1 citations · 2 across the 10 of their papers we have counts for
11 papers
When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval
Tongyao Zhu, Chao-Ming Huang, Min-Yen Kan
While mixed-language querying is ubiquitous in multilingual communities, the sensitivity of dense retrievers to such queries remains poorly understood. We present a ratio-controlle…
SkillCraft: Can LLM Agents Learn to Use Tools Skillfully?
Shiqi Chen, Jingze Gai, Ruochen Zhou +13
Real-world tool-using agents operate over long-horizon workflows with recurring structure and diverse demands, where effective behavior requires not only invoking atomic tools but…
Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective
Shiqi Chen, Tongyao Zhu, Zian Wang +8
Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call exploration collapse: under reinforcement lear…
From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
Haonan Wang, Weida Liang, Zihang Fu +8
Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this…
Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging
Shiqi Chen, Jinghan Zhang, Tongyao Zhu +5
Vision-Language Models (VLMs) combine visual perception with the general capabilities, such as reasoning, of Large Language Models (LLMs). However, the mechanisms by which these tw…
SkyLadder: Better and Faster Pretraining via Context Window Scheduling
Tongyao Zhu, Qian Liu, Haonan Wang +4
Recent advancements in LLM pretraining have featured ever-expanding context windows to process longer sequences. However, our pilot study reveals that models pretrained with shorte…