How LLM Agents Combine Decision-Making and Skill Use in Long-Horizon Tasks
How LLM Agents Combine Decision-Making and Skill Use in Long-Horizon Tasks The paper "Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks" was introduced on arXiv as a new cs.AI submission. It focuses on long-horizon interactive environments, where agents must handle multi-step reasoning, chain multiple skills across many timesteps, and act under delayed rewards and partial observability. In that setting, the paper positions games as a useful testbed for evaluating how well LLM-based agents can use skills over extended interactions. [S1] [S1] Paper overview: what it studies This work studies a practical weakness of LLM agents in long interactive tasks. According to the paper abstract, long-horizon environments require more than single-turn reasoning: agents need to connect several skills over time and keep making robust decisions even when rewards arrive late and the full state is not visible. The paper frames this as a combined decision-making and skill-...