Singapore

We study almost envy-freeness in house allocation, where
$m$ houses are to be allocated among $n$ agents so that
every agent receives exactly one house. An envy-free
allocation need not exist, and therefore we may have to
settle for relaxations. We study different aggregate
measures of envy as markers of fairness. In particular, we
define the amount of envy experienced by an agent $a$
w.r.t. an allocation to be the number of agents that agent
$a$ envies under that allocation. ~\cite{KMS2021} studied
the problem of minimizing the number of envious agents and
showed that it is NP-Complete to find allocations that
minimize the number of envious agents, even for binary
utilities, and this quantity is hard to approximate for
general utilities. In this paper, we explore envy
minimization in house allocation from a broader perspective
and prove algorithmic results not only for minimizing the
number of envious agents but for two other measures of envy
as well---minimizing the amount of maximum envy experienced
by any agent and minimizing the amount of total envy
experienced by all the agents put together.
We prove a host of algorithmic and hardness results. We
also suggest practical approaches for these problems via
integer linear program (ILP) formulations and report the
findings of our experimental evaluation of ILPs. Finally,
we study the price of fairness, which quantifies the loss
of welfare we must suffer due to the fairness requirements,
and present tight bounds as well as algorithms that
simultaneously optimize both welfare and fairness.

AAAI 2026

The Cost and Complexity of Minimizing Envy in House
Allocations

house allocations

parameterized algorithms

fairness

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The core of efficient on-demand ride-pooling lies in solving the Ride-Pool Matching Problem (RMP), which involves assigning multiple customer requests to single vehicles under various service constraints (e.g., pickup windows, detour allowances, and vehicle occupancy). A significant missed opportunity in most current RMP approaches is the assumption that passengers must be picked up and dropped off exactly at their requested locations. Allowing passengers to walk even short distances to meet vehicles could unlock substantial improvements in ride-pooling operations.

Building upon the limitations of existing Ride-Pool Matching Problem (RMP) solutions that neglect passenger walkability, this paper introduces a novel matching method that strategically incorporates flexible pickup and drop-off locations. Our approach simultaneously determines the optimal assignment of vehicles to requests (one vehicle to potentially multiple requests and each request to at most one vehicle), identifies advantageous meeting points for passengers, and plans efficient vehicle routes. This comprehensive optimization respects all service constraints and considers the long-term implications of routing decisions. To achieve this integrated solution, we first employ a tree-based approach to enumerate all feasible pairings between passengers and vehicles. Subsequently, we calculate an optimal route for each of these feasible matches. Finally, we evaluate the quality of all possible assignments and select the most advantageous matching for implementation.

In our experimental evaluation on city-scale taxi datasets, we demonstrate that our method improves the number of served requests by up to 13\% and reduces the average vehicle travel distance by up to 21\%. By serving more passengers with less driving distance, our approach achieves greater efficiency in a more sustainable manner — using fewer resources to deliver better service and creating a win-win outcome for all stakeholders, including customers, drivers, the aggregator, and the environment.

Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility

Recommendation systems shape much of what people, including
youth, encounter online, influencing their exposure to
information and ideas. Understanding their workings and
potential downsides, such as filter bubbles, is
increasingly important. At the same time, Minecraft remains
one of the most popular and accessible game platforms among
students worldwide, making it a promising medium for AI
literacy outreach. Building on a previous in-person
augmented reality application called Beetrap, in which
players, acting as bees, pollinate flowers and see how
similar choices narrow their environment, we created
Beetrap-MC, a Minecraft-based version aimed at broader
reach and accessibility. Unlike the original
facilitator-led group workshop, Beetrap-MC is designed for
students’ individual playthroughs. We conducted a study
with nine middle school participants, using
pre-/post-assessments and qualitative interviews to
evaluate its effectiveness. Results showed significant
learning gains in key AI concepts, such as understanding
filter bubbles and their consequences. We also discuss key
differences in design, usability, and outcomes between
Beetrap-MC and the original, reflecting on trade-offs in
adapting a group-based embodied experience into a shorter,
self-guided digital format.

Beetrap-MC: A Minecraft-Based AI Literacy Tool for Teaching Filter Bubbles to Middle School Students

Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce open-vocabulary change detection (OVCD), a novel task that bridges vision and language to detect changes across any category. Considering the lack of high-quality data and annotation, we propose two training-free frameworks, M-C-I and I-M-C, which leverage and integrate off-the-shelf foundation models for the OVCD task. The insight behind the M-C-I framework is to discover all potential changes and then classify these changes, while the insight of I-M-C framework is to identify all targets of interest and then determine whether their states have changed. Based on these two frameworks, we instantiate to obtain several methods, e.g., SAM-DINOv2-SegEarth-OV, Grounding-DINO-SAM2-DINO, etc. Extensive evaluations on 5 benchmark datasets demonstrate the superior generalization and robustness of our OVCD methods over existing supervised and unsupervised methods. To support continued exploration, we release DynamicEarth, a dedicated codebase designed to advance research and application of OVCD.

DynamicEarth: How Far Are We from Open-Vocabulary Change Detection?

Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Although a number of $k$-WL-based GNNs have been proposed to overcome this limitation, their computational cost increases rapidly with $k$, significantly restricting the practical applicability. Moreover, since the $k$-WL models mainly operate on node tuples, these $k$-WL-based GNNs cannot retain fine-grained node- or edge-level semantics required by attribution methods (e.g., Integrated Gradients), leading to the less interpretable problem. To overcome the above shortcomings, in this paper, we propose a novel Line Graph Aggregation Network (LGAN), that constructs a line graph from the induced subgraph centered at each node to perform the higher-order aggregation. We theoretically prove that the LGAN not only possesses the greater expressive power than the 2-WL, but also has lower time complexity under injective aggregation assumptions. Empirical evaluations on benchmarks demonstrate that the LGAN outperforms state-of-the-art $k$-WL-based GNNs, while offering better interpretability.

LGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation

Massive multi-modality datasets are fundamental to the success of large video-language models. However, existing datasets often focus on providing textual descriptions for visual content, treating audio, particularly music, as weakly related information. This overlooks the inherent semantic correlation between visual narratives and musical scores, limiting the development of models for fine-grained cross-modal understanding and generation. To address this gap, we introduce VMChill, a large-scale, fine-grained multimodal video dataset. We leverage trailers as our data source, as they are professionally edited to create a strong synergy between visual pacing, scene transitions, and background music for narrative and emotional impact. Our dataset comprises over 20 million video clips derived from more than 27.1k hours of high-resolution trailer videos.
To annotate this data, we propose a systematic multimodal captioning framework. This framework first employs specialized unimodal models to extract descriptive features from multiple perspectives, including visual content, motion dynamics, and musical attributes (e.g., genre, instruments, mood). Subsequently, a large language model (LLM) is utilized to adaptively fuse these diverse descriptions into a single, coherent, and rich multimodal caption. This process yields VMChill-2M, a high-quality subset of 2 million clips with detailed multimodal annotations, and VMChill-Test, a manually refined test set for evaluation. We conduct extensive experiments on downstream tasks, including video understanding and generation, to establish benchmarks and demonstrate the dataset's quality. The results validate that VMChill effectively enhances model performance, highlighting its potential to facilitate future research in fine-grained multimodal learning. We will release the dataset, annotation codebase, and processing pipelines to support community research.

VMChill: A Dataset for Fine-Grained Visual-Musical Synergy

Day-to-night unpaired image translation is important to downstream tasks but remains challenging due to large appearance shifts and the lack of direct pixel-level supervision.
Existing methods often introduce semantic hallucinations, where objects from target classes such as traffic signs and vehicles, as well as man-made light effects, are incorrectly synthesized. These hallucinations significantly degrade downstream performance.
We propose a novel framework that detects and suppresses hallucinations of target-class features during unpaired translation. To detect hallucination, we design a dual-head discriminator that additionly performs semantic segmentation to identify hallucinated content in background regions. To suppress these hallucinations, we introduce class-specific prototypes, constructed by aggregating features of annotated target-domain objects, which act as semantic anchors for each class.
Built upon a Schrödinger Bridge-based translation model, our framework performs iterative refinement, where detected hallucination features are explicitly pushed away from class prototypes in feature space, thus preserving object semantics across the translation trajectory.
Experiments show that our method outperforms existing approaches both qualitatively and quantitatively. On the BDD100K dataset, it improves mAP by 15.5% for day-to-night domain adaptation, with a notable 31.7% gain for classes such as traffic lights that are prone to hallucinations.

Bridging Day and Night: Target-Class Hallucination Suppression in Unpaired Image Translation

Social choice theory offers a wealth of approaches for selecting a candidate on behalf of voters based on their reported preference rankings over options. When voters have explicit utilities for these options, however, using preference rankings may lead to suboptimal outcomes vis-a-vis utilitarian social welfare. Distortion is a measure of this suboptimality, and an extensive literature 
uses it to develop and analyze voting rules when utilities have minimal structure. However, in many settings, such as common paradigms for value alignment, available options admit a vector representation, and it is natural to suppose that utilities are parametric functions thereof.

We undertake the first study of distortion for linear utility functions. Our theoretical contributions are organized into two parts: randomized and deterministic voting rules. We obtain bounds that depend only on dimension of the candidate embedding, and are independent of the numbers of candidates or voters. Additionally, we introduce poly-time instance-optimal algorithms for minimizing distortion given a collection of candidates and votes. We empirically evaluate these in two real-world domains: recommendation systems using collaborative filtering embeddings, and opinion surveys utilizing language model embeddings. Our results benchmark the distortion bounds of several standard rules against our instance-optimal algorithms.

Optimized Distortion in Linear Social Choice

Structural measures of graphs, such as treewidth, are central tools in computational complexity resulting in efficient algorithms when exploiting the parameter. It is even known that modern SAT solvers work efficiently on instances of small treewidth. Since these solvers are widely applied in knowledge representation reasoning and symbolic AI, research interests in compact encodings for solving and to understand encoding limitations. Even more general is the graph parameter clique-width, which unlike treewidth can be small for dense graphs, e.g., co-graphs. Although dynamic programming algorithms and logic-based characterizations are available for clique-width, little is known about encodings.
In this work, we initiate the quest to understand encoding capabilities with clique-width by considering abstract argumentation, which is a robust knowledge representation framework widely used for reasoning with conflicting arguments. It is based on directed graphs and asks for computationally challenging properties, making it a natural candidate to study computational properties. We design novel reductions from argumentation problems to (Q)SAT. Our reductions linearly preserve the clique-width, resulting in directed decomposition-guided (DDG) reductions. Thereby, we establish novel results for all argumentation semantics, including counting. We show that the overhead caused by our DDG reductions cannot be significantly improved under reasonable assumptions thereby providing a structurally optimal reduction contributing to the encoding theory of argumentation.

Structure-Aware Encodings of Argumentation Properties for Clique-width

Reconstructing complete and interactive 3D scenes remains a fundamental challenge in computer vision and robotics, particularly due to persistent object occlusions and limited sensor coverage. Even multi-view observations from a single scene scan often fail to capture the full structural details. Existing approaches typically rely on multi-stage pipelines—such as segmentation, background completion, and inpainting—or require per-object dense scanning, both of which are error-prone, and not easily scalable. We propose IGFuse, a novel framework that reconstructs interactive Gaussian scene by fusing observations from multiple scans, where natural object rearrangement between captures reveal previously occluded regions. Our method constructs segmentation-aware Gaussian fields and enforces bi-directional photometric and semantic consistency across scans. To handle spatial misalignments, we introduce a pseudo-intermediate scene state for symmetric alignment, alongside collaborative co-pruning strategies to refine geometry. IGFuse enables high-fidelity rendering and object-level scene manipulation without dense observations or complex pipelines. Extensive experiments validate the framework’s strong generalization to novel scene configurations, demonstrating its effectiveness for real-world 3D reconstruction and real-to-simulation transfer.

IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion

This paper initiates the integration of Spiking Neural Networks (SNNs) into WiFi-based indoor sensing, aiming to enhance performance in challenging signal environments. WiFi signal-based pattern recognition enables a wide range of applications, including human activity recognition, gait analysis for human identification, fine-grained gesture recognition and etc. However, unlike cameras, radar, or LiDAR signals, WiFi measurements are particularly susceptible to noise due to the ubiquitous and interference-prone nature of indoor wireless communication. 
Biologically inspired SNNs, like the human brain, excel at processing information in noisy environments by leveraging stochastic neural dynamics. 
To address this, we propose a hybrid architecture that combines conventional Artificial Neural Networks (ANNs) with SNNs, leveraging the noise-resilient properties of spiking neurons. Our method demonstrates improved accuracy and faster convergence during training. To support this claim, we present a theoretical analysis comparing the noise-handling capabilities of ANN and SNN models in WiFi scenarios. Extensive experiments across three representative WiFi sensing tasks validate the effectiveness and robustness of the proposed ANN-SNN hybrid architecture. For reproducibility, we will release the code upon acceptance.

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