4 citations · 6 across the 9 of their papers we have counts for
11 papers
From Inertia to Objectivity: Improving Deep Research Agents with Noise Isolation
Xiangxin Zhang, Zhanwei Zhang, Zhihang Fu +2
Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent has produced a query, plan, or…
InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
Ke Li, Dong An, Xiaoling Zang +6
Low-bit activation quantization remains a major bottleneck in efficient large language model (LLM) deployment. The difficulty is not only that activations contain outliers, but tha…
Constrained Code Generation with Discrete Diffusion
Lize Shao, Michael Cardei, Zichen Xie +2
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and ena…
Controlling Thinking Speed in Reasoning Models
Zhengkai Lin, Zhihang Fu, Ze Chen +6
Human cognition is theorized to operate in two modes: fast, intuitive System 1 thinking and slow, deliberate System 2 thinking. While current Large Reasoning Models (LRMs) excel at…
GeoCAD: Local Geometry-Controllable CAD Generation with Large Language Models
Zhanwei Zhang, Kaiyuan Liu, Junjie Liu +5
Local geometry-controllable computer-aided design (CAD) generation aims to modify local parts of CAD models automatically, enhancing design efficiency. It also ensures that the sha…
Improving Complex Reasoning with Dynamic Prompt Corruption: A soft prompt Optimization Approach
Sinan Fan, Liang Xie, Chen Shen +7
Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our inv…
Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models
Shaotian Yan, Chen Shen, Wenxiao Wang +3
Few-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reason…
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
Zhengkai Lin, Zhihang Fu, Kai Liu +6
While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Wei Chen, Zhen Huang, Liang Xie +9
Large Language Models (LLMs) tend to prioritize adherence to user prompts over providing veracious responses, leading to the sycophancy issue. When challenged by users, LLMs tend t…
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks
Xiaofeng Zhang, Yihao Quan, Chen Shen +7
Large Vision Language Models (LVLMs) achieve great performance on visual-language reasoning tasks, however, the black-box nature of LVLMs hinders in-depth research on the reasoning…
Concise and Organized Perception Facilitates Reasoning in Large Language Models
Junjie Liu, Shaotian Yan, Chen Shen +4
Exploiting large language models (LLMs) to tackle reasoning has garnered growing attention. It still remains highly challenging to achieve satisfactory results in complex logical p…