Agents8h ago
Cosplay Framework Links LLM Decisions to Skill Banks
The COSPLAY framework enables LLM agents to retrieve and reuse structured skills from a learnable bank during long-horizon tasks. This co-evolution approach solves the problem of inconsistent decision-making in complex game environments. By chaining skills across episodes, agents better handle delayed rewards. Practitioners can now implement more robust memory mechanisms for autonomous LLM agents.