Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints
Authors: Dongjie Yang
Chengqiang Lu
Qimeng Wang
Xinbei Ma
Yan Gao
Yao Hu
hai zhao
Venue: NeurIPS 2025 spotlight ()
Keywords: Real-World Planning
Travel Planning
Simulation-based Evaluation
Large Language Model
Agent
Reinforcement Learning
Primary area: applications

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.

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 in- formation and user preferences. While LLMs show promise, existing methods with long-horizer horizon thinking struggle with handling multifaceted constraints, leading travel plan- Wide-Horizon Thinking and Simulation-Based Evaluation for Real-World LLM Planning with Multifaceted Constraints
Dongjie Yang', Chengqiang Lu", Qimeng Wang", Xinbei Ma', Yan Gao", Yao Hu², Hai Zh 'Shanghai Jiao Tong University, Xiaohongshu Inc. (djyang. tonye, stte,sjttumee umabb,, zhachai@cs Jsjtu. 2 flusuo, haclis 1ig 9, yadun, xiahou 0x1 @xiaccongshu.coo
Abstract
suboptimal solutions. Motivated by the challenges of ning, 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, 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.
Introduction
Large Language Models (LLMs) 4 have shown significant promise in planning by generating action sequences achieve specified goals. However, transitioning from controlled environments to the complexities of real-world planning presents a formidable challenge (5) 6,44 defined by the need manage a multitude of diverse and simultaneous constraints. Prevailing methods 7 8 often rely on task decomposition, a linear and sequential methodology that breaks a complex problem into simpler sub-tasks. This approach is effective domains with limited constraints, such as in GUI automation [9] [0] but its feasibility diminishes sharply in real-worke scenarios where constraints are deeply interconnected. The failure of this step-by-step strategy highlights a fundamental mismatch between the problem-solving approach and the non-linear nature of the challenge itself.
The cognitive model underpinning this sequential approach is analogous to that of logical and mathematical reasoning [2] These domains epitomize long-horizon thinking: a deep, step-by- step deductive process that the entire chain of reasoning funnels toward limited set of deterministic
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Long-horizon (e.g., Math Problem Solving)
Wide-horizzn (ragal Planning)
Question
Question
Question
long context
Quaiation
thought
Strafegist
guidence
Output
Output
Output
Output
(a) CoT
(b) Tree of Thought (ToT)
(c) Long CoT w/ RL (d) Multiple Aspects of Planning
Parallel
X
X
Integrated
X
Figure I: The comparison between long-horizon and wide-horizon thinking reveals distinct cognitive approaches. While long-horizon thinking involves deep exploration of single reasoning trajectory, wide-horizon thinking incorporates heterogeneous information and constraints in long contexts by considering various aspects. It necessitates parallel consideration of multiple dimensions, which are subsequently integrated to generate comprehensive outputs.
outcomes. Real-world planning 66 14 [5]. however, demands a fundamentally different cognitive paradigm. It requires an LLM to move beyond linear deduction to instead simultaneously integrate multifaceted constraints and heterogeneous information, necessitating wide-horizon thinking from various aspects rather than hong-horizon thinking. Success hinges less the deductive soundness of a sequence and more on the holistic feasibility of a plan that concurrently satisfies a wide array of interconnected variables.
In this paper, we explore the limitations and potential of LLMs real-world planning through the lens of the travel planning scenario. We conduct preliminary experiments to investigate the LLM''s zero-shot capability of solving the travel planning problem. Alongside long-horizon baselines, we implement simple wide-horizon approach based on aspect-aware question decomposition. Our results indicate that LLMs struggle to address real-world planning problems using fong-horizon thinking, performing notably better with even naive wide-horizon method.
Our preliminary experiments also reveal that this naive horizoo approach, which relies on decomposing the request into various aspects, has its own significant limitations: 1) lack of inter- aspect associations; 2) dependence on well-crafted artifactual guidance: 3) poor inference-time scalability. To address these limitations, we propose "Multiple Aspects of Planning" (MAoP). In MAoP, instead of directly planning. we introduce strategist to conduct the pre-planning using a two-stage strategy. The strategist first analyzes the context and decomposes the planning request into various aspects to be considered. For each aspect, the strategist provides short guidance for the planner to further analyze subsequently. In the second routing stage, the strategist integrates the independent aspects into a coherent planning blueprint, leading to better inference-tima scaling performance with more aspects considered. In the actual planning of MAoP, the planner concentrates on guidance of single aspect per dialogue turn by following the pre-p pre-planning blueprint, progressively constructing comprehensive planning process via multiple turns of dialogue.
While MAoP improves real-world planning, the evaluation of multi-constraint planning remains a major hurdle. The challenges (e.g, from travel planning) are twofold: 1) The subjective nature of travel planning means there is no universal optimal solution, as users weigh constraints like cost and convenience differently, making objective evaluation elusive. 2) The causal dependency within a journey means a single failure can dynamically violate cascade of subsequent, interconnected constraints, rendering simple, static evaluations inadequate. Previous studies [5] 6 [6] introduce constraint pass rates as metrics, which only reflect the partial feasibility of the plan and the satisfaction of coarse-gras -ganined user requirements. These metrics ignore the real-world influence and causal consistency in real travel scenarios, poorly reflecting actual feasibility.
As the proof is in the pudding, we propose a novel agent-based evaluation framework to simulate trip based on the real-world environment. At its core, an L.L M-poweeedd traveler agent executes plan within an event deent-driven sandbox. By leveraging live traffic data from maps and qualitative insights

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from travel blogs, we enable the simulation to organically capture the dynamics and unforeseen events of real-world scenarios. To better emulate real-world individuals, the traveler agent is meticulously designed with diverse personas and a dynamic stamina engine that simulates physical exertion. Beyond the "static" metrics (constraint pass rates), we introduce "dynamic" metrics that the traveler evaluates the plan based on their experience, offering multi-granularity feedback to assess the experience at multiple levels.
Our contributions can be concluded as follows:
We propose MAoP to enhance wide-horizon thinking capabilities for solving real-world planning with multifaceted constraints.
We propose simulation- bbased evaluation framework to evaluate the feasibility and appeal of the travel plan, offering novel insights for assessing complex scenarios.
2 Preliminaries
2.1 Long-Horizon vs. Wide-Horizon
As shown in Figure previous studies 1177 7 have primarily centered on developing CoT methods and their variants solve planning and reasoning problems requiring deep exploration along single trajectory. Deepseek-R1 [18] further enhances the long-horizon reasoning ability by leveraging Reinforcement Learning. In contrast, real-world planning such travel planning necessitates LLMs to: 1) extract pertinent information from long contexts (e.g., tour guides, spatial information, traveler information, etc); 2) conduct deliberate thinking over multifaceted constraints (e.g., real-world constraints and preferences). Travel planning problem does not require the model to reason deeply, but necessitates considering multiple aspects simultaneously in wide-horizon view. In this paper, we investigate the potential of conducting wide-horizon thinking compared to long- hong-ooizoon thinking in real-world planning.
2.2 Wide-Horizon Thinking with Aspect-Aware Guidance
We conducted a preliminary experiment to com- pare the efficacy of long-horizon versus wide- horizon thinking on real-world planning with multifaceted constraints. The input provided to model comprises two components: a rich context and a structured guidance prompt. The context includes real-world information such as traveler profiles, travel blogs, and spatial data (see Appendix C for processing details). The guidance is a carefully crafted instruction that outlines the key aspects to consider when gen- erating travel plan. To establish our long- horizon thinking baselines, we use this guid- ance to prompt two methods: zero-shot CoT and the Plan-and-Solve framework (I] which relies task decomposition. Our approach to naive wide-horizon thinking is centered on aspect-based decomposition. Critically, unlike prior works [20] 21 that decompose a question into sequence simpler sub-tasks, our method
Table 1: The comparison between long-horizon thinking and wide-horizon thinking in travel plan- ning by evaluating the Feasibility (FEA) and Per- sonalization (PER) scores Travel-Sim.
Method
FEA
PER
Qwen 2.5-32B 19
w/ Artificial Guidance CoT
23.3
36.2
w/ Artificial Guidance Plan&Solve
25.0
39,7
w/ Artificial Guidance Wide
31.9
44.1
w/ Self-Gen. Guidance Wide
29.4
42.0
DeepSeek-Ri R1 [18
w/ Artificial Guidance CoT
52.6
62.5
w/ Artificial Guidance Plan&Solvv
57.4
64.2
Artificial Guidance Wide
58.9
68.0
w/ Self-Gen. Guidance Wide
55.2
64.1
breaks the guidance into multiple aspects designed to be considered concurrently. The LLM then independently analyzes each aspect and finally synthesizes the insights from these parallel analyses to generate an integrated output. In Table I we find that naive wide-horizon thinking with aspect-aware guidance significantly improves the travel plan quality with better feasibility and personalization scores. A Athouggh the wide-horizon thinking demonstrates significant potential, well-designed artificial guidance can further enhance the performance over self self-generated guidance. More details can be checked in Section 5 and Appendix D
3

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RFT
long contistt
Pre- planning
Aspect-aware Thinking
Plan Generation
Quastion
Decempoce Fouse
(Aapect User Inte: By andlyzing to profiis the usor ts young....
Gonsidering energetic chocse to visit 3 in ser day
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Format
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Hobel Inft:: The hotel not neor the wulwwy station,
Figure 2: The overview of the MAoP training and inference process.
3 Wide-Horizon Thinking on Real-world Planning
Based on our preliminary experiments, we find that while the naive wide-horizon thinking approach shows promise, it still exhibits several key deficiencies: 1) Although aspect-aware thinking is concurrent, its independence inherently limits the capture of inter-aspect associations; 2) the artificial guidance demonstrates superiority, yet its deliberate design remains challenging to implement; 3) as the number of aspects increases, the performance can not be scaled with more inference-time compute. Therefore, propose the Multiple Aspects of Planning (MAoP), a method that leverages a strategist to conduct the pre-planning to address these deficiencies in naive wide-horizon thinking.
3.1 MAoP Training
The training of MAoP consists of three stages: reward model training, rejection sampling finetuning for the strategist [ and RL training for the planner. As shown in Figure we mainly focus on the last two stages and provide more details in Appendix D
Rejection Sampling Finetuning (RFT) for Strategist In MAoP, as shown in Figure the strategist mainly does two things in -raanlaning: 1) decomposing the original instruction and generating the aspect-aware guidance; routing the planning trajectories to capture inter-aspect associations. These core operations solve the mentioned deficiencies in naive wide-horizon thinking. To train such strategist, we implement RFT while keeping the planner frozen. For each request, we prompt the strategist to first conduct pre-planning for N times, and subsequently the planner to generate final plans. The rejection strategy is to reject the trajectory where all N plans, as defined in Eq. [ fall below a predefined threshold.
RL Training for Planner Following initial cold-start RFT, we conduct RL training using GRPO 11 on the planner to further improve aspect-aware thinking ability. To mitigate reward hacking, we design multi-diimensional reward function. It is primarily guided by the PER score (a composite of five criteria, see 5.1] Appendix E and also includes an auxiliary reward for proper plan formatting. The overall function is defined follows:
We do not further implement RL on the strategist. The RL pipeline has to go through frozen planner to get rewards, making it hard to optimize.

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2(RpER 0.5), if the format is correct
Roverall
where REER [0, (1)
[[[RER 0.5) 1,
if the format is incorrect
3.2 MAoP Inference
3.2.1 Pre-Planning
Decomposition As shown in Figure 2 the strategist accepts the long context and question as the input and decomposes the planning request into various aspects. For each aspect, the strategist generates concise guidance to instruct the planner how conduct thorough analysis. By parallel sampling the strategist multiple times, we can derive large amount of ssspect-guidane pairs.
Routing Different from the naive wide-horizon thinking that treats the aspects independently, the strategist additionally selects and aggregates the aspects to route the best planning blueprint. From the experiments in Appendix D.2, find that if we directly increase the number of aspects by sampling the strategist more times, the planner only benefits from considering 3 -5 aspects without further inference-time scaling capability. It is because more aspects introduce more information but also more noise. To address this, the strategist aggregates the number of aspects into smaller number and constructs coherent planning blueprint. As shown in Figure subsequent aspect guidance is determined through the influence of preceding multiple aspects. The routing process shifts the burden of considering numerous aspects from the planner the strategist, thus enabling the scalability of considering more aspects to further improve the wide-horizon thinking.
3.2.2 Aspect-Aware Thinking
Based the planning blueprint constructed by the strategist, the planner sequentially conducts thinking over the aspects a coherent multi-turn dialogue. With aspect-specific guidance, the planner can conduct more focused and in-depth analysis over the long context from this aspect. After multiple turns of profound analysis, the planner produces the final plan based on the previous wide-horizon thoughts the last turn of the conversation.
3.3 MAoP Distillation for One-Step Wide-Horizon Thinking
Implementing MAoP is complex, involving two models and a multi-turn planning process. We accelerate and simplify this process by distillation. To create high-quality training data for distillation, we employ a powerful teacher model, composed of a strategist and a planner, to generate MAoP samples. From these generated planning trajectories, guidance and then compress the entire aspect-aware thinking and the final aggregation into a single, consolidated output. By finetuning on this distilled data, the model learns execute complex MAoP planning in single inference step. This capability is what we term one-step wide-horizon thinking.
extract the strategist's aspect-specific
Causal Evaluation via Agent-based Simulation
As shown in Table 2 previous benchmarks typically rely on static, rule-based metries, such as pass rates for individual constraints. However, this approach overlooks fundamental truth: travel is dynamic, causal process, not a static checklist. Each event, from a delayed train to physical exhaustion on the first day, directly impacts the feasibility and enjoyment of the rest of the journey. By neglecting these critical causal dependencies, existing benchmarks fail to adequately evaluate plan's 's real-world viability and its capacity to meet traveler's evolving personal needs.
We introduce Travel-Sim, novel benchmark framework utilizing agent- based simulation. In this framework, a traveler agent, embodied by the advanced Gemini 2.5-Pro-Exx 5xp5 (2I), simulates journey according to given travel plan. Throughout the simulation, the agent provides continuous, experience-driven feedback. This dynamic, simulation-based approach inherently resolves the causal dependency issues of static benchmarks while capturing personalized, multi- guati-granuulrrity evaluations from an authentic traveler's perspective.
5

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Table Comparison of evaluation metrics for travel planning between different benchmarks.
Method
Rule-based
LLM-Judge
Multi-Granularity
Causality
TravelPlanner
UnsatChristmas
TravelAgent 6
ITINERA 23
ChinaTravel 16
Travel-Sim (ours)
4.1 Travel Experience Simulation
To simulate a realistic travel experience, we build a sandbox to provide travelers with any real-world information they need, such time, location, transportation, and even sightseeing experiences from blog posts. Similar to role-playing, we set up detailed profiles for traveler agents, including group sizes, character types, ages, genders, budgets, and preferences. Moreover, we design a stamina engine for the traveler. Stamina is closely related to character types (e.g.. young people vs. elderly, family w/ baby vs. family w/o baby) and has significant impact on the travel experience. The travelers of different types have their own rules for stamina exertion when encountering different events.
Event-Driven State Transition The traveler follows the plan p travel in the sandbox. We define the traveler's 's state as Cr = {t. 8, o, e) at step n, where f, I, s, o, e represent the current time, location, stamina, outlay, and the ongoing event. The traveler employs a policy T ,. P). where action an E A and action space A covers basic actions such as Iransiting, resting, dining, and sightseeing. Each action the traveler takes leads new event, which transitions the previous state to the new one. Similar to ReAct (7), the traveler agent first thinks over the current situation from the traveler's perspective and then makes a decision on the next action.
Real-world Information Integration When coming to a new city, the traveler usually starts the travel at the train station airport. The traveler choose action; in most cases, the first step is to proceed to the hotel. We utilize a map API to provide various modes of transportation as references. The traveler comprehensively considers stamina (being tired from the long flight), schedule (next event in the plan), and budget to make a decision. For example, the traveler opts to take the metro to the hotel, the environment updates the traveler's state with arrival time, new location (hotel), new stamina, etc. Among the events, the simulation of activities like sightseeing or shopping, due to the various experiences and interactions, presents a more significant challenge. Although the traveler agent can not physically visit the POI, we utilize a special event agent to generate how the traveler would do the sightseeing by referring the real experiences in the travel blog posts.
4.2 Multi-granularity Evaluation by Traveler
Evaluation Process To capture detailed feedback, we implement multi-granularity evaluation mechanism. Travelers assess their experience across five core dimensions: experience (ex), interest (it), arrangement (ar), stamina (st), and cost (co) (detailed in Appendix [.4). These evaluations are collected at three distinct levels of granularity: per-POI (after each POI visit), per-day (at the conclusion of each day), and per-trip (upon completion of the entire journey).
Dataset Construction We construct variety of traveler profiles, including 16 distinct types of travelers. Each differs in terms of group size, age, gender, stamina level, and preferences. We carefully select 7 Chinese cities that are ideal for tourism as destinations. We have 112 different distinct (traveler, destination, duration] combinations. See more details in Appendix F3

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Qwen
Qwen 2.5-328
Deepseek-Rl--Dise Qwen-7B
FEA
CPH
CPH
CPH
PER-it
80
100
PER-IT
80
100
PER-I
80
100
PER-ar
PER-ar
PER-ar
PER
PER-agg.
PER PER-agg.
PER
PER-CO
PEЛ-co
Deepseek-R1 FEA
Gemini 1.5-Pr--Exp-0325
PER-oX
PER-ox
CPH
CPH
Zera-shat (Long/Artifact.
Zero-shot (Wide/Artifact.)
PER-It
100
PER-I
B0
100
(Lang/ArtiffacL)
MAoP qwen-7B strategist
MAoP qwen-328 strategist
MAoP r1-78 strategist
PER-ar
PER-ar
PER-agg.
PER-agg.
PER-co
Figure 3: The comprehensive comparison results between MAoP and the baseline methods.
5 Experiment
5.1 Evaluation Setup
Methods For the baseline, reuse the setup of preliminary experiments, including zero-shot long-horizon thinking and naive wide-horizon thinking. We add an additional baseline that we implement RL train the models with Long/Artifact. setting using the same training dataset the MAoP. This additional baseline is to compare the finetuned performance between the long-horizon thinking and the MAoP method. For the MAoP, train the Qwen 2.5-7B [9)) Qwen 2.5-32B [19]. and Prepseck-Rl-Diseee Qwen-7B 7B [18] as the strategists and the planners, respectively. We also include Deepscek-RI [18] and Gemini-2.5-Pro-EBn P-0----x-- (2] as additional zero-shot planners. During the MAoP inference, the strategists only route no more than 8 aspects into the planning blueprint for fair comparison.
To simplify MAoP for one-step iide-horizon thinking, we distill the MAoP synthetic data to train Qwen 2.5-3B [9). Llama 3.2-3B (24] and Deepseek-R1-Distte Qwen-7B. The teachers in the distillation are the Deepseek-R1-DD R1-Dstilll Qwen-7B as the strategist and Gemini 2.5-Pro-Exp-0025 as the planner. More training details be checked in Appendix F
Metrics We evaluate the MAoP on our Travel-Sim to showcase how MAoP better deals with the travel planning problem via wide-horizon thinking. We introduce four metrics (with details in Appendix G): comprehensiveness (CPH), completeness (CPL), feasibility (FEA), and personalization (PER), where the last two metrics have been used rule-based metrics. Comprehensiveness (CPH) evaluates how much relevant information is effectively integrated from the long context into the final plan. Completeness (CPL) evaluates the travel plan strictly follows the formatting instructions and basic constraints according to the artifactual criteria.
preliminary experiment. The first two are
The last two metrics, feasibility (FEA) and personalization (PER), are based on the travel simulation. To evaluate the FEA, also named Travel Plan Similarity Score (TPSS), we develop an algorithm that calculates the similarity between the trajectory from the travel plan and the trajectory from the simulated travel, as shown in Figure 5 and Algorithm 3 This metric measures the discrepancy between what planned and the actual execution the simulation. If the planned trajectory is similar to the simulated one, it means the plan is more feasible. The PER is associated with the feedback of the traveler agent after the simulated travel, indicating if the plan personalized and suitable for this traveler. The PER is comprehensive metric, including the evaluation from multiple dimensions and
7

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granularities: 1) dolli-dimension:: The traveler evaluates the travel experience from five perspectives, ie., experience (ex), interest (it), arrangement (ar), stamina (st), and cost (co). 2) Multi Multi-granularigy the traveler conducts evaluation from three levels (feedback after visiting every POI, finishing the whole day, and finishing the whole journey). We aggregate the scores from various dimensions and granularities of travel experience, according to Equation to calculate a final PER score.
Table 3: Comparison between MAoP distilled models and MAoP combinations.
PER
Model
CPH
CPL
FEA
agg.
MAoP
Qwen 2.5-7B (s.) Qwen 2.5-32B (p.)
64.8
62.5
35.2
59.3
68.4
43.2
49.3
61.5
56.3
RI-Distill (s.) RI-Distill 7B (p.)
72.6
76.5
60.7
77.5
86.4
79.3
76.4
87.6
81.4
MAoP Distillation
Llama 3.2-3B (Distill)
61.3
59.2
52.9
62.0
65.2
63.1
62.5
70.5
65.7
Qwen 2.5-3B (Distill)
64.2
65.8
53.1
64.2
65.9
63.4
61.4
73.5
66.9
RI-Distill Qwen-7B (Distill)
78.2
79.2
73.7
84.5
87.2
83.1
76.0
90.2
84.2
5.2 Result and Analysis
5.2.1 Benchmark Performance
As illustrated in Figure 3 for the finetuned models in the first row, the MAoP demonstrates a sub- stantial performance enhancement over the long- horizon thinking (RI. w/ Long/Artifact. baselines, achieving a remarkable 5% to 40% improvement across all planners. Although trained with the same dataset, MAoP achieves higher scores than RL w/ Long/Artifact. in CPL by better constraint compliance and CPH by integrating more details from the long context to the plan. When com- pared with naive wide-horizon thinking (Zero-shot w/ Wide/Aritifact) MAoP excels especially in stronger strategists, as evidenced by higher FEA and PER scores, suggesting that the strategist plays an important role in the planning process with performance better than artifactual guidance. For advanced models as zero-shot planners, e.g., Deepseek-PP R1 and Gemini-2.5, the strategist also significantly boosts the performance in travel plan- ning compared zhoo baselines.
R1-Distill Qwen-7B +
90
Gemini S--Pr--xxp-022 (p.).
80
Owen 2.5-7B (s.)
Gemini Pr5-Pro 2.5-Pro-Exp-022 (p.)
70
FEA Score
60
R1-Distill Qwen-7B (s.)
Qwen 2.5-78 (p.)
40
2.5-7B Qwen 2,5-78 (p.) -
30
2
8
16
5.2.2 Inference-Time Scaling Capability of the Strategist
Number of Sampled Aspects (Log)
Figure 4: We experiment two strategists (Qwen 7B & RI-Distill Qwen-7B) and planners (Qwen 7B & Gemini 2.5) to showcase the scaling capability of the strategists.
As shown in Figure consistently improve the performance by scaling up more considered aspects. We find that even if the model size is the same, the stronger strategist with thinking (RI-Distill 7B) has better scalability, especially for advanced planners like Gemini 2.5. In contrast, the Qwen 2.5-7B model has limited scalability, because cannot effectively route a suitable planning blueprint when dealing with increasing aspects.
find that strategists can

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5.2.3 MAoP Distillation
distilling RI-Distill 7B (s.) + Gemini 2.5 2.5-Pro-Exp-0325 (p.) enables 3B sized models to outperform the MAoP combinations even with larger model sizes. For RI-Distill 7B with the thinking mode, also distill the thinking part of planning from the Gemini. Compared to the MAoP combination of the same models, RI-Distill 7B (Distill) achieves better performance with distilled one-step vide-horizon thinking. These results demonstrate that distillation from advanced models to smaller models achieves substantial performance improvements even without the original multi-turn MAoP process. The enhancement becomes increasingly pronounced the capability gap between teacher and student models widens, even surpassing the MAoP training models.
As shown in Table B
5.2.4 Emergence of Spontaneous Behaviors in Causal Travel Simulation
Given the causal nature of an itinerary, the traveler agent exhibits emergent behavior by spontaneously adjusting its predetermined plan in response to unfolding events. From trajectories in Figure [ this elderly couple chooses not to have dinner in Nanmen Lamb Hot Pot because they are too tired travel another place, especially since they just suffered from long train trip to Beijing this morning. This indicates that neglecting causal dependencies in lead to significant deviations from the real-world situation.
planning evaluation can
A 3-day trip to Beijing
Trajectory from plan
Trajectory from simulation
Day 1:
10:00 Beijing West Rallway Station
Day
elderly couple
11:00
Beling West Raiwaay Station
and history and prefer a relaxed
who love culture
way of traveling.
20:00
18:00
14:30
Kapok Hotel
Narmen Lamb Hot Pot
Shichanal
Restaurant
dinulation
The older couple
seps this actilly
18:38
14:02 Snichahai
12:04 Siil Mintu Restaurant
10:38 Kapok Hotel
Kapok Hotel
They enjoy visiting
Day
and siglooeeing
Boiling
Day
temples and
08:00
Kapok
savoring local
Museum (Forblidden City)
06:09
The Palace Museum (Forbidden City!
during their
and comfort
cuisine, while also
emphasizing rest
14:30
19:30
12:30
17:30
Donglaishun Restaurant
Kapok Hotel
Jingshan Park
Comer Tower Café (Forbicden City)
12:39
14:11
16:06 Donglaishun Restaurant
Jingshan Park
The Corner Tower Café (Forbidden City)
Hotel
journey.
Day
Day 3:
Kapak
06:00 Kapoi Hotel
08:30 Yonghe Temple [Lama Temple)
09:34 Yanghe Temple (Larna Temple)
12:00 Huguo Temple Snacks
11:21 Huguo Temple Snacks
13:30 Kapok Hotel
14:00
Kapok Hotel
14:00 Beijing South Railway Station
Beling South Rallay Station
Beijing Railwaa Station
Figure 5: The simulation trajectory is not always consistent with the planned one because the traveler agent can change the subsequent itinerary based on the current situation. The FEA score is used to calculate the similarity of these two trajectories.
Related Work
LLM Planning
The planning capabilities of LLMs have become a key research focus due to Recent studies have advanced
their potential as autonomous problem-solving agents 25 7 26
these capabilities through developments in task decomposition, multi-step reasoning [17 27). and adaptive planning [28] 29]. While these planning algorithms have shown promising results 33 31], their planning scenarios are limited to simple tasks with a single objective function. For complex real-world planning like travel planning, previous researchers focus on how to preprocess the various information and constraints to make LLM easier understand, but few them explore how to enhance the planning ability in this complex scenario.
Generative Simulation LLM agents begin to exhibit strong capabilities in mimicking human behaviors 32 33 Some researchers (34
35 36 have been investigating the behavioral patterns of human-like LLM agents within sandbox environments, focusing on simulating human social interactions and lifestyles to study their social behaviors. Although simulation- evaluation has become a prevalent methodology in robotics research [37] 38), our work represents the comprehen-

---

sive investigation into evaluating complex task performance using LLM agents within simulated environments.
7 Conclusion
We propose MAoP to enhance wide-horizon thinking for solving real-world planning problems with multifaceted constraints. In addition, propose Travel-Sim, evaluation benchmark that leverages agent-based simulation to offer causal and multi- graniiarraulrrtty evaluation in travel planning scenarios. Our contributions advance the wide-horizon thinking capabilities of LLMs in real-world planning and offer novel insights for evaluating sophisticated scenarios through agent- simulation.
Acknowledgement
Dongjie Yang and Hai Zhao are with the Department of Computer Science and Engineering, Shanghai Jiao Tong University; Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University; Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3.
This paper was completed during Dongjie Yang's internship at Xiaohongshu Inc. and was supported by the Joint Research Project of Yangtze River Delta Science and Technology Innovation Community (No. 2022CSJGG1400).
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Appendix Contents
Limitations and Ethics Statement
14
A.1 Limitations
14
A.2 Ethics Statement
14
License
14
Preprocessing Framework
15
Proactive Consultant Agent for Implicit Preferences Elicitation
15
Preference-aware POI Selection
17
Cluster-basee Spatial Optimization
17
Entire Preprocessing Workflow
18
Preliminary Experiment Details
19
D.I Detailed Sctup
19
D.2 Scaling Capability of Naive Wide-horizon Thinking
20
MAoP Experiment Details
21
E.1 Training Details
21
Inference Detail
22
Simulation and Evaluation
24
Simulation Process
24
Case Study
26
Travel-Sim Dataset Card
29
Multi-granularity Evaluation by Traveler
30
+
Human Verification for Simulation
basedd based Evaluation
Metrics
32
G.1 1
Rule-based Metrics
32
Simulation based Metries
32
13

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A Limitations and Ethics Statement
A.1
Limitations
While our proposed method (MAoP) and agent-based simulation benchmark (Travel-Sim) demon- strate significant advancements in complex travel planning and evaluation, several limitations warrant discussion.
A.1.1 MAoP Limitations
Though MAoP significantly enhances wide-horizon thinking with deliberate pre-planning by the strategist, MAoP still exhibits a few limitations: 1) MAoP requires the collaboration between the strategist and planner, and the conduction of aspect-aware thinking in multiple turns of dialogue. This complicated process costs more inference time than directly using COT. To alleviate this, we propose that the MAoP distillation compared to COT. 2) We find that weak strategists, especially those with sizes smaller than 7B, can cause severe performance degradation due low-quality planning blueprint and poor inference-time scalability. The recommended way to enhance wide-horizon thinking ability for smaller models is to distill the outputs of the advanced model after MAoP training.
compress this complicated process, gaining similar efficiency
A.1.2 Travel-Sim Limitations
The Travel-Sim is the pioneer in evaluating with agent-based simulation, offering novel solution for complex scenario evaluation, e.g., travel planning. Agent-based simulation and evaluation have several limitations that can be discussed: 1) Similar to other LLM-based evaluation benchmarks, the "traveler", which is an LLM, has the potential bias and unreproducibility over the evaluation. To mitigate this limitation, we stipulate the agent model to be Gemini-2.5-Pro P-o--P-r---O-- Exp-0325 [21] using greedy decoding (0 lemperature) for reproducibility. We also verify the consistency between LLM- based and human-based evaluation in the Appendix F.5 showcasing the 92% consistency in PER score. 2) Although Travel-Sim takes causality and real- woal-world information into account, we do not include all the emergencies that may happen in the real trip, especially some force majeure and human factors. For example, bad weather or delays caused by carelessness also significantly influence the trip. 3) Although Travel-Sim offers causal and multi-granularity evaluation, the whole evaluation process is time-consuming and costs about 20K tokens per sample. To evaluate the 12 samples of the entire dataset, cost around $12 by consuming about 2M tokens in total.
A.2 Ethics Statement
For the traveler profiles the training dataset and Travel-Sim, we do not use any personal information to collect traveler profiles. The diversity of travelers is manually designed, and data is synthesized using Gemini-2.0- Pr..0--r--xx-220- [21]. For the sightseeing event agent that uses travel blog posts to simulate travel experience, collect blog posts from Red Note (Xiaohongshu) that may contain personal information and copyrighted items. Therefore, people using the blog posts should respect the privacy and copyrights of the blog post owner and strictly agree to the license in Appendix
B License
By downloading or using open-source data, you understand, acknowledge, and agree to all the terms the following agreement.
ACADEMIC USE ONLY Any content from the Travel-Sim dataset is available for academic research purposes only. You agree not reproduce, duplicate, copy, trade, or exploit for any commercial purposes.
NO DISTRIBUTION Respect the privacy of the personal information of the original source. With- out the permission of the copyright owner, you are not allowed to perform any form of broadcasting. modification, any other similar behavior to the data set content.
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RESTRICTION AND LIMITATION OF LIABILITY In no event shall we be liable for any other damages whatsoever arising out of the use of, inability use this dataset and its associated software, even if we have been advised of the possibility of such damages.
DISCLAIMER You are solely responsible for legal liability arising from your improper use of the dataset content. We reserve the right terminate your access the dataset at any time. You should delete the Travel-Sim dataset if required.
You must comply with all terms and conditions of these original licenses, including but limited to the Google Gemini Terms of Use, the Copyright Rules & Policies of Red Note (Xiaohongshu). This project does not impose any additional constraints beyond those stipulated in the original licenses.
C Preprocessing Framework
In this section, we introduce how to collect and preprocess the heterogeneous information as the "long context" for travel planning. This context information serves as the information source input for both the training dataset and Travel-Sim.
We break the information and constraints necessary for planning into different categories shown in Figure 6 We leverage our framework collect and preprocess them for further planning. The framework consists of multiple modules that collaborate with each other.
Preferred type: historical
()) Forbidden City:
Must-visit POls: Forbidden City, Great Wall,
Travel blog posts
Interested event/Show: night life, cutural show,
Location: 116.40, 39.92
Physical condition: tinerrry packed
Opening: 8:00 16:00
Tickst RMB; booked
Preference
days ahead Tour Router
Travel Tips:
"type": "couple".
*desc": 'A vibrant in
User Preference
Info POIs
The Great Wall:
3
their 20s, enjoying local food,
culture, nightife, ....
"people": [['aee::
"gender:: "male"),
Spatial Info of POIs
f"age":
"gander": "famale"") 'H,
POI locations: Which POIs can be visited logether? |distance, transportation)
'energy":
"budee": /dly".
POI Which
"preferences":
Spatial Optimization
(opening hrs, shortest routes, best viewing time).
food': "local food
"shapping": "fashinn.... ...]
Cluster-awart
ChuRtor 2: NW
Spatial Preference of User
grouped.
Direction-aware: Directions SEI clusters to hotel. TSP Optimization:
1.5ke
Hotel
Transportation preference: Due to budget
public transport
3km
walking, less taxis.
Hotel location preference: city center, neer metro station
Finding shortest route.
Cluster SE
Figure 6:
The decomposition of multifaceted constraints and heterogeneous information to be considered when creating a travel plan.
C.1 Proactive Consultant Agent for Implicit Preferences Elicitation
Users often provide insufficient information, making challenging for the assistant devise an effective plan. To address this, we not only extract preferences from user requests but also proactively ask questions to elicit implicit preferences. We design two agents to gather users' travel and accommodation preferences, respectively. The travel consultant agent collects basic trip details (group size, duration, traveler type) and integrates insights from travel blogs identify potential challenges for travelers. The accommodation consultant first inquires about hotel preferences and then searches travel blogs and maps to identify optimal hotels. Through iterative interactions, agents uncover implicit preferences for travel planning and accommodation.
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C.1.1 Travel Consultant Agent
We develop an LLM-based agent to serve as a travel consultant, designed to comprehensively dig out the user's implicit preferences. We design six aspects that the travel consultant agent needs to take into account, as shown in Table 4
Table 4: Considerations for implicit preferences elicitation.
Type
Description
Duration of Visit
How long does the user wish tour, is it day, two days, longer?
Group Composition
How many people are the group, what is their approximate age, and are there any special groups, such as the elderly children?
Attraction Type
What type of attractions does the user hope to visit, cultural relics, natural scenery, or shopping and entertainment?
Number of Attractions
Does the user want to visit as many attractions as possible, or not too many?
Mode of Transportation
Under replaceable circumstances, what mode of transportation does the prefer: walking, cycling, public transport, taxi, or driving?
Special Requirements
Does the user have any special needs or preferences, such as particular require- ments for shopping or sightseeing?
Based on the initial user request, the travel consultant agent will proactively inquire about several questions concerning six aspects to better understand the traveler's situation. Upon gathering sufficient information across these six aspects, the agent will synthesize comprehensive conclusion in the form of a traveler profile. The agent will subsequently present this profile to the user for review, allowing them to verify the details and provide any necessary additions or corrections through further interaction with the agent.
Instead of asking the same fixed questions, travel consultant agents have the following advantages: 1) Customized Service: we observe that the LLMs will adjust the questions based on the destination and the traveler type, and also give relevant advice guide the user. 2) Feedback: The agent can receive feedback from the user and raise further interaction to refine the traveler profile.
C.1.2 Hotel Consultant Agent
The selection of the hotel is the key part of travel planning, which previous researchers seldom take into account. The hotel influences not only the accommodation experience but also the planning and logistics of the entire trip, concerning the hotel locations. We develop an LLM--baeed agent to serve as hotel consultant agent to recommend hotels based on travelers' preferences.
We decompose this process into five phases: 1) Inquiry phase: similar the travel consultant agent, the hotel consultant agent has to inquire about the user preferences. 2) Online searching phase: after understanding the hotel and POI preferences, the agent can search the internet to find the compliant hotels from the latest tour guides. 3) Map searching phase: the agent leverages the map API to search for hotels near the most- visited locations and conclude a list that meets the requirements. 4) Conclusion phase: the agent recommends the hotels from the travel blog posts and the map API. 5) Feedback phase: the agent receives the feedback and revises the recommendation.
To be specific, in the inquiry phase, we consider four aspects, as shown in Table 5 After several proactive interactions, the agent will stop asking if has collected enough user preferences. Besides the hotel preference, the hotel agent can leverage POI preferences and spatial information (POI clusters) from other modules. In the online searching phase, we use the search API provided by the Red Note (Xiaohongshu) app, popular Chinese app focused on lifestyle content sharing. We use the Red Note (Xiaohongshu) search API search for blog posts related hotel recommendations. In the map searching phase, we use the Amap (Gaode) Map API, a Chinese map app similar to Google Maps, to search for hotels near the most-visited locations. The probable most-visited locations are predicted by the agent based on the given information. In the conclusion phase, the agent will recommend the hotels provided by the blog posts and the map API by comprehensively taking the locations and preferences into account.
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Table 5: Considerations for accommodation planning.
Type
Description
Hotel Consistency
Does the user prefer to stay the same hotel throughout the trip, or change hotels according to the attractions?
Accommodation Budget
What the user's budget for accommodation?
Location Preference
Does the user have any requirements for the location the accommoda- tion, such as being in the city center, near attractions, etc.?
Facility Requirements
What facilities does the user require from the accommodation?
C.2 Preference-aware POI Selection
As have the user preferences collected by the travel consultant agent, leverage the information to find the potential POIs and activities that will appeal to users. We first leverage L.MSs to generate several search queries based user preferences and use the Red Note (Xiaohongshu) API to search the relevant blog posts. Based on the preferences, we use LLMs to extract the POIs that match user preferences. We use the Amap (Gaode) map API to obtain the POI meta information. We finally filter and deduplicate them to get list of candidate POIs.
C.3 Cluster-based Spatial Optimization
To enhance the coherence of travelers' journeys and minimize unnecessary back-and-forth travel, similar to ITINERA [23], we cluster the POIs based on geographical distances from each other. This way, visitors can follow a well-planned, spatially coherent route for their tours, for example, visiting the POIs in the same cluster in one day, to enjoy a travel experience that is both efficient and pleasant. We use K-means++ cluster the POIs, where the number of clusters is determined jointly by the number of travel days and the candidate POIs.
After obtaining the POI clusters, two perspectives: intra-cluster features and inter-cluster relationships, to enable L.LMs to understand the spatial information.
describe the spatial distribution characteristics of POIs from
Algorithm 1 Find Shortest Route in the POI Cluster
Input: locations set L, start location name end location name
Output: shortest path shortest distance diotal. step distances {d, {d.) function FINDSHORTESS CESORREESRRRRUTRRT ROUTE(T. 8, e)
Extract other locations excluding and e: +- floc E loc & {s,e)}
Let <- [0]
if n then
Compute all permutations of O: 11 {- is permutation of O} Find shortest path and distance from II:
(P, drotai)
+ arg min Σ i=
(T(]i] [[][]]] 1]])
else
Get initial path by finding nearest neighbors: P NearesNeeighhor s,e)
Optimize path using 2-opt search: t- TWoOOpseercc(PP.C..s C, e,
Calculate shortest distance: diotal + IP|- CclculaceDistancc([[[[]]] C[P[i II]
i=
end if
PP-- +- (CClulaeDDsaaneeCIPPPiiCC111 +|)) Calculate step distances: (d.)( return (P, Ptottat,, (P-)) end function
C.3.1 Intra-cluster: Finding the Shortest Routes within the Cluster
Since the POIs in the same cluster are relatively close to each other, travelers have the potential to visit them in sequence within single day. In order to find the shortest route that does not retrace
17

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any part of the path, this problem can be formulated as a variant of the Traveling Salesman Problem (TSP). As the classic Traveling Salesman Problem (TSP) is an NP-hard problem in combinatorial optimization, we use brute force to solve clusters with a small number of POIs (fewer than 6). and for those with larger number, we use the 2-opt algorithm to find an approximate solution. in Algorithm| [ we have the start location & and end location which are generally set as the hotel. Locations besides and e are the POIs in the same clusters. We calculate the shortest route that starts at the hotel and ends at the hotel after visiting the POIs.
shown
C.3.2 Inter-cluster: Relative Cluster Locations to the Hotel
The locations the POI clusters relative to hotels are also key information for LLM to know the spatial distribution of all POIs. We mainly calculate the direction and distance from the cluster center to the hotel, as shown in Algorithm To make directions easier to understand for LLMs, we can simplify directions into eight basic compass points.
Algorithm 2 Calculate Directions and Distances
Input: Start point S (A., ox), set of target points T = {TT, C.) where C, (A₃, de) Output: Set of tuples O containing direction D, and distance de for each target point TŞ function
7)
Initialize empty empty set
for all (T;, C.) T do
Convert coordinates to radians: X₂, S'a Xo of
Calculate geodesic distance d, between S and C₁ Compute bearing 0':
0' arclan 2 (sin X,) cos( cos( sin( sin (o,) cos(e)) cos(X; X2))
Normalize bearing 0: 0 = (0' 180 + 360) mod 360
Map bearing to cardinal direction D,: D; = directions directionsfruun((//18) mod 8) Add tuple (D; d4) end for return end function
C.4 Entire Preprocessing Workflow
Implicit Preferences Elicitation
Cluster-based Spatial Preprocessing
Plan 3 day trip Beijing..
Intra-cluater & Inter-oluster
Hote!
Hotel Consultant
Clustering
Map
Cluster SB
Preference-aware
200 tooe
Blog post
Candidate POI list
Spoopen [Tapet
seerch
1. Great Wall Forbioden City
Frain
Travel Consultant
Tesegg: (iee
Twan Nool
Extraction
Rank
Figure 7: The entire workflow of the preprocessing framework.
Workflow The preprocessing framework consists of these modules above that work together in collaboration rather than operating independently. As shown in Figure 7 we present the entire workflow framework. First, as user raises a request for planning trip, the travel consultant agent gathers user preferences and information by interacting with the user. Based the preferences, we search the relevant blog posts and extract the candidate POIs. We perform clustering on the candidate POIs. In the next step, the hotel consultant inquires about the hotel preference and
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comprehensively considers all the information (user profile, hotel preference, and spatial information) recommend hotels. After we have the location of the hotels, we conduct intra-cluster and inter- cluster analysis.
Table 6: The composition of the long context input.
Type
Source
Description
User Profile& & Preferences
Travel Consultant
The information of the traveler/traveler group and corresponding preferences.
Hotel Information
Hotel Consultant
The location and other information about where the user lives.
POI Blog Posts
Blog Post Search
The relevant blog posts that provide sight- seeing tips for each POL.
Mier-Cluster Information
Spatial Preprocessing
The shortest routes within the POI cluster.
Inter-Cluster Information
Spatial Preprocessing
The relative direction and distance from clus- ter centers to hotels.
Final Input Context for LLMs After we collect and preprocess the information, organize it as the context part of the LLM Input. To give an intuitive view, we structure the context in Table is noted that for every candidate POI, we search for the most relevant blog post that provides the latest sightseeing tips. We can not depend on the intrinsic knowledge of LLMs about the POIs, which is outdated and inaccurate most of the time. The latest blog posts provide most of the information and tips for visiting the POI, for example, the optimal sightseeing routes, which help LLMs to optimize the itinerary and personalize the plan.
D Preliminary Experiment Details
D.1 Detailed Setup
Naive Wide-Horizon Thinking with Artifactual Guidance In the preliminary experiment of exploring naive wide-horizon thinking, we craft the artificial guidance that includes series of aspects to be considered. manually design five aspects as shown below:
Aspect Analyze Key information the Traveler's Question Regional Scope (e.).. Beijng, Shanghai Inner Ring):
Needs
Time Range iduration of the tri)::
Group Composttion (couples, familes, etc., and the number of people): Special Prefarences/ /entrranca//Rqquureeeettts ispacific
travel style, etc.):
Aspect Analyze the POI Selection and Combination
Interests and Hobbies: Select suitable tourist attractions based on the travelers' preferences and group composition, Are there any specific tourist attractions the travelers particuterly Interested in?
Requirements: sightseain??
any specific
constraints (e.g.. opaning hours) visiting
attractions
optimal time
Duration Recurements: How long wil each POI visit take? Can the duration be adjusted flexibly according to the travelers' preferences?
Special Experiences: Are there any unique activiies or experiences available? Any performances special events?
Aspect Analyze the Tourist
Planning
minimizes backtracking? Attraction Locations: Take the travelers' accommodation into account when planning the distances and directions between tourist attractions. For more distant attractions, should transportation time be considered? Can nearby attractions be grouped for the same day? Transportation Suggestions: Offer sultaole transportation recommendatios based on the time factored into schadule.
on [Spatial Optiriizztion Analysis], is a more efficient, logical
and route plan. Ensure transportation
> Aspect IV: Analyzo the itinerary's Comfort Level Travel Pace: Adjusi the pace according to the group demographics and physical starina. Balance sightseeing, rest periods, and free time to ensure pleesant and manageable experience. Scheduling:
number attractions schedules mht lead fatigue overty sparse plans that risk boredom.
day according travelers' Interests energy
overly packed
Dining Racommensations: Schedule meal times thoughtfulyy and provide dining suggestions. Good meal planning ensures: travelers remain energized and comfortable, avoiding exhausiion or hunger.
Aspect Analyze Travel Strategles Precautions
Ticket Purchase: Determine whether tickets need be bought advance, methods for purchasing, ticket prices, discounts, and whether reservations are required.
Scanic Spot Guidelines: Offer tps and precautionss to avoid potential inconveniences. Provide recommended routes and suggestad sequences for touring the scenic spots.
19

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Naive Wide-Horizon Thinking with Self-Generated Guidance Instead of using the artifactual guidance, we leverage the LLM first analyze the aspects to be considered and generate the corresponding guidance. This process can be perceived as the zero-shot strategist, as shown below:
--s-- (Avallabe Information (Start))
Enformation)
[A-[Avvllblle Information (Endi)
[User instruction (Start))
(istruccton)
-Usrr Instruction [End))
(Avallabie Information, multpple sub-steps based on different aspects. do not need to futill request of the instruction [Usar Instruction), but only provide various aspects and suggestions that help address the instruction, For each thinking aspect, you should provide datalied guidance to facilitate more specific thinking in the next step, and attempt to refer information from (Available Information). Additionally, you should priortize these thinking aspect and so ont. Therefore. this coherence thinking process will more effectively address the user's instruction.
analyze
Instruction]
user's instruction
accordance with thinking process (starting thinking
1, followed
Reply in Markdown format:
#n (Aspect 1)...
.....Prreid guidancee quote information, to
instruction)
An (Aspect 2)...
...oviiee guidance + quote information, no need to address the user instruction)
D.2 Scaling Capability of Naive Wide-horizon Thinking
We investigate the potential benefit of scaling inference-time computation with more aspects for naive wide-horizon thinking.
Normalized Attention Score
User Info
aspoct(s)
aspectis)
0.30
aspact(s)
aspect(s)
aspectis)
aspectis)
0.4
3 espect(s)
aspect(s)
0.25
3 aspect(s)
aspect(s)
0.20
aspect(s)
aspect(s)
aspect(s)
aspectis)
0.3
aspectis)
5 aspect(s)
0.15
aspectis)
POI info
Spartel
0.15
0.2
0.10
0.10
0.1
0.05
0.05
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Normalized Position Context
Normalized Position in Context
Normalized Position in Context
0.85
0.70
0.80
0.60
0.65
0.75
0.55
0.70
0.65
Context
0.60
0.50
0:55
0.45
11k
0.45
14k
0.50
17k
0.40
0
2 Number of considered aspects
4
1
3
5
Number of considered aspects
Number of considered aspects
7B model
32B model
72B model
Figure 8: When LLMs consider different numbers of aspects, we analyze the attention pattern of the context (excluding the instruction) on different sizes of Qwen-2.5, varying from 7B, 32B, and 72B (from left to right). (Upper row) We present the distribution of attention scores across the context, aggregated from the output tokens. (Lower row) Additionally, context length (number of context tokens) by examining the context awareness scores. Our analysis reveals that larger models exhibit an enhanced capability for wide-horizon thinking, enabling them to focus on broader range of information within longer contexts by considering multiple aspects simultancously.
investigate the impact of
20

---

Qwen 2.5-7B
Qwen 2.5-32B
Qwen 2.5-72B
Aspect
CPH f
CPI. f
FEA f
PER
CPH
CPI.
FEA
PER
CPH
CPI.
FEA
PER
0
18.2
40.2
15.2
27.8
39.6
51.1
21.7
34.6
50.4
62.5
37.0
44.0
1
20.3
39.5
14.4
28.5
42.8
53.3
25.4
33.3
51.4
60.3
37.2
43.4
2
25.3
36.2
19.0
30.6
47.0
50.1
31.4
38.7
54,9
60.2
39.2
46.0
3
31.5
37.3
16.3
32.6
49.9
50.4
33.0
42.3
56.5
62.9
41.5
49.7
4
29.8
37.8
14.5
29.4
52.9
47.2
32.0
43,5
60.0
61.7
41.8
52.5
5
28.3
38.0
17.8
27.7
52.7
48.8
31.9
43.1
60.3
59.5
42.1
53.5
Table 7: Comparing the effectiveness of wide-horizon thinking when considering different number of aspects by quantitative metrics.
Experiment Setup We investigate scenarios ranging from 0 aspect (i.e., zero-shot CoT baseline without artifactual guidance) maximum of artifactual aspects considered. We evaluate how effectively context is considered in the thinking process by analyzing attention patterns with respect to context. Besides attention patterns, we evaluate if wide-horizon thinking is effective and enhances the quality of travel plans by leveraging four metrics: CPL, CPH, FEA, and PER scores. We conduct the experiments on Qwen-2.5 [19] with different sizes, including 7B, 32B, and 72B.
Result Analysis In the upper part of Figure 8 we show curves of the attention score distributions over the context (excluding the instruction). The attention scores are aggregated from the output tokens and then averaged across layers and attention heads. We observe that larger LLMs (34B and 72B) exhibit higher attention scores across the context when considering more aspects, indicating strong ability to focus on relevant details. In contrast, the smaller LLM (7B) does not follow this pattern, achieving its highest scores when two aspects are considered. In addition, we introduce another indicator, dubbed context awareness score Sc, to indicate the proportion of the most attended tokens in the context out of the total, as calculated in Equation 2
S.
LA (A)),
(2)
where A denotes the attention scores over the context from the output tokens. In the lower part of Figure 8 for larger models like 72B, as the context length increases, the S, curves of longer length rise more steeply. indicates that by considering more aspects, LLMs can capture finer details in longer contexts. This phenomenon emerges only when models are large enough, as smaller LLMs do exhibit explicit improvements in S. with long contexts.
As shown in Table 7 larger LLMs demonstrate enhanced travel planning capabilities as they con- sider more aspects, achieving progressively higher FEA and PER scores. However, this improved performance comes at the cost of formatting compliance, as evidenced by decreasing CPL scores.
E
MAoP Experiment Details
E.1
Training Details
E.1.1
Reward Model
Training Dataset As shown in Table 9 we collect 14 diverse cities as destinations and 37 traveler types. Based on these destinations and travelers, first synthesize 1K travel planning requests. We then separately use 2emmin-2.0-Pro--Exp-00000 and Qwen 2.5-32B to directly generate travel plans. For about 2K generated plans, we conduct the simulation-bart based evaluation get the PER scores as the reward score labels.
To avoid reward hacking, instead of directly predicting the reward (PER-agg- score), the reward model required to generate the corresponding scores for the five criteria the PER score and aggregate them into the final reward.
Model Structure
21

---

Training Setup We conduct the pointwise generative reward modeling. We use the travel plan as the input. Instead of directly using the aggregated scalar score calculated in Equation we extract the assessment text of the traveler and the corresponding PER score after the whole journey as the output. We use 8 H800 GPUs to finetune the Qwen 2.5-7B model for epochs with learning rate of 2e-5, global batch size of 64.
E.1.2 Strategist
Training Dataset To train strategist with RFT, we construct the training dataset consisting of 50K samples with 120 distinct traveler types and 20 Chinese cities. We first conduct RFT on Deepseek- RI-Distill Qwen-7B to get the imitial RFT data. We reject the sample that gets the average PER score lower than 40 after sampling N times. We use 32 H800 GPUs train Deepseek- Distill Qwen-7B for 3 epochs with learning rate of 2e-5, global batch size of 128.
After training the Deepseek-RR Seepseck-R1-Distiee RI-Distill Qwen-7B, we finally collect 22K RFT data with rejection sampling outputs. We reuse this 22K data to finetune the Qwen 2.5 7B and 32B. We use H800 GPUs to train Qwen 2.5 7B for epochs with a learning rate 2e-5, global batch size of 64. We use 32 H800 GPUs to train the Qwen 2.5-32B strategist for epochs with learning rate of le-5, global batch size of 128.
Training Setup
E.1.3 Planner
Training Dataset We first train the planner using the 22K data above as the cold start for subsequent RL training. To train the planner with RL, we additionally construct the training dataset consisting of 20K samples.
Training Setup We conduct the standard GRPO [1] using the veRL. [39 framework. We use 32 H800 GPUs to train Reepseek-R1-Distiie Qwen-7B, Qwen 2.5-32B, and Qwen 2.5-7B, for 200 steps with learning rates of le-6, 5e-7, le-6, respectively, and the KL loss coefficient of 0.001. We use the KL penalty to prevent severe biases. For loss calculation, we mask the aspect-aware guidance from the strategists, preventing the planners from generating the guidance by themselves.
E.1.4 Distillation
Training Dataset We reuse the input of the 22K data and use Deepseek-R1-Disse Qwen-7B (s.) + Demmint-.5-Pro-Expp---
(p.) generate the output. For each sample, we sample the output times. We use the reward model to filter out the output whose reward score is lower than 80. We use the filtered dataset consisting of 15K samples to train the Qwen 2.5-3B, Llama 3.2-3B, and Deepseek-Ree Biieeee--R--iisseee R1-Distill Qwen-7B. For the first two smaller models, we discard the thinking part. For Deepseek-R1-De -I-istilll Qwen-7B, we use the thinking part from Gemini-2.5. P.r-----03-.--
Training Setup We use 32 H800 GPUs to train Deepseek-Distil Qwen-7B for epochs with learning rate 2c-5, global batch size of 128. We use 8 H800 GPUs to train Qwen 2.5 3B and Llama 3.2 3B for 5 epochs with learning rate of 2e-5, global batch size of 64.
E.2
Inference Details
E.2.1 Inference for Strategist
Decomposition
The first stage of pre-planning is to decompose the planning request into multiple aspects to be considered and provide guidance for further analysis. Here is the prompt used in the RFT and inference:
22

---

(Avalabbe Information (Start))-
Information)
-...-- [Available Information (End))--e
[User instruction (Start))
(istruccion)
-User Instruction [End))
the Instruction the request Instruction [User instruction]. only provide various aspects and suggesiions that help address the instruction. For each aspect, you should provide detailed guidance to facilittte more spectfic thinking the next step, and attempt to refer information from (Avallable Information].
instruction) considering [Avaliable Information), muttple different aspects considered do
analyze instruction
Reply Markdown
in (Aspect 1)...
(Provide guldance for further thinking + quote information, na need to address the user instruction)) an (Thinking Aspect 2)...
... guidance thinking quote
address instruction)
Routing If we apply these aspects for subsequent analysis following the naive wide-horizon paradigm, the planner's performance will exhibit severe scalability limitations as the number of aspects increases. Therefore, the strategist reorganizes these aspects and integrates them into coherent planning blueprint. Here is the prompt used in the RFT and inference:
----- (Avallable Information (Start))
(nnormation)
[Available Information (Endi)
[User instruction (Start)--..
(instructian)
-ee.e--Uss Instruction (End)]
[Aspect Reference for Further Anelysis (Start))---
(aspect)
(Aspect Reference for Further Analysis (End))
We have analyzed the Instruction In [User Instruction] with considering the [Avallable Information), and break down the user's instruction multiple different aspects next step, and attempt refer information from (Available Informatinn..
anelysis. For
guidance faciitate more specific thinking
However, the aforementioned aspects for further analysis may involve overtapping or fragmented parts. Your task to reorganize them unifled planning blueprint:
exclusively instuctoon-oriented perspectives and corresponing guidance by consolicating redundant aspects into more refined analytical dimansions.
Develop systematic planning blueprint where each subsaquent aspect logically incorporates insights from preceding aspects, You planning
should prioritzze these aspects accordance guidance. and on). need provide caherence plarning bluaprint more effectivaly helps planning model further conduct n-depth anaylsiis address the user' instruction, so **yau do not need to directly address the user instruction".
(starting with
guidance. followed by aspect 2
Reply Markdown format:
First, (Aspect 1)...
.....Peeevi guidlance quote information, no need to address the user instruction)
## Secondly. (Aspect based previous cantext)...
|Provide guidance information, need address instruction)
E.2.2 Inference for Planner
Aspect-Aware Thinking Based on the planning blueprint, the planner conducts more focused and in-depth analysis following the order the blueprint. The whole process of aspect-aware thinking is implemented through multiple turns of dialogue with shared history. We instruct the planner to follow the guidance to analyze one of the aspects turn of dialogue, as shown below:
--.-- -=ee-[[speet [Aspect Refarence for Further Analysis (Start))
(ane aspects]
[Aspect Reference for Further Analysis (End))---..
Based the previded information and previous analysis, follw [Aspect Reference for Further Analyzis] deliver enhanced detaied analysis that aspect.
Formatting Output Plan To integrate the previous analysis, we design formatting instructions for the model to output specified format. You can refer to the case study in Section F2 to see an example of plan that meets the formatting requirements.
23

---

Summarize the above analyšis and provice very detailed travel plan, recommend sultable attractions and events, and give reasons. The summarize
detailed attraction introductions, tips avoid pittals, visttng order, etc. [Specitic location is the detalled name of the location, such as the name of an attraction or accommodationn placed In "(--]". "octor" specific time point, such as "08:00". should accommodation location.
perspeclives
analysis process:
transportation suggestions,
locations day's itinerary being Lunch should generally start batween 11:30-133 33:0, and dinner should start between 17:30-1 9:30. Ploase list specific time point separately and provide sultable dining suggestions the guide based seerch resuits. Finally,
accommodation location every day,
folloing Markdown format and markdoww
""markdwwn" block:
fit Day x, MM/DD Summary of today's activities
xocoo/ Specific location 1|: Overviaw of the event at (Specific location
Detalled guide [Specific location
follows:
Guide tite 2: Guide content
xocoo// Specific location 2): Overview of the event at (Specific location 2]
Detailed guide (Specific location 2] follows:
#n Day x, MM/DD Summary of today's activities
F
Simulation and Evaluation
F1
Simulation Process
F111
Action Space A
As we have mentioned, there are four kinds actions permitted to transit to the next state: transiting. resting, dining, and sightseeing. In this section, we illustrate the details of how the travel agent takes these actions.
After the traveler completes the event of the previous state, we prompt the traveler to make the decision for the next action follows:
Based on the plan, determine the next engage
location
visiting shopping, entertainment. hiking, etc.)
2) Ta rest the location (such as resting at the hatel for bit, tidying up luggage, or sitting at a resting area within the POI)
To dine the location, with fast food snack dining being hour, regular dining times being
high-end restaurant dining times being hours.
To proceed immediately to the next destination.
Once the traveler chooses an action, we require the traveler agent to give the output formatted as follows:
Transiting: { "ddcissonn": "transit". "departure": "...", "destination": "...", "transport mode": "...",
"arrival time": "xx:xx, "remaining stamina": ... "total expense": ... "next planned location": "..."J
Resting: "decision": "rest", "end time": "xx:xx, "remaining stamina": "total expense":
"next planned location": "
Dining: { "ddcisson"" "dine", "end time": "xxxxx", "remaining stamina": "total expense": "next planned location": "...")
Sightseeing: / "ddecision": "sightsee", "end time": "xx:xx", "remaining stamina": .... "total expense":
"next planned location": "
It is noted that only resting, which means resting in place, usually (If you're heading back to the hotel to rest, choose "transit"), does need additional processing. The following actions, including transiting, dining, and sightseeing, need to be integrated with real-world information.
Transiting If the traveler chooses to transit to the next location according to the plan, there are several modes of transportation, including walking, cycling, public transportation (a combination of walking and bus/metro), and taxis. We use the Amap (Gaode) map API to obtain the travel time and costs of these modes of transportation. The traveler agent needs to comprehensively take into account time, cost, preferences, and stamina make a choice.

---

Dining If the traveler chooses to have meal, we use the Amap (Gaode) map API to find the restaurant near the current location. We provide information on the quality and cost of restaurants for travelers to choose from, which is relevant to the traveler's preferences and budget. Dining usually takes 0.5-2 hours based on the restaurant type, and also counted the time of the sandbox environment.
Sightseeing If the traveler chooses to go sightseeing at the current location (usually a POT), we simulate the sightseeing experience of the POL. To be specific, use the Red Note (Xiaohongshu) API to search for travel blog posts related to POI. Since the blog posts are based on real experiences, we design sightseeing event agent to simulate the probable experience in advance, taking into account the traveler's preferences, available time, and stamina as follows:
Today's [Customized Plan] ls: [layy
Yourlyour group's is initial stamina level is (stamine), and the current state is "(raveler_statte" The current time (time), and you are located at [(location].
Travel Blog Search Results: belog_post__eare results)
[Considerations]
time each POI and to reserve time for traveling the next location [[destnation_pleens
Estimata
you'your group
each Pay attention end you need
2. Estimate the cost of vistting each POI and 1sst the amount for each expense (such as admission fees, show tickets, etc.J.
3 According to the [Stamina Rules), estimate the stamina consumitiion for each visited attraction and describe the stamina consumction
situatio each activity during your visit When yourlyour group's stamina
less than 2.0, you recover stamina and cannot continue visiting, Pay attention the remaining stamina level and whether needs to be reserved subsequent rinerary.
meal
4. Only consider the location [location)]. not other locations after the [Customiizd Plan]. If it's past the opening hours, you choose not visit.
As travelers, based an your current stamina level and time schedule, taking into account the factors mentioned above, how do you plan to praceed with your visit at [[location)]? Please describe how youlyour group will conduct the visit.
Although we have simulated an experience of the POI in advance, the traveler can consider whether to actually conduct such an experience, considering the current time, stamina, or other factors.
F.1.2 Traveler Stamina Engine
Basic Design Many researchers focus on analyzing the preference but neglect the type of traveler. The stamina of different types of travelers is diverse, which significantly influences the number of POIs to be visited in one day. In the travel simulation, stamina expenditure and recovery, which especially vary for different types of travelers. Besides, each type of traveler possesses an initial stamina value, and the travel group is calculated as whole.
design the rule-based engine adjust
As shown in Table X we showcase some examples from Travel-Sim. For younger people, they have higher initial stamina, consume less energy, and recover faster. For elderly people, they would like to have a more relaxed journey and tend be exhausted more quickly when sightseeing and transiting.
To enable LLMs to intuitively comprehend what stamina represents, we design a stamina-to-state conversion table that translates stamina values into specific states of the traveler: 1) If stamina is greater than 6.0, the state is "Energetic". 2) If the stamina is greater than or equal to 4.0 and less than 6.0, the state "Good". 3) the stamina is greater than or equal to 2.0 and less than 4.0, the state is "Slightly Tired". 4) If the stamina is less than 2.0, the state is "Very Tired".
Spontaneous Behavior Although we do not explicitly instruct the LLM to be aware of the state of the traveler, we find it interesting that the traveler agent can automatically adjust the next action based on the current state. For example, if the traveler feels tired, the next action can be "resting in the restaurant" or "transiting by taxi instead of by bus to the next place". In some cases, the traveler modifies the itinerary or skips the next POT depending on the current state, which illustrates that stamina significantly influences the execution of the actual itinerary. This highlights that previous studies, failing to account for the varying stamina of travelers in planning, have resulted in travel plans that are less feasible.
25

---

Table 8: Stamina rules for different types of travelers (from examples in Travel-Sim).
Type
Composition (gender, age]
[Initial stamina] Description
Stamina Rule
Single
[male, 32)
[8.5] An energetic 32-year-old male traveler loves to explore the natural scenery and histori- cal culture, enjoys in-depth experience of local life through public transportation and walking, especially in traditional food and shopping for special souvenirs, preferring to avoid commer- cial attractions.
sightseeing/Ihe Ihr: -1;
dining: +1; resting/Ihr: +1; transiting/Ih: bus/metro+0, taxi+0.5, walking- eycling-0.5, no cycling
Couple
[male,
65),
[6.5] An elderly couple with a passion for culture and history. preferring leisurely travel pace. They enjoy visiting temples and savoring local cuisine, all while prioritizing rest and comfort during their journey..
Bightseeing/hhr: -1.5;
(female, 62)
dining: +0.5; rest-
ing/thr: +0.5; tran- siting/Ih: Ih: bus/metro-l, taxi+0, walking-1.5
Family
(male, 32).
[7.0] A family of four consisting of father who loves natural scenery and technology, mother who loves shopping and scarching for local cui- sine, and two active children, they prefer a com- fortable and convenient way to travel. The fam- ily focuses on parent-child experienees and cul- tural exploration.
sightseeing/Ihr: -1; din- ing: +0.5; resting/Ihr: +0.5; transiting/lh Ih: bus/metro-1, taxi+0, walking- 1.5, cycling
(female, 31).
(male,
7).
(female, 4)
Family
(male, 45),
[6.5] family of 45-ycar-old men, 41-ycar-old women, and 71 71-year-old women who prefer re- laxing cultural and historical exploration, en- joying local cuisine and special performances, while focusing on comfort of travel and qual- ity of family time.
sightsceing/Ihrs /hr: -1.5;
(female, 41}.
dining: +0.5; rest-
(female, 71}
ing/Ihr: +0.5; tran-
siting/ Ih: bus/metro-l, taxi+0, walking-1.5, no cycling
Group
[female, 23).
[7.5] A group of three young female travelers who love to explore local food and culture, fash- ion shopping and photo check-in travel, pre- fer relaxed and comfortable way, enjoy lively nightlife, and not like wake up early.
sightseeing/Ihr: din- ing: +0.5; resting/ Ihr +1; transiting/ lh: hus/metro-0.5, taxi+0.5, walking- I, eycling-1
[female, 24).
(female, 27)
F22 Case Study
In this section, showcase example to intuitively illustrate how the travel simulation works. First of all, we create a traveler agent based on an example in Travel-Sim, e.g., the elderly couple in Table We load the basic information, including the stamina rule and preferences, of the traveler to the system prompt of the LLM agent.
g
This elderly couple is going to have a 3-day journey in Beijing. They start their journey by arriving at the Beijing West Railway Station at 10:00 AM. Their initial stamina will be subtracted by (6.5 - = 4.5) because of the exhaustion of traveling to new city. The couple can choose one of the four actions to go to the next state. We offer four different routes utilizing various modes of transportation, all provided via the map API:
26

---

Day [Custom liierary]:
Day
August
Bellng, Shichanal
10:00/[Beling West Rallway Station]: Arrival at Beilng West Rallway Station Details for [Beijing West Rallway Station]:
Iransportaton
the Cotton Tree Hotel: aporoximataly 30-minute drive. conventant comfortable. crowcled.
Tips: Keep your belongings secure; station
11 11:00/[Cetton Tree Hotell: Arrive the hotel and check-in
Details for (Catton Tree Hotell:
Reminder: Confirm
advance. early check you store your luggage.
Rest Advice: Take shori rest to prepare for the afternon lour.
12:30/(Si) Minfu Restaurant]: Enjoy traditional Beijing lunch Minfu]:
Food Recommendation: Try the Peking Duck; you can request less and salt.
Dining Tip: Make reservation in edvance to avoid walting.
14:30/[Shichahall:
Bejing"
scenic
neighborrhood
Details for [Shichahal]:
Toor Route: Start at Jinding Bridge - Latus Market -+ Yinding Bridge -+ Yandal Xieje.
-Special Activities: Consider 30-minute bost ride enjoy the lake view.
Suggestion:
enjoy
through the hutongs Transport Note: Be prepared for walking: take breaks as neaded.
Cultural Experience:
yourself the Belling atmosphore.
18:00/pNanmen Pot): Enjey traditional Beljing-stll mutton pat
(Nanmen Potl:
Food Recommenddation: Choose traditional copper pot muston hot pot, adjusttng condiments to your laste.
Dining Tpp: Focus on lean meat to avold overly greasy options.
20:00/[Cotton Hotall:
for
Details for [Cotion Tree Hotel]:
Transportation Advice: Take taxi back approxemately 15-minute ride.
Rest Reminder:: Go to bed to recharge for the next day' activities.
Your starting stamina and your current state Is "Good,"
The current time is 10:00, and your location [Bejing West Railway Station]. far, spent yuan today.
Based on the custom ttinrary. do you want start sightseeing. rest, eat, or move to the next location?
If wish proceed
destination, here the available transportation options: Station]
Walking:
[Baling West
[Cotton Hotell),
yuan
Public Transport: public avallable this
Taxi: [Beijing West Railway Station] - Taxi for 37.7 minutes - [Cotton Tree Hotel], cost: 28.0 yuen
Shared Bike (Beijing West Railway Station) Ride shared bike for 49.0 minutes [Cotton Tree Hotell,, cost: 4,9
next steps Analyze stamina consumption (consider the group" overail stamina).
traveler,
en your experience
iist--peroon perspective.
Step 2:
Step 3: Analyze expenses (caiculate the total cost based an the number of people).
Step Provide response fallawing JSON format:..
Considering the travelers' perspective, they decide to take taxi their hotel for their next step, as they feel exhausted from the long journey to Beijing:
Step 1: As an elderty couple newly antved in Beijing, we feal both excited and a bit fatigued After long Journey, our priorty to get the hotel as soon possible, drop off our luggage, and take short rest. Considering our age, preferences, and the inconvenience of carrying kiggage, taking the
the suitable choice. This will looking forward resting briefly hotel, recharging, and preparing for deliggtful meal
to comfortably
destination while conserving energy
day activttees. and our next visit to Shichahal.
Step 2:
Stamina Consumption Analysis
Taling a taxi consumes no stamina Final stamina: 4.5 v 0 4.5
Expense Analysis
Texi fare: 28.0 yuan
Step 4: JSON Response "Jsoo
"decision": "transit',
"departure location': 'Balling West Railway Station",
"transportation_ secttttoo_mdee"__ 'tax""
"arrival time 0:031
"remaining_staar stamina": 4.5,
"total, expenses": 28.0.
"next, planned__occtioon" 'SlI Restaurant"
After the couple arrives at the hotel, they choose to rest for a while before heading to the restaurant to taste the local cuisine, e.g., Peking duck:
27

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Step 1: As newly arried elderly couple in Beiiig, we feel both excited and a bit tired. Despite being good physical conditon, consideerng the fatigue from long-cistance travel, we have decided take some rest at the hotel prepare for the upcoming schedule. This way, can better enjoy the local cuisine and attractions Beljing, We plan Sjiminfu enjoy Beijing-style style lunch. During our rest, we organize our luggage freshen up make ourselves more comfortable. We are looking forward to tasting Paking duck but will also pay attention to controlling our intake of all and salt for health reasons.
until around 12:00 PM, then head
Step 2:
Stamina Consumption Analysis Stamina:
Resting at the hotel for approvimately 1 hour 20 minutes, energy recovery: 1.33 0.5 0.865 Final Stamina: 4.5 0.665 5.165
Step Expense Analysis No additional expersse so far
Step 4: JSON Response "'sson
"decision': "rest", send_time":" "12:00"
'remalningsstamin staaminnt 5.165,
"total_ _expeesees" 28.0,
"rex__planee_lccaall "SUj Minfu Restaurant" 1.
(Skipping the states of transiting and dining) After having a wonderful meal in the restaurant, they plan to visit their first attraction, Shichahai, a historic and scenic area in the heart of Beijing, encompassing three interconnected lakes: Qianhai (Front Sea), Houhai (Rear Sea), and Xihai (West Sea). Known for its well well-preserved traditional Siheyuan courtyard houses and Hutongs, Shichahai offers visitors an authentic glimpse into old Beijing culture. To create a simulated sightseeing experience in Shichahai, we utilize the Red Note (Xiaohongshu) API to gather blog posts relevant to visiting this area. Based on the gathered content, we then guide the sightseeing event agent to craft an immersive experience from the perspective of the travelers:
As an elderly couple with a deep love for culture and history, we are very much looking forward to visiting Shichahai in Beijing, a scenic area brimming with ancient charm. Considering our physical condition and preferences, we have decided on the following ftieerar:
3:30 Oinding Bridge and Lotus Market hour 28 minutes) We start from Cinding Briege, stroling leisurely while enjoying the lake views of Stichehai, Ginding Bridge is an excellentt spot for watching the sunset, though it's not evening yet, we can still admire the beautiful lake scenery. Then we walk to Lotus Market to experience the old Beijing's street life.
Cost:
Physical exertion: 1.5 1.47 2.2
Remaining stamina: 5.8225 2.2 3.6225 (Condiiion: siigtlly tirec)
lakeside teahouse minutes)
After some time of touring. we feel bit weary and decide to take short rest teahouse by the lake, savoring a cup of fragrant jasmine tea whil enjoying the lake view, recovering our strength.
Cost: Approximately 60 RMB (for two people's teal
Stamina recovery: Remaining energy. 3.6225 0.25 3,8725 [Candition: slightly tired)
0.25
4:00 PM - 5:00 PM Yinding Bridge and Yandal Xiejie (1 hour)
After resting, continue tour, arriing Yinding Bridge. another great averizok Shichahal. Following that, meander to Yandai Xiejie, admiing unique hutong architecture and experiencing lifestyle Beijing.
Cost: Free
Physical exartion: 1.5 1 1.5
Remaining stamina: 3.8725 - 1.5 2.3725 (Condition: sightly tired)
5:00 PM - 8:00 PM Row boat ride on the lake (1 hour)
Though our strength has been somewhaat depleted, considering that row boating is characteristic activity of Shichahsi, we decide to give i try. We rent small boal, gently gliding the lake, appreciating the natural beauly, and enjeying the leisurely moments. Cost: Approsimately
RMB (boat rental fee)
Physical exertion: 1.5 1.5
Remaining stamina: 2.3725 - 1.5 0.8725 (Condition: very tired)
Summary:
Total visit time: 3 hours 58 minutes
Total cost: 160 RMB
Final stamina status: 0.8725 (very tired
Our tour largely followed the plan, but due significant physical exertion, we decide to conclude our visit early and return the hotel to rest. This arrangement allows us to fully appreciate the beauty and culture of Shichahal while also taking care of our physical condition. Although we did not visit all racommended attractions, we balleve this experience was already highly satistying, leaving us with beautiful memories trip to Bejing.
Due to the limited space here, we present only a part of the simulation, but it encompasses most scenarios. This case study showcases that causal travel simulation is based on the integration of real-world information and comprehensively reflects the quality of the generated plan.
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"type": "couple", "numberr: 2.
"desc"; "This couple in their 30s are travel enthusiests full curiosity, They enjoy gaining deep understanding of their destinations by brying local culsine, exploring cuttural historical and participatig unique experiences. They prefer using public transportation and choose accommodations that are conventently located and have character', "people":
age" 35, gender":
"age": ": 31, "gender": "temale'
"staminaa" 7.5. r
[Stamina Rules]
The rules for changes stamina are as follow::
hour spent sightsneing deducts
stamina,
2. Each meal can restore 1.0 stamina.
3. Resting for an hour (such as staying at the hotel or resting at scenic spot) can restore 1.0 stamina,
4. Using public transportation for one hour deducts 0.5 from stamine, walking for hour deducts 1.0 from stamine, taking a taxi
does not deduct stamina, cycling for one
deducts
stamina.
Stamina corresponds to the following states:
Stamina grealer than equal 6.0, the state "energetic"
Stamina greater then equal 4.0
the state "good"
3. Stamina greater then or equal to 2.0 and lass than 4.0, the state is "sightly tired".
4. Stamina less than 2.0, the state is "very tired".
...
"preferences':
"Food Freferences". "Thay love to try local specialties, Including street food and tracitional dishes; they are also willing to explore popular internet calebrity restaurants and highl-rraeed local eateries.",
"Shopping Preference": 'The woman enjoys shopping, especially for local handicrafts, souveniss and designer brand clothing," ; beaches, mountains,
"Scanary Prefarance": 'Both
love
scanery,
landscapes; they prefer beautiful
spots suitable photo-taking..
"Cultural interest': "They are enthusiastic about visiting museums, art exhibitions, and other cultural venues to learn about local arts and cuiture,",
"Historical Interest': "Tey enjoy visttng places with rich history, ancient ruins and historic buildings, gain deepar
historical knowledge.."
"Entertainment Activities:: "They enjoy watching live performances like concerts, plays, or dance shows; they also participate in local festivals or other interesting events."
activities, might engage light outdoor activities, such walking "Latsure Preference": 'They tend to keep their itineraries tight, trying to visit as many different places as possible rather than staying long in one place to relax."
"Sports Activties': "Whie not particularly interested
eycling.".
"Adventure Preference:: 'The man has particular interest in adventurous activities such rock climbing, diving, other Uhilling experiences.",
"Religiuss Activities:: "They do not have a special interest in relgious sites unless they hold significant historical or cultural value.". "Leaming New Things": 'During travels, they hope to learn new things, whether it's language, cooking skill, or crafts.". "Transportation Preference": "They prefer public transporaation like subways and buses to better integrate into local life and save taxis save time: they enjoy walking explore alleys streets; might rent car self-crive drive tours.".
costs; they may occasionally
special circumstances, they
"Accommodation Preference": "They tend to choose mid-range hotels or distinctive bed and breakfasts that are conveniently located, clean and comfortable, preferably close to major tourist attractions or transportatio hubs: they value safety and service quality: they don't have high requirements room facilities; possible, they will choose accommodations with romantic elements, such view rooms or thematically decorated rooms.",
"Other Preferences": "They seek uniuee travel experiences, such as hot air balloon rides over the city or night boat tours: they are happy start a new day early and usually choose public transportation to expertence the local atmosphere."
"Total Accommodation Budiget": 1400-800 RMB per right".
"Total Sightseaing Budger": 30001000 AMB per day"
An example in Travel-Sim. The traveler group consists of a couple with one female and one male in their 30s. They have initial stamina of 7.5 and consume less stamina for walking and cycling compared to elders and families with kids. They have specific and detailed preferences that will greatly influence their decision on the itinerary.
Figure 9:
F.3 Travel-Sim Dataset Card
As shown in the example in Figure 9 we construct the Travel-Sim dataset that consists of diverse travelers with detailed preferences. Different from previous travel planning benchmarks that only consider single travelers with limited preferences, Travel-Sim accommodates various types of travelers with diverse group compositions, such as individuals, couples, groups, families, and more. Each traveler type may have distinct preferences regarding activities, accommodation standards, budget constraints, travel pace, and cultural experiences.
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To build such a dataset, we annotate the profiles of 16 types of travelers and leverage Deepseek- R1 to expand the details. We select the most-visited cities China as the destinations and combine them with the travelers to generate 112 (traveler, destination) pairs as the evaluation dataset.
Table 9: Datasets with City Composition.
Dataset
City List
City Count
Traveler Type Count
Reward Model Training
Guilin, Qingdao, Luoyang, Xishuangbanna. Shenyang. Wuhan, Nanchang, Zhengzhou. Changchun. Xianyang. Lanzhou, Yangzhou, Chaozhou, Guangzhou
14
37
RFT/RL Training
Beijing, Chongqing, Nanjing. Chengdu, Dutong. Jingdezhen, Dalian, Hangzhou, Beihai, Lijiang, Kunming. Taiyuan Xianning. Shenzhen, Hong Kong. Changsha. Shantou, Qinhuangdao, Enshi. Tianjin
120
Evaluation
Lhasa, Sanya, Shanghai, Harbin, Dali, Xi'an, an, Xiamen
7
16
F.4 Multi-granularity Evaluation by Traveler
After experiencing the itinerary, the traveler has much share regarding it. We implement multi- granularity evaluation mechanism for the travel experience from three levels: the traveler reflects on the experience and rates the score after each POI visit, at the day's ss conclusion, and after completing the entire journey. We first ask the traveler agent to think from the perspective of a traveler by outputting the psychological activities. We then ask the traveler agent rate the score by inspecting five indicators, e.g., travel experience (ex), scenic spot characteristics (it), sightseeing arrangements (ar), stamina exertion (st), and overall expense (co).
For every end of the visit the POI, we let the traveler assess the POI visiting experience as follows:
Evaluation Criteria]
The evaluation criteria include: travel experience, scenic spat characteristics, sightseeing arrangements, stamina exertion from sightseaing, and overall expense.
1. Traval Experience:
the travel exparience? you feel that activttess attraction fultiling? Were they line with there any regrets?
your preferences?
2. Scenic Spot Characteristics: What special features do the scenic spots have? Were there any special activities or views? Are these places of interest to you?
3. Sightseeing Arrangements: Was there enough time fully enjoy each attraction? Were timing and conditions ideal [too viewing limes]?
any localions visiled 4, Starnina Exartion from Sightsening: Was the schedule too tight or too relaxed? Was overly exhausting? Was there sufficient time for meals and rest (were breakfast, lunch, and dinner arranged]?
crovded/ico few people,
activities
closure?
5. Overall Expense: Was loday's overall spending within budget? Were there money?
overspending? Were there ogportuniless
Please rate your visit to [[location]] on scale of 1 to 5. First, analyze these five indicators based the traveler's psychological activities, then retum following JSON format: "Jsso
"Decision": "Evaluation',
'Travel Experience Rating":
*Scenic Spot Characteristic Rating":...
"Sightseeing Arrangements Reting" ...
"Stamina Exertion from Sightseeing Rating":...
'Overall Expense Rating":
For every end of the whole-day itinerary, we let the traveler assess the travel experience of today as follows:
30

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Today's itinerary has come end. What do think about the arrangement this [customized plan]? Please evaluate today's activities.
[Evaluation Criteria]
The evaluation cnnteria include: enjoyment experience, scenic spot characteristics, sightseeing arrangements, and stamina exertion from sightseaing, and overall expense.
any 2. Scenic Spat Characteristics: What special features did the scenic spats have? Were there any special activities or views? Were these places interest to you?
Travel Experience: regrats?
was experience? day's
fulfiling? align with your preferences?
3, Sightseeing Arrangements:
liming appropriate crowded/too people. missed actMitie best viewing times|? Were there any locations nat visited due lack of time or closure?
enough
fully enjoy each
4. Staminia Everiion from Sighisesing: Was the schedule too light too relaxed? Was it overly exhausting? Was there time for meals and rest (were breakfast, lunch, and dinner arrangedj? Expense: today's to save money?
spending
budgerf?
any overspending
apportuntiies
Please your sightseeing experience today scale of to analyze
indicators based traveler's paychological activilies, return following JSON
"Jsson
"Decision": "Evalusation,
Rating":
"Scenic Spot Characterisics Rating" .m
"Sightseeing Arrangements Railing::
'Staminia Exertion from Sighiseeing Ralingg...
'Overall Expense Rating":..
}.
At the end of the multi-day journey, we let the traveler assess the overall travel experience of the entire trip as follows:
Now, you can evaluate entiee (customized plan] based on your travel experience.
[vvlluttio Criteria]
scenic characteristics, sightseeing arrangements, stamina exertion from 1. Travel Experience: How was the travel experience? Did you feel that the day's activitees were fulfiling? Were they in line with your preferences? Are there any regrets?
The evaluation criteria include: travel
slghtseaing.
2. Scanic Spot Characteristics: What
features
have? Were there any special activities views? these
places
to you?
3. Sightseeing Araangements: Was there enough lime fully enjoy each sitraction? Were the timingg and conditions ideal |loo crovded/cco few people, missed activities or best viewing times)? Were there any locations not visited due to lack of time or closure?
4, Stamina Exertion from Sightseeing: Was schedule Were
relaxed? Was overly exhausting? Was there sufficient time for
rest breakfast, lunch, 5. Overail Expense: Was tocay''s overall spending within budget?? Were there areas overspending? Were there opportunities to save money?
arranged]?
please your (leníself.ee everyday_plan)) days experiences, First, analyze these five Indicators based on traveler's psychological activities, then return folowing JSON format: "json
Based your
points.
"Decision": "Evaluation',
"Trevel Experience Rallig": ...
"Scenic Spot Charactoe tharacteristics Rating":
'Sightseeing Arrangements Rating':
'Stamina Exartion from Sightseeing Rating":
"Overall Expense Rating":...
F.5 Human Verification for Simulation-based Evaluation
Although the simulated travel empirically seems to be effective for evaluating the generated plan, further implement human evaluation verify if the simulation-bast evaluation is consistent with the human evaluation. To be specific, we provide the evaluation results from the simulated travel for humans check if the evaluation from the traveler agent is reasonable. For example, given the evaluation of the POI travel experience, humans inspect it based on the map and blog post, subsequently deciding whether endorse the evaluation. If humans agree with the travel agent's evaluation, the scores remain the same as those of the travel agent. If humans disagree, they have the option modify the scores.
Three individuals take part in this experiment. We record the modified scores and the deviation values to calculate the agreement rates. It is noted that the scores here PER-ex, PER-it, PER-ar, stR-st, and PER-co.
As shown in Table 10 the evaluation of traveler agents has high consistency with the human evaluation, with the agreement rate 92%. We have identified that the PER-co (travel cost) metric exhibits relatively higher level of inconsistency. The travel agent often buys too many expensive souvenirs,
31

---

which leads to going over budget. Additionally, they occasionally make mistakes when calculating total travel expenses, especially when accounting for group of travelers.
Table 10: We implement human evaluation to verify if the simulation- -ased evaluation consistent with humans'.
ex
st
C0
PER agg.
Agree. rate
RI-Distill 7B (s.) + RI-DDstill 7B (p.)
77.5
86.4
79.3
76.4
87.6
81.4
Human
69.2
83.7
73.4
74.2
74.5
75.0
92.1%
G
Metrics
We examine four criteria to evaluate the effectiveness of wide-horizon thinking. Comprehensiveness (CPH) and Completeness (CPL.) rule-based metrics, while Feasibility (FEA) and Personalization (PER) are simulation-based metrics.
G.1 Rule-hased Metrics
G.1.1 Comprehensiveness (CPH)
Comprehensiveness (CPH) evaluates how much relevant information is effectively integrated from the long context into the final plan. To elaborate, for each POI in the plan, first extract the corresponding travel guidance. We calculate the similarity between the POI travel guidance and POI-related blog posts the context. We encode both texts into embedding and calculate the cosine similarity We calculate the average similarity across all POIs in the plan as the comprehensive score.
G.1.2 Completeness (CPL.
To create organized travel plan, we must include several essential elements, such the timeline, destinations, activities, etc. We require LLMs to generate travel plan that follows the specific output structure and includes the required elements. To evaluate the generated plan strictly follows formatting instructions for a thorough itinerary, we inspect four criteria as follows:
1. The origin and destination of the entire journey must be at the specified stations or airports.
2. Each day's itinerary, excluding the first and last day, will begin and end at the hotel where the traveler is accommodated.
3. The introduction for each point of interest's sightseeing should be formally structured according to the format specified in the prompt.
4. In the travel itinerary, activities should be scheduled to include meal arrangements for both lunch and dinner.
For criteria 1, 2, and 3, we use the regex expression to extract the keywords and verify whether the generated plan satisfies these criteria. For the last one, we leverage the Deepseek-RI [18 to verify if itinerary of each day includes arrangements for lunch and dinner. We evaluate each plan against these four criteria. awarding 25 points for each criterion that is met. Therefore, for each plan, have a maximum of 100.0 points for CPL by summing all the scores.
G.2 Simulation- based Metries
Because some criteria, e.g., feasibility and personalization, are hard and less persuasive to simply measured by rule-based metrics, we deal with them via travel simulation based on the real world.
'e use the wwwppphassmmmiiiiinnammmppeeeeee model of the sentence-transfommers library in attps /ttparttt..olooooeeeoocooeeeaaoe
models .tmll
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---

G.2.1 Feasibility (FEA): Travel Plan Similarity Score
We evaluate the feasibility of the generated plan by ensuring that the plan is realistic and executable within the given constraints, such time and spatial optimization. As shown in Figure we can perceive the generated travel plan conducts virtual journey based on the generated travel plan, the traveler also produces trajectory of "time-location" pairs. We aim to assess the feasibility of the generated plan by evaluating whether the plan aligns with the traveler's itinerary.
trajectory of multiple "time-location" pairs. As the traveler
To calculate the similarity between two trajectories of "time-location pairs, there are few things to consider:
1. Completeness; The events of two trajectories should match. Missing or extra events should be penalized.
2. Chronological order:
The events of two trajectories should happen in the same order. The identical events of these two trajectories should occur as closely in time as possible. A penalty shall imposed that corresponds to the temporal discrepancy between identical events. The magnitude of this penalty increases proportionally with the extent of the time gap.
3. Temporal proximity:
Based on the considerations above, shown in Algorithm 3 we design algorithm for calculating the similarity between two trajectories of "time-location" pairs, dubbed Travel Plan Similarity Score (TPSS). We separately deal with the similarity of time and locations and implement dynamic programming to iteratively calculate the score. It is noted that we use TPSS to calculate the similarity of trajectories of one day. For multi-day journeys, calculate the final TPSS by averaging the daily scores.
Algorithm 3 Travel Plan Similarity Score Calculation
Input: generated plan trajectory Tg. simulated Leanull trajectrry T. Output: similarity score (as percentage)
function WAA.CUTAATTPPAANSSIIARRYYY
T.)
m < length of T f length of T,
Initialize dp m + 1][n + 1] with zeros for i + to do
for j + to m do
score + CacuulaeeMacchsoor(((gi..]].
dp[i][i] + max(dpli 1][i] dpli] - dpli - 1[] - 1] + score)
end for end for max. maor_scoreea (ni,
Ideal maximum score
similarity t dp(mi _ccoe
completeness setiitteennsssppnallyyyee t- min(m,n)) n)/ max(m.
Penalize for missing/extra activities
final_ hoiul_simllartt +- similarity * completeness_ * 100
return/ finol_similarii
end function
function
item2)
time__cone & Time Dime time, itme2.time)
locatio item2.location then
location_scom + 1
else
location_score + 0 end if return (time_score + location_score)
end function
function TTMEDIFFSCORE(tieel time2)
difʃ + [tme22 timel in hours
return max(0, 1 d2ff
P Linear decrease within hours
end function
33

---

G.2.2 Personalization (PER): Aggregated Assessment from Travelers
We evaluate the personalization of the generated plan by inspecting whether the travel plan meets the unique needs and preferences of the traveler agent in the simulation. As we have mentioned, our multi-granularity evaluation mechanism via traveler's assessment in Appendix F.4 we further
introduce how we aggregate the scores from multi-granulann assessment Equation as follows:
N
E (BSaay + "SOos_day.. SPER = S11Swh travel + 0:2 N
(3)
As each POI has score after sightseeing, we average the scores each POI to be the overall POI score SPOT _ in the ith day. We weight the Spor_day_ and the score of the whole-day itinerary Sday by 3 and as the aggregated score of ith day. We set / as 0.6 and aggregated score each day and weight it with the score of the whole travel by aj and a2. We set a1 as 0.6 and 0g as 0.4. We normalize the Swhole_travel travel to 100.0 as the final score.
day_
0.4. We average the
34

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Reviews:
[{"review_id": "0Kicast812", "reviewer": "Reviewer_J2GB", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "Thanks for authors’ rebuttal and most of my concerns have been effectively addressed. While I still hold a minor reservation regarding the practical deployment of the proposed method, I find the work to be well-motivated and solid. The rebuttal is comprehensive and solid, led me to raise the Quality and Clarity score. I maintain my borderline accept recommendation and look forward to seeing this work further developed.", "rebuttal": "We are very grateful for thorough review and highly insightful questions.\n\n**W1:** The reward model is trained exclusively on Travel-Sim outputs and reused during optimization, risking self-reinforcing biases and reward hacking.\n\n**A1:** We construct a separate training dataset for the reward model from the ones in RFT and RL. **To avoid the risk of reward hacking you are concerned with, we do not reuse the data (cities and traveler types) in the optimization for RL and RFT.** We exclusively select the 14 cities that are not included in the Travel-Sim training and evaluation dataset. We also additionally construct 37 traveler types specifically for this dataset. \nWe provide the detailed data composition of these three datasets (reward model training, RFT/RL training, evaluation).\n\n\n\n| Dataset| City List | City Count | Traveler Type Count |\n|--------------|-----------|------------|-------------------|\n| **Reward Model Training** | 桂林(Guilin), 青岛(Qingdao), 洛阳(Luoyang),  西双版纳(Xishuangbanna), 沈阳(Shenyang), 武汉(Wuhan), 南昌(Nanchang), 郑州(Zhengzhou), 长春(Changchun), 咸阳(Xianyang), 兰州(Lanzhou), 扬州(Yangzhou), 潮州(Chaozhou), 广州(Guangzhou) | 14 | 37 |\n| **RFT/RL Training** | 北京(Beijing), 重庆(Chongqing), 南京(Nanjing),  成都(Chengdu), 大同(Datong), 景德镇(Jingdezhen), 大连(Dalian), 杭州(Hangzhou), 北海(Beihai), 丽江(Lijiang), 昆明(Kunming), 太原(Taiyuan), 咸宁(Xianning), 深圳(Shenzhen), 香港(Hong Kong), 长沙(Changsha), 汕头(Shantou), 秦皇岛(Qinhuangdao), 恩施(Enshi), 天津(Tianjin) | 20 | 120 |\n| **Evaluation** | 拉萨(Lhasa), 三亚(Sanya), 上海(Shanghai), 哈尔滨(Harbin),  大理(Dali), 西安(Xi'an), 厦门(Xiamen)| 7 | 16 |\n\n\n**W2:** Evaluation is restricted to 7 Chinese cities and 16 traveler profiles, with no evidence of adaptation to unseen geographies, cultures, or constraints.\n\n**A2:** Yes, we leverage the cities in China to verify the effectiveness of MAoP in travel planning. As China is a large country, we can select the cities with diverse cultures and geographies. The meticulous selection of cities and traveler types in the evaluation dataset is to ensure the generalization in unseen geographies, cultures, or constraints. To demonstrate the diversity of our city selection, we present the details of 7 cities from our evaluation dataset that exhibits maximum geographical, cultural, and environmental diversity:\n\n| City | Geo Direction | Location | Cultural Features | Climate Type | Terrain |\n|------|---------------|----------|-------------------|--------------|---------|\n| Lhasa | Southwest | Tibetan Plateau | Tibetan culture, Buddhist holy land | Plateau climate | Plateau |\n| Sanya | South | Southern Hainan Island | Tropical island culture | Tropical marine | Coastal plain |\n| Harbin | Northeast | Northeast Plain | Russian architecture, ice culture | Cold temperate continental | Plain |\n| Shanghai | East | Yangtze River Delta | International metropolis, modern commerce | Subtropical monsoon | Alluvial plain |\n| Xi'an | Northwest | Guanzhong Plain | Ancient capital, Silk Road origin | Warm temperate semi-humid | Loess plateau |\n| Dali | Southwest | Yunnan-Guizhou Plateau | Bai ethnic culture, plateau lakes | Plateau monsoon | Plateau basin |\n| Xiamen | Southeast | Southeast Coast | Minnan culture, garden on the sea | Subtropical marine | Island hills |\n\n**Although many cultures and geographies are still not covered, existing diversity in these cities is sufficient for evaluating the travel planning ability of LLMs. Therefore, expanding more cities is not our primary focus and will be considered in the future work.**\n\n**W3:** The fixed action space (transit, rest, dine, sightsee) omits common activities like shopping, queuing, or performances. TPSS may not correlate with actual user satisfaction, and certain travel patterns may be underrepresented.\n\n**A3:** \n\n**1. Fixed action space omits common activities like shopping, queuing, or performances?**\n\nThe sightseeing action is a general concept that includes various activities happening in the POI. If the POI is a famous shopping spot, the sightseeing action can be shopping in the shopping mall. As our sightseeing event is simulated by referring to the real travel blog posts, the sightseeing agent can consider real-world events, e.g., the necessity of queueing (as there are many blog posts reminding to arrive early to get in line if the POI is very popular).\n\n**2. Correlation between TPSS and actual user satisfaction?**\n\nTPSS, also called the FEA (Feasibility) score in the paper, is specifically used to evaluate the generated plan's feasibility by comparing the trajectory in the plan with the trajectory in the simulation. **We specially design another metric, PER (Personalization) score, to evaluate the user satisfaction.** Therefore, we have these two metrics to evaluate different aspects of the generated plan.\n\n\n**W4:** The paper reports token-level simulation cost but omits end-to-end compute breakdowns across training and inference stages, hindering reproducibility assessment.\n\n**A4:** **We report detailed compute budgets and setups for training in Appendix E.1 and details for inference in Appnedix E.2.** We will release the code and open-source the dataset shortly, following an internal inspection of the dataset (as some of the travel blog posts within it were obtained via an internal corporate API). Anyone who adheres to the license in Appendix B may use our code and dataset for reproducibility.\n\n**W5:** 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\n**A5:** In this paper, our primary focus is to explore the challenging $L^3$ planning problem that most LLMs fail to solve. Existing travel planning datasets (e.g., TravelPlanner [1], ITINERA [2]) provide cases containing **simple constraints and short contexts**, which are not suitable for $L^3$ planning problem evaluation. Therefore, we specially build Travel-Sim to provide the **hard cases**. Through the preprocessing framework in Appendix C, we collect heterogeneous user preferences by interactive agents and real-world information through search engines and map APIs, making the cases in Travel-Sim contain **complicated constraints and long contexts**. \n\n[1] Xie, Jian, et al. \"Travelplanner: A benchmark for real-world planning with language agents.\" arXiv preprint arXiv:2402.01622 (2024).\n\n[2] Tang, Yihong, et al. \"ItiNera: Integrating spatial optimization with large language models for open-domain urban itinerary planning.\" arXiv preprint arXiv:2402.07204 (2024).\n\n\n**Q1:** 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?\n\n**A6:** **Yes, we have measured the agreement between PER scores and human ratings, providing detailed experimental setup in Appendix F.5.** As shown in the following table of PER score evaluation for \"R1-Distill 7B (s.) + R1-Distill 7B (p.)\", the evaluation of traveler agents has **high consistency with the human evaluation, with the agreement rate of 92% (PER-agg. ratio)**. We also identify that the PER-co (travel cost) metric exhibits a relatively higher level of inconsistency. We find that the traveler agents sometimes buy souvenirs but do not include them in the travel budget. In these cases, human evaluators give lower PER-co scores due to the budget overruns.\n\n|  | ex | it| ar | st | co | PER-agg. | Agree. rate|\n|------|---------------|----------|-------------------|--------------|---------|---------|---------|\nSimulation| 77.5 | 86.4 | 79.3 | 76.4 | 87.6 | 81.4 | - |\nHuman | 69.2 | 83.7 | 73.4 | 74.2 | 74.5 | 75.0 | 92.1%|\n\nAs the PER score has high consistency with human evaluation, **the RM that is based on the PER score is also aligned with human ratings.**\n\n\n**Q2:** Have you considered end-to-end reinforcement learning (RL) training of strategists, rather than relying solely on rejection sampling fine-tuning (RFT)?\n\n**A7:** **Yes, we have considered this limitation, as shown in the bottom footnote on the 4th page.** We do not further implement RL on the strategist model because the RL pipeline has to go through a frozen planning model to get rewards, making it hard to optimize.\n\n**Q3:** 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\n**A8:** Yes, we consider noisy but realistic actions in simulation without increasing the simulation cost by following implementations:\n\n1. shopping & waiting: As we mention in W3A3, we leverage the realistic travel-blog post as the reference to simulate the real-world events, e.g., queueing, shopping. \n\n2. transit delay: We collect realistic transportation data from the map APIs, e.g., Amap and Google Map. These map APIs have already considered the real-world traffic conditions, and we do not need to additionally simulate the transit delay."}, {"review_id": "gKW2WWegg3", "reviewer": "Reviewer_Jw3b", "summary": "This 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.", "questions": "Questions 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.", "limitations": "The authors discussed some limitations and broader impacts.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "After carefully reviewing the authors' responses and all reviewers' discussions, I confirm they have fully addressed my concerns and provided additional experimental results. As such, I will revise my rating accordingly. The paper now meets the conference's standards for publication.", "rebuttal": "Thank you for your time and valuable comments. We have carefully considered your suggestions, which have been a great help in improving our manuscript.\n\n**W1:** The method section suffers from confusing logic and organization, making it easy for readers to be confused. \n\n**A1:** \n We wish to clarify our methodology, which is organized into two primary, logical contributions: a novel planning method (MAoP) and a novel evaluation benchmark (Travel-Sim).\n\n**1. The MAoP Method (Plan Your Travel):**\n\n→ **Definition**: We first define a specific complex planning task as the $L^3$ planning problem. Its fundamental challenge is the need to integrate a wide array of parallel and often conflicting information sources and constraints. This problem is typically characterized by a long context (presentation of the information and constraints), long instructions (how to deal with the information and constraints), and a long output (the final plan).\n\n→ **Preliminaries**: We use the travel planning as the testbed. We find simple \"wide-horizon thinking\" approach outperforms standard methods but still fail to connect different aspects and scale effectively. \n\n→ **Solution**: MAoP overcomes this by using a two-model system (Strategist-Planner). A strategist performs high-level \"pre-planning\", decomposing the request into key aspects and creating a coherent blueprint. It overcomes the scalability limits of planning, which is a problem not addressed by traditional planners [1]. This prevents performance degradation from information and constraints overload. \n\n→ **Acceleration**: We introduce MAoP Distillation (Section 3.3) as an optional step to transfer the capability of this two-model system into a single, more efficient model (**one-step wide-horizon thinking**) for faster inference.\n\n**2. The Travel-Sim Benchmark (Travel with Your Plan):**\n\n→ **Evaluation**: To evaluate these plans, we developed Travel-Sim. Existing benchmarks cannot evaluate the dynamic, sequential nature of travel where one event impacts the next (also illustrated as **consistency** in the paper). Travel-Sim solves this by having an LLM-powered traveler agent simulate the trip in a sandbox environment that uses real-world APIs. \n\n\n**W2:** The experimental section lacks sufficient description of the datasets, providing only macro-level descriptions. \n\n**A2:** \nDue to limited space, we provide the details of datasets in Appendix E, F and Figure 9. The Travel-Sim dataset (including the reward model training, RFT/RL training and evaluation datasets) comprises three key components: destination city data, preprocessed information, and traveler profiles.\n\n**1. Destination city data**\n\nWe select cities from the list of China's Excellent Tourism Cities (a city title issued by the National Tourism Administration of China). For each dataset, we ensure selected cities have no overlap while comprehensively covering diverse geographical, cultural, and environmental characteristics.\n\n| Dataset| City List | City Count | Traveler Type Count |\n|--------------|-----------|------------|-------------------|\n| **Reward Model Training** | 桂林(Guilin), 青岛(Qingdao), 洛阳(Luoyang),  西双版纳(Xishuangbanna), 沈阳(Shenyang), 武汉(Wuhan), 南昌(Nanchang), 郑州(Zhengzhou), 长春(Changchun), 咸阳(Xianyang), 兰州(Lanzhou), 扬州(Yangzhou), 潮州(Chaozhou), 广州(Guangzhou) | 14 | 37 |\n| **RFT/RL Training** | 北京(Beijing), 重庆(Chongqing), 南京(Nanjing),  成都(Chengdu), 大同(Datong), 景德镇(Jingdezhen), 大连(Dalian), 杭州(Hangzhou), 北海(Beihai), 丽江(Lijiang), 昆明(Kunming), 太原(Taiyuan), 咸宁(Xianning), 深圳(Shenzhen), 香港(Hong Kong), 长沙(Changsha), 汕头(Shantou), 秦皇岛(Qinhuangdao), 恩施(Enshi), 天津(Tianjin) | 20 | 120 |\n| **Evaluation** | 拉萨(Lhasa), 三亚(Sanya), 上海(Shanghai), 哈尔滨(Harbin),  大理(Dali), 西安(Xi'an), 厦门(Xiamen)| 7 | 16 |\n\n**2. Preprocessed information**\n\nIn Appendix C's preprocessing workflow, we gather real-world data through multiple channels, including:\n\n- Interactive agent simulations\n\n- Travel blog content analysis\n\n- Search engine data extraction\n\n- Map API integrations\n\nFor instance, attraction information is systematically processed from both travel blogs and search engine results, while geographical data is collected through map APIs.\n\n**3. Traveler profiles**\n\nFor traveler profiles, we begin by **manually** creating 30 distinct traveler archetypes based on real-world travel advice requests from blog posts. These initial profiles are then refined using Gemini-2.0-Pro-Exp-0205 to serve as a high-quality seed dataset. To expand the dataset, we employ Gemini-2.0-Pro-Exp-0205 for synthetic data generation. All synthesized profiles undergo **manual human review and filtering** to guarantee accuracy and diversity.\n\n\n**W3:** The paper lacks comparative experiments and fails to compare against other existing methods.\n\n**A3:** Our proposed method, Multiple Aspects of Planning (MAoP), is directly benchmarked against the dominant paradigm for complex travel planning in LLMs [1, 2, 3]: long-horizon thinking, as exemplified by CoT and CoT variants.\n\nOur experiments feature direct comparisons against baselines representing these existing methods. Specifically, we test against zero-shot long-horizon thinking approaches and their finetuned versions. The results consistently show that MAoP yields substantial performance improvements of 5% to 40% over these approaches across different models.\n\n\n**Q1:** 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.\n\n**A4:** As we mention in Section 3.3, the implementation of MAoP requires two different models and a complicated planning process; therefore we propose one-step wide-horizon thinking to **accelerate and simplify** the MAoP process by distillation.\n\nCompared to traditional MAoP, **experimental results (Section 5.2.3)** show that MAoP distillation from advanced models to smaller models achieves substantial performance improvements besides the efficiency improvements.\n\n\n**Q2:** What is the detailed formulation of the reward function for RL/RFT?\n\n**A5:** \nIn the RFT, we use the following reward function:\n$$\nR_{PER} = 2 * (PER_{normal} - 0.5), \\text{where } PER_{normal} \\in [0, 1].\n$$\nThe $PER_{normal}$ score is the normalized PER score (0-100 normalized to 0-1), which is directly predicted by the reward model. We reject the sample that gets the average $R_{PER} < -0.2$ (PER score < 40) after sampling $N = 3$ times.\n\nIn the RL, we add an additional format reward as follows:\n$$\nR_{format} = \\begin{cases} 0, & \\text{if the format is correct,} \\\\ -1, & \\text{if the format is incorrect.} \\end{cases}\n$$\n\nThe overall reward for RL is:\n$$\nR_{overall} = R_{format} + R_{PER}.\n$$\n\n\n**Q3:** Could the preprocessing framework workflow be included in the main text to facilitate reader understanding?\n\n**A6:** We understand your concern that the illustration of the preprocessing framework helps to understand the method details. We exclude the preprocessing framework in the main text due to the following reasons:\n\n1. **Not primary focus**: In this paper, we mainly want to discuss the exploration of $L^3$ planning problem and novel simulation-based evaluation. **The preprocessing workflow is an engineering issue. We avoid adding more content to confuse the reader.** If the readers are interested, they can read the full details in Appendix C.\n\n2. **Space limitation**: Our preprocessing framework is complicated, with several LLM agents with different functions and hierarchical cluster-based algorithms. It is hard to clearly illustrate the details due to the limited space.\n\n\n**Q4:** What is the source of the synthetic data used for training the reward model in MAoP?\n\n**A7:** Please see W2A2 and for further details please refer to Reviewer J2GB W2A2.\n\n\n**Q5:** What is the purpose of the comparison in Section 5.2.4? How does this relate to your proposed Consistent Travel Simulation?\n\n**A8:** Because the events in the simulation happen consistently (not independently), the traveler will be influenced by the previous events and spontaneously adjusts the following schedule, leading to a different trajectory from the plan. **By comparing the trajectories between the simulation and the plan**, we can find out if the plan is feasible (the mechanism of FEA score). The analysis of spontaneous behaviours of in Section 5.2.4 also illustrates that our consistent simulation is close to the real-world travelling scenarios.\n\n\n\n**Q6:** Could you include an evaluation of the simulation itself? The current experiments do not demonstrate the reliability of the simulation component.\n\n**A9:** **Yes, we have included an evaluation of the simulation itself, providing detailed experimental setup in Appendix F.5.** As shown in the following table of PER score evaluation for \"R1-Distill 7B (s.) + R1-Distill 7B (p.)\", the evaluation of traveler agents has **high consistency with the human evaluation, with the agreement rate of 92% (PER-agg. ratio)**. We also identify that the PER-co (travel cost) metric exhibits a relatively higher level of inconsistency. We find that the traveler agents sometimes buy souvenirs but do not include them in the travel budget. In these cases, human evaluators give lower PER-co scores due to the budget overruns.\n\n|  | ex | it| ar | st | co | PER-agg. | Agree. rate|\n|------|---------------|----------|-------------------|--------------|---------|---------|---------|\nSimulation| 77.5 | 86.4 | 79.3 | 76.4 | 87.6 | 81.4 | - |\nHuman | 69.2 | 83.7 | 73.4 | 74.2 | 74.5 | 75.0 | 92.1%|\n\n[1] Xie, Jian, et al. \"Travelplanner: A benchmark for real-world planning with language agents.\" arXiv preprint arXiv:2402.01622 (2024).\n\n[2] Chen, Aili, et al. \"Travelagent: An ai assistant for personalized travel planning.\" arXiv preprint arXiv:2409.08069 (2024).\n\n[3] Tang, Yihong, et al. \"ItiNera: Integrating spatial optimization with large language models for open-domain urban itinerary planning.\" arXiv preprint arXiv:2402.07204 (2024)."}, {"review_id": "tOLPoh1R6l", "reviewer": "Reviewer_RxvN", "summary": "The 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.", "questions": "1) 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thank you for the further explanations. I appreciate the authors’ detailed clarifications regarding my concern. However, I still feel that the generalizability of MAoP would be more convincing if the paper included concrete experiments beyond the travel planning domain. While the mentioned applications in project management and financial advising are reasonable, without empirical demonstrations in these areas, the broader impact remains more speculative than validated.\n\nNonetheless, I agree that the paper tackles an interesting and practical problem, and the proposed wide-horizon planning framework represents a novel and useful direction for LLM-based planning systems. Despite the somewhat limited evaluation scope, I believe the paper offers meaningful contributions. I will therefore maintain my original positive rating.", "rebuttal": "We would like to thank you for your constructive feedback and insightful suggestions.\n\n**W1&Q1:** While Travel-Sim is creative, it is still an LLM simulation of user satisfaction, which may not fully reflect human perception.\n\n**A1:**\n**We include the human evaluation to verify the alignment of simulation-based evaluation with human perception, providing experimental details in Appendix F.5.** As shown in the following table of PER score evaluation for \"R1-Distill 7B (s.) + R1-Distill 7B (p.)\", the evaluation of traveler agents has **high consistency with the human evaluation, with the agreement rate of 92% (PER-agg. ratio)**. We also identify that the PER-co (travel cost) metric exhibits a relatively higher level of inconsistency. We find that the traveler agents sometimes buy souvenirs but do not include them in the travel budget. In these cases, human evaluators give lower PER-co scores due to the budget overruns.\n\n|  | ex | it| ar | st | co | PER-agg. | Agree. rate|\n|------|---------------|----------|-------------------|--------------|---------|---------|---------|\nSimulation| 77.5 | 86.4 | 79.3 | 76.4 | 87.6 | 81.4 | - |\nHuman | 69.2 | 83.7 | 73.4 | 74.2 | 74.5 | 75.0 | 92.1%|\n\n\n**W2&Q2:** 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\n**A2:** We appreciate this crucial point. While tourism serves as a intuitive testbed, the architectural principles of MAoP are intentionally designed to be domain-agnostic. **The core challenge MAoP addresses is not specific to travel but to any task where the primary difficulty is integrating a wide array of parallel, often conflicting, information sources and constraints, rather than executing a deep sequence of actions [1].** The framework's generalizability stems from its unique approach to managing this \"wide-horizon\" complexity.\n\nHere are a few examples of how MAoP can be generalized:\n\n1.  **Complex Project Management (e.g., a product launch):**\n    *   **Wide-Horizon Challenge:** A project manager must synthesize market research, budget constraints, team capabilities, competitor analysis, and stakeholder requirements into a single, coherent launch plan.\n    *   **MAoP Application:**\n        * The `Strategist` would decompose the task into aspects like Budget Allocation, Marketing Channel Strategy, Content Creation, Risk Assessment, and KPIs.\n        * Its `Routing` function would create a blueprint, ensuring that budget decisions inform channel selection, which in turn dictates content needs.\n        * The `Planner` would then use this blueprint to generate a comprehensive project plan, holistically balancing all factors.\n\n2.  **Personalized Financial Advising:**\n    *   **Wide-Horizon Challenge:** An advisor must create a portfolio by integrating a client's risk tolerance, age, income, long-term goals (retirement), short-term needs (liquidity), and complex market data with tax implications.\n    *   **MAoP Application:**\n        * The `Strategist` would identify aspects such as Risk Profile, Asset Allocation, Tax Optimization, and Liquidity Planning.\n        * It would `route` these aspects to ensure the final portfolio is a logical synthesis of the client's profile and goals.\n        * The `Planner` would generate the detailed investment strategy document.\n\nIn each case, MAoP's core innovations—decomposing the *problem space* into parallel aspects rather than the *solution process* into sequential steps, and using a strategist to intelligently \"route\" these aspects, providing a scalable method for complex synthesis. \n\n\n**W3&Q3:** Strategist-planner separation is a logical extension of existing modular or hierarchical planning ideas. While well-executed, Strategist is an adaptation of existing techniques rather than fundamentally new. \n\n**A3:** While our Strategist-Planner (MAoP) framework builds on the established principle of modularity, it introduces fundamental innovations that distinguish it from prior art.\n\nFirst, it shifts the planning philosophy from *long-horizon thinking*—managing a deep sequence of actions, as seen in frameworks like Plan-and-Act [1]—to *wide-horizon thinking*. Our approach is designed for $L^3$ problems where the challenge is synthesizing a *breadth* of parallel constraints, not the *length* of an action sequence.\n\nThis leads to a different decomposition target. Unlike hierarchical planners such as ReAcTree [2], which break down the solution process into sequential sub-goals, our strategist decomposes the problem space into concurrent \"aspects\" (e.g., budget, user preferences, transportation).\n\nFinally, the strategist's two-stage *\"Decompose-then-Route\"* process is a novel mechanism, not an adaptation. The routing step explicitly filters and structures these aspects into a coherent blueprint to overcome the scalability limits of planning, which is a problem not addressed by traditional planners [3]. This prevents performance degradation from information and constraints overload.\n\n[1] Erdogan, Lutfi Eren, et al. \"Plan-and-act: Improving planning of agents for long-horizon tasks.\" arXiv preprint arXiv:2503.09572 (2025).\n\n[2] Choi, Jae-Woo, et al. \"Reactree: Hierarchical task planning with dynamic tree expansion using llm agent nodes.\" (2025).\n\n[3] Xie, Jian, et al. \"Travelplanner: A benchmark for real-world planning with language agents.\" arXiv preprint arXiv:2402.01622 (2024)."}, {"review_id": "TgZAc6YFFI", "reviewer": "Reviewer_uXfj", "summary": "The 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.", "questions": "Q1. 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": false, "final_justification": "", "rebuttal": "We are very grateful for your thorough review and highly insightful comments. Your suggestions were instrumental in helping us strengthen our arguments and improve the paper's clarity.\n\n**Q1:** 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 $L^3$ planning problem concept could improve the readability of your paper?\n\n**A1:** We introduce the $L^3$ planning problem to formalize the specific planning tasks like travel planning, which necessitate wide-horizon thinking. Its fundamental challenge is the need to integrate a wide array of parallel and often conflicting information sources and constraints. This problem is typically characterized by a long context (presentation of the information and constraints), long instructions (how to deal with the information and constraints), and a long output (the final plan). The core challenge MAoP addresses is not specific to travel but to different $L^3$ planning problems, stemming from its Strategist-Planner framework to manage this \"wide-horizon\" complexity.\n\nHere is another example of $L^3$ planning problem that MAoP can potentially be generalized to:\n\n**Complex Project Management (e.g., a product launch):**\n*   **$L^3$ Challenge:** A project manager must synthesize market research, budget constraints, team capabilities, competitor analysis, and stakeholder requirements into a single, coherent launch plan.\n*   **MAoP Application:**\n    * The `Strategist` would decompose the task into aspects like Budget Allocation, Marketing Channel Strategy, Content Creation, Risk Assessment, and KPIs.\n    * Its `Routing` function would create a blueprint, ensuring that budget decisions inform channel selection, which in turn dictates content needs.\n    * The `Planner` would then use this blueprint to generate a comprehensive project plan, holistically balancing all factors.\n\n\nAccording to your concerns, **we will revise the manuscript to better integrate this concept (or rename it) and clarify its connection to our methods and experiments, improving readability.**\n\n\n**Q2:** 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\n**A2:** The Travel-Sim dataset (including the reward model training, RFT/RL training and evaluation datasets) comprises three key components: destination city data, preprocessed information, and traveler profiles.\n\n**1.Destination city data**\n\nWe select cities from the list of China's Excellent Tourism Cities (a city title issued by the National Tourism Administration of China). For each dataset, we ensure selected cities have no overlap while comprehensively covering diverse geographical, cultural, and environmental characteristics.\n\n| Dataset| City List | City Count | Traveler Type Count |\n|--------------|-----------|------------|-------------------|\n| **Reward Model Training** | 桂林(Guilin), 青岛(Qingdao), 洛阳(Luoyang),  西双版纳(Xishuangbanna), 沈阳(Shenyang), 武汉(Wuhan), 南昌(Nanchang), 郑州(Zhengzhou), 长春(Changchun), 咸阳(Xianyang), 兰州(Lanzhou), 扬州(Yangzhou), 潮州(Chaozhou), 广州(Guangzhou) | 14 | 37 |\n| **RFT/RL Training** | 北京(Beijing), 重庆(Chongqing), 南京(Nanjing),  成都(Chengdu), 大同(Datong), 景德镇(Jingdezhen), 大连(Dalian), 杭州(Hangzhou), 北海(Beihai), 丽江(Lijiang), 昆明(Kunming), 太原(Taiyuan), 咸宁(Xianning), 深圳(Shenzhen), 香港(Hong Kong), 长沙(Changsha), 汕头(Shantou), 秦皇岛(Qinhuangdao), 恩施(Enshi), 天津(Tianjin) | 20 | 120 |\n| **Evaluation** | 拉萨(Lhasa), 三亚(Sanya), 上海(Shanghai), 哈尔滨(Harbin),  大理(Dali), 西安(Xi'an), 厦门(Xiamen)| 7 | 16 |\n\n**2. Preprocessed information**\n\nAs illustrated in Appendix C's preprocessing workflow, we gather real-world data through multiple channels, including:\n\n- Interactive agent simulations\n\n- Travel blog content analysis\n\n- Search engine data extraction\n\n- Map API integrations\n\nFor instance, attraction information is systematically processed from both travel blogs and search engine results, while geographical data is collected through map APIs.\n\n**3. Traveler profiles**\n\nFor traveler profiles, we begin by **manually** creating 30 distinct traveler archetypes based on real-world travel advice requests from blog posts. These initial profiles are then refined using Gemini-2.0-Pro-Exp-0205 to serve as a high-quality seed dataset. To expand the dataset, we employ Gemini-2.0-Pro-Exp-0205 for synthetic data generation. All synthesized profiles undergo **manual human review and filtering** to guarantee accuracy and diversity.\n\n**Therefore, most of the data in the finetuning datasets is from real-world data (except for the traveler types). We try to minimize the sim2real gap to leverage as much real-world data as possible, including real travel blog posts, search engine results, and map APIs.**\n\n\n\n**Q3:** Do you have any plan to open-source your code?\n\n**A3:** We will release the code and open-source the dataset shortly, following an internal inspection of the dataset (as some of the travel blog posts within it are obtained via an internal corporate API). **Anyone who adheres to the license in Appendix B may use our code and dataset.**"}]

Author rebuttal:

Meta-review: The submission proposes an LLM-assisted travel planning method and framework. The two main contributions are Multiple aspects of planning approach to travel planning, which involves training the MAoP model in three distinct steps (reward model, rejection sampling fine-tuning, RL planning) and  applying the model to planning where the query is a long question and context which are decomposed into multiple aspects for input to the model. A simulation environment was introduced, TravelSim, evaluated for being an adequate model of a human traveller, and used in experiments for assessment of travel planning via MAoP. The work relies on mature implementations and extensive empirical evaluations.

Initially, some reviewers had a number of concerns about the submissions, however, the authors addressed all reviewers' comments and doubts, provided results of additional experiments, and convinced the reviewers that the paper is worth a publication.
Decision: Accept (spotlight)