AI RESEARCH
Combee: Scaling Prompt Learning for Self-Improving Language Model Agents
arXiv CS.AI
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ArXi:2604.04247v1 Announce Type: new Recent advances in prompt learning allow large language model agents to acquire task-relevant knowledge from inference-time context without parameter changes. For example, existing methods (like ACE or GEPA) can learn system prompts to improve accuracy based on previous agent runs. However, these methods primarily focus on single-agent or low-parallelism settings. This fundamentally limits their ability to efficiently learn from a large set of collected agentic traces.