activity
20192026
most citedPAC Reinforcement Learning without Real-World Feedback

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Toward Cognitive Supersensing in Multimodal Large Language Model

Boyi Li, Yifan Shen, Yuanzhe Liu +12

Multimodal Large Language Models (MLLMs) have achieved remarkable success in open-vocabulary perceptual tasks, yet their ability to solve complex cognitive problems remains limited…

cs.AI2025

Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation

Yu Zhong, Zihao Zhang, Rui Zhang +9

Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions. Recent research underscores the potential of har…

cs.CV2025

MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns

Jiarui Zhang, Yuliang Liu, Zijun Wu +17

Document parsing is a core task in document intelligence, supporting applications such as information extraction, retrieval-augmented generation, and automated document analysis. H…

cs.CV2024

World-Consistent Data Generation for Vision-and-Language Navigation

Yu Zhong, Rui Zhang, Zihao Zhang +9

Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main…

cs.LG2019★ 2 cited

PAC Reinforcement Learning without Real-World Feedback

Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott

This work studies reinforcement learning in the Sim-to-Real setting, in which an agent is first trained on a number of simulators before being deployed in the real world, with the…