LLM Agents

LLM: Large Language Model

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57 papers in the last four weeks, up 58% on the four weeks before. 0.6% of all new papers.

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Latest papers 433

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APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents
May 19, 2026Quan Zheng, Yan Gao, Shaobin He +6Human Behavior Simulation Adaptive Sampling
1Beijing Normal University · 2Zhongguancun Academy, Beijing, China · 3Zhongguancun Institute of Artificial Intelligence
PAVE: A Cognitive Architecture for Legitimate Violation in Generative Agent Societies
May 19, 2026Ahmad Yehia, Abduallah Mohamed, Kun Qian +4Cognitive Architectures for AI Agents LLM Agent Evaluation
The University of Texas at Austin · Meta Reality Labs · University of Calgary
ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
May 18, 2026Yanjun Lin, Zimo Xiao, Kartik Natarajan +7Spoken Dialogue Systems LLM Agents
Amazon
EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL
May 18, 2026Minrui Xu, Zilin Wang, Mengyi DENG +12Tool-Augmented Language Model Agents Agentic RL
LARK, HKUST (GZ) · University of Cambridge · UCL +2
An Empirical Study of Privacy Leakage Chains via Prompt Injection in Black-Box Chatbot Environments
May 18, 2026Hongjang Yang, Hyunsik Na, Daeseon ChoiData Leakage Privacy Leakage in Language Models
Department of Information Security Soongsil University Seoul, Korea · AI Safety Center Soongsil University Seoul, Korea · Department of AI Software Soongsil University Seoul, Korea
PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
May 18, 2026Zile Wang, Qianli Liu, Kaibin Guo +4Multi-Agent Collaboration LLM Agents
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong · School of Software Engineering, Sun Yat-Sen University, China
ASPI: Seeking Ambiguity Clarification Amplifies Prompt Injection Vulnerability in LLM Agents
May 17, 2026Udari Madhushani Sehwag, Zhengyang Shan, Heming Liu +3LLM Agent Security AI Agent Security Benchmarks
Scale AI · 2Boston University · 4Human Frontier Collective +3
MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation
May 17, 2026Chenyu Wang, Yang ShuLLM Agent Orchestration LLM Agent Evaluation
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China · Zhejiang University, Hangzhou, China
OpenJarvis: Personal AI, On Personal Devices
May 16, 2026Jon Saad-Falcon, Avanika Narayan, Robby Manihani +10On-Device Language Model Inference LLM Agents
Stanford University · Lambda Labs
Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents
May 16, 2026Weicheng XueLLM Alignment LLM Auditing
Virginia Tech
Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents
May 16, 2026Jingxing Wang, Chenyu Zhou, Zhihui Fu +4Test-Time Adaptation LLM Agent Skill Learning
1Shanghai Jiao Tong University · 2OPPO Research Institute
The Alpha Illusion: Reported Alpha from LLM Trading Agents Should Not Be Treated as Deployment Evidence
May 16, 2026Yuxuan Ye, Jun Han, Ao Hu +7LLM Agent Evaluation AI Agent Benchmarks
Fudan University · Shanghai University of Finance and Economics · Southwest University of Finance and Economics +3
Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators
May 15, 2026Romain Cosentino, Sarath Shekkizhar, Adam Earle +1Multi-Agent Negotiation Automated Negotiation
Salesforce AI Research
Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
May 15, 2026Igor Bogdanov, Chung-Horng Lung, Thomas Kunz +3Multi-Agent LLM Systems LLM Agent Orchestration
Carleton University Ottawa, Ontario, Canada · Defence R&D Canada Ottawa, Ontario, Canada · Cistel Technology Ottawa, Ontario, Canada
paper.json: A Coordination Convention for LLM-Agent-Actionable Papers
May 15, 2026Arquimedes CanedoDocument Information Extraction LLM Agents
Look Before You Leap: Autonomous Exploration for LLM Agents
May 15, 2026Ziang Ye, Wentao Shi, Yuxin Liu +6RL Exploration LLM Agents
University of Science and Technology of China · Meituan
AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews
May 15, 2026Tobias Priesholm Gardhus, Nikolas Vitsakis, Fie Lejre Frederiksen +2LLM Agents
University of Copenhagen · IT University of Copenhagen
Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
May 14, 2026Kexin Chu, Dawei Xiang, Wei ZhangTool-Augmented Language Model Agents LLM Tool Use
University of Connecticut
How to Interpret Agent Behavior
May 13, 2026Jie Gao, Kaiser Sun, Jen-tse Huang +8Hierarchical Classification AI Agent Monitoring
1Johns Hopkins University · 2California Institute of Technology · 3Northeastern University +1
PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents
May 13, 2026Mikhail Menschikov, Matvey Iskornev, Alexander Kharitonov +8Multi-Hop QA Graph Retrieval-Augmented Generation
Skoltech, Moscow, Russia · SberAI, Moscow, Russia · Huawei, Moscow, Russia +4
Speculative Interaction Agents: Building Real-Time Agents with Asynchronous I/O and Speculative Tool Calling
May 13, 2026Coleman Hooper, Minwoo Kang, Suhong Moon +7Tool-Using Agents LLM Agents
University of California, Berkeley · ICSI · LBNL
IfcLLM: Natural Language Querying of IFC Models through Complementary Relational and Graph Representations
May 13, 2026Rabindra Lamsal, Sisi Zlatanova, Haowen Xu +2Building Information Modeling LLM Agents
GRID Lab, School of Built Environment, The University of New South Wales, Sydney, Australia · School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia
SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
May 13, 2026Yuequan Bao, Xing Li, Huabin Sun +3LLM Agent Orchestration LLM Agents
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin · School of Civil Engineering, Harbin Institute of Technology, Harbin
Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs
May 12, 2026Guinan Su, Yanwu Yang, Xueyan Li +1LLM Agents Instruction Tuning
Max Planck Institute for Intelligent Systems · University Hospital Tübingen · University of Tübingen +3
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
May 12, 2026Eilam Shapira, Moshe Tennenholtz, Roi ReichartNext-Action Prediction LLM Agents
Faculty of Data and Decision Sciences Technion – Israel Institute of Technology Haifa, Israel
SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
May 12, 2026Duling Xu, Zheng Chen, Zaifeng Pan +4LLM Agent Workflow Optimization LLM Agent Skill Retrieval
AetherHeart Tech Co., Ltd., Shanghai, China · Renmin University of China, Beijing, China · University of California San Diego, CA, USA
Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
May 12, 2026Dean Light, Michael Theologitis, Kshitish Ghate +7Structured Reasoning Agentic Reasoning
University of Washington Seattle, WA, USA
NanoResearch: Co-Evolving Skills, Memory, and Policy for Personalized Research Automation
May 11, 2026Jinhang Xu, Qiyuan Zhu, Yujun Wu +11Multi-Agent LLM Systems LLM Agent Skill Learning
1Shanghai Artificial Intelligence Laboratory · 2The Hong Kong University of Science and Technology · 3Peking University +4
An agentic framework for gravitational-wave counterpart association in the multi-messenger era
May 11, 2026Yiming Dong, Yacheng Kang, Junjie Zhao +3AI Agents for Scientific Discovery Gravitational-Wave Astronomy
Department of Astronomy, School of Physics, Peking University, No. 5 Yiheyuan Road, Beijing, 100871, Beijing, China. · Kavli Institute for Astronomy and Astrophysics, Peking University, No. 5 Yiheyuan Road, Beijing, 100871, Beijing, China. · Institute for Gravitational Wave Astronomy, Henan Academy of Sciences, No. 228 Chongshili Road, Zhengzhou, 450046, Henan, China. +2
A Reflective Storytelling Agent for Older Adults: Integrating Argumentation Schemes and Argument Mining in LLM-Based Personalised Narratives
May 11, 2026Jayalakshmi Baskar, Vera C. Kaelin, Kaan Kilic +1Argument Mining LLM Agent Evaluation
Umeå University, Department of Computing Science, Sweden

May 19, 2026, cs.MAJ/K move · Enter open · S save

APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents

Quan Zheng, Yan Gao, Shaobin He, Haoxiang Guan+5

1Beijing Normal University · 2Zhongguancun Academy, Beijing, China · 3Zhongguancun Institute of Artificial Intelligence

Human Behavior Simulation Adaptive Sampling LLM Agents Large Language Model-Based Social Simulation

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LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at every round. We propose Adaptive Prototype Simulation (APS), a framework that reframes scalable LLM-based simulation as a recurrent oracle-allocation problem. APS retains the designated LLM as the online transition oracle while querying adaptive core prototypes, selected singleton-tail agents, and shadow-audit agents. Prototype responses induce local response surfaces for nearby agents, reducing online LLM calls without replacing the underlying transition model. To control approximation bias, shadow-audit residual correction estimates propagation residuals for aggregate correction and future budget allocation, while tail-protected singleton routing directly queries selected isolated, heterogeneous, or high-curvature regions that are vulnerable to smoothing. Theoretically, we treat APS as an estimator for full-scale high-precision individual social simulation and decompose its errors into prototype-coverage error, shadow-audit residual-correction error, local-propagation bias, and temporal context mismatch. Under the reported protocols, APS gives lower reference-aligned distributional discrepancy than scale-oriented and same-budget baselines while reducing online LLM calls, with ablations and compact robustness checks diagnosing the main bias-control mechanisms. In a 10M-agent, multi-round public-opinion simulation, APS achieves a 381.1-fold reduction over full simulation, with reference-aligned final-round JSD of 0.094 against the corresponding full-LLM reference.

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Latest papers 433