2 citations · 4 across the 7 of their papers we have counts for
8 papers
KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents
Siqi Chen, Zhengxi Lu, Yuchen Yan +10
Deep search agents solve knowledge-intensive tasks through long-horizon, multi-turn interactions, where assigning credit to individual search decisions is fundamentally difficult.…
LAPO: Internalizing Reasoning Efficiency via Length-Adaptive Policy Optimization
Xingyu Wu, Yuchen Yan, Shangke Lyu +7
Large reasoning models have achieved remarkable performance through extended chain-of-thought sequences, yet this computational freedom leads to excessive token generation even for…
Cooper: Co-Optimizing Policy and Reward Models in Reinforcement Learning for Large Language Models
Haitao Hong, Yuchen Yan, Xingyu Wu +5
Large language models (LLMs) have demonstrated remarkable performance in reasoning tasks, where reinforcement learning (RL) serves as a key algorithm for enhancing their reasoning…
Hierarchical Budget Policy Optimization for Adaptive Reasoning
Shangke Lyu, Linjuan Wu, Yuchen Yan +7
Large reasoning models achieve remarkable performance through extensive chain-of-thought generation, yet they suffer from a critical inefficiency: applying uniformly extensive reas…
When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
Peizhang Shao, Linrui Xu, Jinxi Wang +2
This paper establishes the first comprehensive review of Large Language Models (LLMs) applied within the legal domain. It pioneers an innovative dual lens taxonomy that integrates…
SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation
Siqi Chen, Xinyu Dong, Haolei Xu +10
Large Language Models (LLMs) and Multimodal LLMs have shown promising capabilities for SVG processing, yet existing benchmarks suffer from limited real-world coverage, lack of comp…
A Survey on (M)LLM-Based GUI Agents
Fei Tang, Haolei Xu, Hang Zhang +12
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-drive…
Hybrid Local Causal Discovery
Zhaolong Ling, Honghui Peng, Yiwen Zhang +4
Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods…