VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning

• URL: http://arxiv.org/abs/2603.25021v1
• Date: Thu, 26 Mar 2026 04:37:54 GMT
• Title: VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning
• Abstract summary: VideoTIR explores Zero-RL and SFT cold-starting to enable MLLMs to retrieve and focus on meaningful video segments/images/regions.<n>We develop a sandbox-based trajectory synthesis framework to generate high-quality trajectories data.
• Score: 47.619860680226964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Multimodal Large Language Models (MLLMs) often suffer from hallucinations in long video understanding (LVU), primarily due to the imbalance between textual and visual tokens. Observing that MLLMs handle short visual inputs well, recent LVU works alleviate hallucinations by automatically parsing the vast visual data into manageable segments that can be effectively processed by MLLMs. SFT-based tool-calling methods can serve this purpose, but they typically require vast amounts of fine-grained, high-quality data and suffer from constrained tool-calling trajectories. We propose a novel VideoTIR that leverages Reinforcement Learning (RL) to encourage proper usage of comprehensive multi-level toolkits for efficient long video understanding. VideoTIR explores both Zero-RL and SFT cold-starting to enable MLLMs to retrieve and focus on meaningful video segments/images/regions, enhancing long video understanding both accurately and efficiently. To reduce redundant tool-calling, we propose Toolkit Action Grouped Policy Optimization (TAGPO), which enhances the efficiency of the calling process through stepwise reward assignment and reuse of failed rollouts. Additionally, we develop a sandbox-based trajectory synthesis framework to generate high-quality trajectories data. Extensive experiments on three long-video QA benchmarks demonstrate the effectiveness and efficiency of our method.

Related papers

• Beyond CNNs: Efficient Fine-Tuning of Multi-Modal LLMs for Object Detection on Low-Data Regimes [0.0]
  We compare fine-tuned traditional CNNs, zero-shot pre-trained multi-modal LLMs, and fine-tuned multi-modal LLMs on the challenging task of artificial text overlay detection in images.<n>A key contribution of our study is demonstrating that LLMs can be effectively fine-tuned on very limited data to achieve up to 36% accuracy improvement.<n>Our work contributes to the broader effort of bridging vision and language, offering novel insights into efficient cross-modal learning strategies.
  arXiv  Detail & Related papers  (2025-10-03T18:53:18Z)
• LLM-I: LLMs are Naturally Interleaved Multimodal Creators [24.64752837827959]
  LLM-Interleaved (LLM-I) is a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem.<n>Our framework empowers a central LLM or MLLM agent to intelligently orchestrate a diverse toolkit of specialized visual tools.<n>LLM-I demonstrates state-of-the-art performance, outperforming existing methods by a large margin across four benchmarks.
  arXiv  Detail & Related papers  (2025-09-17T02:33:29Z)
• Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency [56.475612147721264]
  We propose a dual-reward formulation that supervises both semantic and temporal reasoning through discrete and continuous reward signals.<n>We evaluate our approach across eight representative video understanding tasks, including VideoQA, Temporal Video Grounding, and Grounded VideoQA.<n>Results underscore the importance of reward design and data selection in advancing reasoning-centric video understanding with MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T17:28:26Z)
• AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning [19.68349294206012]
  Large language models (LLMs) have enabled the creation of multi-modal LLMs that exhibit strong comprehension of visual data such as images and videos.<n>We propose a training-free adaptive inference method for multi-modal LLMs that can accommodate a broad range of efficiency requirements.
  arXiv  Detail & Related papers  (2024-12-04T11:47:57Z)
• Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMs [56.040198387038025]
  We present a novel prompt-guided visual perception framework (abbreviated as Free Video-LLM) for efficient inference of training-free video LLMs. Our method effectively reduces the number of visual tokens while maintaining high performance across multiple video question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T12:35:12Z)
• Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement [93.73648674743097]
  Visual program synthesis is a promising approach to exploit the reasoning abilities of large language models for compositional computer vision tasks. Previous work has used few-shot prompting with frozen LLMs to synthesize visual programs. No dataset of visual programs for training exists, and acquisition of a visual program dataset cannot be easily crowdsourced.
  arXiv  Detail & Related papers  (2024-04-06T13:25:00Z)
• ST-LLM: Large Language Models Are Effective Temporal Learners [58.79456373423189]
  Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation. How to effectively encode and understand videos in video-based dialogue systems remains to be solved. We propose ST-LLM, an effective video-LLM baseline with spatial-temporal sequence modeling inside LLM.
  arXiv  Detail & Related papers  (2024-03-30T10:11:26Z)
• VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding [65.12464615430036]
  This paper introduces a Video Understanding and Reasoning Framework (VURF) based on the reasoning power of Large Language Models (LLMs)<n>Ours is a novel approach to extend the utility of LLMs in the context of video tasks, leveraging their capacity to generalize from minimal input and output demonstrations within a contextual framework.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)

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.