Here is a very common problem when building complex agents. Long-horizon agents (in particular) fail in two ways: the decision-maker can't d…

Here is a very common problem when building complex agents. Long-horizon agents (in particular) fail in two ways: the decision-maker can't decompose well, or the skill library goes stale. This new research tackles both at once. The paper introduces a co-evolution framework where an LLM decision agent and a dynamic skill bank improve each other through iterative refinement. The decision agent picks and chains skills. Performance feedback updates both the policy and the skills. New skills emerge by generalizing successful sequences instead of being hand-coded upfront. Why does it matter? Most long-horizon agent stacks treat skills and decision-making as separate optimization problems, which is why they plateau. Co-evolution gives you adaptive planning and a growing library of reusable behaviors from a single loop, which is what you actually want when task structure isn't predetermined: robotics, game agents, complex planning. Paper: https://arxiv.org/abs/2604.20987 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-04-26

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