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
MemSteer generates evidence-grounded QA supervision from unlabeled user histories to optimize agent memory design without model updates, improving benchmark scores significantly.
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. We introduce MemSteer, which turns unlabeled user histories into question-answer (QA) supervision with supporting evidence for memory-design optimization. MemSteer constructs an evidence-grounded answer plan that specifies how facts across related records jointly support a reference answer. The plan guides question generation, while source-grounded…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
MemSteer: Turning User Histories into Supervision for Agent Memory Optimization was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 7 checks on 2 different days. Its best position was #25 of 30, reached on Nov 3, 09:28 UTC. It dropped off the list after Nov 3, 11:28 UTC.
Position on Hugging Face · Daily papers at every check (lower is better).
Best rank per day (UTC).
Platform metric as reported by Hugging Face.
Archived daily, weekly and monthly lists that include this entry.
Not at the moment. It was last on the list on Nov 3, 11:28 UTC, after 2 days of trending.
#25 on Hugging Face · Daily papers, on Nov 3, 09:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 2, 2026 and has been on the list on 2 days (7 checks) since.