DeepSeek团队提出PIS方法，让小模型在前瞻记忆任务上超越大模型，无需训练即可提升性能。
研究人员提出前瞻意图存储(PIS)方法，使小型模型能够执行前瞻记忆任务。在PM-Bench基准测试中，DeepSeek-Chat结合PIS达到82.9% Set-F1分数，显著优于此前65.1%的最佳成绩。Gemma-E2B模型使用PIS后性能提升至66.2%，远超无存储时的4.2%和最多6.6%的回溯记忆方法。
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. On Gemma-E2B, Set-F1 is only 4.2% without a store and at most 6.6% under seven retrospective memories, while PIS reaches 66.2%. PIS further reaches 70.1% Set-F1, where retrospective memory methods stay at most 54.4%. PIS sets a new state of the art on this benchmark and enables small models to surpass the published large-model scaffold.