mlsanityData to 7 Oct 2026

Smart Language Agents in Real-World Planning

arXiv 2407.19667 ↗First posted 29 Jul 2024cs.AIcited by 1

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

Abstract (arXiv)

Comprehensive planning agents have been a long term goal in the field of artificial intelligence. Recent innovations in Natural Language Processing have yielded success through the advent of Large Language Models (LLMs). We seek to improve the travel-planning capability of such LLMs by extending upon the work of the previous paper TravelPlanner. Our objective is to explore a new method of using LLMs to improve the travel planning experience. We focus specifically on the "sole-planning" mode of travel planning; that is, the agent is given necessary reference information, and its goal is to create a comprehensive plan from the reference information. While this does not simulate the real-world we feel that an optimization of the sole-planning capability of a travel planning agent will still be able to enhance the overall user experience. We propose a semi-automated prompt generation framework which combines the LLM-automated prompt and "human-in-the-loop" to iteratively refine the prompt to improve the LLM performance. Our result shows that LLM automated prompt has its limitations and "human-in-the-loop" greatly improves the performance by 139% with one single iteration.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 4 of its 5 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

4 of 5 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb

Other references · 3

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  3. 2018Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunitiescited by 20

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  2. 2024Can We Rely on LLM Agents to Draft Long-Horizon Plans? Let's Take TravelPlanner as an Example
  3. 2023Translating Natural Language to Planning Goals with Large-Language Models
  4. 2024A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
  5. 2025LLMs Can Plan Only If We Tell Them
  6. 2026AI Tour Meeting: Group Travel Planning by LLM Agents
  7. 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
  8. 2024Understanding the planning of LLM agents: A survey