mlsanityData to 7 Oct 2026

Can LLMs plan paths with extra hints from solvers?

arXiv 2410.05045 ↗First posted 7 Oct 2024cs.AI cs.CL cs.ROcited by 3

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Abstract (arXiv)

Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, mathematical problem solving, and tasks related to program synthesis. However, their effectiveness in long-term planning and higher-order reasoning has been noted to be limited and fragile. This paper explores an approach for enhancing LLM performance in solving a classical robotic planning task by integrating solver-generated feedback. We explore four different strategies for providing feedback, including visual feedback, we utilize fine-tuning, and we evaluate the performance of three different LLMs across a 10 standard and 100 more randomly generated planning problems. Our results suggest that the solver-generated feedback improves the LLM's ability to solve the moderately difficult problems, but the harder problems still remain out of reach. The study provides detailed analysis of the effects of the different hinting strategies and the different planning tendencies of the evaluated LLMs.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

25 of 43 references.

References · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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13 more
  1. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  2. 2023FinGPT: Open-Source Financial Large Language Modelscited by 244
  3. 2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 228
  4. 2022Binding Language Models in Symbolic Languagescited by 224
  5. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 204
  6. 2023AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkerscited by 111
  7. 2024Chain of Thoughtlessness? An Analysis of CoT in Planningcited by 100
  8. 2024Is Temperature the Creativity Parameter of Large Language Models?cited by 92
  9. 2023ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planningcited by 77
  10. 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 44
  11. 2024Robust Planning with LLM-Modulo Framework: Case Study in Travel Planningcited by 34
  12. 2024LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Drivingcited by 32
  13. 2017FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planningcited by 21

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  2. 2025Reliable Proof Generation with LLMs via Analogical Retrieval and Symbolic Verification: A Case Study in Euclidean Geometrycited by 7
  3. 2026End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agentcited by 1