Author

Dejing Dou

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Preprint Oct 2026

ADAPT for Science: A Multi-Agent System for Self-Evolving Scientific Optimization

Scientific discovery often requires identifying a small number of high-performing designs in vast, constrained search spaces. This is difficult when evaluations are costly, feasible solutions are sparse, parameters are strongly coupled, and physical constraints are strict. Recent AI for AI methods can automatically imp...

Zhao-Ran Feng, Yu-Cheng Ni, Jia-Nan Liu et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning

By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework for LLM unlearning, and matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility.

Wei Zhai, Xiang Liu, Qiang Huang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks

Proposed Potential-Guided Policy Optimization (PGPO) provides finer-grained step-level credit assignment, especially within failed trajectories, and shows strong overall performance relative to recent group-based RL methods.

Yu-Yao Zheng, Haipeng Sun, Jun-Wei Bao et al. · 1 citation

Preprint Aug 2026

SLIM-0.5B: Learning Action-Grounded Predictive Latents for Robot Manipulation

SLIM (Self-supervised Latent Interaction Model), a compact 0.5B-parameter latent interaction policy, which matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters, no additional embodied pretraining, lower inference latency, and substantially lower GPU memory usage.

Jing-Kai Wang, Zihan Tang, Gu Zhang et al. · 0 citations