Tag
MOAE proposes a Pareto-preserving evolutionary search method to simultaneously optimize multiple objectives like task performance, trajectory quality, and safety for LLM agents without fixed scalarization during search.
This survey paper connects self-evolving LLM-based agents with dynamic graph transformation, proposing a framework to model agent state as dynamic graphs and organizing existing methods for their evolution.
This paper introduces Latent On-Policy Self-Distillation (LOPD), a method that makes the teacher's privileged context learnable end-to-end from experience, providing dense token-level supervision to enhance agent performance and efficiency in agentic tool use and code generation.
This paper proposes the Hybrid Open-Ended Tri-Evolution (HOTE) framework, which uses hybrid-mode reinforcement learning to evolve a proposer, solver, and judge collaboratively for deep research tasks, achieving state-of-the-art results with an 8B model surpassing larger static models.
MetaEvo proposes a two-stage framework for continual evolution of LLM-based agents, using preference-based optimization to enhance principle abstraction and modular architecture for experience reuse, outperforming strong baselines on reasoning benchmarks.
EvoMap introduces GEP (Genome Evolution Protocol), a network protocol enabling agents to convert successful strategies into genes and capsules for self-evolution, reducing repeated exploration.
awesome-autoresearch list updated, adding 6 application cases based on Karpathy's autoresearch pattern, covering scenarios such as customer service agent self-evolution, Shell integration, code configuration self-optimization, RAG tuning, and ASO.