EM^2Mem: Event-Centric Multimodal Memory for Large Language Models
Summary
EM^2Mem proposes an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors for compact, generation-ready memory in long-video question answering, improving accuracy and reducing latency.
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Paper page - EM^2Mem: Event-Centric Multimodal Memory for Large Language Models
Source: https://huggingface.co/papers/2609.00551 Authors:
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Abstract
EM²Mem binds multimodal evidence to event anchors for compact, generation-ready memory in long-video question answering.
Multimodal memoryoffers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as isolated fragments. Although searchable, such fragments are not generation-ready: language models must reconstruct cross-modal and temporal alignments at inference time, when context is limited and attribution is difficult. We propose EM^2Mem, an event-centricmultimodal memoryframework that binds heterogeneous evidence to event anchors during memory construction. Each event-indexed memory cell aligns multimodal records,temporal context,graph-linked relations, semantic facts, andprovenance, enabling compact evidence readout over grounded multimodal events rather than modality-specific fragments. Across threelong-video QAbenchmarks, EM^2Mem improves average accuracy over the strongest memory baseline by 2.0, 2.4, and 3.7 points, improves strict event-level Top-5evidence recallby 7.0 points, and reduces per-query latency by 4.67 times and total inference tokens by 63.66% (The code will be integrated into https://github.com/zjunlp/LightMem).
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