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

Conference Open access Sep 2026

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.

Yi-Hua Shao, Yang-Yang Gu, Min-Xi Yan et al. · Proceedings of the Thirty-Fi... · 1 citation

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