OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. |
| Approach: | They propose to store and retrieve relevant video frames for specific queries and a Divide-and-Conquer loop capable of autonomous reasoning. |
| Outcome: | The proposed model efficiently stores and retrieves relevant video frames for specific queries, preserving the detailed content of videos. |
Similar Papers
LongVideoAgent: Multi-Agent Reasoning with Long Videos (2026.acl-long)
Copied to clipboard
| Challenge: | a key emerging challenge is robust long video understanding, authors say . current methods compress content into lossy summaries or rely on limited toolsets . |
| Approach: | They propose a multi-agent framework where a master LLM coordinates a grounding agent and a vision agent to extract targeted textual observations. |
| Outcome: | The proposed model outperforms strong non-agent baselines on episode-level datasets . the proposed model significantly outperformed existing models on other datasets. |
DocAgent: An Agentic Framework for Multi-Modal Long-Context Document Understanding (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to document understanding are limited due to limited context length or fail to fully leverage multi-modal information. |
| Approach: | They propose a multi-agent framework for long-context document understanding that imitates human reading practice. |
| Outcome: | The proposed framework surpasses human-level benchmarks on long-context document understanding while maintaining a short context length. |
From Detection to Understanding: Multi-Turn Reasoning for Video Misinformation Analysis (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on binary veracity judgments and do not evaluate process-level justifications for misinformation models. |
| Approach: | They propose a video misinformation analysis benchmark that assesses reasoning in video misinterpretation. |
| Outcome: | The proposed framework improves reasoning accuracy and explanation quality compared to existing models . it covers 12 fine-grained deception categories and progresses from perceptual attribution to intent and persuasion analysis. |
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Long video understanding presents unique challenges due to the complexity of reasoning over extended timespans. |
| Approach: | They propose a framework VideoINSTA to leverage large language models for video understanding . they propose 'event-based temporalreasoning' and 'content-based spatial reasoning' |
| Outcome: | The proposed model significantly improves state-of-the-art on three long video question-answering benchmarks. |
PresentAgent: Multimodal Agent for Presentation Video Generation (2025.emnlp-demos)
Copied to clipboard
| Challenge: | Existing methods for generating static slides or text summaries are limited to producing narrated presentations. |
| Approach: | They propose a multimodal agent that transforms long-form documents into narrated presentations. |
| Outcome: | The present agent produces fully synchronized visual and spoken content that closely mimics human-style presentations. |
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering (2026.acl-long)
Copied to clipboard
| Challenge: | Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts. |
| Approach: | They propose a novel agentic framework that explicitly performs visual reasoning directly within the chart’s spatial domain. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on numerically intensive queries. |
OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing prompting methods for large language models (LLMs) are restricted to specialized domains, limited tool types, or require additional training data. |
| Approach: | They propose a training-free, user-friendly, and easily extensible multi-agent framework designed to tackle complex reasoning across diverse domains. |
| Outcome: | The proposed framework outperforms AutoGen, GPT-Functions, and LangChain by up to 10.6% when given the same set of tools. |
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | a surge of deep learning applications for video understanding have led to major advancements in video-related tasks. |
| Approach: | They propose a multimodal video-based conversation model that merges a video-adapted visual encoder with an LLM and a dataset that is easily scalable and robust to label noise. |
| Outcome: | The proposed model can understand and generate detailed conversations about videos. |
TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods that can find relevant information, extract it, and answer video questions in a single pass are not able to adapt if insufficient or incorrect information is collected. |
| Approach: | They propose a modular multi-LMM agent framework that can find relevant information, extract it, and answer the question simultaneously. |
| Outcome: | The proposed model improves performance on several VideoQA benchmarks without fine-tuning on specific datasets. |
Agentic Very Long Video Understanding (2026.acl-long)
Copied to clipboard
Aniket Rege, Arka Sadhu, Yuliang Li, Kejie Li, Ramya Korlakai Vinayak, Yuning Chai, Yong Jae Lee, Hyo Jin Kim
| Challenge: | Existing methods for understanding video over long periods of time are limited . eGAgent system provides tools for structured search and reasoning over entity scene graphs . |
| Approach: | They propose a system that can interpret and recall video over days or weeks . they use entity scene graphs to equip a planning agent with tools for structured search and reasoning . |
| Outcome: | The proposed method achieves state-of-the-art performance on EgoLifeQA and Video-MME-long datasets. |