RRM: Experience-Driven Reflective Retrieval Memory for Long-Horizon Multimodal Reasoning

2026-07-30

AI Summary

Existing long-horizon multimodal memory agents lack the ability to diagnose retrieval failures and adapt their strategies accordingly. This work proposes a reflective retrieval memory framework that dynamically guides current query-based memory retrieval by distilling transferable retrieval strategies from historical task trajectories, ensuring that generated answers rely solely on newly retrieved video evidence. The framework integrates an entity-centric multimodal memory graph, a reflective experience memory mechanism, query-level guided generation, and a lifecycle management strategy that jointly considers usage frequency, feedback signals, and temporal decay to effectively reduce redundancy and noise. Experimental results demonstrate consistent performance gains over state-of-the-art methods across the M3-Bench-Robot, M3-Bench-Web, and Video-MME-Long benchmarks, validating the framework’s effectiveness and generalizability.

Abstract

Existing multimodal long-term memory agents use external memory to overcome the limited context available for long videos. However, most methods emphasize what to store rather than how stored memory should be retrieved. When retrieval becomes inaccurate or repeatedly fails to obtain useful evidence, existing agents lack mechanisms to diagnose failures from previous task trajectories and adapt future search strategies.We introduce Reflective Retrieval Memory (RRM), a reflective memory framework for long-horizon multimodal reasoning. RRM augments an entity-centric multimodal memory graph with reflective experience memory, which distills transferable procedural retrieval knowledge from historical task trajectories. Unlike episodic and semantic memories that preserve factual evidence from the current video, reflective experience memory captures reusable search strategies across tasks. RRM converts retrieved experiences into query-level guidance, while answer generation remains conditioned only on factual evidence newly retrieved from the current video. A lifecycle management mechanism further regulates experience memory through usage frequency, reuse feedback, and temporal decay, thereby reducing redundancy and noise. RRM consistently outperforms previous state-of-the-art approaches on M3-Bench-Robot, M3-Bench-Web, and Video-MME-Long, demonstrating the effectiveness of reflective retrieval memory for long-horizon multimodal reasoning.

Problem

• long-horizon multimodal reasoning
• memory retrieval
• reflective memory
• retrieval failure adaptation
• multimodal memory agents

Innovation

• Reflective Retrieval Memory
• multimodal reasoning
• experience-driven memory
• retrieval strategy
• long-horizon reasoning