Author

Zhichen Zeng

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Preprint Jul 2026

SETA: Scaling Environments for Terminal Agents

This work constructs and releases SETA-Env, the largest open-source verifiable terminal RL dataset to date, containing over 4,500 environments, and demonstrates that SETA- Env provides high-quality training environments for terminal agents and serves as a valuable resource for advancing research on terminal-based agent learning.

Q. Shen, Zhiqi Huang, V. Kamanuru et al. · 8 citations · ⚡2

#artificial intelligence Preprint Aug 2026

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

This work proposes a principled VLM TTA method called \algname, and theoretically reveals that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits.

Qi Yu, Zhichen Zeng, Katherine Tieu et al. · 0 citations