Speculate with Memory: Lossless Acceleration for LLM Agents
Speculative execution accelerates LLM agents by using a smaller, cheaper model to predict and pre-launch the next step while the environment is idle. However, existing speculators are stateless and discard all information between tasks, preventing prediction quality from improving with experience. We equip the speculator with three online memory systems that learn from past agent trajectories: a contrastive transition table tracking action-sequence statistics, an episodic memory retrieving contextually similar segments, and a confusion tracker suppressing recurring errors. We evaluate this approach on six benchmarks spanning three speculation types: action prediction, observation predicti...
| Authors | Yu Li, Qinyuan Ye, Prafulla Kumar Choubey et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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