TriggerBench: Investigating Prospective Memory for Large Language Models
While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prospective memory (PM), the critical ability to spontaneously recall and act on latent constraints without direct prompts, remains largely unevaluated. We introduce TriggerBench, a comprehensive PM benchmark spanning five dimensions across both daily assistants and professional workflows. TriggerBench pairs scenarios with matched RM controls, contrastive positive/negative variants, and overloaded triggers, enabling fine-grained measurement of proactive recall, false-alarm rate, and attentional robustness under a sin...
| Authors | Tianhua Zhang, Xinjiang Wang, Qianxi Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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