Title: When Thinking Drifts: Evidential Grounding for Robust Video Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video Understanding

Video Reasoning

MLLM

GRPO

Video QA
Abstract: Video reasoning, the task of enabling machines to infer from dynamic visual content through multi-step logic, is crucial for advanced AI. While the Chain-of-Thought (CoT) mechanism has enhanced reasoning in text-based tasks, its application to video understanding remains underexplored. This paper presents a systematic analysis revealing that CoT often degrades performance in video reasoning, generating verbose but misleading internal monologues, and leading to hallucinated visual details and overridden correct intuitions—a phenomenon we term "visual thinking drift." We explain this drift through a Bayesian lens, positing that CoT traces often diverge from actual visual evidence, instead amplifying internal biases or language priors, causing models to storytell rather than engage in grounded reasoning. To counteract this, we introduce Visual Evidence Reward (VER), a novel reinforcement learning framework that explicitly rewards the generation of reasoning traces that are verifiably grounded in visual evidence. Comprehensive evaluation across 10 diverse video understanding benchmarks demonstrates that our Video-VER model consistently achieves top performance. 
Our work sheds light on the distinct challenges of video-centric reasoning and encourages the development of AI that robustly grounds its inferences in visual evidence---for large multimodal models that not only "think before answering", but also "see while thinking".


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the limitations of Chain-of-Thought (CoT) prompting in video reasoning tasks for multimodal large language models (MLLMs). The authors identify a critical issue termed \"visual thinking drift,\" where CoT-generated reasoning chains diverge from actual visual evidence, leading to hallucinations and degraded performance. They propose a Bayesian-inspired explanation for this phenomenon, attributing it to the amplification of language priors over visual signals during autoregressive decoding. To mitigate this, the authors introduce Visual Evidence Reward (VER), a reinforcement learning framework that explicitly rewards reasoning traces grounded in verifiable visual evidence. Extensive experiments across 10 video understanding benchmarks demonstrate that VER consistently improves accuracy over baseline models.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. The concept of *visual thinking drift* is an interesting and underexplored challenge in video reasoning. The paper provides empirical findings (Figure 2) and theoretical insights (Bayesian analysis) into such problem.\n2. The experiments span 10 diverse benchmarks, demonstrating robustness across tasks like temporal reasoning, object counting, and hallucination mitigation.\n\n### Weaknesses\n1. The Bayesian analysis in Section 3.2 attributes *visual thinking drift* to the dominance of language priors over visual evidence during autoregressive decoding. However, this explanation is overly generic and fails to address why CoT prompting works relatively better for text-based LLMs (where \"prefill context\" acts as the analog of v) compared to video reasoning tasks. This suggests that the root cause of drift may be more nuanced and specific to video reasoning challenges. The authors should explicitly investigate why CoT fails for video tasks even when the same Bayesian mechanism applies to both modalities.\n2. The experiments in this paper exclusively use `Qwen2.5-VL-7B` as the base model for evaluating the Video-VER framework. While the method demonstrates improvements on this specific architecture, the lack of testing across diverse model architectures (e.g., LongVA, LLaVA-OneVision) or parameter scales (e.g., smaller or larger models) raises concerns about the generalizability of Video-VER.\n3. The generation of visual evidence relies on a strong external MLLM (`Qwen2.5-VL-72B`), which could introduce biases or hallucinations itself. While the paper mentions filtering, the robustness of this process is not fully validated.\n\nQUESTIONS:\n1.  How does Video-VER perform on videos exceeding 32 frames? While the ablation study (Table 3) demonstrates improved performance with increased frame counts, real-world applications often involve much longer sequences.\n2. How does Video-VER compare to modular reasoning approaches (e.g., [46] or [38]) that explicitly decompose video understanding into subtasks like action recognition or temporal grounding?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the issue where Chain-of-Thought (CoT) often degrades performance in video reasoning tasks because the model overlooks crucial visual content during the reasoning process. To address this, the paper introduces a new reward mechanism, called Visual Evidence Reward (VER), which penalizes the model when it fails to infer to actual visual details. Specifically, ground truth evidence is generated by prompting the Qwen2.5-VL-72B model with both a video and a question, and an auxiliary LLM compares this ground truth evidence with the policy model’s response to provide a binary signal indicating whether the two are aligned. This VER is integrated into the GRPO framework. Experimental results on various video reasoning benchmarks show improvements over the baseline.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\nS1. **Interesting and Sound Motivation**: Section 3 presents interesting and meaningful take-away to readers that CoT does not exactly help VideoLLMs to perform temporal reasoning because it actually ignores videos.\n\nS2. **Clear Presentation:** The paper is well-written and easy to follow, with clear explanations that make the context easy to understand.\n\n---\n## Weaknesses\n\nWhile this paper tackles an interesting problem, I have major concerns regarding the method and experimental validations:\n\nW1. **Unfair Comparison Setup:** To provide a fair evaluation, there should be an ablation study comparing the following setups: baseline+SFT, baseline+SFT+GRPO, and baseline+SFT+GRPO+VER. In the current experimental setup, it is unclear whether the observed gains are due to GRPO or VER, making it difficult for readers to assess the individual contributions.\n\nW2. **Reliability of Visual Evidence Source**: Figure 4 indicates that even strong reasoning models, such as GPT-4o, can disregard visual facts. However, the paper then uses Qwen2.5-VL-72B to generate the visual evidence that grounds the training. This raises natural questions about how Qwen2.5-VL-72B is free from the same issue of potentially overlooking or hallucinating visual information when generating this critical evidence.\n\nQUESTIONS:\nUnfair Comparison Setup [W1] is the most significant issue affecting my decision. As it stands, I don't believe readers can accurately assess the experimental impact of the proposed method.\n\nQ1. See [W1]\n\nQ2. See [W2]\n\nQ3. **Regarding Evidence Generation as Distillation for VideoQA**: The paper uses a significantly larger model (Qwen2.5-VL-72B) to generate the visual evidence that guides the training of Video-VER-7B. Could the authors discuss this process from the perspective of knowledge distillation – where the 7B model is learning to better ground its VideoQA reasoning by aligning with the evidence identified by the 72B model? Is the 72B model expected to perform clearly better overall due to its size, or does the VER training enable the smaller 7B model to achieve comparable or even superior performance in VideoQA reasoning on the target benchmarks?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper presents a systematic analysis revealing that CoT often degrades performance in video reasoning, which is related to a phenomenon called "visual thinking drift." Then, the authors introduce a reinforcement framework, “Visual Evidence Reward (VER)”, to explicitly rewards the generation of reasoning traces with visual evidences. 10 diverse video understanding benchmarks demonstrates the Video-VER achieves top performance compared to other baseline models. This work pointed out the importance of visual grounding in the thinking process of multimodal models, and may encourage the development of large models in robust visual grounding-based rational thinking process.

STRENGTHS AND WEAKNESSES:
Strengths：
- Present a phenomenon called visual thinking drift, which is consistent with the well-known AI hallucination problem. This paper further analyze this problem from the perspective of visual grounding, and then propose corresponding RL solutions. 
- Conduct extensive experiments to show that Video-VER achieves better performance. Also, the ablation study provides more useful information to show the effects on different models types and model sizes.

Weakness:
- Maybe more reasoning models should be added as baselines, such as OpenAI o1/o3.

QUESTIONS:
- If visual evidence reward can help improve model performance, which performs better between using agentic workflow and end-to-end training? And what is the foundational reason to lead to the difference?
- If the large model can control the behavior in visual space, will there also be hallucinations? Is it consistent with the cause of visual perception illusion?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-07

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n\"Visual thinking drift\" is the phenom. where CoT generates misleading internal descriptions and steps which hinder performance in visual understanding tasks. The authors propose a \"visual evidence reward\" RL framework to overcome this.\n\nFirst they motivate this drift problem with an analysis of the success rate of 4 models on 20 video QA tasks with and without using CoT. In many cases, using CoT causes a performance **drop**, demonstrating that visual thinking drift is a real issue. Even with GPT4o, it's better to adopt the direct answer strategy over the CoT strategy for 50% of the video QA benchmarks, contra the strict gain under CoT we observe in the text domain.\n\nTheir analysis centers the idea that a \"latent state\" of each step of a reasoning chain can drift away from the semantics of the vision tokens as the influence of the vision tokens is overwhelmed by influence of prior latent states.\n\nVisual evidence reward is based on this idea: each reasoning step is checked against the visual info using an LM judge to train a policy model. This VER is added to the GRPO reward during RL. Specifically, this judging is performed by comparing the text of the step to Qwen2.5-VL-72B-generated descriptions of the visual input conditioned on the known question for the training example. They train the model using Video-R1-COT-165k for SFT followed by GRPO with VER on Video-R1-260k and Reversed-in-Time.\n\nTheir model achieves significant gains over open baselines, particularly in the COT setting.\n\n(Deleted the first review post because I accidentally submitted a review for a different paper)\n\nSTRENGTHS AND WEAKNESSES:\nSolid demonstration of the initial phenomenon (visual thinking drift) which their intervention is intended to fix, with analysis of existing models.\n\nSimple but effective fix to this problem using VER.\n\nComprehensive set of baselines evaluated.\n\nI see no glaring reasons to reject.\n\nQUESTIONS:\nRevisiting the demonstrations in Figures 2, 3, and 4 which motivate the problem of visual thinking drift, can you report numbers for Video-VER *without* chain-of-thought? If your hypothesis is true, I would expect to see more consistent performance gains with CoT.\n\nNitpick: Consider revising to remove  LM-generated flowerly language, such as: \"As the chef **deftly** chops vegetables...**envisioning the delicious outcome**.\" (bolded) Such superfluous adjectives and editorializing are distracting \n\nBeyond this I found the paper quite easy to understand and have no other significant questions or complaints\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-07"}