4 citations · 4 across the 9 of their papers we have counts for
10 papers
HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models
Feng He, Zhenting Wang, Qifan Wang +4
Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses o…
Teacher-Guided Policy Optimization for On-Policy Reasoning Distillation under Large Policy Divergence
Xinyu Liu, Kechen Jiao, Chunyang Xiao +10
On-policy distillation (OPD) has become a promising paradigm for reasoning-oriented post-training of large language models (LLMs), especially when combined with reinforcement learn…
A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models
Zeru Shi, Zhenting Wang, Fan Yang +2
We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the \textbf{Massive Emergence Layer (ME Layer)}, that is consi…
Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
Mingxi Zou, Zhihan Guo, Langzhang Liang +6
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, sali…
Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts
Boxuan Zhang, Jianing Zhu, Qifan Wang +2
Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet existing detectors based on pre-tra…
Meaningless Tokens, Meaningful Gains: How Activation Shifts Enhance LLM Reasoning
Zeru Shi, Yingjia Wan, Zhenting Wang +4
Motivated by the puzzling observation that inserting long sequences of meaningless tokens before the query prompt can consistently enhance LLM reasoning performance, this work anal…
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Xi Zhu, Haochen Xue, Ziwei Zhao +7
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain…
When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations
Huaizhi Ge, Yiming Li, Qifan Wang +2
Large Language Models (LLMs) are known to be vulnerable to backdoor attacks, where triggers embedded in poisoned samples can maliciously alter LLMs' behaviors. In this paper, we mo…
Visual Agents as Fast and Slow Thinkers
Guangyan Sun, Mingyu Jin, Zhenting Wang +7
Achieving human-level intelligence requires refining cognitive distinctions between System 1 and System 2 thinking. While contemporary AI, driven by large language models, demonstr…
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
Qingcheng Zeng, Mingyu Jin, Qinkai Yu +12
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability…