Author

Ling-Xiang Fan

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Jul 2026

Self-Evolving Learning for Embodied AI with Criticality Model

This work proposes a self-evolving method that reduces failure rates by 51--67% relative to trained baselines and by 8-25% relative to state-of-the-art vision-language-action models after replacing redundant nominal scenarios with diverse failure-prone ones.

Linxuan He, Yuying Tian, Ling-Xiang Fan et al. · arXiv.org · 0 citations

Preprint Jul 2026

DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models

This work proposes DC-WAM, a dynamic-centric WAM framework that redistributes supervision and computation in the RGB video branch that consistently improves policy performance, especially under out-of-distribution perturbations in lighting, object appearance, and background texture.

Haoyuan Ji, Lingxiang Fan, Shang Su et al. · 0 citations