Singapore

In voting with ranked ballots each agent submits a strict ranking of the form $a \succ b \succ c \succ d$ over the alternatives, and the voting rule decides on the winner based on these rankings. Although this ballot format has desirable characteristics, there is a question of whether it is expressive enough for the agents. Kahng et. al. address this issue by adding intensities to the rankings. They introduce ranking with intensities ballot format, where agents can use both $\succ\!\succ$ and $\succ$ in their rankings to express intensive and normal preferences between consecutive alternatives in their rankings.
While Kahng et. al. focus on analyzing this ballot format in the utilitarian distortion framework, in this work, we look at the potentials of using this ballot format from the metric distortion view point. We design a class of voting rules coined Positional Scoring Rules, which can be used for different problems in the metric setting, and show that by solving a zero-sum game we can find the optimal member of this class for our problem. This rule takes intensities into account and achieves a lower distortion. In addition, by proving a bound on the price of ignoring intensities, we show that we might lose a great deal in terms of distortion by not taking the intensities into account.

AAAI 2026

Metric Distortion with Preference Intensities

distortion

voting

social choice

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. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

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.

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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 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. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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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 rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular data, despite its ubiquity, has been largely overlooked. Yet, detecting synthetic tabular data is especially challenging due to its heterogeneous structure and unseen formats at test time.
We address the underexplored task of detecting synthetic tabular data in the wild, where tables have variable and previously unseen schemas. We introduce a novel datum-wise transformer architecture that significantly outperforms the only previously published baseline, improving both AUC and accuracy by 7 points. By incorporating a table-adaptation component, our model gains an additional 7 accuracy points, demonstrating enhanced robustness. This work provides the first strong evidence that detecting synthetic tabular data in real-world conditions is not only feasible, but can be done with high reliability.

Robust Detection of Synthetic Tabular Data Under Schema Variability

Generating co-speech gestures in real time requires both temporal coherence and efficient sampling. We introduce a novel framework for streaming gesture generation that extends Rolling Diffusion models with structured progressive noise scheduling, enabling seamless long-sequence motion synthesis while preserving realism and diversity. Our framework is universally compatible with existing diffusion-based gesture generation model, transforming them into streaming methods capable of continuous generation without requiring post-processing. We evaluate our framework on ZEGGS and BEAT, strong benchmarks for real-world applicability. Applied to state-of-the-art baselines on both datasets, it consistently outperforms them, demonstrating its effectiveness as a generalizable and efficient solution for real-time, high-fidelity co-speech gesture synthesis. We further propose Rolling Diffusion Ladder Acceleration (RDLA), a new approach that employs a ladder-based noise scheduling strategy to simultaneously denoise multiple frames. This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 4× speedup with high visual fidelity and temporal coherence in our experiments. Comprehensive user studies further validate our framework’s ability to generate realistic, diverse gestures closely synchronized with the audio input.

Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion

We propose a new General Game Playing (GGP) system called Regular Games (RG).
The main goal of RG is to be both computationally efficient and convenient for game design.
The system consists of several languages.
The core component is a low-level language that defines the rules by a finite automaton.
It is minimal with only a few mechanisms, which makes it easy for automatic processing (by agents, analysis, optimization, etc.).
The language is universal for the class of all finite turn-based games with imperfect information.
Higher-level languages are introduced for game design (by humans or Procedural Content Generation), which are eventually translated to a low-level language.
RG generates faster forward models than the current state of the art, beating other GGP systems (Regular Boardgames, Ludii) in terms of efficiency.
Additionally, RG's ecosystem includes an editor with LSP, automaton visualization, benchmarking tools, and a debugger of game description transformations.

Regular Games -- an Automata-Based General Game Playing Language

Although current large audio language models (LALMs) extend text large language models (LLMs) with generic acoustic understanding abilities, they usually suffer from instruction sensitivity, where different instructions of the same intention can yield drastically different outcomes. In this work, we propose AHAMask, where we simply mask some of the attention heads in the decoder-only LLM backbone of LALMs, to trigger specific acoustic task functionalities without instructions. These masks are efficiently obtained by training on an LALM, with the number of trainable parameters equal to the attention head count in its LLM backbone. We show by experiments that applying such selective attention head masks achieves comparable or even better performance than using instructions, either on single or composite tasks. Besides achieving reliable acoustic task specification for LALMs, this also reveals that LALMs exhibit certain ``functional pathways'' in their attention heads. We will open-source the codebase after the anonymity period.

AHAMask: Reliable Task Specification for Large Audio Language Models Without Instructions

Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD), a paradigm that synthesizes a small, compact dataset to achieve training performance comparable to that of the original dataset, has emerged as a promising solution. However, conventional DD methods are not tailored for time series and suffer from architectural overfitting and limited scalability. To address these issues, we propose Harmonic Dataset Distillation for Time Series Forecasting (HDT). HDT decomposes the time series into its sinusoidal basis through the FFT and aligns the core periodic structure by Harmonic Matching. Since this process operates in the frequency domain, all updates during distillation are applied globally without disrupting temporal dependencies of time series. Extensive experiments demonstrate that HDT achieves strong cross-architecture generalization and scalability, validating its practicality for large-scale, real-world applications.

Harmonic Dataset Distillation for Time Series Forecasting

Understanding when a pre-trained model generalizes well to a new task remains a key challenge in transfer learning. Classical theories bound target risk using divergences such as total variation, MMD, or Wasserstein distance, yet tasks with similar divergences often show very different transfer performance. We propose a structural framework that explains transferability through two factors: the Feature Overlap Rate (FOR), measuring how much target representation lies in the source-induced subspace, and the Effective Task Complexity (ETC), quantifying the entropy of latent subtasks. We derive a PAC-Bayesian bound where target risk depends on FOR and ETC, and show that larger models attenuate their negative effects. Experiments on six GLUE transfer pairs estimate FOR and ETC from encoder representations and compare them to classical divergences. Results show that FOR and ETC together explain over 80% of transfer risk variance, while divergences fail to do so. Our findings provide a geometry-aware perspective for diagnosing and guiding transfer learning.

Unified Structural Factors for Transfer Learning Generalization with PAC-Bayesian Guarantees

Aerial multi-modal visual streams registration and fusion can generate more comprehensive scene information representations for UAVs' cross-modal perception. However, current challenges lie primarily in the essential difficulty of joint spatiotemporal representation learning from dynamic background and moving targets, and a critical shortage exists in large-scale, well-annotated multi-modal visual streams benchmark for UAV platforms. In this paper, we propose AerialFusion, a co-motion-driven unified UAVs visual streams registration and fusion that fully mines modality-invariant common features based on motion-aware, enabling spatiotemporally coherent registration and fusion. Specifically, 1) a Skewed Motion Distribution Field Co-Motion-Driven Image Registration, 2) a Co-Motion Generative Fusion, 3) a Streams-based Unified Learning. Furthermore, we introduce EUM3D, a registration and fusion benchmark for UAVs cross-modal perception. This benchmark contains 60 synchronized visible-infrared visual streams, or 122k spatially and temporally aligned pairs, most of which were taken at low-light scenes. And EUM3D provides pixel-level alignment guarantees via perspective-transform ground-truth. Extensive experiments reveal that AerialFusion surpasses current focus on image and static background fusion methods in aerial sequence scenarios, addressing spatiotemporal mismatches while suppressing cross-modal interference.

AerialFusion: Co-Motion-Driven Unified Registration and Fusion on Multi-modal Data Streams from Aerial View

Large Language Models (LLMs) hold significant potential for enhancing healthcare applications, yet their deployment is hindered by high computational and memory demands. Model compression techniques, such as quantization and sparsification, offer solutions to reduce these demands, but their impact on medical LLMs remains underexplored. We introduce CMedBench, the first comprehensive benchmark for evaluating compressed LLMs in medical contexts. LLMCMedBench assesses five core dimensions: Medical Knowledge Ability, Medical Application Ability, Trustworthiness Maintenance, Compression Cross Combination, and Computational Efficiency. Through extensive empirical studies, we analyze the trade-offs between model efficiency and clinical performance across diverse models, datasets, and compression strategies. Our findings highlight critical limitations in current evaluation practices and provide a robust framework for aligning compression strategies with medical requirements. CMedBench serves as a vital resource for researchers and practitioners, guiding the development of efficient, trustworthy, and clinically effective LLMs for healthcare applications.

CMedBench: A Comprehensive Benchmark for Efficient Medical Large Language Models

Large Language Models (LLMs) have demonstrated strong performance in various NLP tasks but remain limited in emotional intelligence (EI). Benchmarks such as EmoBench attribute this gap to deficiencies in cognitively demanding tasks that require inferring others’ latent mental states, intentions, and emotions in nuanced social contexts. To address this, we propose MACRo, a Multi-Agent Cognitive Reasoning framework that generates a structured Cognitive Chain of Thought comprising Situation, Clue, Thought, Action, and Emotion. Each component is generated by a specialized agent, enabling modular, interpretable multi-step reasoning. To ensure coherence and mitigate hallucinations, a coordinator agent verifies outputs, and a consensus game mechanism enforces alignment across reasoning steps. Extensive Experiments on EmoBench show that MACRo significantly enhances both emotional understanding and application across LLMs. Further evaluations confirm its generalizability to real-world social applications such as emotional support conversations.

Consensus-Driven Multi-Agent Cognitive Reasoning for Enhancing the Emotional Intelligence of Large Language Models

Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expected potential outcomes (or their corresponding utility and desirability) to compare the preferences of candidate actions. In this paper, we study new counterfactual decision-making rules by introducing two new metrics: the probabilities of potential outcome ranking (PoR) and the probability of achieving the best potential outcome (PoB). PoR reveals the most probable ranking of potential outcomes for an individual, and PoB indicates the action most likely to yield the top-ranked outcome for an individual. We then establish identification theorems and derive bounds for these metrics, and present estimation methods. Finally, we perform numerical experiments to illustrate the finite-sample properties of the estimators and demonstrate their application to a real-world dataset.

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Robust Detection of Synthetic Tabular Data Under Schema Variability

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Voting with Preference Intensities