# VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding

arXiv: https://arxiv.org/abs/2610.06672 · HTML: https://mlsanity.com/paper/2610.06672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 14 of 14)

- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding](https://mlsanity.com/paper/2410.17434/) (2024)
- [VideoSeek: Long-Horizon Video Agent with Tool-Guided Seeking](https://mlsanity.com/paper/2603.20185/) (2026)
- [LensWalk: Agentic Video Understanding by Planning How You See in Videos](https://mlsanity.com/paper/2603.24558/) (2026)
- [LongViTU: Instruction Tuning for Long-Form Video Understanding](https://mlsanity.com/paper/2501.05037/) (2025)
- [Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding](https://mlsanity.com/paper/2603.17307/) (2026)
- [MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism](https://mlsanity.com/paper/2606.07512/) (2026)
- [Small Vision-Language Models are Smart Compressors for Long Video Understanding](https://mlsanity.com/paper/2604.08120/) (2026)
- [Bridging Modalities, Spanning Time: Structured Memory for Ultra-Long Agentic Video Reasoning](https://mlsanity.com/paper/2605.08271/) (2026)
- [Visual Agentic Memory: Enabling Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval](https://mlsanity.com/paper/2605.16481/) (2026)
- [PyraVid: Hierarchical Multimodal Memory for Long-Horizon Video Reasoning](https://mlsanity.com/paper/2605.17065/) (2026)
- [Keep It Simple: Multi-Key Episodic Memory Retrieval for Ultra-Long Video Understanding](https://mlsanity.com/paper/2608.07663/) (2026)
- [Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding](https://mlsanity.com/paper/2608.20805/) (2026)
- [Online Video Agent Harness for Long Video Understanding](https://mlsanity.com/paper/2609.12818/) (2026)

## Abstract (arXiv)

Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended temporal spans. Existing approaches broadly follow two paradigms: query-driven exploration, which is sensitive to localization errors, and query-independent memory construction, which may omit question-specific details. We introduce VideoTapestry, a training-free multi-agent framework that adapts a preconstructed hierarchical video memory through coarse-to-fine, query-driven refinement. The preconstructed memory organizes video content into three levels, capturing global narrative context, event-level temporal structure, and fine-grained relational evidence, respectively. To support coarse-to-fine localization and observation, we assign a specialized agent to each level, keeping retrieval and refinement within a scale-specific context. Guided by the query, these agents revisit relevant video regions and enrich layer-wise memories with targeted multimodal observations. Their refinements are assembled according to the original hierarchy into a composite query-adaptive memory, preserving global context in a compact form while retaining fine-grained evidence along query-relevant branches for final reasoning. Compared with direct GPT-5.5 inference, VideoTapestry achieves absolute accuracy gains of 17.2%, 14.9%, 9.8%, and 7.0% on LVBench, LongVideoBench (Long), Video-MME (Long), and EgoSchema, respectively, achieving the state-of-the-art results among all competitors.