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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.