When Users Don't Ask: Benchmarking Context-Driven Memory Retrieval in Conversational Agents
Large language models (LLMs) are increas- ingly deployed as long-horizon conversational agents, motivating growing interest in mem- ory systems. However, existing benchmarks primarily evaluate memory through QA-style probing rather than in-situ conversational usage. We introduce LOCOMO-CONV, a conversa- tional memory benchmark derived from Lo- CoMo with four query styles: dialog, implicit, counterfactual, and composed. Across five rep- resentative memory systems, we evaluate both retrieval recall and end-to-end response qual- ity. Our experiments show that conversational framing exposes substantial retrieval gaps over- looked by QA benchmarks, especially on im- plicit and composed queries...
| Authors | Wen-Yu Chang, Yun-Nung Chen |
|---|---|
| Primary category | cs.CL |
| Published | 3 September 2026 |
| Last updated at source | 22 September 2026 |
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