8 papers
A Hyperbolicity Atlas of Large Language Model Hidden States
Zhichao Yang, Yuanze Hu, Gen Li +7
LLM hidden states are ordinary vectors, but the distances among those vectors may still show hierarchical structure. To our knowledge, this paper is the first systematic study of w…
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
Gen Li, Yuanze Hu, Zhichao Yang +10
Clinical diagnosis requires flexible use of multiple reasoning paradigms under incomplete patient information. Existing LLM-based medical agents show strong medical reasoning abili…
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
Zhichao Yang, Yuanze Hu, Haojie Hao +7
Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, mul…
State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
Yuanze Hu, Gen Li, Yuqin Lan +5
Multimodal large language models (MLLMs) have achieved impressive progress on general multimodal tasks, yet they remain brittle on dial-based measurement reading. In this paper, we…
Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization
Yuqin Lan, Gen Li, Yuanze Hu +6
Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visual prompt attacks or gradient-…
DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing
Hongzhi Zhang, Yuanze Hu, Tinghai Zhang +9
The evolution of Large Language Models (LLMs) towards autonomous agents has catalyzed progress in Deep Research. While retrieval capabilities are well-benchmarked, the post-retriev…
Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Zhichao Yang, Zhaoxin Fan, Gen Li +6
Structured, procedural reasoning is essential for Large Language Models (LLMs), especially in mathematics. While post-training methods have improved LLM performance, they still fal…
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
Yuanze Hu, Zhaoxin Fan, Xinyu Wang +8
Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. The prevailing approach to aligning vision and language models involves freezing…