6 accepted papers
"Thinking with images" has emerged as a central research theme in the realm of Vision-Language Models (VLMs). This multimodal reasoning paradigm typically features interleaved images generated via tool use or code execution as part of the Chain-of-Thought (CoT). While reinforcement learning (RL) has…
Large Language Models (LLMs) can be seen as compressed knowledge bases, but it remains unclear what knowledge they truly contain and how far their knowledge boundaries extend. Existing benchmarks are mostly static and provide limited support for systematic knowledge probing. In this paper, we propos…
Test-time reasoning algorithms such as chain-of-thought, self-consistency, and MCTS enhance LLM problem-solving but can wastefully generate many tokens without improving accuracy. At the same time, we observe that these algorithms exhibit answer stabilization: their intermediate solutions often ceas…
The Fiber Bragg grating (FBG) three-axial force sensor provides force feedback for an endoscopic surgical robot, reducing operational difficulty and risks. However, the packaging method of the optical fiber sensor demonstrates limited adaptability to both high-temperature sterilization environments
Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has s…
In Large Language Model (LLM) inference, the output length of an LLM request is typically regarded as not known a priori. Consequently, most LLM serving systems employ a simple First-come-first-serve (FCFS) scheduling strategy, leading to Head-Of-Line (HOL) blocking and reduced throughput and servic…