ClueTracer: Question-to-Vision Clue Tracing for Training-Free Hallucination Suppression in Multimodal Reasoning

• URL: http://arxiv.org/abs/2602.02004v1
• Date: Mon, 02 Feb 2026 12:03:56 GMT
• Title: ClueTracer: Question-to-Vision Clue Tracing for Training-Free Hallucination Suppression in Multimodal Reasoning
• Abstract summary: We introduce ClueRecall, a metric for assessing visual clue retrieval.<n>We present ClueTracer, a training-free, parameter-free, and architecture-agnostic plugin for hallucination suppression.
• Score: 11.30122927323085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large multimodal reasoning models solve challenging visual problems via explicit long-chain inference: they gather visual clues from images and decode clues into textual tokens. Yet this capability also increases hallucinations, where the model generates content that is not supported by the input image or the question. To understand this failure mode, we identify \emph{reasoning drift}: during clue gathering, the model over-focuses on question-irrelevant entities, diluting focus on task-relevant cues and gradually decoupling the reasoning trace from visual grounding. As a consequence, many inference-time localization or intervention methods developed for non-reasoning models fail to pinpoint the true clues in reasoning settings. Motivated by these insights, we introduce ClueRecall, a metric for assessing visual clue retrieval, and present ClueTracer, a training-free, parameter-free, and architecture-agnostic plugin for hallucination suppression. ClueTracer starts from the question and traces how key clues propagate along the model's reasoning pathway (question $\rightarrow$ outputs $\rightarrow$ visual tokens), thereby localizing task-relevant patches while suppressing spurious attention to irrelevant regions. Remarkably, \textbf{without any additional training}, ClueTracer improves all \textbf{reasoning} architectures (including \texttt{R1-OneVision}, \texttt{Ocean-R1}, \texttt{MM-Eureka}, \emph{etc}.) by $\mathbf{1.21\times}$ on reasoning benchmarks. When transferred to \textbf{non-reasoning} settings, it yields a $\mathbf{1.14\times}$ gain.

Related papers

• TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents [54.88208747581851]
  Training-free visual token pruning can reduce this cost, but cache reuse introduces a fundamental constraint.<n>We propose textbfmethod, a training-free framework for emphtextbfTrajectory-textbfrobust textbfAdmission and textbfCoverage-aware textbfEvidence ordering.
  arXiv  Detail & Related papers  (2026-09-09T15:12:48Z)
• RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought [50.36951914894845]
  Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments.<n>Current vision-language models rely on text-only or coordinate-augmented chain-of-thought.<n>We propose Pinned Chain-of-Thought, a structured reasoning paradigm that pins every reasoning step to visual evidence.
  arXiv  Detail & Related papers  (2026-06-14T11:30:42Z)
• LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards [53.339700196282905]
  Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task.<n>Existing methods are limited by low-confusability distractors and sparse, outcome-only reward signals.<n>textscLongTraceRL produces training contexts that are far more challenging than those built by random sampling or one-shot search.
  arXiv  Detail & Related papers  (2026-05-29T17:51:40Z)
• Shorthand for Thought: Compressing LLM Reasoning via Entropy-Guided Supertokens [4.0704009036918025]
  Reasoning in Large Language Models incurs significant inference-time compute.<n>We show that reasoning tokens split into two functional types: low-entropy textitstructural tokens (recurring phrases that scaffold the reasoning process) and higher-entropy textitorganic tokens (problem-specific content that drives toward a solution)<n>We derive textitsupertokens that capture frequent structural patterns, then teach the model to adopt them via supervised fine-tuning.
  arXiv  Detail & Related papers  (2026-04-29T07:06:43Z)
• Clue Matters: Leveraging Latent Visual Clues to Empower Video Reasoning [14.945921705882725]
  This work bridges the perception-to-generation gap in MLLM video understanding, providing an interpretable, faithful reasoning paradigm for VideoQA applications.<n>Inspired by hierarchical human visual cognition, we propose ClueNet, a clue-aware video reasoning framework with a two-stage supervised fine-tuning paradigm.
  arXiv  Detail & Related papers  (2026-03-16T09:15:12Z)
• Seeing Through the Chain: Mitigate Hallucination in Multimodal Reasoning Models via CoT Compression and Contrastive Preference Optimization [78.94590726578014]
  multimodal reasoning models (MLRMs) remain prone to hallucinations, and effective solutions are still underexplored.<n>We propose C3PO, a training-based mitigation framework comprising textbfCompression and textbfPreference textbfOptimization.
  arXiv  Detail & Related papers  (2026-02-03T11:00:55Z)
• MentisOculi: Revealing the Limits of Reasoning with Mental Imagery [63.285794947638614]
  We develop MentisOculi, a suite of multi-step reasoning problems amenable to visual solution.<n> evaluating visual strategies ranging from latent tokens to explicit generated imagery, we find they generally fail to improve performance.<n>Our findings suggest that despite their inherent appeal, visual thoughts do not yet benefit model reasoning.
  arXiv  Detail & Related papers  (2026-02-02T18:49:06Z)
• Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy [75.66913260900726]
  Reinforcement Learning with Verifiable Rewards has significantly advanced reasoning capabilities in Large Language Models.<n>Existing paradigms, driven by text-centric outcome rewards, encourage models to bypass visual perception.<n>We propose textbfThinking with Deltas, a framework driven by a textbfDifferential Visual Reasoning Policy.
  arXiv  Detail & Related papers  (2026-01-11T08:25:34Z)
• CogFlow: Bridging Perception and Reasoning through Knowledge Internalization for Visual Mathematical Problem Solving [28.57547723919984]
  We present CogFlow, a novel cognitive-inspired three-stage framework that incorporates a knowledge internalization stage.<n>To guarantee faithful integration of extracted visual cues into subsequent reasoning, we introduce a Knowledge Internalization Reward model.<n>We also contribute a new dataset MathCog for model training, which contains samples with over 120K high-quality perception-reasoning aligned annotations.
  arXiv  Detail & Related papers  (2026-01-05T08:02:18Z)
• Deep But Reliable: Advancing Multi-turn Reasoning for Thinking with Images [53.373427633330515]
  We propose DRIM, a model that enables deep but reliable multi-turn reasoning when thinking with images in its multimodal CoT.<n>Based on a high-resolution image dataset, we construct high-difficulty and verifiable visual question-answer pairs.<n>In the SFT stage, we collect tool trajectories as cold-start data, guiding a multi-turn reasoning pattern.<n>In the RL stage, we introduce redundancy-penalized policy optimization, which incentivizes the model to develop a self-reflective reasoning pattern.
  arXiv  Detail & Related papers  (2025-12-19T07:44:43Z)
• Thinking Before You Speak: A Proactive Test-time Scaling Approach [54.8205006555199]
  We implement our idea as a reasoning framework, named emphThinking Before You Speak (TBYS)<n>We design a pipeline for automatically collecting and filtering in-context examples for the generation of emphinsights.<n>Experiments on challenging mathematical datasets verify the effectiveness of TBYS.
  arXiv  Detail & Related papers  (2025-08-26T03:43:32Z)
• VLM-R$^3$: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought [51.43082554363725]
  We introduce textbfVLM-R$3$ (textbfVisual textbfLanguage textbfModel with textbfRegion textbfRecognition and textbfReasoning), a framework that equips an MLLM with the ability to decide emph when additional visual evidence is needed.<n>Experiments on MathVista, ScienceQA, and other benchmarks show that VLM-R$3$ sets a new
  arXiv  Detail & Related papers  (2025-05-22T03:50:13Z)
• Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning [53.790502697674754]
  We propose Take-along Visual Conditioning (TVC), a strategy that shifts image input to critical reasoning stages.<n>TVC helps the model retain attention to the visual components throughout the reasoning.<n>Our approach achieves state-of-the-art performance on average across five mathematical reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-03-17T16:45:12Z)
• PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model [0.0]
  hallucinations often arise from the progressive weakening of attention weight to visual tokens.<n>textbfPAINT (textbfPaying textbfAttention to textbfINformed textbfTokens) is a plug-and-play framework that intervenes in the self-attention mechanism of the Large Vision Language Models.
  arXiv  Detail & Related papers  (2025-01-21T15:22:31Z)
• Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild [35.91285472401222]
  We devise an innovative training and reasoning framework suitable for lightweight Multimodal Large Language Models (MLLMs)<n>Our self-questioning approach organically guides MLLMs to focus on visual clues relevant to the target problem, reducing hallucinations and enhancing the model's ability to describe fine-grained image details.<n>Our experiments on various benchmarks demonstrate SQ's remarkable capabilities in self-questioning, zero-shot visual reasoning and hallucination mitigation.
  arXiv  Detail & Related papers  (2025-01-06T12:16:56Z)
• Interpretable Visual Question Answering via Reasoning Supervision [4.76359068115052]
  Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task. We propose a novel architecture for visual question answering that leverages common sense reasoning as a supervisory signal. We demonstrate both quantitatively and qualitatively that the proposed approach can boost the model's visual perception capability and lead to performance increase.
  arXiv  Detail & Related papers  (2023-09-07T14:12:31Z)

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.