Memory ranking is the stage in personalized Large Language Models (LLMs) that decides which of the candidate memories returned by a retriever enter the limited context, so that the answer is grounded in the right parts of the user's history. Existing memory rankers operate in a single pass, scoring each candidate once...
Peng-Yue Jia, Shi-Da Yan, Shunyu Liu et al.· 0 citations
The memory capabilities of Large Language Models (LLMs) have garnered increasing attention recently. Despite great success achieved, existing retrieval-based memory approaches typically overlook the differences between memories and employ a unified strategy to process all memories, leading to suboptimal performance. Th...
Yi Wen, De-Rong Xu, Peng-Yue Jia et al.· 0 citations
Inspired by Multiple-Trace Theory in cognitive psychology, this work revisit long video understanding from a probe-echo perspective, in which human episodic memories are activated and integrated in parallel, and proposes ProEchoMem, a cognitive-inspired framework that simulates the probe-echo mechanism.
Derong Xu, Yanxin Chen, Wanyu Wang et al.· Annual International ACM SIG...· 0 citations
A lightweight training framework that learns a single Behavior-Equivalent Token that substantially reduces inference cost and frees nearly the entire context window for user inputs and model outputs.
Jiancheng Dong, Pengyue Jia, Jingyu Peng et al.· 2 citations
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