3 citations · 4 across the 13 of their papers we have counts for
15 papers
TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
Yuhang Zhou, Kai Zheng, Haoling Li +3
On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. H…
The Scaling Laws of Skills in LLM Agent Systems
Charles Chen, Qiming Yu, Yuhang Gu +12
As agent systems scale, skills accumulate into large reusable libraries, yet their scaling laws remain poorly understood. Across 15 frontier LLMs, 1,141 real-world skills, and over…
Do Coding Agents Understand Least-Privilege Authorization?
Zheng Yan, Jingxiang Weng, Charles Chen +9
As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an agent should receive enough autho…
Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems
Dengyun Peng, Qiguang Chen, Bofei Liu +6
Ensuring large language model (LLM) reliability requires distinguishing objective unsolvability (inherent contradictions) from subjective capability limitations (tasks exceeding mo…
Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks
Jiannan Guan, Qiguang Chen, Libo Qin +5
Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks that require generating comprehensive and d…
The Universal Landscape of Human Reasoning
Qiguang Chen, Jinhao Liu, Libo Qin +14
Understanding how information is dynamically accumulated and transformed in human reasoning has long challenged cognitive psychology, philosophy, and artificial intelligence. Exist…
AutoPR: Let's Automate Your Academic Promotion!
Qiguang Chen, Zheng Yan, Mingda Yang +10
As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to en…
Beyond Surface Reasoning: Unveiling the True Long Chain-of-Thought Capacity of Diffusion Large Language Models
Qiguang Chen, Hanjing Li, Libo Qin +7
Recently, Diffusion Large Language Models (DLLMs) have offered high throughput and effective sequential reasoning, making them a competitive alternative to autoregressive LLMs (ALL…
Boundary-to-Region Supervision for Offline Safe Reinforcement Learning
Huikang Su, Dengyun Peng, Zifeng Zhuang +4
Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action g…
Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models
Qiguang Chen, Dengyun Peng, Jinhao Liu +4
Recent advancements in large language models (LLMs) have greatly improved their capabilities on complex reasoning tasks through Long Chain-of-Thought (CoT). However, this approach…
AI4Research: A Survey of Artificial Intelligence for Scientific Research
Qiguang Chen, Mingda Yang, Libo Qin +13
Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in co…
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective
Dengyun Peng, Yuhang Zhou, Qiguang Chen +3
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requ…
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
Qiguang Chen, Libo Qin, Jinhao Liu +7
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like math…
Electronic Circuit Principles of Large Language Models
Qiguang Chen, Libo Qin, Jinhao Liu +6
Large language models (LLMs) such as DeepSeek-R1 have achieved remarkable performance across diverse reasoning tasks. To uncover the principles that govern their behaviour, we intr…
Reinformer: Max-Return Sequence Modeling for Offline RL
Zifeng Zhuang, Dengyun Peng, Jinxin Liu +2
As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as sequence modeling that conditions on the hindsight information including returns, goal or futu…