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论文提出 TGR-EvoDAG，一个基于有向无环图（DAG）的多智能体代码进化框架，用于生成式推荐流水线的自动演化，离线指标最高提升 37.4%，并在腾讯工业部署中带来 0.40% 的在线 CTR 提升。
PACE-Bench introduces a simulator-grounded benchmark for evaluating self-evolving agents on physics adaptation tasks involving iterative code redesign after environmental mutations, revealing that mechanism redesign is a major bottleneck compared to parameter inference.
CurateEvo is a failure-driven dynamic evolution framework for agentic post-training data curation. It iteratively rewrites curation strategies using failed trajectories, improving effectiveness and efficiency on benchmarks like ACEBench-Agent and BFCL-V4.
NVIDIA proposes HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution, achieving 100% benchmark completion across several hardware design suites.
This paper proposes three co-evolutionary mechanisms (evaluator co-evolution, hierarchical deep evaluation, and weakness pressure) for LLM-driven code evolution in adversarial multi-agent games, achieving state-of-the-art results on the MCTF 2026 maritime capture-the-flag task.