4 citations · 4 across the 7 of their papers we have counts for
13 papers
Teacher-Guided Tool Learning for Small Language Models via Bidirectional KL Constraints
Haoxuan Gao, Qin-Wen Luo, Xiang Chen +5
External tools can extend the capabilities of small language models (SLMs), enabling their deployment in resource constrained environments. However, their limited capabilities make…
Query-Adaptive Neuron Steering for LLMs
Yanxi Pan, Jingwen Wu, Yan Ma +8
Effectively eliciting the capabilities of large language models (LLMs) at inference time remains an important challenge. Existing studies address this challenge through context eng…
Mind the Execution Gap: Action-Semantic Mismatch in World-Model Control
Shengtao Wen, Xiang Chen, Yu Tian +3
World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronously due to communication delay,…
Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting
Chang Liu, Yu Tian, Rui Xie
Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge caused by visual uncertainty…
PrivMeSA: Privacy-Aware Self-Evolving Multi-Agent System for Medicine via Local-Remote LLM Collaboration
Dannong Wang, Yuran Zhang, Bian Sun +4
Clinical large language model (LLM) agents deployed locally can consult more capable remote models, but doing so risks exposing patient information. Privacy-conscious delegation pl…
Decoupling Knowledge and Privacy: Post-Task Self-Distillation Replay for LLM Continual Learning
Shengtao Wen, Yunying Yang, Xiang Chen +3
Privacy-preserving continual learning (PPCL) must reduce the reproduction of sensitive content while retaining useful knowledge across sequential tasks. Formal privacy guarantees c…
When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
Feiyang Ren, Shengtao Wen, Lingbing Guo +6
Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput. Output length prediction enable…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models
Lifan Zheng, Xue Yang, Jiawei Chen +6
With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel paradigm in Personality Edit…
Skills-Coach: A Self-Evolving Skill Optimizer via Training-Free GRPO
Yu Tian, Jiawei Chen, Lifan Zheng +5
We introduce Skills-Coach, a novel automated framework designed to significantly enhance the self-evolution of skills within Large Language Model (LLM)-based agents. Addressing the…
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction
Shuoxin Wang, Chang Liu, Gowen Loo +5
Large Language Model (LLM)-based mobile agents have made significant performance advancements. However, these agents often follow explicit user instructions while overlooking perso…
Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level
Xinyi Zeng, Yuying Shang, Jiawei Chen +2
Large language models (LLMs) have demonstrated immense utility across various industries. However, as LLMs advance, the risk of harmful outputs increases due to incorrect or malici…
From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
Yuying Shang, Xinyi Zeng, Yutao Zhu +6
Hallucinations in large vision-language models (LVLMs) are a significant challenge, i.e., generating objects that are not presented in the visual input, which impairs their reliabi…