TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
- URL: http://arxiv.org/abs/2608.30924v1
- Date: Mon, 31 Aug 2026 15:02:37 GMT
- Title: TRIPPULSE: Multi-Agent Travel Planning with Review-Grounded Reasoning
- Abstract summary: We propose TRIPPULSE, a multi- agent framework for review-grounded travel planning.<n>We augment TRIPCRAFT with 100K+ real-world reviews and introduce Review-Grounded Per- sona Alignment (RGPA), an LLM-as-a-Judge metric for evaluating alignment with human-centric travel experiences.
- Score: 8.15237621583531
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on structured attributes and pre- defined traveler personas, but real travel deci- sions are often shaped by reviews that reveal experiential factors such as comfort, safety, ser- vice quality, ambiance, crowding, and hidden risks absent from structured databases. Incor- porating such review information is therefore critical to realistic, user-centric itinerary gen- eration. We propose TRIPPULSE1, a multi- agent framework for review-grounded travel planning. Instead of relying on a monolithic planner (and face context and reasoning bot- tlenecks), TRIPPULSE2 decomposes itinerary generation into specialized agents (each op- erating over localized contexts) for accom- modations, transportation, meals, attractions, and events, coordinated through a global or- chestrator with scheduling mechanisms that enforce temporal and budget feasibility. We augment TRIPCRAFT with 100K+ real-world reviews and introduce Review-Grounded Per- sona Alignment (RGPA), an LLM-as-a-Judge metric for evaluating alignment with human- centric travel experiences. Experiments across multiple trip durations and diverse proprietary and open-source models show that TRIPPULSE maintains strong constraint satisfaction while generating more personalized and experien- tially grounded itineraries.
Related papers
- Shift & Drift: A Zero-Shot Benchmark for Generalizable and Robust Autonomous Driving Motion Planning [60.888463449435484]
We present Shift & Drift, a novel dual-track benchmark designed to rigorously stress-test motion planners across two critical axes of distribution shift.<n>We show that imitation learning methods achieve high scores in ID benchmarks, but suffer from persistent drift when subjected to temporally correlated actuation noise.<n>Our findings reveal an empirical trade-off between imitation fidelity and closed-loop resilience, providing the community with a rigorous benchmark to evaluate progress toward reliable deployment.
arXiv Detail & Related papers (2026-07-08T18:23:48Z) - Trip+: Benchmarking Agents in Personalized Interactive Travel Planning [12.167681198611744]
We introduce Trip+ to measure the ability of agents to plan travel holistically.<n>Given traveler profiles and dynamic interactions, agents must generate and revise minute-level itineraries.<n>End-to-end traveler experiences are evaluated via an LLM-based simulator.
arXiv Detail & Related papers (2026-06-19T07:17:40Z) - TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents [16.732203115366584]
We introduce TravelEval, a realistic and comprehensive benchmark for evaluating large language travel models.<n>TravelEval 1) a novel six-dimensional evaluation framework to holistically assess plans across accuracy, compliance, temporality, spatiality, economy, and utility dimensions.
arXiv Detail & Related papers (2026-05-31T06:29:19Z) - GroupTravelBench: Benchmarking LLM Agents on Multi-Person Travel Planning [21.321198339300334]
We introduce textbfGroupTravelBench, the first benchmark for textbfmulti-user, multi-turn travel planning.<n>Based on real user profiles, POI data, and ticket price data, we synthesize 650 tasks and divide them into three difficulty levels.<n>We evaluate a wide range of LLMs and find that even frontier models still show substantial weaknesses in preference coverage and group fairness.
arXiv Detail & Related papers (2026-05-24T17:59:58Z) - APEX: Autonomous Policy Exploration for Self-Evolving LLM Agents [54.213455157510445]
Self-evolving agents learn by accumulating memory and reflection across episodes rather than requiring model-weight updates.<n>As memory grows, behavior concentrates around familiar high-reward routines, reducing the chance of discovering better alternatives.<n>We propose Autonomous Policy EXploration (APEX), which builds and maintains an explicit strategy space through a strategy map.
arXiv Detail & Related papers (2026-05-20T14:29:27Z) - Agentic Spatio-Temporal Grounding via Collaborative Reasoning [80.83158605034465]
Temporal Video Grounding aims to retrieve thetemporal tube of a target object or person in a video given a text query.<n>We propose the Agentic Spatio-Temporal Grounder (ASTG) framework for the task of STVG towards an open-world and training-free scenario.<n>Specifically, two specialized agents SRA (Spatial Reasoning Agent) and TRA (Temporal Reasoning Agent) constructed leveraging on modern Multimoal Large Language Models (MLLMs)<n>Experiments on popular benchmarks demonstrate the superiority of the proposed approach where it outperforms existing weakly-supervised and zero-shot approaches by a margin
arXiv Detail & Related papers (2026-02-10T10:16:27Z) - OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions [66.84396313837765]
We introduce OdysseyArena, which re-centers agent evaluation on long-horizon, active, and inductive interactions.<n>We provide a set of 120 tasks to measure an agent's inductive efficiency and long-horizon discovery.<n>We also introduce OdysseyArena-Challenge to stress-test agent stability across extreme interaction horizons.
arXiv Detail & Related papers (2026-02-05T16:31:43Z) - ATLAS: Constraints-Aware Multi-Agent Collaboration for Real-World Travel Planning [53.065247112514534]
ATLAS is a general multi-agent framework designed to handle complex nature of constraints awareness in real-world travel planning tasks.<n>We demonstrate state-of-the-art performance on the TravelPlanner benchmark, improving the final pass rate from 23.3% to 44.4% over its best alternative.
arXiv Detail & Related papers (2025-09-29T23:23:52Z) - TripTailor: A Real-World Benchmark for Personalized Travel Planning [28.965273870656446]
TripTailor is a benchmark for personalized travel planning in real-world scenarios.<n>This dataset features over 500,000 real-world points of interest (POIs) and nearly 4,000 diverse travel itineraries.<n>We identify several critical challenges in travel planning, including the feasibility, rationality, and personalized customization.
arXiv Detail & Related papers (2025-08-02T16:44:02Z) - Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints [39.01715254437105]
This paper introduces the Multiple Aspects of Planning (MAoP) to solve planning problems with multifaceted constraints.<n>Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planners.
arXiv Detail & Related papers (2025-06-14T09:37:59Z) - SynthTRIPs: A Knowledge-Grounded Framework for Benchmark Query Generation for Personalized Tourism Recommenders [6.910185679055651]
This paper introduces a novel SynthTRIPs framework for generating synthetic travel queries using Large Language Models (LLMs)<n>Our approach combines persona-based preferences (e.g., budget, travel style) with explicit sustainability filters (e.g., walkability, air quality) to produce realistic and diverse queries.<n>While our framework was developed and tested for personalized city trip recommendations, the methodology applies to other recommender system domains.
arXiv Detail & Related papers (2025-04-12T16:48:35Z) - TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning [7.841787597078323]
TripCraft establishes a new benchmark for LLM driven personalized travel planning, offering a more realistic, constraint aware framework for itinerary generation.<n>Our parameter informed setting significantly enhances meal scheduling, improving the Temporal Meal Score from 61% to 80% in a 7 day scenario.
arXiv Detail & Related papers (2025-02-27T20:33:28Z) - ChinaTravel: An Open-Ended Benchmark for Language Agents in Chinese Travel Planning [38.44879526364259]
We introduce emphChinaTravel, the first open-ended benchmark grounded in authentic Chinese travel requirements.<n>We design a compositionally generalizable domain-specific language for scalable evaluation, covering feasibility, constraint satisfaction, and preference comparison.<n> Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a 37.0% constraint satisfaction rate on human queries.
arXiv Detail & Related papers (2024-12-18T10:10:12Z) - TravelPlanner: A Benchmark for Real-World Planning with Language Agents [63.199454024966506]
We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario.
It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans.
Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%.
arXiv Detail & Related papers (2024-02-02T18:39:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.