MemSteer builds memory-optimization labels from unlabeled user histories
Original title: MemSteer: Turning User Histories into Supervision for Agent Memory Optimization
MemSteer-Opt lifts ATM-Bench Question Score by 13.4 points over Base-BM25 without changing model weights.
MemSteer turns unlabeled user histories into question-answer supervision with supporting evidence for agent memory design. An evidence-grounded answer plan guides question generation, and source-grounded auditing plus blind answer recovery check the annotations. MemSteer-Opt uses that supervision to revise memory store, retrieval and context construction without updating model parameters. On ATM-Bench and RHELM the authors report results competitive with official-QA-guided optimization, including gains for two existing optimizers and continued optimization on new users.
- ATM-Bench Question Score rises 13.4 points versus Base-BM25.
- RHELM accuracy rises 4.1 points without external data and 6.6 with it.
- The supervision also benefits two existing optimizers on ATM-Bench.
- New-user tests keep optimizing designs from target-history supervision.
From the original article
1 min read at huggingface.co
An effective agent memory system should adapt to a user's history and task requirements. Human-annotated supervision can guide this adaptation by providing feedback for evaluating and improving memory designs. Yet suitable labels are rarely available for new user histories, and manually constructing QA pairs and supporting evidence is costly.
By Jie Huang, Cuiling Lan, Qize Yu · huggingface.co
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