Abstract
Ranking items is a central task in many information retrieval and recommender systems. User input for the ranking task often comes in the form of ratings on a coarse discrete scale. We ask whether it is possible to recover a fine-grained item ranking from such coarse-grained ratings. We model items as having scores and users as having thresholds; a user rates an item positively if the item's score exceeds the user's threshold. Although all users agree on the total item order, estimating that order is challenging when both the scores and the thresholds are latent. Under our model, any ranking method naturally partitions the $n$ items into bins; the bins are ordered, but the items inside each bin are still unordered. Users arrive sequentially, and every new user can be queried to refine the current ranking. We prove that achieving a near-perfect ranking, measured by Spearman distance, requires $\Theta(n^2)$ users (and therefore $\Omega(n^2)$ queries). This is significantly worse than the $O(n\log n)$ queries needed to rank from comparisons; the gap reflects the additional queries needed to identify the users who have the appropriate thresholds. Our bound also quantifies the impact of a mismatch between score and threshold distributions via a quadratic divergence factor. To show the tightness of our results, we provide a ranking algorithm whose query complexity matches our bound up to a logarithmic factor. Our work reveals a tension in online ranking: diversity in thresholds is necessary to merge coarse ratings from many users into a fine-grained ranking, but this diversity has a cost if the thresholds are a priori unknown.

Abstract
As urban mobility integrates traditional and emerging modes, public transit systems are becoming increasingly complex. Some modes complement each other, while others compete, influencing users' multimodal itineraries. To provide a clear, high-level understanding of these interactions, we introduce the concept of a macroscopic multimodal hierarchy. In this framework, trips follow an "ascending-descending" order, starting and ending with lower hierarchical modes (e.g., walking) that offer high accessibility, while utilizing higher modes (e.g., subways) for greater efficiency. We propose a methodology to identify the multimodal hierarchy of a city using multimodal smart card trip chain data and demonstrate its application with actual data collected from Seoul and the surrounding metropolitan area in South Korea.

Abstract
Understanding human mobility during emergencies is critical for strengthening urban resilience and guiding emergency management. This study examines transitions between returners, who repeatedly visit a limited set of locations, and explorers, who travel across broader destinations, over a 15-day emergency period in a densely populated metropolitan region using the YJMob100K dataset. High-resolution spatial data reveal intra-urban behavioral dynamics often masked at coarser scales. Beyond static comparisons, we analyze how mobility evolves over time, with varying emergency durations, across weekdays and weekends, and relative to neighborhood boundaries, linking the analysis to the 15-minute city framework. Results show that at least two weeks of data are required to detect meaningful behavioral shifts. During prolonged emergencies, individuals resume visits to non-essential locations more slowly than under normal conditions. Explorers markedly reduce long distance travel, while weekends and holidays consistently exhibit returner-like, short distance patterns. Residents of low Points of Interest (POI) density neighborhoods often travel to POI rich areas, highlighting spatial disparities. Strengthening local accessibility may improve urban resilience during crises. Full reproducibility is supported through the project website: https://github.com/wissamkontar

Abstract
In this paper, we study two years of access card validation records from Bogot\'a's multimodal public transport system, comprising over 2.3 billion trips across bus rapid transit, feeder buses, dual-service buses, and an aerial cable network. By reconstructing user trajectories as motifs, we identify recurrent mobility patterns that extend beyond simple round trips, enabling the construction of an integrated origin-destination (OD) matrix covering 2,828 urban zones. Similarity analysis using the Jensen-Shannon divergence confirms the temporal stability of mobility structures across semesters, despite infrastructure changes and fare policy adjustments. From the obtained OD matrices, we derive transition probabilities between zones and uncover a robust power-law relationship with geographical distance, consistent with L\'evy flight dynamics. We validate our model using Monte Carlo simulations showing that reproduces both local and long-range displacements, with similar scaling exponents across time. These findings demonstrate that Bogot\'a's public transport mobility can be effectively modeled through L\'evy processes, providing a novel framework for analyzing complex transportation systems based solely on user access records.

Abstract
Updating diffusion models in an incremental setting would be practical in real-world applications yet computationally challenging. We present a novel learning strategy of Concept Neuron Selection (CNS), a simple yet effective approach to perform personalization in a continual learning scheme. CNS uniquely identifies neurons in diffusion models that are closely related to the target concepts. In order to mitigate catastrophic forgetting problems while preserving zero-shot text-to-image generation ability, CNS finetunes concept neurons in an incremental manner and jointly preserves knowledge learned of previous concepts. Evaluation of real-world datasets demonstrates that CNS achieves state-of-the-art performance with minimal parameter adjustments, outperforming previous methods in both single and multi-concept personalization works. CNS also achieves fusion-free operation, reducing memory storage and processing time for continual personalization.

Abstract
Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses. However, current personalization methods are ill-suited for new users, as they typically require either slow, resource-intensive fine-tuning or a substantial amount of pre-existing user data, creating a significant cold-start problem. To address this challenge, we introduce a new paradigm for real-time personalization by learning from online pairwise preference feedback collected during text generation. We propose T-POP (Test-Time Personalization with Online Preference Feedback}), a novel algorithm that synergistically combines test-time alignment with dueling bandits. Without updating the LLM parameters, T-POP steers the decoding process of a frozen LLM by learning a reward function online that captures user preferences. By leveraging dueling bandits, T-POP intelligently queries the user to efficiently balance between exploring their preferences and exploiting the learned knowledge to generate personalized text. Extensive experiments demonstrate that T-POP achieves rapid and data-efficient personalization, significantly outperforming existing baselines and showing consistent improvement with more user interactions.

Abstract
While Large Language Models (LLMs) have shown remarkable advancements in reasoning and tool use, they often fail to generate optimal, grounded solutions under complex constraints. Real-world travel planning exemplifies these challenges, evaluating agents' abilities to handle constraints that are explicit, implicit, and even evolving based on interactions with dynamic environments and user needs. In this paper, we present ATLAS, a general multi-agent framework designed to effectively handle such complex nature of constraints awareness in real-world travel planning tasks. ATLAS introduces a principled approach to address the fundamental challenges of constraint-aware planning through dedicated mechanisms for dynamic constraint management, iterative plan critique, and adaptive interleaved search. ATLAS demonstrates state-of-the-art performance on the TravelPlanner benchmark, improving the final pass rate from 23.3% to 44.4% over its best alternative. More importantly, our work is the first to demonstrate quantitative effectiveness on real-world travel planning tasks with live information search and multi-turn feedback. In this realistic setting, ATLAS showcases its superior overall planning performance, achieving an 84% final pass rate which significantly outperforms baselines including ReAct (59%) and a monolithic agent (27%).

Abstract
Problem definition: Transportation terminals such as airports often experience persistent oversupply of idle ride-sourcing drivers, resulting in long driver waiting times and inducing externalities such as curbside congestion. While platforms now employ virtual queues with control levers like dynamic pricing, information provision, and direct admission control to manage this issue, all existing levers involve significant trade-offs and side effects. This limitation highlights the need for an alternative management approach. Methodology/results: We develop a queueing-theoretic framework to model ride-sourcing operations at terminals and propose a novel lottery-based control mechanism for the virtual queue. This non-monetary strategy works by probabilistically assigning a driver's entry position. By directly influencing their expected waiting time, the mechanism in turn shapes their decision to join the queue. We reformulate the resulting infinite-dimensional, non-smooth optimization into a tractable bi-level program by leveraging the threshold structure of the equilibrium. Theoretically, we prove that the lottery mechanism can achieve higher or equal social welfare than FIFO-queue-based dynamic pricing. Numerical experiments in unconstrained markets show that in profit maximization, our approach only narrowly trails dynamic pricing and significantly outperforms static pricing. Furthermore, it is shown that under commission fee caps, the lottery mechanism can surpass dynamic pricing in profitability. Implications: This study introduces a new, non-monetary lever for managing idle ride-sourcing drivers at transportation terminals. By aligning operational practices with queue-based dynamics, the proposed lottery mechanism offers a robust and implementable alternative to pricing-based approaches, with advantages in both unconstrained and regulated markets.