1 citations · 3 across the 16 of their papers we have counts for
8 papers · 1 filter
Making LLMs Say What They Think: Measuring and Improving CoT-Interpretability Alignment
Yihuai Hong, Shauli Ravfogel, Chen Zhao +1
Chain-of-thought (CoT) traces often serve as a proxy for how Large Language Models (LLMs) arrive at their answers. However, growing evidence shows that models' CoT often fails to r…
Memory Is a Derivation: The Distributed-Evidence Paradox in Long-Term Agents
Hongjun Liu, Chen Zhao
Long-running LLM agents compress past interactions into persistent memories that may be reused as premises for later tasks. This creates a distinct derivation problem: whether the…
Small Language Models as Judges for Rubric-Based Reinforcement Learning
Fengyu Xie, Yilun Zhao, Bingsen Chen +2
Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scoring responses against instance-specific criteria. However, this makes…
MMGist: A Comprehensive Multimodal Benchmark for 2027
Wenzhen Yuan, Jiacheng Ruan, Wutao Xiong +3
We conduct a systematic study of 18 widely used vision-language benchmarks and identify three major issues: 1) many items do not rely on visual cues and therefore fail to effective…
GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
Xiao Zhou, Siyue Zhang, Yilun Zhao +4
Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with d…
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning
Tianyu Yang, Sihong Wu, Yilun Zhao +6
Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities.…
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
Kaiser Sun, Xiaochuang Yuan, Hongjun Liu +4
Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematica…
SAGE: Benchmarking and Improving Retrieval for Deep Research Agents
Tiansheng Hu, Yilun Zhao, Canyu Zhang +2
Deep research agents have emerged as powerful systems for addressing complex queries. Meanwhile, LLM-based retrievers have demonstrated strong capability in following instructions…