16 citations · 19 across the 13 of their papers we have counts for
8 papers · 1 filter
HERA: Harness-Environment Co-Evolution for Reliable Agentic Abstention
Han Luo, Bingbing Wen, Guang Yang +3
Large language model (LLM) agents are increasingly capable of acting in complex tool-use environments, yet they often fail to recognize when tasks are infeasible and no valid solut…
You're Hired: Strategic Model Selection for LLM Collaboration
Zongwan Cao, Ziyuan Yang, Shangbin Feng +5
While multi-agent and model collaboration algorithms gain traction to combine the strengths of diverse Large Language Models (LLMs), existing systems remain bottlenecked on pre-def…
Spurious Tool Use: When RL Agents Learn the Wrong Reason to Act
Yiwei Yang, Haoxiang Zhang, Bingbing Wen +6
Large language model (LLM) agents increasingly interleave natural language reasoning with external tools such as web search and code execution. These tool-use policies are often op…
Agentic Abstention: Do Agents Know When to Stop Instead of Act?
Han Luo, Bingbing Wen, Lucy Lu Wang
LLM agents are expected to act over multiple turns, using search, browsing interfaces, and terminal tools to complete user goals. Yet not every goal is well specified or achievable…
Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation
Katelyn Xiaoying Mei, Yi-Li Hsu, Minjoon Choi +5
Human evaluation plays a critical role in assessing the quality of generated text. However, the reliability and reproducibility of these evaluations depend on transparent and well-…
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
Chenjun Xu, Zhennan Zhou, Zhan Su +3
Long chain-of-thought (Long CoT) reasoning improves performance on multi-step problems, but it also induces overthinking. This inefficiency is especially problematic in low-data fi…
MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining
Bingbing Wen, Sirajul Salekin, Feiyang Kang +4
Domain reweighting can improve sample efficiency and downstream generalization, but data-mixture optimization for multimodal midtraining remains largely unexplored. Current multimo…
Clarify or Answer: Reinforcement Learning for Agentic VQA with Context Under-specification
Zongwan Cao, Bingbing Wen, Lucy Lu Wang
Real-world visual question answering (VQA) may be context-dependent: an image-question pair could be under-specified, such that the correct answer depends on external information t…