The authors treat memory retrieval as a set-level decision rather than independently estimating the relevance of individual memories.
Targeted feedback improves memory retrieval for language model agents
UpliftMem learns which sets of stored examples improve task outcomes, then directs limited training rollouts toward alternatives most likely to change retrieval decisions.
Top University
Mengkun Liang · Haoran Qiang · Guannan Liu · Junjie Wu
MIIT Key Laboratory of Data and Decision Intelligence · Beihang University
Research Digest··3 min read
Liang and colleagues introduce UpliftMem, a retrieval method that scores an entire set of memories by its execution uplift relative to running the same frozen agent without memory.
Why this paper
From Beihang University and MIIT Key Laboratory of Data and Decision Intelligence
In one line
Memory retrieval for LLM agents improves by learning set-level uplift compared to no-memory execution.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
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- ·No stated limitations found
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Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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