Delivered, Not Used: Evaluating Agent Memory at Its Point of Use
Changran Xu, Zeju Li, Qiang Xu
Abstract
Task success, retrieval quality, and cost do not show whether an LLM agent used a delivered memory or what followed from that use. We introduce MemUse, which evaluates memory at its point of use in recorded trajectories: each memory delivery is compared with what the agent could already see and followed through the agent's later actions and their consequences. An agentic judge rates each delivered entry's relevance, accuracy, novelty, influence, and observed effect; programmatic checks enforce complete reading of cited evidence, and fixed rules aggregate the ratings into verdicts linked to memory histories and delivery cost. On 8,000 SWE-bench Verified episodes from three memory systems, resolve rates stay within 0.4 percentage points of a no-memory baseline, yet MemUse shows that most delivered entries were irrelevant, most relevant entries went unused, and in two systems most deliveries restated content the agent could still see. It also surfaces effects that outcomes hide: 23.2\% of episodes with beneficial memory use still failed, and 5 of 16 with harmful use still succeeded.
Create a lesson
Related papers
Route-Guided CBF-QP Repair for Safe Vision-Language-Action Models
Chan Park, Sunwoo Hong, Kirak Kim et al.
Just-in-Time Skill Retrieval via Long-Horizon State Tracking
Xin Lin, Ziheng Wang, Zerui Chen et al.
MemHarmBench: Evaluating Cross-Session Prompt Injection in Memory-Persistent AI Agents
Zhihua Wang, Yunhao Feng, Yifeng Gao et al.
SPAR: Experience-Driven Strategy Evolution in Multi-Session B2B Sales
Hangming Zhang, Yao Zhao, Haotian Kuang et al.
Xiaomi Auto World Model 2.0: Causal World Models for Real-World Driving and Embodied Intelligence
Zhenxin Zhu, Zhijian Huang, Cheng Chi et al.
Local or Escalate? Post-Training Expert Routing for Real-Time Duplex Speech Models
Ruoyu He, Jisen Li, Anthony Ji et al.