Singapore

Amid recent advances for multivariate time series forecasting, self-supervised learning has emerged as a promising paradigm for deriving transferable knowledge from multi-domain data. Despite its effectiveness, existing approaches exhibit two critical limitations: (1) Underestimating the significance of multivariate dependencies in learning generalizable representations and (2) Failing to reconcile the complementary strengths of autoregressive and one-shot generative paradigms. In this work, we propose TimeCAP, a novel channel-aware pre-training framework that internalizes latent causal relationships among variables inherent in multi-domain data, and effectively transfers the acquired knowledge to downstream applications. Technically, we present a flexible channel-grouping learning approach, complemented by an adaptive meta-routing mechanism, enabling TimeCAP to parallel recognize intra-group local patterns while maintaining global coherence. Intra- and inter-group multivariate dependencies are captured through the self- and cross-attention with channel-aware mask, which strictly confine interactions among time-aligned, fine-grained multivariate tokens. To seamlessly unify two advanced generative paradigms, we propose a novel dynamic dual-head decoding and optimization strategy, empowering TimeCAP to leverage critical dependencies in the output series while avoiding cumulative errors over time. In the few-shot evaluation, TimeCAP achieves average MSE and MAE reductions of 11.8% and 6% over leading baselines, while also outperforming state-of-the-art models in full-shot and zero-shot settings by large margins.

AAAI 2026

TimeCAP: A Channel-Aware Pre-Training Framework for Multivariate Time Series Forecasting

ml: time-series/data streams

self-supervised learning

pre-training

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

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AAAI 2025

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-25 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.



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.

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.

Species distribution models (SDMs), which aim to predict species occurrence based on environmental variables, are widely used to monitor and respond to biodiversity change. Recent deep learning advances for SDMs have been shown to perform well on complex and heterogeneous datasets, but their effectiveness remains limited by spatial biases in the data. In this paper, we revisit deep SDMs from a Bayesian perspective and introduce BATIS, a novel and practical framework wherein prior predictions are updated iteratively using limited observational data. Models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns. We benchmark an extensive set of uncertainty quantification approaches on a novel dataset including citizen science observations from the eBird platform. Our empirical study shows how Bayesian deep learning approaches can greatly improve the reliability of SDMs in data-scarce locations, which can contribute to ecological understanding and conservation efforts.

BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models

Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In this work, we investigate strategies to enhance the data analysis capabilities of open-source LLMs. By curating a seed dataset of diverse, realistic scenarios, we evaluate models across three dimensions: data understanding, code generation, and strategic planning. Our analysis reveals three key findings: (1) Strategic planning quality serves as the primary determinant of model performance; (2) Interaction design and task complexity significantly influence reasoning capabilities; (3) Data quality demonstrates a greater impact than diversity in achieving optimal performance. We leverage these insights to develop a data synthesis methodology, demonstrating significant improvements in open-source LLMs' analytical reasoning capabilities.

Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study

Self-supervised learning (SSL) methods have achieved remarkable success in learning image representations by focusing on invariance — discarding transformation information that some computer vision tasks actually require. While recent approaches like SIE attempt to address this limitation by learning equivariant features using linear operators in feature space, they impose restrictive assumptions that constrain flexibility and generalization.
We introduce a novel SSL auxillary task that learns equivariant-coherent representations through intermediate transformation reconstruction, which can be integrated with existing augmentation-based SSL methods. Our key idea is to reconstruct images at intermediate points along transformation paths, e.g. when training on 30° rotations, we reconstruct the 10° and 20° rotation states. 
Reconstructing intermediate states uses augmentation information, rather than suppressing it, and therefore requires equivariant features carrying such information.Our method decomposes feature vectors into invariant and equivariant parts, training them with standard SSL losses and reconstruction losses, respectively. We demonstrate substantial improvements on synthetic equivariance benchmarks while maintaining competitive performance on downstream tasks requiring invariant representations. The approach seamlessly integrates with existing SSL methods (iBOT, DINOv2) and consistently enhances performance across diverse tasks, including segmentation, detection, depth estimation, and video dense prediction. Our framework provides a practical way for augmenting SSL methods with equivariant capabilities while preserving invariant performance.

Self-Supervised Learning Based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation

Vector quantization (VQ) transforms continuous image features into discrete representations, providing compressed, tokenized inputs for generative models. However, VQ-based frameworks suffer from several issues, such as non-smooth latent spaces, weak alignment between representations before and after quantization, and poor coherence between the continuous and discrete domains. These issues lead to unstable codeword learning and underutilized codebooks, ultimately degrading the performance of both reconstruction and downstream generation tasks. To this end, we propose VAEVQ, which comprises three key components: (1) Variational Latent Quantization (VLQ), replacing the AE with a VAE for quantization to leverage its structured and smooth latent space, thereby facilitating more effective codeword activation; (2) Representation Coherence Strategy (RCS), adaptively modulating the alignment strength between pre- and post-quantization features to enhance consistency and prevent overfitting to noise; and (3) Distribution Consistency Regularization (DCR), aligning the entire codebook distribution with the continuous latent distribution to improve utilization. Extensive experiments on two benchmark datasets demonstrate that VAEVQ outperforms state-of-the-art methods.

VAEVQ: Enhancing Discrete Visual Tokenization Through Variational Modeling

Multi-Agent Reinforcement Learning (MARL) has gained significant interest in recent years, enabling sequential decision-making across multiple agents in various domains. However, most existing explanation methods focus on centralized MARL, failing to address the uncertainty and nondeterminism inherent in decentralized settings. We propose methods to generate policy summarizations that capture task ordering and agent cooperation in decentralized MARL policies, along with query-based explanations for “When,” “Why Not,” and “What” types of user queries about specific agent behaviors. We evaluate our approach across four MARL domains and two decentralized MARL algorithms, demonstrating its generalizability and computational efficiency. User studies show that our summarizations and explanations significantly improve user question-answering performance and enhance subjective ratings on metrics such as understanding and satisfaction.

Explaining Decentralized Multi-Agent Reinforcement Learning Policies

Clustering is a long-standing research problem and a fundamental tool in AI and data analysis. The traditional $k$-center problem, known as a fundamental theoretical challenge in clustering, has a best possible approximation ratio of $2$, and any improvement to a ratio of $2 - \epsilon$ would imply $\mathcal{P} = \mathcal{NP}$.
In this work, we study the constrained $k$-center clustering problem, where instance-level cannot-link (CL) and must-link (ML) constraints are incorporated as background knowledge. Although general CL constraints significantly increase the hardness of approximation, previous work has shown that disjoint CL sets permit constant-factor approximations. However, whether local search can achieve such a guarantee in this setting remains an open question. To this end, we propose a novel local search framework based on a transformation to a \textit{dominating matching set} problem, achieving the best possible approximation ratio of $2$.
The experimental results on both real-world and synthetic datasets demonstrate that our algorithm outperforms all baseline methods in solution quality.

Approximation Algorithm for Constrained k-Center Clustering: A Local Search Approach

Linguistic steganography seeks to conceal secret
information within natural language text. However, existing
methods often struggle to balance stego text quality with
embedding efficiency, largely due to limitations in
generation strategies and coding mechanisms. We propose
SA-ANS, a self-adaptive linguistic steganography framework
based on a self-adjusting Asymmetric Numeral System. SA-ANS
allows user-specified embedding rates and employs
probabilistic coding with adaptive candidate selection,
dynamically tailoring the token pool to the language
model’s probability distribution. This design produces
fluent, semantically coherent stego text while preserving
statistical indistinguishability from natural language.
Extensive experiments on multiple benchmark datasets,
evaluated across embedding efficiency, linguistic quality,
statistical similarity, robustness to steganalysis, and
human judgment, show that SA-ANS consistently outperforms
state-of-the-art methods, demonstrating both effectiveness
and practicality.

Linguistic Steganography via Self-Adjusting Asymmetric
Number System

This paper studies submodular maximization over matroids in the fully dynamic setting, where elements of an underlying ground set undergo sequential insertions and deletions. The goal is to maintain an approximate optimal solution for the current element set with low amortized update time. For monotone submodular functions. we propose a dynamic algorithm achieving a $(0.3178 - \varepsilon)$-approximation using $\tilde{O}_{\varepsilon}(k^3)$ expected amortized queries. Furthermore, we extend our approach to the non-monotone submodular maximization setting, obtaining a $(0.1921-\varepsilon)$-approximation with the same update complexity. Both algorithms improve upon the best known approximation guarantees, which are $(0.25 - \varepsilon)$ for the monotone case (Banihashem et al., 2024) and $(0.0932 - \varepsilon)$ for the non-monotone case (Liu and Yang, 2024).

Improved Fully Dynamic Submodular Maximization Under Matroid Constraints

Learning representation of the enclosing subgraph of node pairs is recognized as an efficient approach for link-oriented prediction tasks in network applications. The core challenge within this subgraph encoding approach is how to effectively distinguish and then properly aggregate the contribution of nodes in the subgraph into a single vector to indicate the relation between the target node pair. In this work, we propose a novel sphere-based subgraph encoding architecture, namely BS-SubGNN, to address the challenge. In detail, we design two key building blocks, including Bicentric Sphere Node Labeling (BSNL) and Bicentric Sphere Subgraph Pooling (BSSP) to assist message passing in BS-SubGNN. BSNL endows each node a label according to the sphere it belongs to in the subgraph to distinguish the contribution of nodes, while BSSP adopts an attention mechanism to aggregate the contribution of nodes in each sphere. Theoretically, we prove that BS-SubGNN can unify existing node distance labeling methods, and yield discriminative node features with less time complexity. We evaluate the performance of BS-SubGNN in link prediction tasks over a variety of network types, including undirected networks, attribute networks, directed networks, and signed directed networks. Our experimental results demonstrate that BS-SubGNN consistently achieves significant performance improvements over the above diverse types of networks. In particular, compared to those methods with a requisite of multi-hop neighborhood information, BS-SubGNN can obtain better performance even when only one-hop neighborhood information of the node pair is utilized.

Subgraph Encoding with Bicentric Sphere Node Labeling and Pooling for Link Prediction

Understanding complex physical systems often requires
integrating data from multiple diagnostics, each with
limited resolution or coverage. We present a machine
learning framework that reconstructs synthetic
high-temporal-resolution data for a target diagnostic using
information from other diagnostics, without direct target
measurements during the inference. This multimodal
super-resolution technique improves diagnostic robustness
and enables monitoring even in case of measurement failures
or degradation. Applied to fusion plasmas, our method
targets edge-localized modes (ELMs), which can damage
plasma-facing materials. By reconstructing super-resolution
Thomson Scattering data from complementary diagnostics, we
uncover fine-scale plasma dynamics and validate the role of
resonant magnetic perturbations (RMPs) in ELM suppression
through magnetic island formation. The approach provides
new observation supporting the plasma profile flattening
due to these islands. Our results demonstrate the
framework’s ability to generate high-fidelity synthetic
diagnostics, offering a powerful tool for ELM control
development in future reactors like ITER. The approach is
broadly transferable to other domains facing sparse,
incomplete, or degraded diagnostic data, opening new
avenues for discovery.

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Next from AAAI 2026

BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models

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TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents