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Recent research on instant runoff voting (IRV) shows that it exhibits a striking combinatorial property over one-dimensional preferences: there is an exclusion zone around the median voter such that the winner must come from the exclusion zone, unless no such candidate exists. Thus, in one dimension, IRV cannot elect an extreme candidate as long as a sufficiently moderate candidate runs. In this work, we examine the mathematical structure of exclusion zones as a broad phenomenon in more general preference spaces. We prove that with voters uniformly distributed over any $d$-dimensional hyperrectangle (for $d &gt; 1$), IRV has no nontrivial exclusion zone. However, we also show that IRV exclusion zones are not solely a one-dimensional phenomenon. For irregular higher-dimensional preference spaces with fewer symmetries than hyperrectangles, IRV can have nontrivial exclusion zones. As a further exploration, we study IRV exclusion zones in graph voting, where nodes represent voters who prefer candidates closer to them in the graph. Here, we show that IRV exclusion zones present a surprising computational challenge: even checking whether a given set of positions is an IRV exclusion zone is NP-hard. We develop an efficient randomized approximation algorithm for checking and finding exclusion zones. We also report on computational experiments with exclusion zones: (i) applying our approximation algorithm to a collection of real-world school friendship networks, we find that about 60\% of these networks have probable nontrivial IRV exclusion zones; and (ii) performing an exhaustive computer search of small graphs and trees, we find nontrivial IRV exclusion zones in most graphs. While our focus is on IRV, the properties of exclusion zones we establish provide new methods for analyzing voting systems in metric spaces more generally.

AAAI 2026

Exclusion Zones of Instant Runoff Voting

Recent research on instant runoff voting (IRV) shows that it exhibits a striking combinatorial property over one-dimensional preferences: there is an exclusion zone around the median voter such that the winner must come from the exclusion zone, unless no such candidate exists. Thus, in one dimension, IRV cannot elect an extreme candidate as long as a sufficiently moderate candidate runs. In this work, we examine the mathematical structure of exclusion zones as a broad phenomenon in more general preference spaces. We prove that with voters uniformly distributed over any $d$-dimensional hyperrectangle (for $d > 1$), IRV has no nontrivial exclusion zone. However, we also show that IRV exclusion zones are not solely a one-dimensional phenomenon. For irregular higher-dimensional preference spaces with fewer symmetries than hyperrectangles, IRV can have nontrivial exclusion zones. As a further exploration, we study IRV exclusion zones in graph voting, where nodes represent voters who prefer candidates closer to them in the graph. Here, we show that IRV exclusion zones present a surprising computational challenge: even checking whether a given set of positions is an IRV exclusion zone is NP-hard. We develop an efficient randomized approximation algorithm for checking and finding exclusion zones. We also report on computational experiments with exclusion zones: (i) applying our approximation algorithm to a collection of real-world school friendship networks, we find that about 60\% of these networks have probable nontrivial IRV exclusion zones; and (ii) performing an exhaustive computer search of small graphs and trees, we find nontrivial IRV exclusion zones in most graphs. While our focus is on IRV, the properties of exclusion zones we establish provide new methods for analyzing voting systems in metric spaces more generally.

technical paper

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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.

Phase transitions in condensed matter systems traditionally require prior knowledge of order parameters for identification. We present Prometheus, a variational autoencoder framework for unsupervised discovery of phase transitions and order parameters in the two-dimensional Ising model without prior physical knowledge. Our approach combines convolutional neural networks with beta-variational autoencoders to learn compressed representations that naturally separate ordered and disordered phases. Experimental validation demonstrates automatic discovery of the order parameter with 0.85 correlation to theoretical magnetization and critical temperature detection within 0.27% of the theoretical value, achieving 89% improvement over principal component analysis while requiring no supervision.

Prometheus: Unsupervised Discovery of Phase Transitions and Order Parameters in the 2D Ising Model Using Variational Autoencoders (Student Abstract)

Transformer models have achieved remarkable success across diverse deep learning fields, including natural language processing (NLP) and computer vision (CV). One drawback of these models is that the computational cost of the softmax attention, the core component of the transformer, exhibits quadratic complexity in both time and memory. As data scales up various attempts have been reported to overcome this bottleneck. The objective of this study is to propose a novel attention mechanism, “Cumulant Attention,” that systematically balances efficiency and accuracy. This proposal introduces a statistical-mechanics perspective and a reliable approximation based on cumulant expansion into the attention layer. The low-order variant reduces computational complexity to linear order, similar to the linear attention, while keeping nonlinearity of the softmax attention. We evaluate several variants on CV tasks, including image classification with ViT on ImageNet-100 and video classification with ViViT on UCF-101. Experimental results demonstrate that the cumulant attention outperforms the linear attention and achieves accuracy comparable to the softmax attention. These findings validate the effectiveness of our approach and highlight future directions, including scaling to larger models, extending to other modalities, and optimizing implementations for GPU hardware.

Cumulant Attention in Vision Transformers (Student Abstract)

Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model’s detection capability to new object classes requires large amounts of annotated training data, which is costly and time-consuming to acquire, especially for long-tailed classes with insufficient representation in existing datasets. We compare four different methods of generating synthetic data to finetune object detection models on novel object categories, particularly when limited data is available in the form of object-centric data (multi-view images or 3D models). Our approaches are based on simple image processing techniques, 3D rendering, and image generation models, each varying in complexity and image realism. We assess the ability of models finetuned using synthetic data to generalize to novel object classes in real-world data, and we achieve significant performance boosts in our data-constrained experimental setting.

Object-Centric Data Synthesis for Category-level Object Detection (Student Abstract)

Continuous cardiac monitoring during sleep is vital for detecting silent arrhythmia and other nocturnal cardiac events. While electrocardiogram (ECG) is the clinical gold standard, its reliance on electrodes and physical contact makes it intrusive for daily long-term use. Millimeter-wave (mmWave) radar offers a compelling non-contact alternative by capturing cardiac-induced chest-wall micro-vibrations. Existing radar-to-ECG methods often rely on direct waveform regression, assuming posture-stable mappings that break under natural sleep movements and obscure true cardiac rhythms. Inspired by the modality-invariant perception observed in speech and vision, we introduce mmJEPA-ECG, a physiology-guided framework for reconstructing clinical ECGs by anchoring radar sensing to invariant cardiac dynamics. It addresses two fundamental challenges: (i) disentangling robust cardiac representations from posture-induced artifacts, and (ii) generalizing ECG reconstruction across individuals under signal ambiguity. To address these challenges, Physiology-Oriented Self-Supervised Pretraining builds on a Joint Embedding Predictive Architecture (JEPA) with domain-informed masking and heart rate consistency to extract posture-robust cardiac embeddings. Conditional Diffusion-based ECG Reconstruction then generates personalized ECG waveforms through a hierarchical conditional diffusion process by spectral fidelity and denoising constraints. Extensive experiments on both public and self-collected multi-subject datasets demonstrate that our method outperforms state-of-the-art across waveform and rhythm metrics, halving R-R peak errors even under posture shifts and arrhythmic conditions. Codes and dataset are released at https://github.com/lanyangyang/mmJEPA-ECG.

mmJEPA-ECG: Cross-Posture Robust Contactless Electrocardiogram Monitoring via Millimeter Wave Radar Sensing

Conventional feedback, even when accompanied by brief explanations, rarely uncovers the hidden contradictions that trigger a learner's mistake. We bridge this gap with counterfactual question generation (CFQG): given a learner's answer, generate a follow-up question that deliberately contradicts it, compelling the learner to confront the underlying conflict. CFQG thus transforms assessment from passive scoring into an interactive and contradiction-centered dialogue that supports knowledge repair. To automate CFQG, we propose GapProbe, which probes the knowledge gap between a learner’s belief and curated facts through a knowledge graph (KG), then designs counterfactual questions (CFQs) that negate the belief. Identifying contradiction-aware triples, and more importantly, selecting those most likely to confuse the learner, are highly challenging in large-scale KGs. GapProbe tackles these challenges with an iterative ProConB cycle coupled with a schema-aware KGMap. By caching one- and multi-hop schema patterns of the KG, KGMap provides ``roadmap'' to guide LLMs jump to deep and contradiction-aware triples, beyond traditional step-wise graph traversal. We present the CFQG benchmark and corresponding metrics for evaluating how generated CFQs trigger, focus, and deepen learner reflection through explicit contradictions. 
Experiments on multiple datasets and LLMs show that GapProbe boosts LLM reasoning over KGs and generates follow-up questions that consistently promote deeper and more focused learner reflection.

Counterfactual Question Generation Uncovering Learner Contradictions

Predicting the distribution of future states in a stochastic system, known as belief propagation, is fundamental to reasoning under uncertainty. However, nonlinear dynamics often make analytical belief propagation intractable, requiring approximate methods. When the system model is unknown and must be learned from data, a key question arises: can we learn a model that (i) universally approximates general nonlinear stochastic dynamics, and (ii) supports analytical belief propagation? This paper establishes the theoretical foundations for a class of models that satisfy both properties. The proposed approach combines the expressiveness of normalizing flows for density estimation with the analytical tractability of Bernstein polynomials. Empirical results show the efficacy of our learned model over state-of-the art data-driven methods for belief propagation, especially for highly non-linear systems with non-additive, non-Gaussian noise.

Universal Learning of Stochastic Dynamics for Exact Belief Propagation Using Bernstein Normalizing Flows

Electric bicycles (e-bikes) have become the dominant mode of transportation in China’s urban instant delivery industry. However, many riders lack the experience to navigate complex traffic networks and diverse road conditions, leading to reduced delivery efficiency. To address this issue, we present Talking Trails, an e-bike delivery route planning system built upon an LLM-enhanced spatiotemporal trajectory model. Trained on millions of real-world delivery trajectories, fused with spatiotemporal and semantic data information, the model achieves a top-5 rider displacement prediction accuracy of 95\% and a route optimization rate of 82.1\%. In practice, we augment the core planner with an LLM-driven semantic layer that translates high-level user intent into executable tasks, then pair it with a battery-swap module that continuously validates route feasibility so the vehicle never runs out of charge mid-mission. Currently serving tens of thousands of riders, the system is projected to reduce average delivery mileage by 17\% and lower annual carbon emissions by 3978 tons. Overall, Talking Trails significantly improves delivery efficiency, offering a scalable and sustainable solution for instant delivery operations.

Talking Trails: LLM-Enhanced Spatiotemporal Trajectory Modeling for E-Bike Delivery Route Planning

Recent advances have focused on training high-performance spiking neural networks (SNNs) by leveraging surrogate gradient (SG) learning to estimate the derivatives of non-differentiable spiking activity. However, the distribution of neuronal membrane potentials varies across timesteps and progressively deviates toward both sides of the firing threshold during training. When threshold and SG remain fixed, this may lead to imbalanced spike firing rates and diminished gradient signals, which prevent SNNs from performing well. To address these issues, we propose a novel dual-stage synergistic learning algorithm that achieves forward adaptive thresholding and backward dynamic SG. In forward propagation, we adaptively adjust thresholds based on the distribution of membrane potential dynamics (MPD) at each timestep, which enriches neuronal diversity and effectively balances firing rates across layers. In backward propagation, drawing from the association between MPD and SG, we dynamically optimize the SG driven by thresholds to enhance gradient estimation through spatio-temporal alignment, effectively mitigating gradient information loss. Experimental results demonstrate that our method achieves significant performance improvements. Moreover, it allows neurons to fire stable proportions of spikes at each timestep and increases the proportion of neurons that obtain gradients in deeper layers. Code is available at Supplementary Materials.

DS-ATGO: Dual-Stage Synergistic Learning via Forward Adaptive Threshold and Backward Gradient Optimization for Spiking Neural Networks

Alignment is crucial for text-to-image (T2I) models to ensure that the generated images faithfully capture user intent while maintaining safety and fairness. Direct Preference Optimization (DPO) has emerged as a key alignment technique for large language models (LLMs), and its influence is now extending to T2I systems. This paper introduces **DPO-Kernels** for T2I models, a novel extension of DPO that enhances alignment across three key dimensions: *(i) Hybrid Loss*, which integrates embedding-based objectives with the traditional probability-based loss to improve optimization; *(ii) Kernelized Representations*, leveraging Radial Basis Function (RBF), Polynomial, and Wavelet kernels to enable richer feature transformations, ensuring better separation between safe and unsafe inputs; and *(iii) Divergence Selection*, expanding beyond DPO’s default Kullback–Leibler (KL) regularizer by incorporating alternative divergence measures such as Wasserstein and Rényi divergences to enhance stability and robustness in alignment training. We introduce **DETONATE**, the first large-scale benchmark of its kind, comprising approximately 100K curated image pairs, categorized as chosen and rejected. This benchmark encapsulates three critical axes of social bias and discrimination: *Race*, *Gender*, and *Disability*. The prompts are sourced from the hate speech datasets, while the images are generated using state-of-the-art T2I models, including Stable Diffusion 3.5 Large (SD-3.5), Stable Diffusion XL (SD-XL), and Midjourney. Furthermore, to evaluate alignment beyond surface metrics, we introduce the **Alignment Quality Index (AQI)**: a novel geometric measure that quantifies latent space separability of safe/unsafe image activations, revealing hidden model vulnerabilities. While alignment techniques often risk overfitting, we empirically demonstrate that DPO-Kernels preserve strong generalization bounds using the theory of Heavy-Tailed Self-Regularization (HT-SR). To foster further research, we publicly release DETONATE along with the complete codebase for DPO-Kernels.

DETONATE – A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization

Artificial neural networks are popular for computer vision as they often give state-of-the-art
performance, but are difficult to interpret because of their complexity. This black box modeling is especially troubling when the application concerns human well-being such as in medical image analysis or autonomous driving. In this work, we propose a technique called routing path visualization for capsule networks, which reveals how much of each region in an image is routed to each capsule. In turn, this technique can be used to interpret the entity that a given capsule detects, and speculate how the network makes a prediction. We demonstrate our new visualization technique on several real world datasets. Experimental results suggest that routing path visualization can precisely localize the predicted class from an image, even though the capsule networks are trained using just images and their respective class labels, without additional information defining the location of the class in the image.

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Prometheus: Unsupervised Discovery of Phase Transitions and Order Parameters in the 2D Ising Model Using Variational Autoencoders (Student Abstract)

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