LLM Agent Orchestration

LLM: Large Language Model

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26 papers in the last four weeks, up 53% on the four weeks before. 0.3% of all new papers.

Jul 13Week of Sep 28

Latest papers 294

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GraSP: Graph-Structured Skill Compositions for LLM Agents
Apr 20, 2026Tianle Xia, Lingxiang Hu, Yiding Sun +5LLM Agent Orchestration LLM Agent Workflow Optimization
Tencent
Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
Apr 20, 2026Jiahao Huang, Peilan Xu, Xiaoya Nan +1Evolutionary Optimization Large Language Model-Guided Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China · Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, Guangdong, China
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration
Apr 18, 2026Sukwon Yun, Jie Peng, Pingzhi Li +5LLM Agent Orchestration Multi-Agent Collaboration
UNC Chapel Hill · Eigent AI · MIT-IBM Watson AI Lab +1
Beyond Task Success: An Evidence-Synthesis Framework for Evaluating, Governing, and Orchestrating Agentic AI
Apr 18, 2026Christopher Koch, Joshua Andreas WellbrockLLM Agent Orchestration AI Agent Evaluation
Independent Researcher
HiveMind: OS-Inspired Scheduling for Concurrent LLM Agent Workloads
Apr 18, 2026Justice Owusu Agyemang, Jerry John Kponyo, Obed Kwasi Somuah +3LLM Inference Efficiency AI Coding Agents
Sperix Labs · VIA Cybersecurity Lab, KNUST · Quantum and Assistive Technologies Lab, KNUST
Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)
Apr 18, 2026Tianbao ZhangSafety-Critical Control LLM Guardrails
Independent Researcher
Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition
Apr 18, 2026Wenzhen Yuan, Wutao Xiong, Fanchen Yu +7Multi-Agent System Optimization Multi-Agent Orchestration
Shanghai Jiao Tong University · Sichuan University · Shanghai AI Lab +2
Agentic Frameworks for Reasoning Tasks: An Empirical Study
Apr 17, 2026Zeeshan Rasheed, Abdul Malik Sami, Muhammad Waseem +3LLM Agent Orchestration AI Agent Evaluation
*Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
Apr 14, 2026Jiacheng Liu, Xiaohan Zhao, Xinyi Shang +1AI Coding Agents LLM Agent Orchestration
1VILA Lab, Mohamed bin Zayed University of Artificial Intelligence · University College London
Authoring for Living Worlds: Tool-Constrained LLM Agents for Executable Multi-Actor Scenarios
Apr 11, 2026Nicolae Cudlenco, Mihai Masala, Marius LeordeanuLLM Agent Orchestration Tool-Using Agents
Institute of Mathematics of the Romanian Academy, Bucharest, Romania · Büchi Labortechnik AG, Flawil, Switzerland · National University of Science and Technology Politehnica Bucharest, Romania
AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control
Mar 27, 2026Borui Zhang, Nariman Mahdavi, Subbu Sethuvenkatraman +1LLM Agent Orchestration Power Systems
University of New South Wales Sydney, NSW, Australia · CSIRO Newcastle, NSW, Australia
Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent
Mar 1, 2026Achuth Chandrasekhar, Janghoon Ock, Amir Barati FarimaniLLM Agent Orchestration Materials Science
Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Chemical and Biomolecular Engineering, University of Nebraska–Lincoln, Lincoln, NE 68588, USA · Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA +3
Agentic AI for Scalable and Robust Optical Systems Control
Feb 23, 2026Zehao Wang, Mingzhe Han, Wei Cheng +12LLM Agent Orchestration AI Agent Benchmarks
Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA · NEC Laboratories America, Princeton, NJ 08540, USA · Duke Quantum Center and Department of Physics, Duke University, Durham, NC, USA 27708 +4
A General Equilibrium Theory of Orchestrated AI Agent Systems
Feb 23, 2026Jean-Philippe GarnierMulti-Agent LLM Systems Multi-Agent Coordination
Br.AI.K · BrainiaK
ThunderAgent: A Simple, Fast and Program-Aware Agentic Inference System
Feb 14, 2026Hao Kang, Ziyang Li, Weili Xu +7LLM Agent Orchestration LLM Inference Scheduling
University of Illinois Urbana-Champaign · 5Together AI · 1Georgia Institute of Technology
Nalar: Workflow-Aware Management of Agentic Applications
Jan 8, 2026Saurabh Agarwal, Marco Laju, Donghyun Son +4LLM Inference Efficiency LLM Agent Orchestration
UT-Austin · Cisco-Research
Monadic Context Engineering
Dec 27, 2025Yifan Zhang, Yang Yuan, Mengdi Wang +1Multi-Agent Orchestration LLM Agent Orchestration
IIIS, Tsinghua University
Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response
Nov 19, 2025Philip DrammehMulti-Agent LLM Systems LLM Agent Orchestration
Independent Researcher
CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation
Oct 24, 2025Jinhui Lou, Yan Yang, Zhou Yu +4Chest X-Ray Classification LLM Agent Orchestration
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, China · Department of Automation, Tsinghua University, Beijing 100084, China · Department of Colorectal Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China +2
ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism
Aug 1, 2025Rui Sun, Li Zhao, Zuoyou Jiang +5Multi-Agent LLM Systems LLM Agent Orchestration
1Stepfun · 2FinStep
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Jun 4, 2025Dhaval Patel, Shuxin Lin, James Rayfield +7LLM Agent Orchestration AI Agent Evaluation
IBM · University of South Carolina · Steel Authority of India +1
Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversations
Oct 14, 2024Garima Agrawal, Sashank Gummuluri, Cosimo SperaRetrieval-Augmented Generation Customer Support Automation
Minerva CQ, Sunnyvale, California, USA
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
Date pendingRakibul Hasan Rajib, Mengxin Zheng, Qian LouMulti-Agent LLM Systems Multi-Agent System Optimization
University of Central Florida
Omni Interaction Agent Technical Report
Date pendingOrantqing, Shengpeng Ji, Junlong Tong +19Unified Multimodal Models LLM Agent Orchestration
Hunyuan Speech Team, Tencent · Shanghai Jiao Tong University · Zhejiang University +2

Apr 20, 2026, cs.CLJ/K move · Enter open · S save

GraSP: Graph-Structured Skill Compositions for LLM Agents

Tianle Xia, Lingxiang Hu, Yiding Sun, Ming Xu+4

Tencent

LLM Agent Orchestration LLM Agent Workflow Optimization LLM Agents LLM Agent Planning

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Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve performance -- focused sets of 2-3 skills outperform comprehensive documentation, and excessive skills actually hurt. The bottleneck has shifted from skill availability to skill orchestration: agents need not more skills, but a structural mechanism to select, compose, and execute them with explicit causal dependencies. We propose GraSP, the first executable skill graph architecture that introduces a compilation layer between skill retrieval and execution. GraSP transforms flat skill sets into typed directed acyclic graphs (DAGs) with precondition-effect edges, executes them with node-level verification, and performs locality-bounded repair through five typed operators -- reducing replanning from O(N) to O(d^h). Across ALFWorld, ScienceWorld, WebShop, and InterCode with eight LLM backbones, GraSP outperforms ReAct, Reflexion, ExpeL, and flat skill baselines in every configuration, improving reward by up to +19 points over the strongest baseline while cutting environment steps by up to 41%. GraSP's advantage grows with task complexity and is robust to both skill over-retrieval and quality degradation, confirming that structured orchestration -- not larger skill libraries -- is the key to reliable agent execution.

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Latest papers 294