1 citations · 1 across the 20 of their papers we have counts for
22 papers
: An End-to-End Agent Auditing Engine
Haoning Wang, Mingxun Zhang, Chenyue Yu +5
With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving ha…
SEER: Long-Context Reasoning via Selective Visual-Text Compression
Jiawei Xu, Zhilin Zhai, Jinrui Fang +6
Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a p…
ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models
Xinye Li, Lingshuai Lin, Lei Wang +8
Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step vi…
Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control
Haoze Liu, Run Liu, Haiying Xu +6
Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM per…
SkillEval: Decomposing Agent Skill Quality into Interpretable Signals
Jiahui Han, Qinuo Li, Ziheng Peng +6
Agent skills provide reusable knowledge that helps agents solve specialized tasks. As their use expands, evaluating skill quality becomes increasingly important. Existing evaluatio…
Right Answer, Wrong Tokens: Tail-Aware Credit Calibration for RLVR
Xiuyi Lou, Zicheng Xu, Yu-Neng Chuang +4
Although reinforcement learning (RL) improves the reasoning capabilities of large language models (LLMs), GRPO-style methods can suffer from a positive-credit contamination failure…
DynamicMem: A Long-Horizon Memory Benchmark in Real-World Settings
Wenya Xie, Shengming Zhou, Zelin Li +9
LLM agents increasingly act as personal assistants that must remember a user's profile over months: who they are (attributes), what they routinely do (habits), and what they prefer…
Learning at the Right Pace: Adaptive Data Scheduling Improves LLM Reinforcement Learning
Zicheng Xu, Ruixuan Zhang, Yu-Neng Chuang +7
Large Language Models (LLMs) achieve remarkable reasoning capabilities through reinforcement learning (RL) post-training. However, existing RL post-training commonly relies on unif…
Model Merging to Evolution: Parameter Space Exploration for Expert Models
Chao Wang, Yuchen Guo, Zheng Tan +4
Model merging integrates the capabilities of multiple expert models to create strong models for multiple tasks without additional training, thereby reducing computational resource…
PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
Mingxuan Zhang, Jiahui Han, Dadi Guo +5
LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users. However, agents often acquire more sensitive information than t…
Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
Haiyan Zhao, Zirui He, Guanchu Wang +3
Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own ac…
Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +4
On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify a failure mode of OPD: as t…
Task-free Adaptive Meta Black-box Optimization
Chao Wang, Licheng Jiao, Lingling Li +4
Handcrafted optimizers become prohibitively inefficient for complex black-box optimization (BBO) tasks. MetaBBO addresses this challenge by meta-learning to automatically configure…
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le +9
Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LL…
Think Before You Prune: Self-Reflective Structured Pruning for Reasoning Language Models
Ziyan Wang, Enmao Diao, Qi Le +5
Reasoning LLMs (RLMs) such as OpenAI o1, DeepSeek-R1, and Qwen3 deliver strong multi-step reasoning through chain-of-thought generation, but their large model sizes and lengthy dec…
Catastrophic Forgetting in Kolmogorov-Arnold Networks
Mohammad Marufur Rahman, Guanchu Wang, Kaixiong Zhou +2
Catastrophic forgetting is a longstanding challenge in continual learning, where models lose knowledge from earlier tasks when learning new ones. While various mitigation strategie…
DTS: Enhancing Large Reasoning Models via Decoding Tree Sketching
Zicheng Xu, Xiuyi Lou, Guanchu Wang +6
Large Reasoning Models (LRMs) achieve remarkable inference-time improvements through parallel thinking. However, existing methods rely on redundant sampling of reasoning trajectori…
Self-ensemble: Mitigating Confidence Mis-calibration for Large Language Models
Zicheng Xu, Guanchu Wang, Guangyao Zheng +4
Although Large Language Models (LLMs) perform well in general fields, they exhibit a confidence distortion problem on multi-choice question-answering (MCQA), particularly as the nu…
AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models
Feng Luo, Yu-Neng Chuang, Guanchu Wang +8
Reasoning-capable large language models (LLMs) achieve strong performance on complex tasks but often exhibit overthinking after distillation, generating unnecessarily long chain-of…
Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization
Yu-Neng Chuang, Leisheng Yu, Guanchu Wang +6
Large language models (LLMs) are increasingly deployed and democratized on edge devices. To improve the efficiency of on-device deployment, small language models (SLMs) are often a…
Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
Haochen Zhang, Junze Yin, Guanchu Wang +5
Low-rank optimization has emerged as a promising approach to enabling memory-efficient training of large language models (LLMs). Existing low-rank optimization methods typically pr…
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
Boning Zhang, Dongzhu Liu, Osvaldo Simeone +3
To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantificatio…