mlsanityData to 14 Oct 2026

Planning with Multi-Constraints via Collaborative Language Agents

arXiv 2405.16510 ↗First posted 26 May 2024cs.AI cs.CL cs.LGcited by 26

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) have emerged as a promising paradigm for achieving artificial general intelligence (AGI) due to their superior reasoning and generalization capabilities. Effective planning is crucial for the success of LLM agents in real-world tasks, making it a highly pursued topic in the community. Current planning methods typically translate tasks into executable action sequences. However, determining a feasible or optimal sequence for complex tasks with multiple constraints at fine granularity, which often requires compositing long chains of heterogeneous actions, remains challenging. This paper introduces Planning with Multi-Constraints (PMC), a zero-shot methodology for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. Each subtask is then mapped into executable actions. PMC was assessed on two constraint-intensive benchmarks, TravelPlanner and API-Bank. Notably, PMC achieved an average 42.68% success rate on TravelPlanner, significantly higher than GPT-4 (2.92%), and outperforming GPT-4 with ReAct on API-Bank by 13.64%, showing the immense potential of integrating LLM with multi-agent systems. We also show that PMC works with small LLM as the planning core, e.g., LLaMA-3.1-8B.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 37 of its 58 references that are in the map. First posted on arXiv 26 May 2024.

Builds on

37 of 58 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey17 Apr
  2. 2024Understanding the planning of LLM agents: A survey5 Feb
  3. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb
  4. 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives19 Dec
  5. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  6. 2023Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency29 Sep
  7. 2023The Rise and Potential of Large Language Model Based Agents: A Survey14 Sep
  8. 2023A Survey on Large Language Model based Autonomous Agents22 Aug
  9. 2023ExpeL: LLM Agents Are Experiential Learners20 Aug
  10. 2023SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks27 May
  11. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 26

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  7. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  8. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  9. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  10. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  11. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  12. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
14 more
  1. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  2. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  3. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 668
  4. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  5. 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 353
  6. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructioncited by 248
  7. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 207
  8. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  9. 2024EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instructioncited by 154
  10. 2020Inductive logic programming at 30: a new introductioncited by 85
  11. 2024SciAgent: Tool-augmented Language Models for Scientific Reasoningcited by 70
  12. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  13. 2023LLM A*: Human in the Loop Large Language Models Enabled A* Search for Roboticscited by 26
  14. 2023Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Webcited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 12; 2026: 13.

26 papers.

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  1. 2024Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Examplecited by 22
  2. 2025HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinkingcited by 14
  3. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directionscited by 14
  4. 2025TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planningcited by 12
  5. 2025Blueprint First, Model Second: A Framework for Deterministic LLM Workflowcited by 12
  6. 2026LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agentscited by 10
  7. 2025TripTide: A Benchmark for Adaptive Travel Planning under Disruptionscited by 8
  8. 2025Select-Then-Decompose: From Empirical Analysis to Adaptive Selection Strategy for Task Decomposition in Large Language Modelscited by 5
  9. 2025LLMPC: Large Language Model Predictive Controlcited by 4
  10. 2025GraphicBench: A Planning Benchmark for Graphic Design with Language Agentscited by 4
  11. 2025LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systemscited by 4
  12. 2026Unified-MAS: Universally Generating Domain-Specific Nodes for Empowering Automatic Multi-Agent Systemscited by 4
14 more
  1. 2026From Plan to Action: How Well Do Agents Follow the Plan?cited by 4
  2. 2025Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agentscited by 3
  3. 2025Cochain: Balancing Insufficient and Excessive Collaboration in LLM Agent Workflowscited by 2
  4. 2026Decoupled Travel Planning with Behavior Forestcited by 1
  5. 2026State-Centric Decision Processcited by 1
  6. 2026Does The Way You Plan Matter? An Empirical Study of Planning Representations for LLM Web Agentscited by 1
  7. 2026Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profilingcited by 1
  8. 2026Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptionscited by 1
  9. 2025GenEscape: Hierarchical Multi-Agent Generation of Escape Room Puzzlescited by 0
  10. 2025IMAGINE: Integrating Multi-Agent System into One Model for Complex Reasoning and Planningcited by 0
  11. 2026Revisiting the Travel Planning Capabilities of Large Language Modelscited by 0
  12. 2026Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learningcited by 0
  13. 2026CARL: Constraint-Aware Reinforcement Learning for Planning with LLMscited by 0
  14. 2026Agents in the Wild: Where Research Meets Deploymentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
  2. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  3. 2025Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination
  4. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challenges
  5. 2026Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation
  6. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  7. 2026Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
  8. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects