ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework
Authors: Yusong Wang
Chuang Yang
Jiawei Wang
Xiaohang Xu
Jiayi Xu
Dongyuan Li
Chuan Xiao
Renhe Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Human Mobility Generation
Large Language Models
Event-Driven Mobility
Urban Computing
Primary area: other topics in machine learning (i.e., none of the above)

Abstract: Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture deviated mobility during large-scale societal events. This limitation stems from two critical gaps: (1) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile competitions between users' habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with a twofold contribution. First, we construct the first event-annotated mobility dataset covering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate trajectories that are both habitually grounded and event-responsive. Extensive experiments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness.

Published as a conference paper at ICLR 2026
ELLMOB: EVENT-DRIVEN HUMAN MOBILITY GEN- ERATION WITH SELF-ALIGNEI LLM FRAMEWORK
Yusong Wang': Chuang Yang"; Jiawei Wang', Xiaohang Xu², Jiayi Xu², Dongyuan Li² Chuan Xiao², Renhe Jiang²t
Institute of Science Tokyo, The University of Tokyo, 'The University of Osaka
wangyi [xujy.lidy] } u--sis. chuanx @ist.ossaa ast.ooaaa-u.c..jpp jip. esis.u-tokyo.acjjp
chuang.yang www.s....oooo.ac........... (jiawei.xhxu] Saaixuj@aaa
ABSTRACT
Human mobility generation aims synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating routine trajectories, they struggle to capture devi- ated mobility during large-scale societal events. This limitation stems from two critical gaps: (I) the absence of event-annotated mobility datasets for design and evaluation, and (2) the inability of current frameworks to reconcile com- petitions between users' habitual patterns and event-imposed constraints when making trajectory decisions. This work addresses these gaps with twofold contribution. First, we construct the first event-annotated mobility dataset cov- ering three major events: Typhoon Hagibis, COVID-19, and the Tokyo 2021 Olympics. Second, we propose ELLMob, a self-aligned LLM framework that first extracts competing rationales between habitual patterns and event constraints, based on Fuzzy-Trace Theory, and then iteratively aligns them to generate tra- jectories that are both habitually grounded and event-responsive. Extensive ex- periments show that ELLMob wins state-of-the-art baselines across all events, demonstrating its effectiveness. Our codes and datasets are available at ht https: / // //ithub. com/ hwtmm//eeppasssiwwaaee /ELLMMb.
INTRODUCTION
Human mobility generation aims synthesize plausible spatio-temporal trajectories of human movement (Kim et al., 2024). The study of such trajectories offers deep insights for urban plan- ning, transportation management, and public health (Duan al., 2023; Chen al., 2023; Li al., 2024). Moreover, synthetic trajectories provide privacy-preseriing alternative that permits broader access and usage than sensitive real- wul-w data. The emergence of Large Language Models (LLMs) has modeled trajectories as a "spatio-temporal language on map," shifting the task from data dis- tribution learning of traditional methods to instruction-bart et al., 2025). They leverage powerful contextual understanding and reasoning capabilities, offering advantages in semantic interpretability and versatility to different scenarios (Wang et al., 2024).
text generation (Choi et al., 2020; Feng
Current LLM-based methods explore various modeling strategies for generating realistic trajecto- ries. One line of work studies single-stage direct prompting. For example, Wang et al. (2023); Feng al. (2024) concatenate available information such long and short-term check-ins and instruct an LLM to jointly model user preferences, geospatial distance, and sequential dynamics, yielding coherent trajectories. Profile augmented modeling with multi-stage pipeline is also prominent research direction. For instance, Wang et al. (2024); Gong et al. (2024); Ju et al. (2025) first apply an LLM infer semantic profiles from user histories such as personas and travel motivations, and then condition generation on these high-level abstractions to produce personalized trajectories.
Although these methods achieve remarkable success, they still suffer from two key weaknesses. The first is data scarcity leading to evaluation bias. These methods are developed and evaluated pri- marily on datasets dominated by non-event days (stable period), resulting in questionable reliability
"quaal contribution.
Corresponning author.

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Published as a conference paper at ICLR 2026
LLM agent
Habitual mobility
Event-adapted trajectories
Affected area
Habitual Trajectories
Area under voluntary restrictions
Typhoon
COVID-19
Olympics OQQ
Altractions closed, trafic disruption, stay away from the seaside
Self-restraint, restricted commercial and entertainment facilities
Watch games, leisure trip and family visit, restricted zone
ZRE
Figure 1: Event-driven mobility generation by LLMs, which incorporates event context to capture real-world human mobility on three different events: (1) Typhoon: evacuation from seaside, (2) COVID-19 Pandemie: self-restraint, and (3) Olympies: restricted zones and traffic jam.
when modeling non-routine deviations caused by large-scale societal events (e.g., natural disasters, public-health emergencies) (Zhong et al., 2024). As Figure (a) illustrates, during a typhoon, travel shifts away from coastal areas and unnecessary commutes are eliminated. Without reliable data to evaluate their performance in modeling these dynamics, the reliability of these models for down- stream applications, such as emergency response planning and transportation management under stress, is severely reduced (Li et al., 2017). Another limitation is lack of a mechanism to reconcile competing decisions. In these events, real-world human mobility combines habitual regularities with shock-induced deviation (Song et al., 2014). As Figure (b) and (c) show, while overall mobil- ity patterns are altered, event-adapted trajectories preserve visits to essential anchor points (shared nodes for both lines) of user's routine, such as workplaces. Current methods struggle to navigate this duality, producing trajectories that either default to habitual patterns or are dominated by event constraints. Thus, explicit reconciliation mechanism is needed to generate plausible trajectories.
To tackle these challenges, first develop event-centric dataset to provide the necessary em- pirical foundation for studying non-routine mobility. It covers trajectories from over a thousand users in the Tokyo metropolitan area cross three large-scale societal events (COVID-19 Pandemic, Typhoon Hagibis, Tokyo Olympics) with distinct mobility effects, in addition to a normal period for baseline comparison. Second, we introduce ELLMob, a self-aligned LLM framework that incorpo- rates cognitive theory to shift generic self- self-alignment from error correction to conflict reconciliation, explicitly arbitrating between these competing decisions. Our key insight draws from Fuzzy- -racce Theory (FTT) (Reyna & Brainerd, 1995), which posits that gist, the essential meaning distilled from information, guides decisions under uncertainty. Event-driven mobility naturally fits this per- spective, as individuals weigh habitual patterns against event-imposed constraints. Crucially, FTT reveals that gist can be linguistically expressed, enabling us to analyze the decision basis LLMs. Building these insights, ELLMob extracts three forms of gist to capture competing decision tionales: pattern gist (habitual tendencies) and event gist (constraint requirements). along with the action gist (LL.''s current trajectory decision). Through iterative alignment of these gists, ELLMMob transforms trajectory decision into a traceable process where competitions are explicitly identified and reconciled, generating habitually grounded and event-responsive trajectories.
Experiments show that on our event-centric dataset, existing methods produce trajectories that either default routine patterns or overfit to event shocks (Figure 4), leading to poor generation quality. In contrast, ELLMob effectively reconciles this duality, achieving the best performance across four metrics, and surpasses strongest baselines by an average of 46.9 %% across all three events (Table 5). Ablation studies confirm the critical role of gist-level reconciliation: incorporating cognitive-base based self-alignment improves performance by an average of 69.5% % over non-aligned variants, highlight- ing its necessity for event-driven mobility modeling. Our contributions are summarized as follows.
We construct the first event-centric human mobility dataset with detailed semantic information, providing foundation for studying the non-routine deviations caused by societal events.
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We provide the first empirical evidence that current LLM-based methods struggle to model human mobility under societal events, revealing a critical research gap.
We propose an FTT-inspired framework that constructs decision variables to explicitly reconcile conflicts between habitual patterns and event constraints, enabling traceable decision-makin making.
ELLMob achieves state-of-the ahrt (SOTA) performance across all evaluated scenarios, demon- strating its effectiveness in generating plausible human mobility behaviors.
2 RELATED WORK
2.1 HUMAN MOBILITY GENERATION
The task of human mobility generation focuses on synthesizing realistic trajectories (Sun et al,, 2023; Gong et al., 2023). Early deep learning methods applied sequential models like LSTMs and attention-based RNNs to predict temporal dependencies and personal preferences (Hochreiter & Schmidhuber, 1997; Kulkarni & Garbinato, 2017; Gao et al., 2017; 2018; Wang et al., 2018; Feng et al., 2018; Luo et al., 2021). To improve trajectory fidelity. subsequent research shifted to deep generative models, including VAFs (Huang al., 2019), GANs (Choi et al., 2020; Wang et al., 2021; Zhao & Wang, 2023; Jia et al., 2024), and diffusion models (Zhu et al., 2023b;a; Chu et al., 2024), which excel at generating high-resolution location sequences. The emergence L.LMs introduced new approach, re-framing trajectory generation
sequence generation task conditioned on con- textual prompts (Xue et al., 2022; Wang et al., 2023; Feng et al., 2024). However, the defined task for all these preceding models has been to simulate the routine activity trajectories of users. Their ability to generate faithful trajectories under sudden, non-stationary conditions such as disasters or public health crises therefore remains unknown, compromising their real-world application. Our work addresses this deficiency by defining the task of event-driven human mobility generation.
2.2 LLM FOR HUMAN MOBILITY MODELING
LLMs are currently applied across range of human mobility modeling strategies (Wang et al., 2023; Feng et al., 2024; Wang et al., 2024; Tang al., 2024; Zhang et al., 2024; Beneduce et al., 2025). For example, Wang et al. (2023) incorporated both long- and short-term dependencies from histori- cal mobility data into LLMs to generate the next visiting location. Liang et al. (2024) used LLM with historical mobility Origin-Destinattnn Destination data to generate travel demand during public events at the Barclays Center. Wang et al. (2024) integrated diverse user contexts, such activity patterns, motivations, and profiles, into an L.M to generate more interpretable daily trajectories. Existing LLM-based methods fail reconcile competing objectives during events: they either blindly follow habitual patterns or event constraints, making them unable to effectively adapt to sharp mobility be- havioral changes driven by events (Luo et al., 2024). In contrast, ELLMob is cognitive theory-drived and incorporates a self-aligned mechanism that iteratively adjusts generated trajectories, shifting the generation goal from maximizing statistical likelihood cognitive plausibility.
PROBLEM DEFINITION
In this section, we define the terminology and formulate the event-driven trajectory genera- tion problem. A trajectory т is a time-ordered sequence of visited places, represented as ((po, to), (pI, (),,4)..... (Pn, b..), where each tuple (p,, t,) denotes visit to place p; at time t, Event Context, denoted Eat.. is structured data describing the exogenous shock associated with a specific event For
partition their historical trajectories within pre-event window WPPee() (c) into two disjoint sets based on a short-term duration Tshort-term relative to the start time of event la: long-term trajectories D ((v) long-term = (7((()) d < t. - Tehorttterml } and short-term trajectories teho These two datasets jointly characterize the prior mo- bility patterns the user. The objective of this task is to develop a generative model that generates
D(1)
Dahort-term (u.dd)
the event-driven trajectory: F: (D(m) long-termi short-term Eesa) + T.
(M)
EVENT HUMAN MOBILITY DATA
To develop and evaluate the performance of models in capturing mobility shifts under varying events, construct dataset from Tokyo trajectories collected via Twitter and Foursquare (2019-202) It encompasses three distinct events selected to represent a spectrum of societal conditions, as well as normal period to establish baseline. Detailed specifications are provided in Table 1.
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Table 1: Specifications for experimental evaluation scenarios.
Event
Event Period
Pre-Event Period
Description
Typhoon Hagibis
2019-10-11
10-13
2019-08-13 10-11
Natural disaster.
COVID-19 Pandemic
2020-04-07 04-13
2020-02-07 04-06
Public health emergency.
Tokyo 2021 Olympics
2021-07-23 07-29
2021-05-24~ ~ 07-22
Pandemic-ers large event.
Normal Period
2019-09-01 ~ 09-30
2019-07-03 ~ 08-31
Regular urban mobility.
For prolonged events (COVID-19 Pandemic and Tokyo Olympics), focus on the first seven days to capture pronounced behavioral shifts. COVID-19 Pandemic began with the State of Emergency of Japan. 30-day window for normal period is used establish a robust baseline of typical mo- bility, averaging out weekly fluctuations. Pre-event period (two months) acts training data for deep learning baselines and a source for user pattern extraction for LLM-based baselines. Follow- ing collection process described in Appendix B, curated dataset of 1,100 users who exhibited consistently dense check-in activity throughout the study period. Each sample includes user ID, geo- graphical coordinates, subcategory, subcategory ID, category, timestamp, and comment, shown in Appendix C. Table shows key statistics of this dataset. Table compares data dimensions across major mobility datasets (GeoLife (Zheng et al., 2009), Gowalla (Cho et al., 201 1). Foursquare (Yang et al., 2015), and Yelp (Asghar, 2016)), revealing that ours cover all standard mobility dimensions. dataset is the first to
To the best of our knowledge, types (long-term vs. short-term, diverse semantics) with continuous, dense pre- and during-event trajectories, enabling the precise analysis of behavioral transitions during societal shifts.
broad spectrum of distinct event
Table 2: Dataset statistics by scenario, detailing the counts of check-ins, unique POIs, and POI categories, with per-scenario totals.
Table 3: L, C, TC. N, and E denote time, lo- cation, subcategory, comments, normal period and explicit event annotation, respectively.
Event
Check-ins
POIs
Subcat.
Dataset
T
L (lat,lon)
C
TC
N
E
Typhoon Hagibis
4,330
3,284
334
GeoLife
X
X
/
X
COVID-19 Pandemic
8,448
4,629
330
Gowalla
X
X
/
X
Tokyo 2021 Olympics
16,069
9,586
467
Foursquare
/
/
X
/
X
Normal Period
104,781
36,487
655
Yelp
J
J
J
/
X
Agg. Pre-Event
641,747
125,710
764
Ours
V
/
/
/
To quantitatively ground this study, we present statistical analysis to reveal distinct impacts each event on collective mobility. We adopt four widely-used metrics from the human mobility work (Pappalardo et al., 2015; Alessandretti al., 2020): daily check-ins, capturing activity intensity, the radius of gyration and total travel distance, measuring spatial extent and volume, respectively, and daily activity duration, quantifying temporal span. Results are shown in Figure Specifi- cally, COVID-19 Pandemic and Typhoon Hagibis significantly suppressed the scope and frequency of movement. contrast, Olympics reversed suppressive trend in activity of COVID-19 Pandemic.
Gyration
During event Pre-ever
Distance
Check-ins
Daily
Normal Period
Typhoon Hagibls
COVID-19 Pandemic
Tokyo 2021 Olympics
Figure 2: Normalized radar charts of four mobility metrics on pre-event and during- patterns.
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Trajectory Generation
Reflection
Data
Context Short-term traj.
0
110
Gist
Exaluate Fictton gist's
acharo
Alignment
spacifie connuct
detection
Internal allgnment
analytical justifiction explaiing how used the
term (raj [Shoet-term traj.. and [Evend] Prosede
The
Trajectory generation 10
(Action git vs. Fatter giet)
Evalurle acton a correctailyy approprate
It inuu... stale care pattern dontifled.. mentin
Planned trajectory
rigst
c Gist Extraction
Guidance
Analyaa event. bayond surfsce
poLo wwd.
allgrment
unified
Gist extraction
Condicd Event
the
Synthoobe pattern giet by capturng the points of inertia The points fallure The gai should be underlying purposs namrativ....
Acsess plar resporda
Final trajectory
requiements and Convidar inmodiate safitty and contingency paaning.....
(Anshya planned tnapectory and planned)
glet
Event gist Pattern
Action gist
Figure 3: The ELLMob framework architecture comprising three interconnected modules: Trajec- tory Generation, Gist Extraction, and Reflection-based alignment.
METHODOLOGY
5.1 EVENT SCHEMA CONSTRUCTION
challenge in event-driven mobility generation that real-world events are typically described in lengthy, free-form text (e.g., news reports and policy documents), which often leads the LLM to overlook critical information during trajectory generation (Liu et al., 2024; An et al., 2024). To address this, event schema construction step is introduced to transform raw event narratives into structured representation that explicitly outlines the event's ' impact on population mobility patterns. The event schema is designed around four distinct but complementary aspects that collectively cover the key information required to assess mobility changes:
Event profile: Records the fundamental elements of the event (e.g., type, name, occurrence time, and affected regions). This provides an anchor for spatio-temporal alignment.
Intensity and scale: Quantifies key metrics of the event's 's severity, such as wind speed and amount of precipitation, to inform travel risk assessment.
Infrastructure and service impact: Describes the operational status of critical resources (e.g.. transportation, public venues), defining the physical constraints mobility.
Official directives: Captures governmental orders and recommendations (e.g., a request for resi- dents to avoid non-essential travel), including their applicable populations and geographic scope, ensuring generated trajectories refer to policy mandates.
An LLM is leveraged to process the raw event text E, into structured key-value format, referred to as event context Ecte- which subsequently serves as the input to the trajectory generation task. The prompt is provided in Figure A6 and the generated contents are presented in Appendix E.
5.2 SELF-ALIGNED L.M FRAMEWORK
In Figure 3, we propose ELLMob, a cognitive theory-driven framework that employs an iterative refinement process to reconcile competition between a user's 's habitual patterns and event constraints.
5.2.1 THEORY OF PLANNED MOBILITY BEHAVIOR
Fuzzy-Trace Theory (FTT) (Reyna & Brainerd, 1995) provides a cognitive perspective on decision- making under uncertainty, emphasizing that decisions are driven by gist, which refers the bottom- line (essential) meaning of information rather than verbatim details. A classic example is evacuation, where the action is driven not by the exact probability that a tsunami will strike (e.g., 15%) but by the gist that the risk is "high." According to FTT, gist can be linguistically expressed, making the decision-making basis transparent. In event-driven mobility generation, uncertain disruptions such as natural disasters epidemics require the model to navigate between two independent decision bases: adhering to habitual mobility routines or complying with event-imposed constraints. Existing LLM-based methods lack explicit mechanism for arbitrating between these competing decisions, leaving their rationale difficult to audit or control and tending to follow only one gist. By extracting the gist underlying these decisions, we expose the model's 's decision basis and resolve competition in
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transparent manner. We extract relevant gists: Pattern Gist and Event Gist, corresponding to two independent decision bases, and Action Gist, representing LLM's tentative plan.
Pattern gist: A representation of the essential tendencies distilled from the user's habitual mobility patterns, reflecting stable movement routines.
Event gist: A representation of the essential tendencies distilled from contextual constraints, cap- turing constraints or incentives imposed by external events.
Action gist: A representation of LLM's immature mobility decision, extracted from the candidate trajectory during planning.
We heuristically formalize these concepts as structured representations, where each gist is derived by assessing the relevant source data along set of core attributes, which is illustrated in Table 4. Building on this, we propuse a reflection module in which the LLM audits the alignment of these gist. This alignment process explicitly identifies conflicts that are then resolved through guided finement to ensure that the final generated trajectory is grounded in a unified decision basis. Notably, FTT offers architecture design basis. It motivates multi-gis -iitt decision framework, guides mapping heterogeneous inputs into a unified gist space for consistent alignment, and drives the use of inter- pretable bottom-line attributes over arbitrary features. Ablation study is provided in Appendix
Table 4: A set of defined core attributes that guide the gist extraction from source information.
Gist Type
Attribute
Description
Example
Core Behavior
The dominant pattern of action.
Daily commute to office.
Deeply embedded,
Returning home to a specific
Points of Inertia
Pattern Gist
non-negotiable components.
neighborhood at night.
Critical dependencies and
Reliance on a single train line
Points of Fracture
single points of failure.
that might be suspended.
Core implication of the event
High risk outdoors, strong incentive to stay home.
Primary Intent
for mobility decisions.
Survival, social dynamics, and compliance.
Evacuation from coastal areas, seeking indoor shelter.
Behavioral Implications
Event Gist
A cost-benetit analysis of the
Risk of injury outweighs reward of a non-essential outing.
Risk-Reward Calculus
response to event risks.
Main purpose driving this trajectory choice.
To get essential supplies from nearby store.
Primary Intent
Degree preservation
Low; this trip deviates from the
Habit Adherence
Action Gist
habitual patterns.
usual work commute.
Trajectory's ' level adherence to event constraints.
High; the trip short and
Event Compliance
avoids dangerous areas.
5.2.2 REFLECTION BASED ALIGNMENT
We replace single-pass decoding with an iterative reflect-refine loop that externalizes the model's s decision basis for transparent reasoning. Moreover, unlike generic self-alignment approaches that primarily correct errors such as hallucinations, our mechanism targets the decision-making dilemma inherent in event-driven mobility scenarios. Our alignment performs in two stages:
Alignment Auditing. This process is dedicated to rigorously auditing the plausibility of a planned trajectory. Each candidate trajectory is checked along two binary dimensions: Internal alignment is to ascertain whether the planned trajectory reflects a coherent expression of the user's intrinsic habitual mobility patterns and current behavioral tendencies. External aligument determines if the planned trajectory represents a rational and compliant response to the constraints and implications of the event. A trajectory is accepted only if both criteria are satisfied. The auditor outputs two binary judgments, accompanied by concise rationales that indicate any violated criterion and its cause.
Corrective Refinement. Should a planned trajectory fail to satisfy the criteria of either the internal or external alignment audit, ELLMob initiates corrective refinement loop. During this loop, the precise reasons for the audit failure are provided as feedback to the trajectory generator, guiding to regenerate revised trajectory that explicitly addresses the identified semantic misalignments and logical flaws. This loop repeats up to a maximum of K iterations. A trajectory that satisfies both criteria within the K-step budget is accepted as final trajectory. In the event that constraints remain unmet after K iterations, the system executes a fallback strategy. It accepts the last validated trajectory available in the agent's 's buffer and reports unmet constraints ensure transparency.
To clearly understand the ELLMob, we show the overall procedure in pseudo-code form Appendix G with complete contents of all related prompts provided in Appendix M.

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Table 5: Comparison of different methods under three events, Performance is evaluated by JSD across four dimensions with the best performance highlighted in bold.
Typhoon Hagibis
COVID-19 Pandemic
Tokyo 2021 Olympies
Models
STL
SDL
CDL SGD]
S1L SDL
CDL
SGDJ
S11
SDL
CDL
SGDJ
LSTM
0.1336 O. 1030 0.0555 0.1111 0.1928
0.1047 0.1300 0.2571 00 0.0651 0.0598 0.0634
DeepMove
0.1697 0.0826 0.0266 0.0759 0.1838 0.0834 0.0423 0. 061688
0.1667 0.0492 0.0587 0.0555
GETNext
0.3031 0.2007 0.0274 0.1037
0.2891 0.2241 0.0142 0.1354 0.2701 0.1473 0.0176 0. 1202
MHSA
0.1430 0.1815 0.0118 0.0711 0.2180 0.3083 0.0254 0.0437 0.1815 0.2013 0.0120 0.0525
TrajGAIL
0.1034 0.3591 0.0155 0.0275 0.1600 0,3557 0.0195 0.0444
0.0863 0.2913 0.0121 0,0104
DiffTraj
0.1271 0.2450 0.0385 0.0761
0.1405 0.2766 0.0554 0.0454
0.0732 0.2171 0.0342 0.0282
LLMOB
0.0949 0.1195 0.0123 0.0256
0.1011
0.1051 0,0186 0.0286
0.0973 0.0274 0.0110 0.0051
LLM-MOB
0.1214 0.0468 0.0285 0.0344
0.1166
0.0532 0.0234 0.0353
0.1047 0.0286 0.0085 0.0052
LLM-Move
0.1267 0.0392 0.0136 0.0303 0.1408 0.0567 0.0127 0.0503
0.1967 0.0298 0.0101 0.0057
LLM-ZS
0.1574 0.1348 0.0153 0.0724
0.1146
0.0576 0.0552 0.0570
0.0938
0.0330 0.0132 0.0052
ELLMob
0.0642 0.0200 0.0041 0.0173
0.1003 0.1003
0.0444 0.0080 0.0268
0.0617
0.0061 0.0022 0.0035
w/o &.A.
0.1304 0,1270 0.0139 0.0723 0.2331 0.1077 0.1 0.0733
0.1465 0.0340 0.0093 0.0095
w/ I.A.
0.0835 0.0720 0.0135 0.0436
0.12 0.0950 1053 0.0300
0.1555 0.0316 0.0088 0.0086
w/o E.A.
0.0680 0.0258 0.0077 0.0229
0.2237 0.0860 0.0283 0.0430
0.1392 0.0291 0.0083 0.0064
w/o Eve. Ext.
0.0736 0.0273 0.0045 0.0227
0.2037
0.0741
0.0269 0.0405
0.0686
0.0213
0.0030 0.0041
EXPERIMENTS
6.1 EXPERIMENTAL SETUP
Baselines. ELLMob is evaluated against two types of baselines: 1) deep learning-basee meth- ods which include predictive models: LSTM (Hochreiter & Schmidhuber, 1997), DeepMove (Feng et al., 2018), GETNext (Yang et al., 2022), and MHSA (Hong al., 2023); and generative models: TrajGAIL (Choi et al., 2020) and DiffTraj (Zhu et al., 2023b). 2) LLM-based models: LLM-MOB (Wang et al., 2023), LLM-Move (Feng al., 2024), LLMOB (Wang et al., 2024), LLM-ZS (Bene- duce et al., 2025). For fairness, the input event information remains consistent across all LLM--bsed methods. Specifically, detailed event descriptions (including type, time, location, and constraints) are integrated as natural language context at the beginning of each prompt, ensuring that all baselines have equal access to the event information despite differences in their specific prompt designs.
Evaluation Metrics. Step Interval (SI). The time between consecutive activities, defined as SI, 7+11 -T, where Ti denotes the timestamp at step t; Step Distance (SD). The distance between consecutive locations, defined as SD, = ||4++1 2, where 4, E IR2 denotes the location at step Category Distribution (CD). This metric captures the distribution of activity types. To calculate it, we aggregate the total number of visits N(ca) for each location category Cg- Spatial Grid Dis- tribution (SGD). It captures the population-level spatial footprint of activities. All visited locations discretized onto fixed S x S grid covering the Tokyo metropolitan area, with visit counts ac- cumulated per grid cell. To mitigate sparsity, following Ouyang et al. (2018); Feng et al. (2020), the top 23% frequently visited cells are retained for evaluation. For each of the four metrics, a distribution from the generated trajectories and compare it against the ground truth distribution using the Jensen-Shannon Divergence (JSD), following Zhu al. (2023b); Wang et al. (2024).
form
Implementation Details. We primarily use GPT-4o-mini (2025-01-01 -preview) (Achiam et al., 2023) as the backbone for its capability-coss balance, with additional LLM evaluations reported in Appendix H. Following Wang et al. (2024), we set the temperature to 0.1 to curb randomness, Top-p to 1, and model trajectories a 10-minute resolution. Grid size parameter is set to 10. K is set to 3 to balance refinement quality and inference cost based on the parameter study in Appendix K. A stability analysis verifying result consistency is provided in Appendix L.
6.2 QUANTITATIVE RESULTS
Table 5 summarizes our main results, demonstrating ELLMob's consistent superiority across all event-driven settings. For instance, it improves the SI score by 32.3% for Typhoon Hagibis and the SD score by 16.5% for the COVID-19 Pandemic compared to the strongest baselines. We observe that LLM-based approaches generally outperform traditional deep learning models, particularly on spatial coherence metrics (SD, SGD), benefiting from their ability to integrate event context. Fur- thermore, to verify spatial generalizability beyond the Tokyo area, we extended evaluations to Osaka during the COVID-11 pandemic, with detailed results provided in Appendix I.
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Disitg Driniig
Recall
0.88
0:45
0.56
0.48
0.30
0.84
0.00
0.24
0.80
6.7
5i0 LA VSS Corg.
na L.A. Comp. (a)
0.5 W0IA RULL Ceep.
0.16
LLM--M0 LLMOB LLM NOVE LLM-ZZ
ELLMob
(c)
Arts Erterteisment
Dring & Drieting
Pesfth Medkine
0.100
0.818
1-1--
8.00
0.35
3.000
Pndwart LAMC LMN
LUVE UA-
(b)
LLO BLM
Figure 4: (a) Comparison of generated activity proportions (relative to the total number of activities) during the COVID-19 Pandemic. Each chart contrasts the ground truth (G.T.), distribution with: Without internal alignment (wo L...). without external alignment (w/o E.A.), and the complete model (Comp.). (b) Comparison of three key activity categories distributions generated by ELLMob with various LLM-based baselines. (c) Performance comparison on the active user prediction task.
The ablation study further dissects our ELLMob's performance. Removing either the reflection mod- ule (w/o I.A.& or the event schema (w/o Eve. Ext.) consistently degrades performance. The two components of the reflection module show distinct roles: Internal alignment (w/o E.A.) provides corrective. The
foundational plausibility and external alignment (w/o L.A.) acts as a scenario-speciio importance of external aligmment is particularly evident during the COVID-19 Pandemic, where its removal causes a catastrophic 132.4% performance degradation in the SI score. This highlights its critical role in aligning with rational behaviors that substantially deviate from habitual patterns.
6.3 MODEL ANALYSIS
Analysis of Self-alignment. To dissect the distinct roles of internal and external alignment, we analyze the generated distributions of three sensitive top-categories (Arts & Entertainment, Din- ing & Drinking, and Hcalth & Medicine) within the COVID- 19 Pandemic. As shown in Figure (a), removing either alignment leads to distinct failures. Lacking internal aligmment, the model over-corrects for the event, generating an unrealistic surge in Health & Medicine while excessively suppressing Arts & Entertainment and Dining & Drinking activities. Conversely, without external alignment, the model rigidly adheres habitual patterns, producing the opposite failure. Further- more, Figure 4 (b) reveals that most LLM-based baselines default to habitual patterns (PreEvent) such as overestimating entertainmen//dining while ignoring health-related travel, while LLM-ZS overcorrects by suppressing social activities entirely. Both extremes demonstrate that these base- lines are unable to reconcile habitual patterns with event constraints in trajectory decision. The complete EL.LMob successfully considers these two forces to produce distribution that closely matches the ground truth, demonstrating the effectiveness of the self-alignment mechanism.
F Fundamental Decisions in Disasters. Accurately identifying individuals who travel during extreme weather critical for targeted early warnings and effective emergency response. We frame this as a binary classification task to identify potentially high-risk cohort, which we define as the positive class of "active" users (at least one trip) during the typhoon. We evaluate LLM-based basclines for their strong ability to incorporate event context. As shown in Figure 4 (c), ELLMob achieves the highest F1-Score, driven by its superior recall of 59.3% in identifying this "active" high-risk population. This effectiveness is likely attributed to the iterative alignment process, which enhances LLM's joint understanding of individual user mobility patterns and event constraints.
Case Study. To illustrate ELLMob's 's reasoning process, Figure 5 presents case study of user with a strong culinary exploration pattern during the COVID-19 Pandemic. The initially planned trajectory (stay at home) over-aligns with the event's public health constraints and is flagged by our reflection module for conflicting with the user's 's habitual patterns. Guided by this internal feedback, the model iteratively refines the plan into plausible trajectory that weighs both factors, ultimately limiting rather than eliminating dining outings. This case highlights the ELLMMobss 's ability to recon- cile user patterns with event constraints to generate realistic behaviors that match the ground truth.
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Ground Trush: Asteliss 2620-66-0 Food Re
Table 6: Comparison of different methods under the Normal period with the best in bold.
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Normal period
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Models
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dinig
SIL
SDL
CDL
SGD|
engoo
Auditor (Reflection)
the estel shed coutne, potentiall leating feeling of dhectitoction cattess sha to lack culnary expioition care actvtses appreptaae
Festylo
devistion
LSTM
0.1140
0.0696
0.0746
0.1499
DeepMove
0.1423
0.0428
0.0300
0.0742
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GETNext
0.3071
0.1628 0.0126
0.0502
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"eoson"" new
MHSA
0.1546
0.2346 0.0069
0.0269
Generator
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TrajGAIL
0.0953 0.3432 0.0035 0.0104
DiffTraj
0.0748 0.1832 0.0361 0.0393
Coheronoo pabee:: looheronce, with
LLMOB
0.1460
0.1007 0.0051 0.0045
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The
LLM-MOB
0.0654
0.0186 0.0059
0.0030
LLM-Move
0.1836
0.0261 0.0067 0.0036
Auditor (Reflaction)
nastained in the plan lintiig outings tep key reeds. This domonst uno comtoot ofly approp odagksSon to tho ourrece stuaton houtth
LLM-ZS
0.0746
0.0311
0.0164
0.0027
pooy whott completely acondoning toir out interosts...
prictiiie
ELLMob
0.0496 0.0164
0.0025
0.0025
w/o L.A&E.A.
0.0639 0.0210
0.0041
0.0032
Figure 5: A case study of ELLMob's workflow on the mobility of User No.003.
w/o L.A.
0.0545
0.0198
0.0026
0.0028
w/o E.A.
0.0556 0.0201
0.0037
0.0028
6.4 EVALUATION ON ROUTINE MOBILITY
To assess ELLMob's generality, we evaluate its performance in routine scenarios. Since ELLMob is inherently drivenr this evaluation requires defining context for the normal period. We there- fore classify target days as either weekdays or weekends, defining their respective contexts by the typical societal operating status (e.g., differences in business hours, public transport schedules). As shown in Table 6, ELLMob outperforms all baselines. This confirms that the ELLMob's 's alignment mechanisms are not narrowly tailored to disruptive events, but instead constitute robust foundation that also excels in standard scenarios.
6.5 COMPARATIVE ABLATION ON ALIGNMENT STRATEGIES
Iterative-reflcction methods such as Reflexion (Shinn et al., 2023), SELF-REFINE (Madaan et al., 2023), and Air (Liu et al., 2025) enhance LLM reasoning via self-correction, mainly targeting hal- lucinations or logical flaws in unstructured text. However, event-driven mobility requires resolving conflicts between habitual inertia and event-induced constraints. ELLMob introduces method- ological shift by grounding alignment in cognitive theory. At representation level, it replaces un- structured trajectories with structured decision variables to disentangle drivers. At decision level, rather than prompting the model to "improve" an answer, ELLMob employs dual-axis alignment to arbitrate competing objectives. This ensures trajectory adjustments are cognitively grounded rather than surface-level, locally reasonable fixes. To isolate the effectiveness of this design, we replaced ELLMMob's s alignment module with each baseline strategy, while keeping all other settings identical. As the results shown in Table 7, ELLMob outperforms these variants in all metrics. This validates the necessity of the proposed alignment strategy for event-driven human mobility generation.
Table 7: Comparison with iterative-reflection baselines with the best performance in bold.
Typhoon Hagibis SDJ
COVID-19 Pandemic
Tokyo 2021 Olympies
Models
STS
CDJ
SGDJ
SIL
SDL
CDL SGDJ
SIL
SDJ CDJ SGDJ
Reflexion
0.1106 0.1282 0.0841 0.0855 0. 0616 0.0588 0.0146 0.0269
0.1741 0.0308 0.0378 0.0704
SELF-REFINE
0.1979 0.0637 0.0135 0.0193
0.2122 0. 0.1053 0.0344 0.0294
0.0826 0.0320 0.0073 0.0058
Air
0.0764 0.0710 0.0198 0.0204
0. 08188 0.0454 0.0256 0.0291
0.0774
0.0441 0.0053 0.0035
ELLMob
0.0642 0.0200 0.0041 0.0173
0.1003 0.0444 0.0080 0.0268
0.0617
0.0061
0.0022 0.0035
6.6 COMPUTATIONAL EFFICIENCY
We evaluated computational overhead via token consumption and inference latency on a per person per day basis, with results in Table 8. Under GPT-4o-mini pricing, ELLMob uses 9,569 tokens and 18.68 seconds to generate one-day mobility for single person, at $0.001 70. While this multi-stage
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Table Computational Efficiency Analysis. The reported total token count includes both input and output tokens, and the overall cost is computed by accounting for their respective pricing rates.
Model
Token Count
Inference Time (s)
Cost (USD)
LLMOB
1,271
10.12
0.00030
LLM-MOB
3,954
3.72
0.00064
LLM-MOVE
4,954
4.05
0.00078
LLM-ZS
5,184
3.34
0,00080
Reflexion
26,057
27.12
0.00417
SELF-REFINE
15,382
21.16
0.00258
Air ELLMob
15,514
20.50
0.00260
9,569
18.68
0.00170
architecture entails additional overhead compared to single-pass models, it demonstrates superior efficiency relative generic reflection baselines like Reflexion (Shinn et al., 2023). This efficiency might stem from the integration of FTT, where structured alignment provides targeted guidance to accelerate convergence and avoids the excessive resource cost of open-ended iterative refinement. Independent user-level generation allows parallelization, ensuring city-scale simulations feasible.
6.7 VISUALIZATION SPATIAL MOBILITY PATTERNS
Figure 6 presents heatmaps of the spatial mobility distribution during two high-impact events (Ty- phoon Hagibis and the COVID-19 Pandemic), comparing ground truth against ELLMob, its ablated version without the reflection module, and the strongest baseline LLMOB. Both our ablated model and the strongest baseline LLMOB exhibit the core limitations of single-pass generation with im- plicit trajectory decision, producing flawed spatial patterns: Excessive contraction during the ty- phoon and incomplete decentralization during the COVID-19 Pandemic. In contrast, ELLMob's reflection module uses iterative alignment achieve a fine- ana-ggrieed understanding of both user pat- terns and event constraints, enabling it to reproduce realistic mobility patterns.
-175
Covld
1.0
-009
Typhoon
-209
-00%%
Figure 6: Spatial mobility patterns. Darker red in the heatmaps indicates higher visit frequency.
7 CONCLUSION
This work addresses the critical challenge of modeling human mobility during large-scale soci- etal events. We contribute comprehensive event-centric dataset covering three major events in Tokyo and introduce ELLMob, a framework that explicitly reconciles competing mobility decisions through gist-based alignment. Through extensive experiments, ELLMob demonstrates substantial improvements over existing methods, enabling more reliable mobility generation for emergency planning and urban management applications. However, acknowledge that data from these plat- forms may introduce demographic biases, such as skewing towards younger users, which a com- mon limitation in LBSN research. Future work will aim incorporate diverse data sources.
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ETHICS STATEMENT
All data used in our study is fully anonymized. The dataset was collected through Twitter Inc.'s Academic Research Product Track and Foursquare in strict compliance with their privacy policies and terms of service. Our data collection and preprocessing methodology follows the established work (Yang et al., 2016; Wang et al., 2024). While the data sources are legitimate, we recognize that refereninng with original Twitter posts potentially enable re-identification. To miti- gate this risk, we implemented the following anonymization measures: (1) User IDs are replaced with random numbers; (2) Specific venue names are generalized categories; (3) Exact timestamps are discretized into time intervals; and (4) User-generated comments are provided only in translated form prevent linguistic fingerprinting. These comprehensive measures ensure that reverse identifi- cation is computationally infeasible even with access to the original platforms. Detailed descriptions of these preaeevii processes are included in the Appendix B.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have made our code and data publicly available in the supplementary materials. Our experimental setup, including evaluation metrics and parame- ter settings, are described in Section 6.1. Our data collection and filtering criteria are detailed in Appendix B.
ACKNOWLEDGMENTS
This work supported by JSPS KAKENHI Grant JP24K02996, JP23K17456, JP23K25157, JP23K28096, JP25H01117 and JST CREST Grant JPMJCR2IM2, JPMJCR22M2.
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
Laura Alessandretti, Ulf Aslak, and Sune Lehmann. The scales of human mobility. Nature, 587 (7834):402 2020.
Shengnan An. Zexiong Ma. Zeqi Lin. Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. Make your I1m fully utilize the context. In A. Globerson, L. Mackey, D. Bel- grave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances in Neural In- formation Processing Systems, volume 37, pp. 11160-6188.. Curran Associates, Inc., 2024. URL https: http::////prcceednggs www.iinggn..euusseeeeee.e... neurips cc/ httppppfflleepppppeeeeepeeee file (1t.7711133111116444444228666
fa-per Conference pdf.
Nabiha Asghar. Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv.1605.05362. 2016.
Ciro Beneduce, Bruno Lepri, and Massimiliano Luca. Large language models are zero-shot next location predictors. IEEE Access, 2025.
Enhui Chen, Yang Liu, and Min Yang. Revealing senior mobility patterns and activities in urban transit systems. JEEE Transactions on Intelligent Transportation Systems, 24(10): 1424-111774 2023. doi: 10. 109/TTTS.2023333755555
Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: user movement in location-bass based social networks. In Proceedings of the 17th ACM SIGKDD International Confer- Knowledge Discovery and Data Mining, KDD '11, pp. 1082-1090, New York, NY. USA, 201 Association for Computing Machinery. ISBN 9781450308137. doi: 10.1145/2020488 wrw/10....0 000...11152220000000 2020579.
2020579. URL wwwph:ttt/hoo...cc /doii. http:///ooooooog////////

---

Published as a conference paper at ICLR 2026
Seongjin Choi, Jiwon Kim, and Hwasoo Yeo. Trajgail: Generating urban vehicle trajectories using generative adversarial imitation learning. Transportation Research Part C: Emerging Technolo- gies, 2020. URL https:
Chen Chu, Hengcai Zhang. Peixiao Wang, and Feng Lu. Simulating human mobility with tra- jectory generation framework based on diffusion model. International Journal of Geographical Information Science, 38:847 878, 2024. URL https:////// /api semant commanticschann www.cccccccaccooooooooooooooeoe. orgg/ CorpusID: 26722177755
DeepSeek-AL, Daya Guo, and et al. Deepseek-rl: Incentivizing reasoning capability in Ilms via reinforcement learning. ArXiv, abs/2501.12948, 2025. URL attps://apls
semantics iittannisccolarr.. Org/CorpusID :IID:275785 275779950
Xiaoqi Duan, Tong Zhang, Zhibang Xu, Qiao Wan, Jinbiao Yan, Wangshu Wang, and You- liang Tian. Discovering urban mobility structure: a spatio-temporal representational learn- ing approach. International Journal of Digital Earth, 16:4044 - 4072, 2023. URL https: //api. wwwanticscholas.tt.....e...c.aae wotp/oooorsD 2636441D226644488
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. Deepmove: Predicting human mobility with attentional recurrent networks. WWW '18, pp. 1459-1468, Re- public and Canton of Geneva, CHE, 2018. International World Wide Web Conferences Steer- ing Committee. ISBN 978 1503566398. //doi. org/ 10. 1145/ 31 7876 3186058.
doi: 10.1145/3178866.1 366058.
URL https:
Jie Feng, Zeyu Yang. Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. Learning to simulate hu- man mobility. In Proceedings of the 26th ACM SIGKDD international conference knowledge discovery & data mining, pp. 3426-3433, 2020.
Jie Feng, Yuwei Du, Jie Zhao, and Yong Li. Agentmove: large language model based agentic framework for zero-shot next location prediction. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume I: Long Papers), pp. 1322 1338, 2025.
Shanshan Feng. Haoming Lyu, Fan Li, Zhu Sun, and Caishun Chen. Where to move next: Zero- shot generalization of Ilms for next poi recommendation. In 2024 IEEE Conference on Artificial Intelligence (CAI). pp. 1530-1535. IEFF, 2024.
Qiang Gao, Fan Zhou, Kumpeng Zhang, Goce Trajcevski, Xucheng Luo, and Fengli Zhang. Identify- ing human mobility via trajectory embeddings. In International Joini Conference on Artificial In- telligence, 2017. URL https: /api. semant wwwctoccchhoooooo ar. www..r.....ooo...e..........eee. Barg/CorpusTT. 9552064 64.
Qiang Gao, Goce Trajcevski, Fan Zhou, Kunpeng Zhang, Ting Zhong, and Fengli Zhang- Trajectory-based social circle inference. Proceedings of the 26th ACM SIGSPATIAL Inter- national Conference on Advances in Geographic Information Systems, 2018. URL https: //api. www.nticscholes.e
Haofeng Gong, Xinning Zhu, Zheng Hu, and Jianzhou Diao. Two-stage trajectory generation model for realistic human mobility simulation. 2023 IEEE International Conference on Iligh Perfor- mance Computing & Communications, Data Science & Systems, Smart City & Dependabiliry in Sensor, Cloud & Big Data Systems & Application (HPCC/DDSSSmarrCin//Dpeeeddd 941, 2023. URL wwwpt:ttt//...m / api semanti cscholar cesaantcscholar......ooomo org :D:p://oooouuuuooooo00
pp. 934-
Letian Gong, Yan Lin, Xinyue Zhang. Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, and Huaiyu Wan. Mobility-IIm: Learning visiting intentions and travel prefer- ence from human mobility data with large language models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomezak, and C. Zhang (eds.), Advances in Neu- ral Information Processing Systems, volume 37, pp. 36185-36217. Curran Associates, Inc., www.diinnnnn.nnunuieeeee neurips......... co cc/ wttp//pper__iiieeeee files/ /ailes/ppper/224// 2024. URL 1eeo22eeeeee000eccee 74d www.rpapeeeaeeeecceeeeeea .dff. hilee3fbbc52aebbl1
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv 178.2 2024.
12

---

Published as a conference paper at ICLR 2026
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Comput., 9(8): 1735-1780, November 1997. ISSN 0899-7667. doi: 10.1 (e.11///coo..999...... 17355. URL
https://doi..rgggge http://..112//eccce 1997. 99.899...87
Ye Hong. Yatao Zhang, Konrad Schindler, and Martin Raubal. Context-aware multi-head self- attentional neural network model for next location prediction. Transportation Research Part C: Emerging Technologies, 156:104315, 2023. ISSN 0968-090X. doi: https://bb.orr//10.1116j.. 2023. 104315. URL https: wwwww www.wwwwnndd.ee.t...... wtt.cccc/ccccccc ccmm//cience/arttceeeeeeeeeee 50968090X2300304
Dou Huang, Xuan Song, Zipei Fan, Renhe Jiang, Ryosuke Shibasaki, Yu Zhang, Haizhong Wang, and Yugo Kato. variational autoencoder based generative model of urban human mobility. In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pp. 425-430. IEEE, 2019.
Jia Jia, Linghui Li, Pengfei Qiu, Binsi Cai, Xu Kang, Ximing Li, and Xiaoyong Li. Domain- knowledge enhanced gans for high-quality trajectory generation. In International Conference on Intelligent Computing, 2024. URL https: http://aap........eeeee.aee www.nticschoooeeoe.ooe...occcooe org/ CorpusID: 271770070.
Chenlu Ju, Jiaxin Liu, Shobhit Sinha, Hao Xue, and Flora D. Salim. Trajllm: A modular 11m- enhanced agent-based framework for realistic human trajectory simulation. Companion Proceed- ings of the ACM on Web Conference 2025, 2025. URL https: /atp://appppeeee wew.sssescanssooll.... org/CozppsID::2 2766177777766777
Joon-Seok Kim, Gautam Malviya Thakur, Licia Amichi, Annetta Burger, Chathika Gunaratne, Joseph Tuccillo, Taylor Hauser, Joseph Bentley, Kevin Sparks, Debraj De, Chance Brown, Eliz- abeth McBride, Jesse McGaha, James Gaboardi, Xiuling Nie, and Steven Carter Christopher. Humonet: A framework for realistic modeling and simulation of human mobility network. In 2024 25th IEEE International Conference on Mobile Data Management (MDM), pp. 185-194, 2024. doi: 10. 1099/DDM60037.2024.000000
Vaibhav Kulkarni and Benoît Garbinato. Generating synthetic mobility traffic using mns. Pro- ceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, 2017. URL htthhtts///ee http::::/app.smmaniii.ssse....e semanticscholle OrggCoorr/II:
28123397.
Yang Li, Xudong Wang, Shuo Sun, Xiaolei Ma, and Guangquan Lu. Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transportation Research Part C-emerging Technologies, 77:306-328, 2017. URL https:// api. wew.ssemanticc.. wcschclar......cc org/ Ltss/CoopsII::11555555
Zhonghang Li, Lianghao Xia, Jiabin Tang, Yong Xu, Lei Shi, Long Xia, Dawci Yin, and Chao Huang. Urbangpt: Spatio-temporal large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '24, pp. 5351-5362, New York, NY. USA, 2024. Association for Computing Machinery. ISBN 9798400704901. doi: 10. /o/.
1145/3637528.367155 URL https:////// : http:://oooooo////// http///...1113333333333 311.5/33377228.61555777 3671578.
Yuebing Liang, Yichao Liu, Xiaohan Wang, and Zhan Zhao. Exploring large language mod- cls for human mobility prediction under public events. Computers. Environment and Urban Systems, 1 112:102153, 2024. ISSN 0198-9715. doi: sor.dooo.rr/1...0011..oommmeep
2024.102153. URL https:://wwwtt http//wwwciinceeee rect wtt.///iiene//rriiccc...... S01 50198971524000822.
Kai Liu, Zhihang Fu, Chao Chen, Wei Zhang, Rongxin Jiang, Fan Zhou, Yaowu Chen, Yue Wu, and Jieping Ye. Enhancing Ilm's cognition via structurization. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances Neural Curran Associates, Inc., Information Processing Systems, volume 37, pp. hpappr/appee..eee files/ / 2024. URL htttps://proceeddggs..eee.
file/
13

---

Published as a conference paper at ICLR 2026
Wei Liu, Yancheng He, Yu Li, Hui Huang, Chengwei Hu, Jiaheng Liu, Shilong Li, Wenbo Su, and Bo Zheng. AIR: Complex instruction generation via automatic iterative refinement. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.). Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 31952-31974, Suzhou, China, November 2025. Association for Computational Linguistics. doi: 10.18653/ 11/2025.emnlp-man 1628. URL https: 1628/.
patps://aclantinn /tt///aaaaaantoyyycaaaaaa htwpa/oooo...ogg///ooo
Yingtao Luo. Q. Liu, and Zhaocheng Liu. Stan: Spatio-temporal attention network for next location recommendation. Proceedings of the Web Conference 2021, 2021. URL https: //api. saaapi..smanticcccoooe.e .rgg www.:oaa.....oooooi........
Yuxiao Luo, Zhongcai Cao, Xin Jin, Kang Liu, and Ling Yin. Deciphering human mobil- ity: Inferring semanties of trajectories with large language models. 2024 25th IEEE Interna- tional Conference on Mobile Data Management (MDM), pp. 289-294, 2024, //api. semantccscoolar
URL https:
2700023364
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao. Sarah Wiegreffe, Uni Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Itera- tive refinement with self-feedback. ArXiv, abs/2303.17651,, 2023. URL https://api. semant Semannicscholar org/
wtw.aaarrrrCCoooCoIIIIIIoI I ID:
Kun Ouyang. Reza Shokri, David S Rosenblum, and Wenzhuo Yang. A non- com-parmettric generative model for human trajectories. In IJCAI, volume 18, pp. 3812-3817, 2018.
Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, and Albert- László Barabási. Returners and explorers dichotomy in human mobility. Nature communications, 6(1):8166, 2015.
V.F. Reyna and C.J. Brainerd. and Individual Differences, 7(1):1-75, 1995. ISSN 1041-6080. doi: https://oi. org/10. 1016/1041-6000005999000
Fuzzy-trace theory: An interim synthesis. Learning
URL https://wwat www.:science. eeleencedirect. cot..com/ae ccmm/cciece/ atticllep111101111800900000
Special Issue: Fuzzy-Trace Theory.
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: language agents with verbal reinforcement learning. In Neural Information
Processing Systems, 2023. URL https://aat semant cscholar. www.oolrrrrrrrr//o..oooo CorgCCrrpusID)
258833055.
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki. Prediction of human emergency behavior and their mobility following large-scale disaster. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp- 5-14, 2014.
Xinyue Sun, Qingqing Ye, Haibo Hu, Yuandong Wang, Kai Huang, Tianyu Wo, and Jie Xu. Synthe- sizing realistic trajectory data with differential privacy. IEEE Transactions on Intelligent Trans- portation Systems, 24:5502-5515, 2023. URL https: /tpp.aap..../// aescannccccclarr....
ID:
Peizhi Tang, Chuang Yang, Tong Xing, Xiaohang Xu, Renhe Jiang, and Kaoru Sezaki. Instruction- tuning 1lama-3-8b excels in sctye mobility prediction. Proceedings of the 2nd ACM SIGSPA- TIAL International Workshop on Human Mobility Prediction Challenge, 2024. URL https:
//api.
ptrg/Corpuss.. : 277706998.
Guowei Wang, Dongliang Liao, and Jing Li. Complete user mobility via user and trajectory em- beddings. IEEE Access, 6:72125-72136, 2018. URL https: /tppapp...// sew.sticcmntccchoo..oo
Dxc/CorpusID::6111 56170111
Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Noboru Koshizuka, and Chuan Xiao. Large language models as urban residents: An LLM agent framework for personal mobility generation. In The Thirty- Anmual Conference Neu- ral Information Processing Systems, 2024. URL https:/ /wtp//pppwweeeee////pp/p net hft.moorrmmi https:://pperrevies
1 HmhMHNya.
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---

Published as a conference paper at ICLR 2026
Xinglei Wang. Meng Fang, Zichao Zeng, and Tao Cheng. Where would go next? large language models as human mobility predictors. arXiv preprint arXiv:2308. 15197, 2023.
Xingrui Wang, Xinyu Liu, Ziteng Lu, and Hanfang Yang. Large scale gps trajectory generation using map based on two stage gan. Journal of Data Science, 2021. URL https://apt semanti csccollclooooooo waw.lrrr.......oo...........oooa org/ Corpus 2341 69684.
Hao Xue, Bhanu Prakash Voutharoja, and Flora D. Salim. Leveraging language foundation models for human mobility forecasting. Proceedings of the 30th International Conference Advances in Geographic Information Systems, 2022. URL https: httptpp://pp..ssmatttsssssseeess
www.entiiscchheem org/
CozpusID:252212331
Dinggi Yang. Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. Modeling user activity preference by leveraging user spatial temporal characteristies in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1):129-144 2015, doi: 10.1 N0.1099TTMMC2224222227000
Dingqi Yang. Daqing Zhang, and Bingqing Qu. Participatory cultural mapping based on collective behavior data in location-bar based social networks. ACM Trans. Intell. Syst. Technol, 7(3), Jan- uary 2016. ISSN 2157-6904. doi: 10.1145/2814575. URL https: //ci:///doo//ee// ott//oo.g1ll.. 2814575.
Dingqi Yang, Bingging Qu, Jie Yang, and Philippe Cudre-Mauroux. Revisiting user mobility and social relationships in lbsns: A hypergraph embedding approach. In The World Wide Web Conference, WWW '19, pp. 2147-2151 New York, NY. USA, 2019. Association for Com- puting Machinery. ISBN 9781450366748. doi: 10. 10.11457308558.311366.
URL https:
//doi. 111111135/33551 0tt.//3388558833333333 3313635.
Qwen An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jian Yang. Jianhong Tu, Jianwei Zhang, Jianxin Yang. Jiaxin Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yi-Chao Zhang, Yunyang Wan, Yuqi Liu, Zeyu Cui, Zhenru Zhang, Zihan Qiu, Shanghaoran Quan, and Zekun Wang. Qwen2.5 technical report. ArXiv, abs/2412.15115,, 2024. URL puttps://agg iomanticschollo org/ Corpus ID: 27 274859421.
Song Yang, Jiamou Liu, and Kaiqi Zhao. Getnext: Trajectory flow map enhanced transformer for next poi recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 222 pp. 1 1144-1153, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450387323. doi: 10.1145/ 3477495.3531983 URL.
Kunyi Zhang, Yanbo Pang. Yurong Zhang, and Yoshihide Sekimoto. Mobglm: A large lan- guage model for synthetic human mobility generation. Proceedings of the 32nd ACM Inter- national Conference on Advances in Geographic Information Systems, 2024. URL https: //api. www.nticscnnollaaa.....eeeoa http://cccc..aa..oooooosssassa /Copp//orpussD. :
Kefan Zhao and Nana Wang. Generating spatiotemporal trajectories with gans and conditional gans. In International Conference on Neural Information Processing, 2023. URL https:/ :/api.
semanti cechhtaasccl........oooo... wtto/orrgppeeeeoooooooo Cctp/orrrpiiccccee 265455724.
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. Mining interesting locations and travel sequences from gps trajectories. Proceedings of the 18th International Conference on World Wide Web, WWW '09, pp. 791-800, New York, NY, USA, 2009. Association for Computing Machinery, ISBN 9781605584874. doi: 12.1145/1 0eg/10.1145/1555
URL https://doi.
1526816.
Xiaofeng Zhong, Yinfeng Xiang, Fang Yi, Chao Li, and Qinmin Yang. Hmp-Ilm: Human mobility prediction based pre-trained large language models. In 2024 IEEE 4th Interna- tional Conference on Digital Twins and Parallel Intelligence (DTPI), pp. 687-692, 2024. doi:
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Yuanshao Zhu, Yongchao Ye, Ying Wu, Xiangyu Zhao, and James Yu. Synmob: Creating high-fidelity synthetic gps trajectory dataset for urban mobility analysis. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (eds.), Advances in Neural Informa- fion Processing Systems, volume 36, pp. 22961-22977. Curran Associates, Inc., 2023a. URL https://proceedingg neurips cc/paper www.flllepppe haales/paper/2222/ fill/ 4T880019567788111573366080000000 Paw.oob8obboeeeeppeeeeeeeeeeeeee -aaeer-Dataetss.tm and Benchmarks. pdf.
Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, and James Yu. Difftraj: Generat- ing GPS trajectory with diffusion probabilistic model. In Thirty- Conference Neural Information Processing Systems, 2023b. URL WtRLtht:///pppeeeeeee /ttp:///ppeeeeeeee///// net met/forum forum?irn ykMdzevPkJ.
A USE OF LARGE LANGUAGE MODELS
We employed LLMs for grammar checking and polishing the English expression throughout this manuscript. It is important note that while research focuses on leveraging LLMs for human mobility modeling, the LLMS studied in this work are the subject of our research rather than tools for research ideation scientific writing. All experimental design, analysis, and scientific conclusions were developed independently by the authors.
B DATA COLLECTION, CLEANING AND ANONYMIZATION
Data Collection. The raw data used in this study are derived from Foursquare check-in records that users publicly synced to Twitter, which are accessible through the Twitter API. We first used the Twitter API to identify users who were active within a 100 km radius Tokyo Station during April 2021, and then retrieved all of their tweets from 2019 to 2021. We subsequently identified Foursquare check-in tweets (auto-posted via the Foursquare Twitter integration) and extracted the associated metadata, including the Point-of-Interest (POI) name. category, and subcategory! geo- graphic coordinates (latitude & longitude), timestamp, and user-provided comment text. The ex- tracted check-in- served as the raw dataset for this study. Importantly, these user-level records span 2019 to 2021, providing longitudinal dataset that captures diverse social and environmental con- texts, including but not limited the Typhoon Hagibis, COVID-19 pandemic, and the Tokyo 2021 Olympics, which collectively form the foundation of this study.
Data Cleaning. After obtaining the raw dataset, we performed several cleaning steps improve data quality. First, we discarded users with missing check-ins for an entire year. Next, we parsed geographic coordinates to obtain prefecture information and assigned each user to their most fre- quently visited one. For example, users whose check-ins were primarily in Tokyo were labeled as "Tokyo users". We filtered the dataset to include only Tokyo users for two primary reasons: To ensure homogeneous dataset by mitigating confounding variables from adjacent prefectures and to leverage the wide spectrum of mobility behaviors characteristic of a global mega city. We re- tained only users with consistently dense check-in activity throughout the study period, yielding final dataset of 1,100 users. As illustrated in Figure A2, the check-in distribution of these sampled users follows power-law characteristic, which consistent with real-world human activity patterns (Yang et al., 2019).
Data Anonymization. To protect privacy, we applied deterministic, one-way pseudonymization all identifiers. Twitter user IDs were irreversibly mapped to integer surrogates, and POI identifiers were processed in the same manner. POI names (e.g., "Yoshinoya Shinjuku") were removed while retaining only category information (e.g., major category "Dining and Drinking" and subcategory "Donburi Restaurant"). Precise time information was obfuscated resolution of one minute and all comments were translated into English and paraphrased.
'https://occs www.rsppree.com/tttataraote httpp///w.....ooooo....r////r//s
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10a
10a
10-1
CCDF
10
10-1
10
10-2
10
10-3
10-1
10"
10.
10'
10a
10*
10 Check-in counts per user
10
Check-in counts per POI
Figure A2: Log-log plots of the Complementary Cumulative Distribution Function (CCDF) of check-in counts. Left part of the figure shows the distribution of check-ins per POI. Right part of the figure shows the distribution of check-ins per user. Both distributions exhibit a linear trend, characteristic of power-law.
C MOBILITY DATA SAMPLE
To provide a concrete illustration of the user activity trajectories, Table A2 presents an exemplary sequence from single user. This sample highlights the data structure, integrating precise spatio- temporal features (latitude, longitude, time) with functional semantics (Location Name, Category), which forms the foundation for our mobility analysis.
Table A2: A user's mobility sample, showcasing activity trajectories with spatio-temporal features and comments.
Long.
Subcategory
Category
Timestamp
Comments
Inted and rewritten)
0118
35.652
139.543
1003
Home Appliance Store
Retail
2020-04-07 18:33
Chofu-chan: Oh no, an emergency doclaration bas been announced!!
139.577
Clothing Store
response como- navirus precautions, credit card transactions have transitioned self-scanning! (This aiming For.).
35,632
4859
Rail Station
Travel Transport
2020-04-10 19:21
Wait minute? this considered close contact?
D BEHAVIOR DISTRIBUTION
Figure A3 shows that different events impose distinct mobility behavior. For instance, Typhoon Hagibis disrupts transportation, leading to widespread cancellations. Similarly, the declaration of COVID-19 Pandemic canceled nearly all entertainment activities due to self-quarantine require- ments.
E EVENT SCHEMA
This section provides three complete instances of the event schema introduced in the main paper (Typhoon Hagibis, Tokyo 2021 Olympics, and COVID-19 Pandemic). Each instance follows the same four aspect structure used to derive the event context Eat from raw text E,.- The content is shown in Figure A4. Note that omit the event category here due to its limited sample size.
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Arts & Entertainment Business Prof. Services Community Government
Dining & Drinking
Retail Sports Recreation Travel Transportation
Health Medicine
Landmarks Outdoors
5.80%
6.87%
5.40%
17.13%
49.85%
18.77%
42.84%
18.11%
46.77%
16.87%
47.86%
7.42%
8.95%
9.33%
12.01%
14.74%
16.09%
12.25%
Normal Period
Typhoon Hagibis
COVID-19 Pandemic
Tokyo 2021 Olympics
Figure A3: A visualization of category distributions across four event scenarios. Each data point represents the percentage share of the category out of the total activities in that scenario.
Furthermore, this structured approach is highly extensible, allowing for the integration of custom information to generate tailored outputs for any given event.
Event Schema Typhoon Hagibis:
Event Profile: Typhoon Hagibis struck the Tokyo metropolitan
October 2019.
Intensity Scale: The typhoon reached Category 5 strength, bringing violent winds and heavy rainfall, with Tokyo recording 240 of rain in 24 hours.
Infrastructure & Service Impact: More than 370,000 homes experienced power outages, leading the suspension of all major transportation services and closure of popular attractions.
Official Directives: Authorities issued evacuation orders to over 800,000 households and urged approximately six million residents to stay indoors and monitor official advisories.
Event Schema of COVID-19 Pandemic:
The COVID-19 state of emergency declared by Prime Minister Shinzo Abe under Article 32 of the Act Special Measures for Pandemic Influenza and New Infectious Diseases. The state of emergency covered Tokyo, Saitama, Chiba, Kanagawa, Osaka, Hyogo, and Fukuoka. Intensity Scale:
Event Profile:
entail full lockdown but allowed prefectural governors request residents to stay home for essential activities.
Infrastructure Service Impact: Essential services grocery stores, pharmacies, banks, public transport, utilities asked to continue operating with infection-control measures. Large commercial and entertainment facilities requested suspend restrict operations.
Official Directives: Prefectural governors authorized to request residents to stay home except for essentials like medical visits, shopping, commuting. Non-compliant businesses could have their names disclosed.
Event Schema Tokyo 2021 Olympics:
Event Profile:
The 'Tokyo 2021 Olympics', starting from July in Tokyo, Japan.
to with Intensity Scale: Significant traffic jams occurred and around central Tokyo and Olympic notable but separate lockdowns imposed by organizers and construction restricted access limited increase travel regional tourist destinations during the holiday period.
Infrastructure & Service Impact: Public transportation reduced usage, while regional tourist infrastructure experienced temporary uptick in visitors. However, overall tourism sector recovery remained incomplete compared to pre-pandemic levels.
Official Directives: Goverment advisories 'stay at home' measures during the COVID-19 state of emergency, targeting the population of Tokyo and surrounding areas.
Figure A4: This figure shows three event schema instances (Typhoon Hagibis, COVID-19 pandemic, and Tokyo 2021 Olympics), serving as structured contexts for event-driven mobility generation.
F COMPONENT ANALYSIS OF FTT
To verify the concrete impact of FTT-guided design choices, we conducted additional ablation stud- ies: A variant that relies mainly
features (verbatim) without gist abstraction (w/ verbatim), and a variant that removes the bottom-line gist component, using generic summaries instead (w/o bottom-line). As shown in Table A3, ELL consistently outperforms these variants across all
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Algorithm ELLMob: trajectory generation under event
Require: long-term data Daia, short-term data Dshort-terms event context Eg, max iters max
Ensure: Event-aware trajectory Thinal
1: Eetw
Be)
2: Gpat f EXTERACTPAATTERNIIST( Dshort-tem)
3: Gon f EXTRACTEVENTTISTTEE)
4: Feedback +- NONE, Fprev +- NONE
to Naax do 6: if' Feedback = NONE then (rJJusiifiaation GENERATEENNTTTATRAUCCCOO (Dhist, Dshort-term; Ecta) 8: else
5: for
7:
9:
(u.tu fication) REGENERATETRAJECTORN (Dniss D Dshort-term: Ectz, Tprev, Feedback) end
11: Gza EXTRACTACTIONGIITTT, Justi fication)
12: (Alignment, Feedback) + AUDITALIGNMENT (Gatts Gpat: Gen)
13:
if Alignment then
14:
Thnal T T return Tinal
Accept
15:
16: end
Tprev T-
18:
end for
Thinal <
Last candidate
20:
return Thoal
event scenarios. These results confirm that the FTT--guided framework concretely improves genera- tion quality under event-driven mobility.
Table A3: Ablation study on the effectiveness of different FTT components with the best perfor- mance highlighted in bold.
Typhoon Hagibis
COVID-19 Pandemic
Tokyo 2021
Olympies
Models
SIL
SDJ
CD1
SGDJ
ST.
SDL
CDJ
SGDJ
S1\
SDJ
CDJ
SGDJ
w/ verbatim
0.1511 0.0724 0.0230 0.0595
0.2071
0.0713
0.0324 0.0422
0.1030 0.0452 0.0054 0.0189 0.0892
w/o bottom
0.0978 0.0443 0.0101 0.0327
0.1687
0.0690 0.0305 0.0333
0.0364 0.0036 0.0082
ELLMob
0.0642
0.0200 0.0041
0.0173
0.1003
0.0444
0.0080
0.0268
0.0617
0.0061
0.0022 0.0035
G ALGORITHM PSEUDO-CODES
To clearly present the proposed framework, we outline the event-driven trajectory generation process of ELLMob. This procedure integrates long-term and short-term mobility records with structured event contexts, iteratively generating and auditing candidate trajectories until a satisfactory align- ment with mobility patterns and event-specific constraints is achieved. Algorithm 1 summarizes the key steps, from constructing the event schema and extracting representative gists to producing, evaluating, and refining trajectories under feedback-guie guided auditing.
H ROBUSTNESS ACROSS ARCHITECTURES
To verify that the superior performance of ELLMob is intrinsic to our cognitive framework rather than dependent on the specific LLM, we conducted a comparative evaluation using Gemini Flash 2.0 as the uniform backbone for all methods. As detailed in Table A4, ELLMob maintains the lowest JSD scores across all event scenarios, mirroring its superior performance on previous benchmarks. In contrast, baseline methods exhibit volatility when subjected to this backbone shift. For instance, the SI of LLM-MOB deteriorates markedly from 0. 1214 to 0.3180 the Typhoon Hagibis, indi- cating strong dependency of the specific backbone. This divergence highlights that while baseline
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performance is often contingent model-specific characteristics, ELLMob effectively captures mo- bility patterns through explicit cognitive alignment, ensuring consistent superiority independent of the proprietary backbone.
To further substantiate the robustness and reproducibility of our framework, we extended the exper- imental evaluation to include three representative open-source large language models: LLaMA3-8B (Grattafiori et al., 2024), Qwen-2.5-14B (Yang et al., 2024), and DeepSeek-R1-Diss -ise--stlll---wen-- (RI-Q7B) (DeepSeek-AI et al., 2025). This expansion mitigates concerns regarding the reliance on proprietary APIs and confirms the adaptability of the method to local computing environments. As presented in Table A5, ELLMob achieves consistent alignment performance across these diverse backbones. The effectiveness on open- spen-source LLMs confirms that the capability of ELLMob stems from the cognitive alignment strategy rather than the inherent capacity of the underlying model.
Table A4: Performance comparison of different methods using Gemini Flash 2.0. Performance is evaluated by JSD across four dimensions with the best performance highlighted in bold.
Typhoon Hagibis
COVID-19 Pandemic
Tokyo 2021
Olympics
Models
STL
SDL
CDJ
SGDJ
SLL
SDL
CDJ
SGDJ
SL.
SDJ
CDJ
SGDJ
LLMOB
0.1069 0.0743 0.0126 0.0196 0.0846
0.0693
0.0105 0.0292 0.0572 0.0281 0.0031 0.0087
LLM-MOB
0.3180
0.1726 0.2004 0.0968
0.1428
0.0660 0.0280 0.0459
0.0379
0.0154 0.0053 0.0065
LLM-Move
0.2383
0.0721 0.0887 0.0465
0.3683
0.0644 0.0378 0.0353 0.1979
0.0287 0.0097 0,0063
LLM-ZS
0.3466
0.1537 0.2556 0.1084
0.2788
0.1344 0.2489 0.1479
0.0967
0,0313 0.0137 0.0057
BLLMob 0.0850 0.0267 0.0087 0.0160 (0.0546 0.0586 0.0069 0.0210 0.0113 0.0074 0.0016 0.0048
Table A5: Performance comparison of ELLMob across different open-source LLM backbones. Per- formance evaluated by JSD across four dimensions
Typhoon Hagibis SDL
COVID-19 Pandemic
Tokyo 2021 Olympies SDJ
Models
STL
CD1
SGDL
STL
SDJ
CDJ
SGDJ
STL
CDL SGDJ
LLaMA3-8B
0.0663 0.0512 0.0004 0.0132
0.0669 0.0624 0.0087 0.0263 0.0407 0.0322 0.0023 0.0030 0.0836 0.0646 0.0016 0.0225 0.0530 0.0238 0.0035 0.0021
Qwen-2.5-14p
0.1594 0.0607 0.0091 0.0115
RI RI-Q7B
0.0881 0.0516 0.0033 0.0203
0.1 104
0.0993
0.0163 0.0392
0.0570
0.0251
0.0015 0.0071
I REGIONAL GENERALIZABILITY EVALUATION
The primary experiments in the main text focus on the Tokyo metropolitan area. To verify that ELLMob generalizes to other geographical contexts and is not overfitted a specific urban layout, we extended our evaluation to Osaka. We constructed new dataset comprising 1,100 users during the COVID-19 pandemic, maintaining scale consistent with the Tokyo dataset. Table A6 presents the performance comparison against baseline methods, where EL. consistently outperforms all competitors. This result demonstrates its adaptability to diverse urban layouts beyond Tokyo.
J EVENT GENERALIZABILLTY EVALUATION
The primary experiments in the main text focus on events characterized by external restrictions or exogenous shocks. To verify that ELLMob generalizes to traditional cultural festivities that induce distinct, voluntarily driven deviation patterns, we extended our evaluation to the New Year scenario. Table A7 presents the performance comparison against baseline methods. ELLMob consistently outperforms baselines across all metries, demonstrating its capability to model diverse event types.
Discussion of Event Scalability. The scalability of ELLMob stems from its ability to generalize across distinct event semantics rather than overfitting to specific scenarios. The Event Schema mod- ule converts diverse narratives into standardized semantic descriptors (e.g., traffic impact), creating universal conflict-reollution logic via the FTT-based alignment. To rigorously verify this generaliza- tion, selected four events represent diverse and contrasting typologies along three key dimensions:
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Table A6: Regional generalizability analysis on the Osaka data during the COVID-19 Pandemic.
Performance is evaluated by JSD across four dimensions with the best performance in bold.
Model
SI
SD
CD
SGD
LLMOB
0.1934
0.1589
0.0344
0.1201
LLM-MOB
0.1732
0.1617
0.0304
0.0988
LLM-MOVE
0.2134
0.1555
0.0698
0.1227
LLM-ZS
0.1531
0.1788
0.0299
0.1161
ELLMob
0.1131
0.1001
0.0120
0.0556
Restrictive vs. Promotive: COVID-19 Pandemic and Typhoon Hagibis restrict movement, whereas the New Year scenario promotes social gatherings.
Stochastic vs. Periodic: Typhoon Hagibis is unpredictable and sudden, while New Year's is peri- odic and cyclic.
Global vs. Local (Hybrid): The COVID-19 Pandemic affects the entire city globally, whereas the Tokyo 2021 Olympics imposes hybrid constraints concentrated in specific zones.
Notably, we applied the identical framework and parameter settings across scenarios. The consis- tent performance across these contrasting types confirms that ELLMob can capture the underlying logic of event-driven mobility. While extensive empirical validation is currently constrained by the scarcity of high-quality went-mobility data in the community, this work, the best of our knowl- edge, is the first attempt to validate scalability across a wide spectrum of distinct event types.
Table A7: Event generalizability analysis the New Year scenario in Tokyo. Performance is evaluated by ISD across four dimensions with the best performance in bold.
Model
SI
SD
CD
SGD
LLMOB
0.0776
0.0391
0.0230
0,0422
LLM-MOB
0.1061
0.0413
0.0318
0.0379
LLM-MOVE
0.2248
0.0493
0.0272
0.0550
LLM-ZS
0.0996
0.0497
0.0230
0.0481
ELLMob
0.0598
0.0250
0.0200
0.0317
K PARAMETER SENSITIVE STUDY OF ITERATION
As shown in Figure A5, performance improves substantially in the first three iterations with only marginal gains thereafter, justifying our choice of K=3 as an effective trade-off between refinement quality and computational cost.
0.30
0.16
Performance (JSD)
CD
0.14
0.08
0.12
0.06
0.10
0.06
0.08
0.04
0.02
0.02
0.00
0.00
fteration number R
Typhoon Hagibis
COVID-19 Pandemic
Tokyo 2021 Olympics
Figure A5: Performance comparison across iteration numbers K = to 5 for three major events:
Typhoon Hagibis, COVID-19 Pandemic, and Tokyo 2021 Olympics. Performance measured using Jensen-Shannon Divergence (ISD), where lower values indicate better performance.
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L STABILITY ANALYSIS
Given the inherent stochastic nature of LLMs, generated outputs may vary across different runs with the same input. To verify the robustness our approach, we conducted repeated experiments across 5 distinct random seeds with the results shown in Table A8, Table A9, Table A10, As indicated by the standard deviations, ELLMob consistently maintains high stability across different initializations.
These results demonstrate that while absolute performance may vary, ELLMob provides statistically consistent predictions and less sensitive random seed variations.
Table A8: Stability analysis on the Typhoon Hagibis dataset. Results are reported as Mean +
Standard Deviation across 5 independent runs.
Model
SI
SD
CD
SGD
LLMOB
0.1057
0.0389
0.0497
0.0319+-000100
LLM-MOB
0.1101=0.0110
0.0558=0.0208
0.0352-000096
0.0532:10.0430
LLM-MOVE
0.1242:10.035 0.040
0.0031
0.02901000014
LLM-ZS
0.1601=10.001 0.1335-0.00S5
0.0155=0.0008
0.0750+10.0025
ELLMob
0.0649=0.0030
0.0220-0.0044
0.0046:0.0012
0.0162:1000018
Table A9: Stability analysis on the COVID-19 Pandemic dataset. Results are reported as Mean + Standard Deviation across 5 independent runs.
Model
SI
SD
CD
SGD
LLMOB 0.1128:0.0173
0.1221=0.0298
0.0158=0.0038
0.0295+00.0477
LLM-MOB
0.1099=10.0107
0.0613=0.0263
0,0191=0.0072
0,0359:10.0066
LLM-MOVE
0.1789+0.0241
0.0511=0.0044
0.0457=0.0185
0.054410.0067
LLM-ZS
0.1108+0.0100
0.0551 00001-0.0025
0.0025
0.0442++0.004
0.0605+000006
ELLMob
0.1002+0.0094
0.0443 0.0037
0.0079
0.0279++0.0027
Table A10: Stability analysis on the Tokyo 2021 Olympics dataset. Results are reported as Mean +
Standard Deviation across independent runs.
Model
SI
SD
CD
SGD
LLMOB 0.1042++0.011
0.0291 0.0006
0.0115
0.0054::00006
LLM-MOB
0.1123+:0.067
0.0338-0.0083
0.0157=000084
0.0172+t0.0127
LLM-MOVE
0.2027+0.0050 0.095311..021 0.0320=0.0014
0.0304=0.0014
0.011110.0009
0.0053+10.0044
LLM-ZS
0.0147+000021
0.0060110.001
ELLMob
0.000611.0016
0.0063=0.0002
0.0021#0.0002
0.0037+0.0002
M PROMPT
This appendix details the sequence of prompts utilized within the ELLMob framework. These prompts work in concert to guide the L.LM through the entire process, from initial data process- ing to the final reflective generation of mobility trajectories.
Event context generation. To enable the LLM to interpret unstructured event descriptions, the following prompt is used transform raw into structured intelligence brief. instructs the model to act as an analyst, extracting key details and their implications for public behavior.
Initial trajectory generation. Once the event context is established, this prompt generates an initial trajectory. It takes the user's tong-term and short-term trajectories, and the structured event context as input, instructing the model to synthesize this information into a plausible daily plan and provide analytical justification for its reasoning.
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To facilitate the reflection module, the framework extracts three distinct forms of gist: Event Gist, Action Gist, and Pattern Gist.
Event Gist Generation. This prompt distills the core behavioral takeaway for the public from the event information.
Action Gist Generation. This prompt infers the underlying intent from the generated trajectory and its accompanying justification.
Pattern Gist Generation.
This prompt analyzes the user's s long-term and short-term trajectory logs to synthesize their core behavioral patterns, including strengths (points of inertia) and weak- nesses (points of fracture).
Conflict Judgment. This prompt serves as the core of the auditing mechanism. instructs the model to act as Critical Trajectory Auditor to evaluate the coherence between the Action Gist, the user's Pattern Gist, and the situational Event Gist.
Trajectory Regeneration. If the planned trajectory fails the conflict audit, this prompt is invoked. instructs the model to act as a Trajectory Plan Corrector, using the auditor's 's feedback to regenerate revised plan that specifically resolves all identified inconsistencies.
Event context generation
SYSTEM ROLE
You are an expert event impact analyst. Your primary mission is to dissect the provided unstructured event text and transform into a clear, actionable intelligence brief.
The data are as follows:
<EVENT TEXT>: |<INPUT 0>!
OUTPUT
1. Event Profile:
Extract the type, name, time, and location the event.
Intensity Scale:
Detail the key metrics of event's power direct physical consequences (e.g., rainfall, storm surge).
3. Infrastructure Service Impact:
List the critical infrastructure failures (e.g., power outages) and public service shutdowns (e.g.. transportation, attractions).
4. Official Directives:
State the official orders and advisories issued, including the scale of the population they targeted.
Figure A6: The details of event schema under three different events.
23

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Published as a conference paper at ICLR 2026
Initial trajectory generation
SYSTEM ROLE
Your task is generate user's trajectory based activity patterns.
You will be provided with:
--LONG-TERM>: The user's historical stays showing their personal patterns.
-SHORT-TERM>: Recent contextual information about the user's activities.
<EVENT>: Current day event information (holidays, emergencies. or normal operations)
<DAY TYPE>: Indicates whether the current day is a weekday a weekend
# CONTEXT & GOAL
Please generate the trajectory considering:
Event Impact Assessment Check <EVENT> first to understand the day's context:
Event: First, check the <EVENT> and it establishes the main context for the day. should be treated as the reference for today's trajectory generation.
During Holidays/Weekends: Expect an increase leisure, social, and shopping activities. During Normal Weekdays: Assume regular routines.
Personal Patterns Priority: The user's individual patterns from <LONG-TERM> the guide. Look for: Regular visits specific places certain times. Sequential activity patterns (places that frequently follow other places).
Recent-Aware Adaptation: Recent activities <SHORT-TERM> may override personal they indicate change in routine.
Temporal Consistency: Ensure all timestamps are chronologically ordered and realistic for travel times between locations.
5. Analytical Justification (For the "reason" field): It must be third-person, analytical summary explaining how you used the inputs to generate the plan. It should state the core pattern identified and mention the influence of the event.
The data are as follows:
<LONG-TERM>: !<INPUT 0s!
<SHORT-TERM>: |INNPTT
<EVENT>: KINNUT 2-!
-DAY_TYPE> :INNPTT 3>!
OUTPUT
Response STRICTLY the prompt above in JSON the "following" format:
("plan": [<Location> <Time>, <Location> <Time>...]] "reason"....
Figure A7: The prompt of initial trajectory generation.
24

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Published as a conference paper at ICLR 2026
Event gist generation
# SYSTEM ROLE
You are an expert event impact analyst.
Your analysis must go beyond surface-level facts to unearth the subtle cuss that predict and explain public behavior in an event.
INSTRUCTION
Analyze the [EVENT TEXT] provided below. Extract following key information from provided inputs to synthesize the event gist:
1. Primary Intent:
The simplified understanding of what event means for mobility decisions.
2. Behavioral Implications:
Survival, information gathering, social dynamics, and compliance
3. Risk-Reward Calculus:
Returns responding event risks.
The data are as follows: <EVENT TEXT>: KINNPT 0>
# OUTPUT
"Event Gist:**
Directly state the "*llst" that the public likely formed from the event information.
"Gist" can show how behaviors related survival, information gathering, social dynamics, and risk-reward are logical consequences the public operating on this core interpretation.
Figure A8: The prompt of event gist generation.
Action gist generation
# SYSTEM ROLE
You are Decision Rationale Analyst. You specialize in deconstructing planned action and its justification to assess its core intent.
You will be provided with: <<PLANNED TRAJECTORY>; candidate trajectory, defined as a specific sequence of planned locations and actions.
<<PLANNED TRAJECTORY REASON>> The justification logic explaining the purpose behind the
planned trajectory.
INSTRUCTION
Analyze the <PLANNED TRAJECTORY> and <PLANNED TRAJECTORY >RESOON>>R> explicitly state the user's inferred "Action Gist" considering following key information:
1. Primary Intent:
The main purpose driving this trajectory choice.
2. Habit Adherence:
Preservation versus compromise of habitual patterns.
3. Event Compliance:
Adherence to event-imposed constraints.
The data are as follows:
<<PLANNED TRAJECTORY>: IINNUT D=!
<PLANNED TRAJECTORY REASON>: |<NNPUT 1>!
OUTPUT
**Action Gist:*
Directly the ""gist" that summarizes intent planned trajectory.
Figure A9: The prompt of action gist generation.
25

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Published as a conference paper at ICLR 2026
Pattern gist generation
SYSTEM ROLE
You are an expert Behavioral Patter Analyst. Your expertise is synthesizing detailed activity logs into high- level understanding of person's life structure, identifying both core strengths critical dependencies. mission to analyze an individual's long-term and short-term activity data to derive "Pattem Gist". You consider the routine's points of inertia (strengths) points fracture (weaknesses).
You will be provided with:
-LOONGTTEMM: The user's historical stays showing their personal pattems. -SHORT-TERM>: Recent contextual information about the user's activities.
INSTRUCTION
1. Synthesize the Core Behavior: Based on the [SHORT-TERM] and considering the [SHORT-TERM]
first synthesize and clearly state the initial Pattem Gist. This should be high-level summary of their dominant pattern of action.
Critical guidance: Your analysis must go beyond simply listing the mast frequent, generic locations.
The goal is to find the underlying purpose or narrative that connects these individual data points.
Focus on what makes this person's pattern specific and characteristic.
For example, is the combination regular visits a Govermment Office, an Elementary School, and large Supermarket indicative a "structured public servant's life".
"a parent's s daily routine" or "a Your gist must capture this deeper meaning and consider points inertia fracture.
professional and family responsibilities"
Identify Points of Inertia (Strengths):
Based on the Pattern Gist, what are the most deeply embedded, almost non-negotiable components of the routine? identify the rituals, obligations, habits that create strongest "pull" to maintain this pattern.
3. Identify Points of Fracture (Weaknesses):
What are the external dependencies required for the Pattem Gist to function?
Identify the single points of failure (e.g.. reliance on public transport, specific store availability, power grid) that, disrupted, would make this pattern impossible to follow.
The data are as follows: <LONG-TERM>: KINPUT 0>!
<SHORT-TERM> IINNUTT 1>!
OUTPUT
"Pattem-Gist."
Direclly state "'gis!" that embodies the core structure of user's routine.
Figure A10: The prompt pattern gist generation.
26

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Published as a conference paper at ICLR 2026
Conflict judgement
SYSTEM ROLE
You critical Trajectory Auditor. task perform strict, logical audit to decide planned trajectory is aligned based user's habitual mobility pattern and the current event context.
You will be provided with:
<<VENNT GIST>: Reflects the core impact, restrictions, and incentives of external event trajectory decisions.
<PATTERN GIST>; Distills the user's long-term, habitual mobility patterns.
<ACTION GIST>; Represents the intention of current mobility.
#CORE JUDGMENT
Your judgments must not be based on "*superfiiial or literal matching words" Instead, you must evaluate the inferred Action Gist represents a rational decision by balancing the Pattern Gist and the Event Gist. You must not evaluate them isolation.
1. Does the Action Gist conflict with Pattern
Evaluate whether the
fundamentally contradicts core pattern whether contextually
appropriate adaptation.
Pattern Gist reflects general tendencies, rigid rules. Use commonsense reasoning decide whether action still aligns with the character's motivations, if the surface behavior differs.
Do not consider temporary. situationally driven behaviors conflict if they *'lleally justified the context**
2. Does the Action Gist conflict with the Event Gist?
Evaluate whether the plan appropriately respond to the event's constraints. Risk assessment should be comprehensive, not just manue-based. Consider both immediate safety and contingency planning.
The data are as follows:
<EVENT GIST>; IINNUT 0>1
<PATTERN GIST>; KINPUT 1>!
<ACTION GIST>: I<NNUT 2>!
output MUST JSON
OUTPUT
Your output MUST be in strict JSON format with the following three keys:
"coherence with patternn: A boolean value (true or false).
"coherence with event": boolean value (true false).
"reason": detailed justification for the two judgments above with weakness if necessary.
Figure All: The prompt of conflict judgment.
27

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Published as a conference paper at ICLR 2026
Trajectory regeneration
SYSTEM ROLE
You are "Trajectory Plan Corrector", Your sole purpose is to fix flawed plan based on an expert's audit report.
CONTEXT & GOAL
user's initial plan <LAST PLAN> evaluated by Rationality Auditor and deemed irrational. The auditor's report <FAILED PLAN_ REASON> explains what was wrong. Your task to generate revised plan that "specifically addresses and resolves every concer" raised in auditor's report. The auditor's feedback should be followed.
You will be provided with:
-LOONGTERM>: The user's historical stays showing their personal patterns.
<SHORT-TERM> Recent contextual information about the user's activities.
<EVENT>: Current day event information (holidays, emergencies, or normal operations)
<DAY TYPE>:
<DAY_TYPE>> Indicates whether the current day is weekday or weekend
<LAST PLAN>: The previous plan JSON that failed checking
FAILED BEALED_LAST_PUUE PLAN REASON>: The auditor's failure report
MANDATORY CORRECTION PROCESS
1. EXTRACT every failure point from <FAILED PLAN REASON>
MAP each failure to specific correction
VERIFY no failure point is missed
VALIDATE new plan against ALL criteria
The new "plan" should be the corrected trajectory.
times between locations. The "reason" should be overall justification for the new plan. Analytical Justification
Temporal Consistency: Ensure all timestamps are chronologically ordered and realistic
"reason" field): must be third-person, analytical summary explaining how you used the inputs generate the plan. It should state the pattern identified and mention the influence of event. Generate your response single JSON object.
The data follows: <LONG-TERM>: I<INPUT 0>!
-SHORT-TERM>: KINNPT 1>
<EVENT>: |<INPUT 2>!
<DAY <DAY_TYPE TYPE: !<INPUT 3>!
<LAST_ LLASTPPLAN= IKINPUT 4>!
<FAILED LLET_ PLAN_ SEANONRRAONNNNNEAA KINPUT 5>!
OUTPUT
Response STRICTLY to the prompt above in JSON in "following" format:
("plan": [<Location> at <Time>, <Location> at <Time>....j "reason"...]
Figure A12: The prompt of trajectory regeneration.
28


Reviews:
[{"review_id": "E1vx8jDgVF", "reviewer": "Reviewer_WGcR", "summary": "The paper introduces ELLMob, an event-driven human-mobility generator that reconciles a user’s habitual patterns with exogenous event constraints via a self-alignment loop. It also provides a new event-annotated mobility dataset for Tokyo spanning three events. Evaluation shows ELLMob outperforms baselines on the dataset.", "questions": "Can you compare the token usage / latency of ELLMob and the baselines?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "-paper is overall well-written and easy to follow\n\n-new dataset with event context has been provided\n\n-the proposed method ELLMob is neat and shows consistent improvements across SI/SD/CD/SGD over the baseline methods. The ablation study shows both the schema and the reflection matter.", "weaknesses": "-methodology contribution is relatively on the incremental side. The contribution is more on applying the concept of self-alignment along with some domain specific heuristic into the human-mobility prediction task.\n\n-evaluation can be more comprehensive:\n(1) variance of results are absent\n(2) all scenarios are single-city (Tokyo)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CvnjRYw56Y", "reviewer": "Reviewer_bqV1", "summary": "This paper introduces the task of event-driven human mobility generation. To address the shortcomings of existing methods under atypical scenarios, which tend either to replay routine travel or to over-conform to event constraints, this paper constructs an explicitly event-annotated dataset covering three Tokyo events plus a normal period, and proposes ELLMob, a cognition-theory–driven framework. ELLMob first distills long-form news and policy text into a structured event context, then extracts three kinds of gists (pattern, event, and action) and runs an iterative reflect–refine loop that conducts dual auditing for internal alignment  and external alignment, until a trajectory is produced that is both habitually grounded and event-compliant. Using Jensen–Shannon Divergence (JSD) on the distributions of Step Interval (SI), Step Distance (SD), Category Distribution (CD), and Spatial Grid Distribution (SGD) for evaluation, ELLMob significantly outperforms strong baselines across all three events, and ablations confirm that the self-alignment mechanism is the key source of improvement.", "questions": "(1) The study’s geographic and event scope is relatively narrow, which focused solely on Tokyo and three event types within Tokyo, does it have a certain degree of transferability?\n(2) The paper does not discuss how the choice of LLM might affect the method’s performance, does model selection materially influence results?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(1) This paper introduces the first event-centric, fully featured, explicitly annotated dataset, providing a solid foundation for studying non-routine deviations in mobility.\n(2) This paper converts event understanding from free text into a structured event context directly consumable by models, reducing loss of critical information.\n(3) This paper proposes the ELLMob framework, which uses gist-based alignment to explicitly reconcile competing mobility decisions.\n(4) ELLMob achieves significant improvements over existing methods.", "weaknesses": "(1) The data come exclusively from Twitter/Foursquare, which may skew the sample toward younger, more homogeneous users, introducing selection and behavioral biases.\n(2) The paper lacks details about the LLM type and hyperparameters; providing a fuller description would improve clarity and reproducibility.\n(3) For the reflect–regenerate procedure, if constraints remain unmet after K iterations, please offer a further remedy or fallback strategy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GWrmgOAz80", "reviewer": "Reviewer_3JM6", "summary": "This paper introduces ELLMob, a self-aligned LLM framework for event-driven human mobility generation. It highlights two main gaps in current work: (1) the lack of event-annotated mobility datasets, and (2) the inability of LLM-based trajectory generators to reconcile habitual patterns with event-induced constraints. To address these, the authors construct a Tokyo-based event-annotated dataset (Typhoon Hagibis, COVID-19, Tokyo 2021 Olympics) and propose a Fuzzy-Trace Theory (FTT)-inspired framework that extracts three “gists” (pattern, event, action) and performs iterative “reflection-based alignment” to generate plausible trajectories.", "questions": "Please refer to weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Strength:\nS1: The “event-driven mobility” setup moves beyond routine trajectory generation and has real-world applications in urban crisis modeling and planning.\nS2: The constructed event-annotated dataset is potentially useful for studying mobility under disruptions. The data collection and anonymization steps are clearly documented.", "weaknesses": "Weakness:\nW1: The core of ELLMob lies in a prompt-based reflection loop with heuristic “alignment auditing.” While reflection-based prompting has been extensively studied in the LLM literature, this work does not introduce any new architecture or learning algorithm. Essentially, the contribution remains at the level of prompt engineering rather than methodological contribution and is quite limited. \n\nW2: Although the proposed dataset includes event annotations (e.g., typhoon, pandemic, Olympics), the data collection process appears to cover only trajectory information, and the event annotation itself seems rather trivial. In this sense, many comparable datasets are already publicly available. Meanwhile, the dataset’s scale is small (only 1,100 users), and it is restricted to the Tokyo area, which further limits its generalizability and practical utility. \n \nW3: Although the paper claims to be “theory-driven,” the use of FTT is largely metaphorical. The separation of gist into pattern-based and event-based forms is very intuitive for this task, but not necessarily derived from the cognitive theory itself. The attributes used to analyze trajectories as “pattern gist” or “event gist” are also intuitive and do not require FTT for guideline or serving as a core component. Meanwhile, it remains unclear for me how FTT concretely improves the model’s reasoning or generation performance.\n\nW4: Baseline choice. It is unclear how event information is incorporated into the baseline methods. In addition, all LLM-based baselines are single-pass models without reflection or iterative refinement. Given that reflection mechanisms are now common in the LLM prompting literature, the comparison should include other iterative-reflection baselines.\n \nW5: Backbone choice. All modules rely on proprietary models such as GPT-4 or Gemini for prompting (w/o any open-source LLMs). The reflection loop is not quantified in terms of computational cost or token usage. Reproducibility might be limited due to closed-source LLM dependencies.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xiEuaL0Lq3", "reviewer": "Reviewer_k5vy", "summary": "The paper proposes ELLMob, an event-driven human mobility generation framework that leverages large language models (LLMs) to synthesize realistic human trajectories under large-scale societal events. Unlike existing approaches that mainly learn habitual mobility patterns, ELLMob explicitly models the competition between routine behaviors and event-induced disruptions (e.g., natural disasters, pandemics, or major events). Following this, this paper proposed the first human-mobility dataset that is annotated with the event label, and then proposed an LLM-based method with ''reflect–refine'' paradigm for human mobility generation.", "questions": "Please see the weakness above.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "(1) The paper tackles event-driven mobility generation, a previously overlooked challenge. While most prior works model routine movements, this study explicitly considers non-routine deviations caused by major events—a critical step toward urban simulations, especially contexted by big social event.\n\n(2) The paper is well-writen and the proposed method is technically sound.\n\n(3) This paper contributes a new dataset that has event-label  along with the human trajectory data.", "weaknesses": "(1) One concern is number of events in the paper, which only covers Typhoon, COVID-19 Pandemic, and Tokyo 2021 Olympics. Though they cover serveral typical patterns of different social events, the number of events is somehow limited. While in real-world, there are lots of  different events. In that sense, the proposed event-dataset and method is somehow limited. How can the dataset  and the model scale up w.r.t the event types?\n\n(2) LLM-based methods can have different output with the same input, it would be better to report the results across different runs to see whether the performance is stable or consitent.\n\n(3) What is the running time at the inference time, since the model requires multiple LLM runs? which could cost a lot time and tokens, especially when simulation for a mega-city is required.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)