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arXiv AI By BHVSP Subrahmanyam, Neeraj Choudhary

Beyond Marginal Influence: Interaction-Aware Memory Selection for LLM Agents

Read the original on arXiv AI →

The paper introduces JointCoreBench, a benchmark for evaluating memory selection in large language model agents, and JointCore, a cost‑ordered group‑deletion baseline that identifies minimal sufficient memory subsets. JointCore achieves perfect recovery of minimum‑cost sufficient sets across 6,912 conditions, cuts subset evaluations by 62.9%, and saves 87.8% of active memory usage. Experiments across multiple model families and tasks demonstrate that JointCore preserves full‑memory behavior while reducing model queries and cost‑aware recovery rates compared to singleton deletion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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