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The fair division of indivisible goods is not only a subject of theoretical research, but also an important problem in practice, with solutions being offered on several online platforms. Little is known, however, about the characteristics of practical fair-division instances and how they compare to the characteristics of synthetic fair-division instances. Taking inspiration from the work of Szufa et al.~(2020), we devise a map of fair-division instances. This map identifies two key axes along which fair-division instances differ, which help distinguish synthetic distributions, predict various features of the fair-division instances, and can be conceptually interpreted.

AAAI 2026

Putting Fair Division on the Map

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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.<br><br>

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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.

Motivated by the increasing risks of data misuse and fabrication, we investigate
the problem of identifying synthetic time series generated by Time-Series Large
Models (TSLMs) in this work.
While there are extensive researches on detecting model generated text, we find
that these existing methods are not applicable to time series data due to the
fundamental modality difference, as time series usually have lower information
density and smoother probability distributions than text data, which limit the
discriminative power of token-based detectors.
To address this issue, we examine the subtle distributional differences between
real and model-generated time series and propose the contraction
hypothesis, which states that model-generated time series, unlike real ones,
exhibit progressively decreasing uncertainty under recursive forecasting.
We formally prove this hypothesis under theoretical assumptions on model
behavior and time series structure.
Model-generated time series exhibit progressively concentrated distributions
under recursive forecasting, leading to uncertainty contraction.
We provide empirical validation of the hypothesis across diverse datasets.
Building on this insight, we introduce the Uncertainty Contraction
Estimator (UCE), a white-box detector that aggregates uncertainty metrics
over successive prefixes to identify TSLM‑generated time series.
Extensive experiments on $32$ datasets show that UCE consistently outperforms
state-of-the-art baselines, offering a reliable and generalizable solution for
detecting model-generated time series.

A Theoretical Analysis of Detecting Large Model-Generated Time Series

Confidence estimation for text-to-SQL aims to assess the reliability of model-generated SQL queries without having access to gold answers. We study this problem in the context of large language models (LLMs), where access to model weights and gradients is often constrained. We explore both black-box and white-box confidence estimation strategies, evaluating their effectiveness on cross-domain text-to-SQL benchmarks. Our evaluation highlights the superior performance of consistency-based methods among black-box models and the advantage of SQL-syntax-aware approaches for interpreting LLM logits in white-box settings. Furthermore, we show that execution-based grounding of queries provides a valuable supplementary signal, improving the effectiveness of both approaches.

Confidence Estimation for Text-to-SQL in Large Language Models

In the field of sketch generation, raster-format trained models often produce non-stroke artifacts, while vector-format trained models typically lack a holistic understanding of sketches, leading to compromised recognizability. Moreover, existing methods struggle to extract common features from similar elements (e.g., eyes of animals) appearing at varying positions across sketches. To address these challenges, we propose StrokeFusion, a two-stage framework for vector sketch generation. It contains a dual-modal sketch feature learning network that maps strokes into a high-quality latent space. This network decomposes sketches into normalized strokes and jointly encodes stroke sequences with Unsigned Distance Function (UDF) maps, representing sketches as sets of stroke feature vectors. Building upon this representation, our framework exploits a stroke-level latent diffusion model that simultaneously adjusts stroke position, scale, and trajectory during generation. This enables high-fidelity stroke generation while supporting stroke interpolation editing. Extensive experiments across multiple sketch datasets, demonstrate that our framework outperforms state-of-the-art techniques, validating its effectiveness in preserving structural integrity and semantic features. Code and models will be made publicly available upon publication.

StrokeFusion: Vector Sketch Generation via Joint Stroke-UDF Encoding and Latent Sequence Diffusion

The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall short in dynamic, human-centric environments due to two fundamental limitations: (1) their reliance on static graph topologies, which fail to capture physical, event-driven dynamics, and (2) their tendency to confuse spurious correlations with true causality, undermining robustness in human-centric environments. To address these gaps, we propose the Dynamic Causal Spatio-Temporal Graph Network (DyC-STG), a novel framework designed for real-time data credibility analysis in IoT. Our framework features two synergistic contributions: an event-driven dynamic graph module that adapts the graph topology in real-time to reflect physical state changes, and a causal reasoning module to distill causally-aware representations by strictly enforcing temporal precedence. To facilitate the research in this domain we release two new real-world datasets. Comprehensive experiments show that DyC-STG establishes a new state-of-the-art, outperforming the strongest baselines by 1.4 percentage points and achieving an F1-Score of up to 0.930.

DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT

The multi-modality remote sensing foundation model (MM-RSFM) has made notable progress recently. However, most existing approaches remain limited to medium-resolution, single-modality, restricting their performance in fine-grained downstream applications such as disaster response and urban planning. In this work, MaRS is proposed, a multi-modality very-high-resolution (VHR) remote sensing foundation model designed for cross-modality granularity interpretation of complex scenes. To achieve this, a multi-modality VHR SAR-optical dataset, MaRS-16M, is constructed through large-scale collection and semi-automated processing, comprising over 16 million paired samples. Unlike previous work, MaRS tackles two fundamental challenges in VHR SAR-optical self-supervised learning (SSL) techniques. Cross-granularity contrastive learning (CGCL) is introduced to alleviate alignment inconsistencies caused by imaging differences, and meta-modality attention (MMA) is designed to unify heterogeneous physical characteristics across modalities. Compared to existing remote sensing foundation models (RSFMs) and general vision foundation models (VFMs), MaRS performs better as a pre-trained backbone across nine multi-modality VHR downstream tasks.

MaRS: A Multi-modality Very-high-resolution Remote Sensing Foundation Model with Cross-Granularity Meta-Modality Learning

Searching for the $k$-nearest neighbors (KNN) in multimodal data retrieval is computationally expensive, particularly due to the inherent difficulty in comparing similarity measures across different modalities. Recent advances in multimodal machine learning address this issue by mapping data into a shared embedding space; however, the high dimensionality of these embeddings (hundreds to thousands of dimensions) presents a challenge for time-sensitive vision applications. This work proposes Order-Preserving Dimension Reduction (OPDR), aiming to reduce the dimensionality of embeddings while preserving the ranking of KNN in the lower-dimensional space. One notable component OPDR is a new measure function to quantify KNN quality as a global metric, based on which we derive a closed-form map between target dimensionality and key contextual parameters. We have integrated OPDR with multiple state-of-the-art dimension-reduction techniques, distance functions, and embedding models; experiments on a variety of multimodal datasets demonstrate that OPDR effectively retains recall high accuracy while significantly reducing computational costs.

Order-Preserving Dimension Reduction for Multimodal Semantic Embedding

Mixture-of-Experts (MoE) models enable scalable performance by activating large parameter sets sparsely, minimizing computational overhead. To mitigate the prohibitive cost of training MoEs from scratch, recent work employs upcycling, reusing a single pre-trained dense model by replicating its feed-forward network (FFN) layers into experts. However, this limits expert diversity, as all experts originate from a single pre-trained dense model. This paper addresses this limitation by constructing powerful MoE models using experts sourced from multiple identically-architected but disparate pre-trained models (e.g., Qwen2.5-Coder and Qwen2). A key challenge lies in the fact that these source models occupy disparate, dissonant regions of the parameter space, making direct upcycling prone to severe performance degradation. To overcome this, we propose Symphony-MoE, a novel two-stage framework designed to harmonize these models into a single, coherent expert mixture. First, we establish this harmony in a training-free manner: we construct a shared backbone via a layer-aware fusion strategy and, crucially, alleviate parameter misalignment among experts using activation-based functional alignment. Subsequently, a stage of post-training coordinates the entire architecture. Experiments demonstrate that our method successfully integrates experts from heterogeneous sources, achieving an MoE model that significantly surpasses baselines in multi-domain tasks and out-of-distribution generalization.

Symphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-Experts

Recent advances in Large language models (LLMs) have shown remarkable capabilities across textual and multimodal domains. In parallel, diffusion-based language models have emerged as a promising alternative to the autoregressive paradigm, offering improved controllability, bidirectional context modeling, and robust generation. However, their application to the audio modality remains underexplored. In this work, we introduce DIFFA, the first diffusion-based Large Audio-Language Model designed to perform spoken language understanding. DIFFA integrates a frozen diffusion language model with a lightweight dual-adapter architecture that bridges speech understanding and natural language reasoning. We employ a two-stage training pipeline: first, aligning semantic representations via an ASR objective; then, learning instruction-following abilities through synthetic audio-caption pairs automatically generated by prompting LLMs. Despite being trained on only 960 hours of ASR and 127 hours of synthetic instruction data, DIFFA demonstrates competitive performance on major benchmarks, including MMSU, MMAU, and VoiceBench, outperforming several autoregressive open-source baselines. Our results reveal the potential of diffusion-based language models for efficient and scalable audio understanding, opening a new direction for speech-driven AI.

DIFFA: Large Language Diffusion Models Can Listen and Understand

Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating ligand molecules that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the dynamic nature of protein structure and ligand-induced conformational changes, limiting their applicability in practical drug discovery. Here, we propose Apo2Mol, a diffusion-based generative framework for structure-based 3D molecule design that explicitly accounts for conformational flexibility in protein binding pockets. To support this, we curate a new dataset of over 24,000 experimentally resolved Apo-Holo protein structure pairs from the Protein Data Bank, enabling the modeling of protein conformational changes associated with ligand binding. Apo2Mol employs a hierarchical graph-based diffusion model that simultaneously generates 3D ligand molecules and their corresponding induced Holo pocket conformation from an input Apo state. Extensive experiments demonstrate that Apo2Mol can produce chemically valid ligands with state-of-the-art binding affinities and realistic pocket conformation changes. To our knowledge, Apo2Mol is the first open-sourced model trained on experimental Apo-Holo structure pairs to explicitly model coupled ligand-pocket dynamics, representing a crucial advance and offering a valuable resource for future research in structure-based drug design.

Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models

Text-to-image generation tasks have driven remarkable advances in diverse media applications, yet most focus on single-turn scenarios and struggle with iterative, multi-turn creative tasks. Recent dialogue-based systems attempt to bridge this gap, but their single-agent, sequential paradigm often causes intention drift and incoherent edits. To address these limitations, we present Talk2Image, a novel multi-agent system for interactive image generation and editing in multi-turn dialogue scenarios. Our approach integrates three key components: intention parsing from dialogue history, task decomposition and collaborative execution across specialized agents, and feedback-driven refinement based on a multi-view evaluation mechanism. Talk2Image enables step-by-step alignment with user intention and consistent image editing. Experiments demonstrate that Talk2Image outperforms existing baselines in controllability, coherence, and user satisfaction across iterative image generation and editing tasks.

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