12 citations · 27 across the 19 of their papers we have counts for
26 papers
AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
Jingbo Sun, Wenyue Chong, Songjun Tu +7
Agentic retrieval-augmented generation (RAG) systems enable large language models (LLMs) to solve complex tasks through multi-step interaction with external retrieval tools. Howeve…
Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning
Jingbo Sun, Qichao Zhang, Songjun Tu +5
Zero-shot unsupervised reinforcement learning (URL) offers a promising direction for building generalist agents capable of generalizing to unseen tasks without additional supervisi…
Learning from Mistakes: Post-Training for Driving VLA with Takeover Data
Yinfeng Gao, Deqing Liu, Qichao Zhang +7
Current Vision-Language-Action (VLA) paradigms in end-to-end autonomous driving rely on offline training from static datasets, leaving them vulnerable to distribution shift. Recent…
PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
Yinfeng Gao, Qichao Zhang, Deqing Liu +8
End-to-end autonomous driving policies based on Imitation Learning (IL) often struggle in closed-loop execution due to the misalignment between inadequate open-loop training object…
PaperAudit-Bench: Benchmarking Error Detection in Research Papers for Critical Automated Peer Review
Songjun Tu, Yiwen Ma, Jiahao Lin +6
Large language models can generate fluent peer reviews, yet their assessments often lack sufficient critical rigor when substantive issues are subtle and distributed across a paper…
Spec-o3: A Tool-Augmented Vision-Language Agent for Rare Celestial Object Candidate Vetting via Automated Spectral Inspection
Minghui Jia, Qichao Zhang, Ali Luo +5
Due to the limited generalization and interpretability of deep learning classifiers, The final vetting of rare celestial object candidates still relies on expert visual inspection-…