mlsanityData to 7 Oct 2026

TriFlow: A Progressive Multi-Agent Framework for Intelligent Trip Planning

arXiv 2512.11271 ↗First posted 12 Dec 2025cs.AIcited by 1

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

Abstract (arXiv)

Real-world trip planning requires transforming open-ended user requests into executable itineraries under strict spatial, temporal, and budgetary constraints while aligning with user preferences. Existing LLM-based agents struggle with constraint satisfaction, tool coordination, and efficiency, often producing infeasible or costly plans. To address these limitations, we present TriFlow, a progressive multi-agent framework that unifies structured reasoning and language-based flexibility through a three-stage pipeline of retrieval, planning, and governance. By this design, TriFlow progressively narrows the search space, assembles constraint-consistent itineraries via rule-LLM collaboration, and performs bounded iterative refinement to ensure global feasibility and personalisation. Evaluations on TravelPlanner and TripTailor benchmarks demonstrated state-of-the-art results, achieving 91.1% and 97% final pass rates, respectively, with over 10x runtime efficiency improvement compared to current SOTA.

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 6 of its 6 references that are in the map. First posted on arXiv 12 Dec 2025.

Builds on

6 of 6 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning21 Nov
  2. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agents2 Feb

Other references · 4

  1. 2024Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Toolscited by 47
  2. 2024TravelAgent: An AI Assistant for Personalized Travel Planningcited by 38
  3. 2024Robust Planning with LLM-Modulo Framework: Case Study in Travel Planningcited by 34
  4. 2025TripTailor: A Real-World Benchmark for Personalized Travel Planningcited by 22

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2025ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning
  2. 2026TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
  3. 2026TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning
  4. 2025TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning
  5. 2024A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
  6. 2026Agentic AI for Trip Planning Optimization Application
  7. 2025DeepTravel: An End-to-End Agentic Reinforcement Learning Framework for Autonomous Travel Planning Agents
  8. 2026DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints