Topic
evolution
SkillsVote Framework Governs Agent Skills Lifecycle from Collection to Evolution
SkillsVote is a lifecycle-governance framework for Agent Skills that addresses redundancy, quality, and environment sensitivity in open skill ecosystems. It profiles a million-scale open source corpus, decomposes trajectories into skill-linked subtasks, and admits only successful reusable discoveries to evidence-gated updates. Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show performance improvements on challenging agentic coding benchmarks.
Hybrid Open-Ended Tri-Evolution Framework Boosts Deep Research AI Performance
Researchers propose the Hybrid Open-Ended Tri-Evolution (HOTE) framework that uses hybrid-mode reinforcement learning to collaboratively evolve a proposer, solver, and judge for deep research tasks. An 8B model trained with HOTE surpasses static open 8-32B models and state-of-the-art deep research training methods while requiring less time overhead.
MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design
A new AI framework called MeEvo cyclically couples natural evolution and metacognitive evolution for automatic heuristic design. It addresses limitations of existing LLM-based approaches by combining population exploration with reflective refinement. Experiments show stronger and more stable performance on complex optimization tasks.