EvoCog: Composable Cognitive Programs for Adaptive Reasoning in LLM Agents

Large language-model-based agents have demonstrated remarkable capabilities in complex decision-making and tool-use tasks. However, existing adaptive reasoning methods typically adapt computation within a predefined set of reasoning modes, limiting their ability to optimize and evolve the structured cognitive procedures required across diverse interactive environments. In this paper, we introduce EvoCog, a framework that represents an agent’s cognitive space as executable programs built from four typed operations: Ground, Plan, Check and Act. A causal grammar constrains dependencies among these operations, defining a space of structurally valid programs. EvoCog learns and evolves these programs through a three-stage pipeline. First, program-conditioned supervised fine-tuning teaches a shared executor to follow assigned programs while preserving expert actions. Second, Sparse Counterfactual Program Optimization (SCPO) optimizes task execution and a cost-aware router for step-level program selection. Finally, EvoCog supports test-time adaptation by iteratively repairing its cognitive programs from interaction failures, without updating model parameters. Experiments on ALFWorld, ScienceWorld and WebShop demonstrate that EvoCog improves interactive task solving while reducing inference cost. With Qwen2.5-7B, it reaches a 77.47% success rate, outperforming GPT-5(+26.57%) and GRPO(+15.1%), while using 51.4% fewer tokens.