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VideoMind: A Chain-of-LoRA Agent for Temporal-Grounded Video Reasoning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Multi-modal AgentVideo UnderstandingVideo Temporal Grounding
Abstract:

Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in text-based reasoning with large language models, multi-modal reasoning – especially for videos – remains limited. In this work, we fill this gap by introducing VideoMind, a novel video-language agent for temporal-grounded video reasoning. Our method involves two key innovations: (1) We identify four essential capabilities for grounded video reasoning and propose a role-based agentic workflow, comprising a planner to coordinate roles, a grounder for temporal event localization, a verifier to assess event candidates, and an answerer for question answering. (2) To efficiently integrate these roles during inference, we propose a novel Chain-of-LoRA mechanism, where a unified base model with multiple LoRA adapters is leveraged to enable seamless role switching, balancing efficiency and flexibility. Extensive experiments on 14 benchmarks across 3 tasks, including Grounded VideoQA, Video Temporal Grounding, and General VideoQA, demonstrate the effectiveness of the proposed scheme in advancing video agent, test-time scaling, and long-form video reasoning. Code, models, and data will be publicly available.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

VideoMind proposes a role-based agentic workflow for temporal-grounded video reasoning, combining a planner, grounder, verifier, and answerer to decompose complex video understanding tasks. The paper resides in the 'Agentic and Tool-Augmented Video Reasoning' leaf under 'Video-LLM Temporal Reasoning', which contains four papers total including the original work. This represents a moderately populated research direction within the broader taxonomy of 50 papers across 36 topics, suggesting active but not overcrowded exploration of multi-agent and tool-based approaches to video reasoning.

The taxonomy reveals that VideoMind's leaf sits within a larger branch exploring how large language models handle temporal dependencies in video. Neighboring leaves include 'Fine-Grained Temporal Video-LLMs' (3 papers) focusing on segment-level understanding through specialized architectures, and 'Long-Form Video Understanding with Grounding' (3 papers) addressing extended video processing. The taxonomy's scope notes clarify that agentic methods emphasize multi-agent workflows or external tools, distinguishing them from single-model end-to-end approaches in sibling categories. VideoMind's role-based decomposition aligns with this boundary while connecting to broader temporal reasoning mechanisms explored elsewhere in the taxonomy.

Among 30 candidates examined across three contributions, the role-based agentic workflow shows one refutable candidate out of 10 examined, suggesting some prior work in multi-agent video reasoning exists within this limited search scope. The Chain-of-LoRA mechanism examined 10 candidates with zero refutations, indicating this parameter-efficient role-switching approach may be more distinctive. The performance claims across three tasks also examined 10 candidates without clear refutation. These statistics reflect a focused semantic search rather than exhaustive coverage, meaning additional relevant work may exist beyond the top-30 matches analyzed.

Based on the limited literature search of 30 semantically similar papers, VideoMind appears to occupy a moderately explored niche within agentic video reasoning, with the Chain-of-LoRA mechanism showing stronger novelty signals than the overall workflow architecture. The taxonomy context suggests this work contributes to an active but not saturated research direction, though the search scope leaves open the possibility of additional relevant prior work in adjacent areas like tool-augmented reasoning or parameter-efficient video adaptation.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: temporal-grounded video reasoning. This field addresses the challenge of understanding and localizing events, actions, or moments within video sequences based on natural language queries or questions. The taxonomy reveals a diverse landscape organized around several complementary directions. Video Temporal Grounding Methods focus on retrieving specific time intervals matching textual descriptions, while Spatio-Temporal Video Grounding extends this to jointly localize objects and moments in both space and time. Video Question Answering with Temporal Grounding tackles QA tasks that require pinpointing relevant temporal segments to answer questions accurately. Meanwhile, Video-LLM Temporal Reasoning explores how large language models can be adapted or augmented to handle temporal dependencies in video, and Temporal Reasoning Mechanisms investigates the underlying architectures and attention schemes that enable models to capture temporal relations. Specialized Temporal Reasoning Tasks and Benchmarks and Evaluation round out the taxonomy by addressing domain-specific challenges and standardized assessment protocols. Within the Video-LLM Temporal Reasoning branch, a particularly active line of work centers on agentic and tool-augmented approaches that equip video-language models with external reasoning modules or iterative refinement strategies. VideoMind ChainLoRA[0] exemplifies this direction by introducing a chain-of-thought style framework that decomposes complex temporal reasoning into manageable steps, closely related to efforts like Thinking with videos[18] and LongVT[48], which similarly emphasize structured reasoning over extended video content. In contrast, works such as Context-guided grounding[3] and Visually grounded VQA[5] prioritize tighter integration of visual context and question semantics without explicit tool invocation, highlighting a trade-off between modular agentic pipelines and end-to-end learned representations. VideoMind ChainLoRA[0] sits naturally among these tool-augmented methods, sharing the emphasis on decomposing reasoning found in Video-of-thought[2] and VideoITG[1], yet differing in its use of parameter-efficient adaptation to enable flexible temporal grounding across diverse video lengths and question types.

Claimed Contributions

VideoMind: A role-based agentic workflow for temporal-grounded video reasoning

The authors introduce VideoMind, a video-language agent that decomposes video reasoning into four essential roles: a planner to coordinate tasks, a grounder for temporal event localization, a verifier to assess event candidates, and an answerer for question answering. This framework addresses challenges in long video reasoning through a structured, progressive approach.

10 retrieved papers
Can Refute
Chain-of-LoRA: An efficient test-time scaling mechanism for seamless role switching

The authors propose Chain-of-LoRA, a novel mechanism where a unified base model uses multiple LoRA adapters to implement different roles. This design allows efficient role switching during inference by caching LoRA parameters in memory, balancing flexibility and efficiency without requiring multiple full model copies.

10 retrieved papers
State-of-the-art performance across three video understanding scenarios

The authors report that VideoMind achieves state-of-the-art results on 14 benchmarks spanning Grounded VideoQA, Video Temporal Grounding, and General VideoQA tasks. Their 2B model surpasses larger proprietary models like GPT-4o and Gemini-1.5-Pro on multiple long video benchmarks, demonstrating the effectiveness of their approach.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VideoMind: A role-based agentic workflow for temporal-grounded video reasoning

The authors introduce VideoMind, a video-language agent that decomposes video reasoning into four essential roles: a planner to coordinate tasks, a grounder for temporal event localization, a verifier to assess event candidates, and an answerer for question answering. This framework addresses challenges in long video reasoning through a structured, progressive approach.

Contribution

Chain-of-LoRA: An efficient test-time scaling mechanism for seamless role switching

The authors propose Chain-of-LoRA, a novel mechanism where a unified base model uses multiple LoRA adapters to implement different roles. This design allows efficient role switching during inference by caching LoRA parameters in memory, balancing flexibility and efficiency without requiring multiple full model copies.

Contribution

State-of-the-art performance across three video understanding scenarios

The authors report that VideoMind achieves state-of-the-art results on 14 benchmarks spanning Grounded VideoQA, Video Temporal Grounding, and General VideoQA tasks. Their 2B model surpasses larger proprietary models like GPT-4o and Gemini-1.5-Pro on multiple long video benchmarks, demonstrating the effectiveness of their approach.

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