mlsanityData to 14 Oct 2026

Adaptive Domain Modeling with Language Models: A Multi-Agent Approach to Task Planning

arXiv 2506.19592 ↗First posted 24 Jun 2025cs.AI cs.ROcited by 6

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

Abstract (arXiv)

We introduce TAPAS (Task-based Adaptation and Planning using AgentS), a multi-agent framework that integrates Large Language Models (LLMs) with symbolic planning to solve complex tasks without the need for manually defined environment models. TAPAS employs specialized LLM-based agents that collaboratively generate and adapt domain models, initial states, and goal specifications as needed using structured tool-calling mechanisms. Through this tool-based interaction, downstream agents can request modifications from upstream agents, enabling adaptation to novel attributes and constraints without manual domain redefinition. A ReAct (Reason+Act)-style execution agent, coupled with natural language plan translation, bridges the gap between dynamically generated plans and real-world robot capabilities. TAPAS demonstrates strong performance in benchmark planning domains and in the VirtualHome simulated real-world environment.

Key facts. Cited by 6 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 30 of its 32 references that are in the map. First posted on arXiv 24 Jun 2025.

Builds on

30 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner30 Sep

Other references · 29

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  6. 2024OpenVLA: An Open-Source Vision-Language-Action Modelcited by 2,887
  7. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
  8. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  9. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
  10. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
  11. 2023Open X-Embodiment: Robotic Learning Datasets and RT-X Modelscited by 862
  12. 2023Reasoning with Language Model is Planning with World Modelcited by 861
17 more
  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  2. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  3. 2018VirtualHome: Simulating Household Activities via Programscited by 415
  4. 2022PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Changecited by 382
  5. 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 294
  6. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 207
  7. 2024LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworkscited by 179
  8. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 170
  9. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  10. 2023AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkerscited by 113
  11. 2023Dynamic Planning with a LLMcited by 61
  12. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  13. 2024DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Modelscited by 33
  14. 2024NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptionscited by 22
  15. 2024AutoGPT+P: Affordance-based Task Planning with Large Language Modelscited by 19
  16. 2024Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generationcited by 5
  17. 2024MAkEable: Memory-centered and Affordance-based Task Execution Framework for Transferable Mobile Manipulation Skillscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026Recursive Multi-Agent Systemscited by 10
  2. 2025CaRT: Teaching LLM Agents to Know When They Know Enoughcited by 7
  3. 2025MADRA: Multi-Agent Debate for Risk-Aware Embodied Planningcited by 6
  4. 2026Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisationcited by 1
  5. 2026The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modescited by 0
  6. 2026HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
  2. 2023ADaPT: As-Needed Decomposition and Planning with Language Models
  3. 2026GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
  4. 2025Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study
  5. 2026Scale-Plan: Scalable Language-Enabled Task Planning for Heterogeneous Multi-Robot Teams
  6. 2024AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
  7. 2024MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation
  8. 2026AdaPlanBench: Evaluating Adaptive Planning in Large Language Model Agents under World and User Constraints