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Dylan Zhang

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#artificial intelligence Preprint Oct 2026

MiniCorp: The Last Mile of the AI Agent Firm

The last mile toward enterprise AGI is a company that runs itself. Training and adapting such agents require longitudinal enterprise data, which remain scarce, costly to acquire, and often restricted by privacy constraints. Historical archives are also frequently incomplete and record only what actually happened. They...

Jing-Ying Zeng, Zhen-Wei Dai, Jin-Ning Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Selecting Diverse SFT Traces Improves Post-RL Generalization

Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variation in the sequences of reasoning steps in supervised fine-tuning (SFT) data, and propose a lightweight, rule-based fingerprint to select for it. From...

Dylan Zhang, Ming-Yuan Wu, Jin-Ning Li · 0 citations

Useful Memories Become Faulty When Continuously Updated by LLMs

This work traces the regression to the consolidation step rather than the underlying experience: the same trajectories yield qualitatively different memories under different update schedules, and an episodic-only control that simply retains those trajectories remains competitive with the consolidators the authors test.

Dylan Zhang, Yan-Shan Lin, Zheng Wu et al. · 10 citations

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