TIM-PRM: Verifying multimodal reasoning with Tool-Integrated PRM

• URL: http://arxiv.org/abs/2511.22998v1
• Date: Fri, 28 Nov 2025 09:01:38 GMT
• Title: TIM-PRM: Verifying multimodal reasoning with Tool-Integrated PRM
• Authors: Peng Kuang, Xiangxiang Wang, Wentao Liu, Jian Dong, Kaidi Xu, Haohan Wang,
• Abstract summary: Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning.<n>They remain vulnerable to visual hallucinations and logical inconsistencies that standard outcome-based supervision fails to mitigate.<n>We introduce TIM-PRM, a novel agentic framework that transforms verification from a passive classification task into an active, tool-augmented investigation.
• Score: 45.91545449507256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual hallucinations and logical inconsistencies that standard outcome-based supervision fails to mitigate. While Process Reward Models (PRMs) promise step-by-step verification, current approaches typically operate as scalar scorers or generative critics that suffer from sycophancy, blindly validating the flawed hypotheses rather than grounding them in visual reality. To bridge this gap, we introduce TIM-PRM (Tool-Integrated Multimodal PRM), a novel agentic framework that transforms verification from a passive classification task into an active, tool-augmented investigation. TIM-PRM is trained to explicitly plan verification strategies and utilizes a mechanism of Independent Question Asking to query evidence via external tools, effectively decoupling verification from the reasoning context to eliminate confirmation bias. We instantiate this method by curating a high-quality dataset of tool-integrated verification trajectories. Extensive experiments on VisualProcessBench demonstrate that our 8B parameter model surpasses existing open-source multimodal PRMs, significantly outperforming much larger models like Qwen2.5-72B and InternVL-78B, while offering interpretable insights into the verification process.

Related papers

• Multimodal Fact-Level Attribution for Verifiable Reasoning [80.60864342985748]
  Multimodal large language models (MLLMs) are increasingly used for real-world tasks involving multi-step reasoning and long-form generation.<n>Existing multimodal grounding benchmarks and evaluation methods fail to assess attribution in complex multimodal reasoning.<n>We introduce MuRGAt, a benchmark for evaluating fact-level multimodal attribution in settings that require reasoning beyond direct observation.
  arXiv  Detail & Related papers  (2026-02-12T03:10:02Z)
• Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition [51.68340973140949]
  Multimodal Named Entity Recognition (GMNER) aims to extract text-based entities, assign them semantic categories, and ground them to corresponding visual regions.<n> MLLMs exhibit $textbfmodality bias$, including visual bias and textual bias, which stems from their tendency to take unimodal shortcuts.<n>We propose Modality-aware Consistency Reasoning ($bfMCR$), which enforces structured cross-modal reasoning.
  arXiv  Detail & Related papers  (2026-02-04T12:12:49Z)
• Guided Verifier: Collaborative Multimodal Reasoning via Dynamic Process Supervision [11.159231524113764]
  Reinforcement Learning (RL) has emerged as a pivotal mechanism for enhancing the complex reasoning capabilities of Multimodal Large Language Models (MLLMs)<n>In this paper, we propose the textbfGuided Verifier framework to address these structural limitations.<n>We develop a specialized data synthesis pipeline targeting multimodal hallucinations, constructing textbfCoRe dataset of process-level negatives and textbfCorrect-guide textbfReasoning trajectories to train the guided verifier.
  arXiv  Detail & Related papers  (2026-02-04T07:38:42Z)
• A Comprehensive Evaluation of LLM Reasoning: From Single-Model to Multi-Agent Paradigms [20.241519889633285]
  Large Language Models (LLMs) are increasingly deployed as reasoning systems, where reasoning paradigms play a critical role.<n>We conduct a comprehensive and unified evaluation of reasoning paradigms, spanning direct single-model generation, CoT-augmented single-model reasoning, and representative MAS.<n>We introduce MIMeBench, a new open-ended benchmark that targets two foundational yet underexplored semantic capabilities.
  arXiv  Detail & Related papers  (2026-01-19T17:23:45Z)
• ARM-Thinker: Reinforcing Multimodal Generative Reward Models with Agentic Tool Use and Visual Reasoning [103.7657839292775]
  ARM-Thinker is an Agentic multimodal Reward Model that autonomously invokes external tools to ground judgments in verifiable evidence.<n>We train ARM-Thinker with multi-stage reinforcement learning, jointly optimizing tool-calling decisions and judgment accuracy.<n>Our results demonstrate that agentic capabilities significantly enhance both accuracy and interpretability of reward models.
  arXiv  Detail & Related papers  (2025-12-04T18:59:52Z)
• GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning [34.42899160708635]
  We introduce GroundedPRM, a tree-guided and fidelity-aware framework for automatic process supervision.<n>GroundedPRM is trained on only 40K automatically labeled samples, amounting to just 10% of the data used by the best-performing PRM trained with auto-labeled supervision.<n>It achieves up to a 26% relative improvement in average performance on ProcessBench.
  arXiv  Detail & Related papers  (2025-10-16T17:54:07Z)
• Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning [68.89572566071575]
  Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to improve their internal reasoning ability by integrating external tools.<n>We propose Tool-Light, a framework designed to encourage LLMs to perform TIR efficiently and accurately.<n> Experimental results on 10 datasets demonstrate the effectiveness of Tool-Light.
  arXiv  Detail & Related papers  (2025-09-27T12:53:37Z)
• GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning [12.724393910603299]
  We introduce the Generative Multimodal Process Reward Model (GM-PRM)<n>Instead of a simple scalar score, GM-PRM provides a fine-grained, interpretable analysis of each reasoning step.<n>We show that GM-PRM achieves state-of-the-art results on multiple multimodal math benchmarks.
  arXiv  Detail & Related papers  (2025-08-06T05:10:29Z)
• PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier [18.771754895027616]
  Policy as Generative Verifier (PAG) is a framework that empowers Large Language Models to self-correct by alternating between policy and verifier roles.<n>It alleviates model collapse and jointly enhances both reasoning and verification abilities.
  arXiv  Detail & Related papers  (2025-06-12T06:59:35Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo [55.452453947359736]
  We introduce a novel verification method based on Twisted Sequential Monte Carlo (TSMC)<n>We apply TSMC to Large Language Models by estimating the expected future rewards at partial solutions.<n>This approach results in a more straightforward training target that eliminates the need for step-wise human annotations.
  arXiv  Detail & Related papers  (2024-10-02T18:17:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.