LLM Agent Training

LLM: Large Language Model

Follow this topic Ask about this topic

Momentum

54 papers in the last four weeks, up 145% on the four weeks before. 0.5% of all new papers.

Jul 13Week of Sep 28

Latest papers 354

All topics

SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
Jun 6, 2026Yichen Chen, Siying Li, Yuhang Liang +2Multi-Agent LLM Systems Financial Fraud Detection
National University of Singapore · School of Computing, Singapore 117417 · University of Chinese Academy of Sciences +4
Agentopia: Long-Term Life Simulation and Learning in Agent Societies
Jun 5, 2026Xintao Wang, Sirui Zheng, Hongqiu Wu +10Multi-Agent LLM Systems Human Behavior Simulation
Fudan University · Independent Researcher · Johns Hopkins University +1
SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating
Jun 5, 2026Zequn Xie, Junjie Wang, Dan Yang +4Reward Shaping LLM Agent Training
Zhejiang University
From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents
Jun 4, 2026Yuhao Sun, Jiacheng Zhang, Shaanan Cohney +3LLM Guardrails LLM Agent Training
1The University of Melbourne · 2Tsinghua University · †Corresponding author.
CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement
Jun 4, 2026Hong Qian, Yuanhao Liu, Zihan Zhou +7Game-Playing Agents Human Behavior Simulation
Shanghai Institute of AI for Education, and School of Computer Science and Technology, East China Normal University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · Tencent Inc., Shenzhen, China
AdaMEM: Test-Time Adaptive Memory for Language Agents
Jun 4, 2026Yunxiang Zhang, Yiheng Li, Ali Payani +1Agent Memory Test-Time Adaptation
University of Michigan · 2Cisco Research.
Self-Evolving Deep Research via Joint Generation and Evaluation
Jun 3, 2026Han Zhu, Chengkun Cai, Yuanfeng Song +3LLM Evaluation LLM Agent Training
1The Hong Kong University of Science and Technology · 2ByteDance, China · University College London
AgentJet: A Flexible Swarm Training Framework for Agentic Reinforcement Learning
Jun 3, 2026Qingxu Fu, Boyin Liu, Shuchang Tao +2Multi-Agent LLM Systems Agentic RL
Tongyi Lab, Alibaba Group
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
Jun 2, 2026Kai Zhang, Xinyuan Zhang, Hongda Jiang +11Persistent Memory for Language Models Memorization in Generative Models
Meta Reality Labs
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Jun 2, 2026Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4Synthetic Data Generation Reinforcement Learning with Verifiable Rewards
IBM Research
What Makes Interaction Trajectories Effective for Training Terminal Agents?
Jun 2, 2026Sidi Yang, Chaofan Tao, Jierun Chen +11AI Agent Benchmarks LLM Agent Training
1The University of Hong Kong · 2Huawei Technologies · 3Nanyang Technological University
RUBAS: Rubric-Based Reinforcement Learning for Agent Safety
Jun 2, 2026Xian Qi Loye, Qinglin Su, Zhexin Zhang +5Reinforcement Learning AI Agent Safety
The Conversational AI (CoAI) group, DCST, Tsinghua University · Huawei Noah’s Ark Lab
Uncertainty-Aware Clarification in LLM Agents with Information Gain
Jun 2, 2026Mengyi Deng, Zhiwei Li, Xin Li +4Clarification Question Generation Tool-Using Agents
Information Hub, HKUST(GZ) · Jilin University · et al.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Jun 2, 2026Guhong Chen, Yingcheng Shi, Yongbin Li +6RL for Code Generation Agentic RL
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · 2Tongyi Lab , Alibaba Group · 3Alibaba Group +1
Libra: Efficient Resource Management for Agentic RL Post-Training
Jun 2, 2026Kaiwen Chen, Xin Tan, Jingzong Li +6Agentic RL Reinforcement Learning
The Chinese University of Hong Kong · The Hang Seng University of Hong Kong · Independent Researcher
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
Jun 1, 2026Hengrui Gu, Xiaotian Han, Kaixiong ZhouTool-Augmented Language Model Agents Tool-Using Agents
North Carolina State University · Case Western Reserve University
Adaptive Latent Agentic Reasoning
Jun 1, 2026Dongwon Jung, Peng Shi, Yi Zhang +2Agentic Reasoning LLM Agents
University of California, Davis · University of Waterloo · Greenshoe, Inc.
Policy and World Modeling Co-Training for Language Agents
Jun 1, 2026Ning Lu, Baijiong Lin, Shengcai Liu +9Agentic RL World Model Learning
Southern University of Science and Technology · Hong Kong University of Science and Technology · Hong Kong University of Science and Technology (Guangzhou) +2
CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
Jun 1, 2026Youwei Liu, Jian Wang, Hanlin Wang +1LLM World Models LLM Agent Training
Central South University · College of Computer Science, Sichuan University · Department of Computing, The Hong Kong Polytechnic University
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Jun 1, 2026Wenhang Shi, Jinhao Dong, Yiren Chen +4Synthetic Data Generation LLM Agent Training
Renmin University of China · Peking University · Tencent
HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
May 31, 2026Yi Gu, Huacan Wang, Shuo Zhang +10AI Agent Benchmarks LLM Agent Training
1Midea Group · 2Beijing University of Posts and Telecommunications · 3Donghua University +1
CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation
May 31, 2026Ruihui Hou, Ziyue Huai, Chennuo Zhang +5Tool-Using Agents Clinical Reasoning
East China University of Science and Technology, Shanghai, China · Zhongshan Hospital, Fudan University, Shanghai, China
BAGEN: Are LLM Agents Budget-Aware?
May 29, 2026Yuxiang Lin, Zihan Wang, Mengyang Liu +9LLM Agent Evaluation AI Agent Benchmarks
1Northwestern University · 2O2 Lab · 3Independent +5
Learning to Construct Practical Agentic Systems
May 29, 2026Aditya Kumar, Zhihan Lei, Jerry Yan +6LLM Agent Orchestration Agentic Workflows
Carnegie Mellon University · Dept. of Computer Science Emory University
Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information
May 29, 2026Antonio Valerio Miceli-Barone, Vaishak Belle, Shay B. CohenMulti-Agent LLM Systems Multi-Agent Negotiation
University of Edinburgh
Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration
May 29, 2026Weile Chen, Bingchen Miao, Qifan Yu +6LLM Agent Training Self-Improving Agents
1Zhejiang University
COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents
May 29, 2026Wenkai Shen, Pengyang Zhou, Jiahe Xu +5LLM Safety Alignment LLM Agents
Zhejiang University
AgentDoG 1.5: A Lightweight and Scalable Alignment Framework for AI Agent Safety and Security
May 28, 2026Dongrui Liu, Yu Li, Zhonghao Yang +47LLM Safety Alignment AI Agent Security
LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents
May 28, 2026Xiaoxuan Peng, Kaiqi Zhang, Xinyu Lu +5RL for Language Models Terminal Agents
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences
GrepSeek: Training Search Agents for Direct Corpus Interaction
May 28, 2026Alireza Salemi, Chang Zeng, Atharva Nijasure +4Tool-Augmented Language Model Agents Agentic Search
University of Massachusetts Amherst · Princeton University · Carnegie Mellon University

Jun 6, 2026, cs.AIJ/K move · Enter open · S save

SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection

Yichen Chen, Siying Li, Yuhang Liang, Lijun Wang+1

National University of Singapore · School of Computing, Singapore 117417 · University of Chinese Academy of Sciences+4

Multi-Agent LLM Systems Financial Fraud Detection LLMs for Cybersecurity LLM Agent Training

Open paper AskPDF arXiv

Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers. Existing methods fall at least one of these requirements: automated machine learning systems search a fixed numerical space without semantic awareness of the dataset; graph neural network-based methods require pre-defined relational graphs and remain opaque at the individual-decision level; and the design of general-purpose large language model (LLM) agents does not consider the recall and precision constraints specific to real-world fraud detection. In this paper, we propose SAGE, the first end-to-end LLM-driven multi-agent framework for fraud detection. SAGE coordinates three dedicated agents that make decisions based on a six-layer Data Diagnostic Tree (DDT) and a Markov decision process guided by natural-language gradients, automatically optimizing the model under a fraud-specific reward. On five fraud datasets and five LLM backbones, SAGE wins

96.00\%

of method--dataset comparisons and improves F1 by an average of

40.86\%

over baselines. The code is available at https://github.com/yichenC1c/SAGE.

Momentum

Related topics

Latest papers 354