2 citations · 3 across the 15 of their papers we have counts for
15 papers
What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents
Xingshan Zeng, Zishan Xu, Boju Zhang +11
LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Zishan Xu, Zhiyuan Yao, Yuxin Chen +9
Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verificatio…
ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation
Xingshan Zeng, Lingzhi Wang, Weiwen Liu +5
Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for a…
From Verifiable Dot to Reward Chain: Harnessing Verifiable Reference-based Rewards for Reinforcement Learning of Open-ended Generation
Yuxin Jiang, Yufei Wang, Qiyuan Zhang +6
Reinforcement learning with verifiable rewards (RLVR) succeeds in reasoning tasks (e.g., math and code) by checking the final verifiable answer (i.e., a verifiable dot signal). How…
ACE-Router: Generalizing History-Aware Routing from MCP Tools to the Agent Web
Zhiyuan Yao, Zishan Xu, Yifu Guo +6
With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. Howe…
Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
Yihong Tang, Kehai Chen, Liang Yue +11
With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence.…
ToolACE-MT: Non-Autoregressive Generation for Agentic Multi-Turn Interaction
Xingshan Zeng, Weiwen Liu, Lingzhi Wang +6
Agentic task-solving with Large Language Models (LLMs) requires multi-turn, multi-step interactions, often involving complex function calls and dynamic user-agent exchanges. Existi…
Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
Xinda Jia, Jinpeng Li, Zezhong Wang +6
Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reaso…
Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning
Zezhong Wang, Xingshan Zeng, Weiwen Liu +7
Mathematical reasoning through Chain-of-Thought (CoT) has emerged as a powerful capability of Large Language Models (LLMs), which can be further enhanced through Test-Time Scaling…
ToolACE-DEV: Self-Improving Tool Learning via Decomposition and EVolution
Xu Huang, Weiwen Liu, Xingshan Zeng +8
The tool-using capability of large language models (LLMs) enables them to access up-to-date external information and handle complex tasks. Current approaches to enhancing this capa…
Advancing and Benchmarking Personalized Tool Invocation for LLMs
Xu Huang, Yuefeng Huang, Weiwen Liu +5
Tool invocation is a crucial mechanism for extending the capabilities of Large Language Models (LLMs) and has recently garnered significant attention. It enables LLMs to solve comp…
Position: The Real Barrier to LLM Agent Usability is Agentic ROI
Weiwen Liu, Jiarui Qin, Xu Huang +10
Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, plan…
ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool Learning
Xingshan Zeng, Weiwen Liu, Xu Huang +8
Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabiliti…
ACEBench: Who Wins the Match Point in Tool Usage?
Chen Chen, Xinlong Hao, Weiwen Liu +13
Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex…
GUI Agents with Foundation Models: A Comprehensive Survey
Shuai Wang, Weiwen Liu, Jingxuan Chen +12
Recent advances in foundation models, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs), have facilitated the development of intelligent agents…