Making Prospective Memory SLM-Shaped: Typed Intention Stores for Small-Model Agents
Prospective memory means carrying out a deferred intention at the right future cue while other work continues. Benchmarks now isolate it as an agent skill, yet frontier LLMs still struggle: the best published PM-Bench scaffold reaches only 65.1% Set-F1. We argue that this loop is schema-constrained state tracking rather than open-ended reasoning, and that small models can execute it when the action space is typed. We propose the Prospective Intention Store (PIS) that puts lifecycle logic in code and scoped language work on the model. The scaffold is agentic and training-free: no selector fine-tuning and no trajectory distillation. On PM-Bench, DeepSeek-Chat with PIS reaches 82.9% Set-F1. ...
| Authors | Jinqing Zhao, Chengcan Wu |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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