Memory Optimization
Training-free optimization of agent memory for long-horizon tool use.
Training-free optimization of an agent’s memory — deciding what to retrieve, write, and evict — without any gradient updates.
- Treats memory policy as a search / optimization problem over read–write–evict decisions.
- Targets long-horizon, multi-step tool-use agents, where naïve context growth becomes the bottleneck.
- Complements context management to keep the working set small and relevant.
Research line from my AI research internship at TikTok (ByteDance); paper in preparation.