Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
On the map: LLM reasoning → Spatial and embodied reasoning
Abstract (arXiv)
We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e., in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained via reinforcement learning (RL). Inspired by human problem-solving strategies, CoTT decomposes complex reasoning into modular steps, with the RL agent invoking specific tools, one per step, to iteratively and collaboratively answer sub-questions tackling such tasks as temporal retrieval and multi-modal understanding. We design a two-stage training paradigm involving supervised finetuning (SFT) of a pretrained language model using CoTT data and RL to enable our agent to dynamically propose step-by-step tools for long-range reasoning. To facilitate training, we construct a dataset called Ego-R1 Data, which consists of Ego-CoTT-25K for SFT and Ego-QA-4.4K for RL. Furthermore, our Ego-R1 agent is evaluated on a newly curated week-long video QA benchmark, Ego-R1 Bench, which contains human-verified QA pairs from hybrid sources. Extensive results demonstrate that the dynamic, tool-augmented chain-of-thought reasoning by our Ego-R1 Agent can effectively tackle the unique challenges of understanding ultra-long egocentric videos, significantly extending the time coverage from few hours to a week.
Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 61 of its 73 references that are in the map. First posted on arXiv 16 Jun 2025.
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- 2026Insight-V++: Towards Advanced Long-Chain Visual Reasoning with Multimodal Large Language Modelscited by 2
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- 2026Bridging Modalities, Spanning Time: Structured Memory for Ultra-Long Agentic Video Reasoning8 May 2026
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- 2026Shape of Thought: Progressive Object Assembly via Visual Chain-of-Thought28 Jan 2026
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- 20263rd Place at CVPR 2026 CASTLE Challenge: Agentic Multi-View Long-Context Video Understanding via Hierarchical Knowledge Graph Retrieval1 Jun 2026
- 2026VideoSEAL: Mitigating Evidence Misalignment in Agentic Long Video Understanding by Decoupling Answer Authority12 May 2026
- 2026HippoCamp: Benchmarking Contextual Agents on Personal Computers1 Apr 2026
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- 2025TAMA: Tool-Augmented Multimodal Agent for Procedural Activity Understanding30 Sep 2025
12 newest of 14 All 14 (JSON)
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12 most cited of 21 All 21 (JSON)
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- 2026Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding21 Aug 2026
- 2026SCOUT: Self-Checking and Recovery-Aware Tool-Thought Agents for Ultra-Long Egocentric Video Reasoning8 Aug 2026
- 2026LightMem-Ego: Your AI Memory for Everyday Life13 Jul 2026
- 2026Statistical Adversaries: Natural Backdoor-like Adversarial Features in Clean Vision Datasets6 Jul 2026
- 2026X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding1 Jun 2026
- 2026VideoOdyssey: A Benchmark for Ultra-Long-Context and Omni-Modal Video Understanding21 May 2026
- 2026Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs16 Apr 2026
- 2026Evaluating Memory Capability in Continuous Lifelog Scenario13 Apr 2026
- 2026LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs19 Mar 2026
12 newest of 21 All 21 (JSON)
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