Title: Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints
Venue: NeurIPS 2025
Accepted: yes
Keywords: Real-World Planning

Travel Planning

Simulation-based Evaluation

Large Language Model

Agent

Reinforcement Learning
Abstract: Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and potentially conflicting information and constraints. For example, in travel planning scenarios, it requires the integration of diverse real-world information and user preferences. While LLMs show promise, existing methods with long-horizon thinking struggle with handling multifaceted constraints, leading to suboptimal solutions. Motivated by the challenges of real-world travel planning, this paper introduces the Multiple Aspects of Planning (MAoP), empowering LLMs with "wide-horizon thinking" to solve planning problems with multifaceted constraints. Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planners, enabling strong inference-time scalability by scaling aspects to consider various constraints. In addition, existing benchmarks for multi-constraint planning are flawed because they assess constraints in isolation, ignoring causal dependencies within the constraints, e.g, travel planning, where past activities dictate future itinerary. To address this, we propose Travel-Sim, an agent-based benchmark assessing plans via real-world simulation, thereby inherently resolving these causal dependencies. This paper advances LLM capabilities in complex planning and offers novel insights for evaluating sophisticated scenarios through simulation.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors formulated travel planning as an $L^3$ planning problem (long context, long instruction, and long output). This type of wide-horizon thinking is characterized by parallel information processing followed by information integration at each decision step. To handle wide-horizon thinking, the authors proposed Multiple Aspects of Planning (MAoP). In this approach, a strategist first decomposes the planning request into independent aspects, then integrates these aspects into a coherent planning blueprint.\nThe MAoP training consists of three components: reward model training, rejection sampling fine-tuning for the strategist model, and reinforcement learning training for the planning model. MAoP inference includes pre-planning (decomposition and routing) and aspect-aware thinking, which allows the planning blueprint to be turned into the final plan.\nTo train their agent, the authors constructed the Travel-Sim benchmark. To evaluate the experimental results, the authors proposed several metrics. For information integration, they used comprehensiveness (CPH), completeness (CPL), feasibility (FEA), and personalization (PER). For user experience, they assessed experience (ex), interest (it), arrangement (ar), stamina (st), and cost (co). The experiments verified the effectiveness of MAoP.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The proposed MAoP demonstrates a strong real-world application for travel planning, and the travel planning pipeline is practical.\n2. The Travel-Sim benchmark would be valuable to the community if the authors could make it publicly available.\n\nWeaknesses:\n\n1. The proposal of unnecessary concepts affects readability.\n2. The code is not available.\n\nQUESTIONS:\nQ1. You proposed MAoP to overcome the limitations of wide-horizon thinking. However, you also mentioned that MAoP is designed for the proposed $L^3$ planning problem, but this concept is barely discussed in the methods and experiments sections. Do you think removing the L3 planning problem concept could improve the readability of your paper?\n\nQ2. You mentioned that the agent is trained in Travel-Sim. Did you perform any fine-tuning using real-world data? How do you view the sim2real gap, and what are your plans to overcome it?\n\nQ3. Do you have any plan to open-source your code?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles the challenge of producing high‑quality travel itineraries with large language models (LLMs) when the task involves long context (many heterogeneous facts), long instructions (formatting rules, chain‑of‑thought prompts), and long outputs (multi‑day plans). The authors name this the L³ planning problem. They observe that conventional “long‑horizon” reasoning (deep, step‑by‑step chains) is ill‑suited to travel planning, which instead demands wide‑horizon thinking—parallel consideration of many distinct aspects such as budget, stamina, spatial constraints, and personal preferences. The paper propose MAoP (Multiple Aspects of Planning), use strategist model performing pre‑planning. Strategist can decompose the user request into aspect‑specific guidance snippets and then “routes” or aggregates these aspects into a coherent planning blueprint that captures cross‑aspect interactions. They also propose Travel‑Sim which can evaluate their method more better. Overall, the work contributes a novel planning framework (MAoP), a distillation strategy for small models, and an agent‑based evaluation benchmark (Travel‑Sim) that together advance LLM‑based solutions for complex, real‑world planning tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1) This paper addresses a real and growing use case: Travel planning, which is a realistic, high-impact application for LLMs.\n\n2) The authors go beyond standard rule-based metrics by proposing Travel-Sim, a realistic agent-based evaluation framework with multi-granularity, multi-dimension feedback. The combination of objective trajectory similarity and subjective traveler satisfaction is a notable improvement over prior evaluation strategies.\n\n2) The paper presents quantitative improvements over several baselines, and shows how MAoP scales better with the number of planning aspects. The experimental protocol is clearly described, with relevant baselines and ablations.\n\n\nWeaknesses:\n\n1) While Travel-Sim is creative, it is still an LLM simulation of user satisfaction, which may not fully reflect human perception.\n\n2) Though tourism is a strong testbed, the paper could further discuss how MAoP generalizes to other wide-horizon planning settings beyond travel. Without this, its broader impact may be undercut.\n\n3) Strategist-planner separation is a logical extension of existing modular or hierarchical planning ideas. While well-executed, Strategist is  adaptation of existing techniques rather than fundamentally new.\n\nQUESTIONS:\n1) Have you considered validating Travel-Sim’s user satisfaction results with real human feedback to confirm its alignment with human perception?\n\n2) Can you provide more discussion or experiments on how MAoP might generalize to wide-horizon planning problems outside of travel?\n\n3) What specific innovations differentiate your Strategist-planner framework from prior modular or hierarchical planning approaches?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a comprehensive approach for travel planning utilizing large language models (LLMs), implemented through the development of two key components: a Multi-aspect Planning (MAoP) framework designed to enhance LLM planning capabilities by addressing three critical challenges—long context, long instruction, and long output—and an agent-based evaluation system called Travel-Sim that overcomes fundamental limitations in evaluation methodologies for complex real-world scenarios through the simulation of real-world travel experiences.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe MAoP framework addresses broad-scope reasoning challenges through a strategy-planning model architecture that decomposes complex planning into manageable, aspect-oriented components, making it particularly well-suited for travel planning tasks.\nThe paper proposes a comprehensive evaluation framework incorporating innovative metrics that provides solid support for experimental validation.\nThe successful distillation to smaller models and integration with real-world data sources (maps, travel blogs) demonstrates immediate applicability for consumer-facing travel planning services.\n\nWeaknesses:\nThe methodology section suffers from confusing logic and organization, making it easy for readers to become misled or confused.\nThe experimental section lacks sufficient description of the datasets, providing only macro-level descriptions without in-depth elaboration.\nThe paper lacks comparative experiments and fails to compare against other existing methods, presenting only comparisons between different large language models.\n\nQUESTIONS:\nQuestions and Concerns:\n1. What is the purpose of One-Step Wide-Horizon Thinking in Section 3.3? Experimental evidence is needed to support the rationale for adopting a one-step approach.\n2. What is the detailed formulation of the reward function used in the RFT and RL processes?\n3. Could the preprocessing framework workflow be included in the main text to facilitate reader understanding?\n4. What is the source of the synthetic data used for training the reward model in MAoP?\n5. What is the purpose of the comparison in Section 5.2.4? How does this relate to your proposed Consistent Travel Simulation?\n6. Could you include an evaluation of the simulation itself? The current experiments do not demonstrate the reliability of the simulation component.\n\nLIMITATIONS:\nThe authors discussed some limitations and broader impacts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper addresses the L³ planning problem—long context, instruction, and output—via a novel two-stage framework (MAoP) combining high-level aspect routing with step-wise planning. It introduces Travel-Sim, a simulation-based evaluation environment with LLM agents, real-world constraints, and multi-dimensional feedback. A reward model trained on simulated data is used to further improve planning via RFT and RL. Experiments show consistent gains over strong baselines, highlighting the framework’s effectiveness in complex, multi-objective planning tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper clearly defines the L³ planning challenge—Long context, Long instruction, Long output—and grounds it in the concrete and impactful domain of travel itinerary planning. The formulation is broadly applicable to other multi-constraint tasks.\n2. The MAoP pipeline introduces a two-stage (strategist → planner) design that supports controllable and wide-horizon reasoning.\n3. The use of an event-driven sandbox (Travel-Sim) with LLM-based agents for realistic plan execution and feedback is novel and enables richer, behavior-grounded evaluation.\n\nWeaknesses\n1. The reward model is trained exclusively on Travel-Sim outputs and reused during optimization, risking self-reinforcing biases and reward hacking.\n2. Evaluation is restricted to 7 Chinese cities and 16 traveler profiles, with no evidence of adaptation to unseen geographies, cultures, or constraints.\n3. The fixed action space (transit, rest, dine, sightsee) omits common activities like shopping, queueing, or performances. TPSS may not correlate with actual user satisfaction, and certain travel patterns may be underrepresented.\n4. The paper reports token-level simulation cost but omits end-to-end compute breakdowns across training and inference stages, hindering reproducibility assessment.\n5. The approach is only validated within the Travel-Sim setup. There is no transfer experiment on existing travel planning datasets (e.g., TravelPlanner, ITINERA), which limits understanding of generalization to other task formats.\n\nQUESTIONS:\n1. Have you measured the agreement between RM or PER scores and human ratings (or real travel diaries)? If not, how confident are you in their generalizability beyond your simulator?\n2. Have you considered end-to-end reinforcement learning (RL) training of strategists, rather than relying solely on rejection sampling fine-tuning (RFT)? \n3. If you were to include noisy but realistic actions (e.g., shopping, waiting, transit delays), would the simulation cost become intractable? Are you considering macro-actions or hierarchical planning to address this?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}