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EM^2Mem is proposed, an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors during memory construction, enabling compact evidence readout over grounded multimodal events rather than modality-specific fragments.
This work identifies memory-induced cognitive traps: even faithfully recorded and semantically relevant memories can distort model reasoning or beliefs and degrade current task performance, and proposes AdaptiveMem, a simple yet effective inference-time method that instructs LLMs to avoid memory traps.
TokenPilot is presented, a dual-granularity context management framework that reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems.
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