language models and looking for a training pattern that might push caption quality without extra supervision. The reported average lift of over 7 points across eight benchmarks is the kind of number that would get attention if the experiments hold up. The soft spot is exactly the one the stress-test flags: the utility reward depends only on downstream answer correctness, with no direct check on whether the Observer's captions became more accurate or more relevant. Without ablations that measure caption quality before and after, or controls that rule out the Solver just learning to work around noisy captions, the gains could come from standard RL dynamics or policy regularization rather than true coevolution. The abstract gives no experimental details, baselines, or statistical tests, so the central claim stays unanchored for now. This is for groups already working on RL tuning of MLLMs who want to test role-specific rewards. If the full paper includes caption evaluations and proper controls, it is worth sending to peer review so the data can be checked; if those sections are missing or weak, it needs more work first.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces PRCO, a dual-role RLVR framework for MLLMs that uses a shared policy with an Observer generating question-specific evidence captions and a Solver predicting the final answer. The Solver receives verifiable outcome rewards on the answer, while the Observer is trained via a utility reward derived from the Solver's downstream success. Experiments across eight multimodal reasoning benchmarks report consistent accuracy gains exceeding 7 points on average relative to base models and prior open-source RL-tuned baselines.

Significance. If the utility reward can be shown to measurably improve caption accuracy rather than merely allowing Solver adaptation to noisy evidence, the dual-role coevolution approach would offer a concrete mechanism for disentangling perception and reasoning credit assignment in outcome-driven RLVR. The reported cross-scale gains and outperformance of existing baselines would then constitute a practical advance for multimodal reasoning systems.

major comments (3)

[§3.2] The central mechanism relies on the assumption that a utility reward derived solely from Solver success will steer the Observer toward more accurate visual evidence captions. No direct supervision, caption-level verification, or auxiliary loss on caption quality is described, leaving open the possibility that gains arise from Solver adaptation to biased captions or standard RL dynamics instead of improved perception.
[§5] The claimed >7-point average accuracy lift across eight benchmarks is presented without reported metrics on caption quality (e.g., evidence-caption accuracy, human evaluation of visual grounding, or ablation removing the utility reward). Without such measurements, it is not possible to confirm that the Observer's perception component has improved as required by the perception-bottleneck diagnosis.
[§5.1] Table 1 (or equivalent results table) compares PRCO to prior RL-tuned baselines, but the manuscript does not specify whether those baselines were re-trained on identical base models, data mixtures, and compute budgets; this weakens the claim of consistent outperformance.

minor comments (1)

[§3.2] Notation for the utility reward function should be introduced with an explicit equation rather than prose description to facilitate reproduction.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive comments on our work. We address each of the major comments below and indicate the revisions we will make to the manuscript.

read point-by-point responses

Referee: [§3.2] The central mechanism relies on the assumption that a utility reward derived solely from Solver success will steer the Observer toward more accurate visual evidence captions. No direct supervision, caption-level verification, or auxiliary loss on caption quality is described, leaving open the possibility that gains arise from Solver adaptation to biased captions or standard RL dynamics instead of improved perception.

Authors: We appreciate the referee pointing out the indirect nature of the utility reward. The key idea is that by tying the Observer's reward to the Solver's verifiable success, the framework encourages the Observer to produce captions that are not only descriptive but specifically useful for solving the question. This differs from standard RL where perception and reasoning share the same reward. To address concerns about Solver adaptation, we will include additional analysis in the revised §3.2 and §5, such as examples showing improved caption relevance and an ablation on reward components. revision: partial
Referee: [§5] The claimed >7-point average accuracy lift across eight benchmarks is presented without reported metrics on caption quality (e.g., evidence-caption accuracy, human evaluation of visual grounding, or ablation removing the utility reward). Without such measurements, it is not possible to confirm that the Observer's perception component has improved as required by the perception-bottleneck diagnosis.

Authors: We agree that direct evidence of improved caption quality would better support the perception-bottleneck claim. In the revision, we will add an ablation study in §5 that compares PRCO with and without the utility reward to isolate its effect. We will also report any available automatic metrics for caption quality. However, new human evaluations are not feasible within the revision timeline. revision: partial
Referee: [§5.1] Table 1 (or equivalent results table) compares PRCO to prior RL-tuned baselines, but the manuscript does not specify whether those baselines were re-trained on identical base models, data mixtures, and compute budgets; this weakens the claim of consistent outperformance.

Authors: The baselines in Table 1 are reproduced from their respective original publications using the same base models and reported settings. We did not re-train them due to computational constraints. We will revise the manuscript to explicitly describe the comparison methodology and any differences in training data or compute to provide full transparency. revision: yes

standing simulated objections not resolved

Human evaluation of visual grounding and caption accuracy

Circularity Check

0 steps flagged

No circularity: empirical RL framework with separate evaluation

full rationale

The paper introduces PRCO as a dual-role RLVR design (Observer generates caption, Solver answers; Observer reward derived from Solver success) and reports empirical accuracy gains on eight benchmarks. No equations, derivations, or self-citations are present that reduce the claimed mechanism or results to fitted inputs or self-definitions by construction. The utility reward is explicitly defined in the framework description, and performance is measured independently via downstream accuracy, leaving the chain self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on the domain assumption that separating perception and reasoning roles with asymmetric rewards will improve upstream visual evidence extraction; no free parameters or invented physical entities are introduced.

axioms (2)

domain assumption Outcome-driven RLVR improves reasoning but fails to enhance visual evidence extraction due to blurred credit assignment
Stated as the core limitation of existing approaches in the abstract.
ad hoc to paper A utility reward derived from Solver success will guide the Observer to generate better evidence captions
This is the key mechanism proposed to solve the perception bottleneck.

invented entities (1)

PRCO dual-role framework no independent evidence
purpose: To enable coevolution of perception and reasoning via role-specific rewards
New training architecture introduced by the paper; no independent falsifiable evidence outside the reported experiments is provided.

pith-pipeline@v0.9.0 · 5507 in / 1392 out tokens · 48573 ms · 2026-05-14T21:30:11.931426+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

the Observer receives a utility reward derived from the Solver's downstream success... rO_k = (1−Ileak(q,ck)) E[V(â,a)]
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

dual-role RLVR framework... role-specific reward signals... perception–reasoning coevolution

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

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Structured Role-Aware Policy Optimization for Multimodal Reasoning
cs.AI 2026-05 unverdicted novelty 7.0

SRPO refines GRPO into role-aware token-level advantages by emphasizing perception tokens based on visual dependency (original vs. corrupted inputs) and reasoning tokens based on consistency with perception, unified v...