Author

Minxi Yan

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Preprint Jul 2026

MetaSkill-Evolve: Recursive Self-Improvement of LLM Agents via Two-Timescale Meta-Skill Evolution

MetaSkill-Evolve is introduced, a two-timescale framework that makes agentic skill improvement recursive and outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks, improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.

Zefeng Wang, Minxi Yan, Jinhe Bi et al. · 4 citations

Gradient Enhancement Task Aware Post-training Quantization

This paper introduces Gradient Enhancement Task Aware Post-training Quantization, i.e., GTAQ, to address the generalization issue of Large Language Models, and extensively evaluates the LLaMA family of language models on WikiText, C4, and MMLU.

Yihua Shao, Yangyang Gu, Minxi Yan et al. · 0 citations

Cross Domain Test Time Scaling: Scale Knowledge and Reasoning on Cross Domains

Cross-Domain TTS is proposed, a novel framework that enables task-tailored scaling in broader domains and achieves an improvement of up to 17% in pass@1 accuracy while reducing inference latency and saving up to 30% in token consumption.

Minxi Yan, Yihua Shao, Yanling Pan et al. · 0 citations