mlsanityData to 7 Oct 2026

LLMPC: Large Language Model Predictive Control

arXiv 2501.02486 ↗First posted 5 Jan 2025cs.AI cs.CLcited by 4

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Recent advancements in prompting techniques for Large Language Models (LLMs) have improved their reasoning, planning, and action abilities. This paper examines these prompting techniques through the lens of model predictive control (MPC). We show that LLMs act as implicit planning cost function minimizers when planning prompts are used. We propose a unified MPC framework for planning with LLMs and demonstrate improved performance over few shot prompting on several planning benchmarks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 20 of its 38 references that are in the map. First posted on arXiv 5 Jan 2025.

Builds on

20 of 38 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Scattered Forest Search: Smarter Code Space Exploration with LLMs22 Oct
  2. 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents13 Aug
  3. 2023Reasoning with Language Model is Planning with World Model24 May

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  5. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  6. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  7. 2022LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Modelscited by 459
  8. 2024Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agentscited by 144
  9. 2024NATURAL PLAN: Benchmarking LLMs on Natural Language Planningcited by 125
  10. 2024Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigationcited by 116
  11. 2023ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planningcited by 77
  12. 2024TrustAgent: Towards Safe and Trustworthy LLM-based Agentscited by 62
5 more
  1. 2024Enhancing LLM Reasoning with Reward-guided Tree Searchcited by 58
  2. 2024LiteSearch: Efficacious Tree Search for LLMcited by 30
  3. 2024TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Modelscited by 29
  4. 2024Planning with Multi-Constraints via Collaborative Language Agentscited by 26
  5. 2024Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Examplecited by 22

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Rethinking the Unsolvable: When In-Context Search Meets Test-Time Scalingcited by 2
  2. 2026Test-Driven Agentic Framework for Reliable Robot Controllercited by 0
  3. 2026AI Control Scientist: LLM-driven Agentic System for Automated Control Designcited by 0
  4. 2026ReHoPER: Receding-Horizon Planning for Enhanced Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning
  2. 2024Non-myopic Generation of Language Models for Reasoning and Planning
  3. 2025Leveraging Pre-trained Large Language Models with Refined Prompting for Online Task and Motion Planning
  4. 2025On the Limits of Innate Planning in Large Language Models
  5. 2025Prompt reinforcing for long-term planning of large language models
  6. 2025LLMs Can Plan Only If We Tell Them
  7. 2024Exploring and Benchmarking the Planning Capabilities of Large Language Models
  8. 2024Towards Generalist Prompting for Large Language Models by Mental Models