MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents
The effectiveness of agent memory ultimately depends on whether the underlying LLM gives each memory in context an appropriate degree of influence over its response. Yet this capability has remained largely overlooked. To assess this capability, we introduce MemCalib, a benchmark grounded in realistic memory-system scenarios for evaluating memory use and advancing optimization algorithms. Results on the MemCalib test set reveal that frontier open- and closed-source models struggle to use memory appropriately. They frequently over-use or under-use memory rather than matching each proposition's actual use to its target level, leading to biased, low-quality responses. Experiments with common...
| Authors | Ruike Cao, Fanyu Zhao, Fugen Yao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 September 2026 |
| Last updated at source | 8 October 2026 |
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