Singapore

Solar irradiance forecast aims to accurately estimate future solar irradiance based on historical data, playing a vital role in energy production and grid management. 
While ground-based station measurements provide local accuracy, geostationary satellites offer much broader environmental contexts, such as cloud coverage, which serves as a key factor for accurate forecasting. 
However, effectively integrating these multimodal observations remains a challenge, with existing methods suffering from inflexibility and high computational costs.
To address this problem, we propose SatSolarCast, a flexible and efficient multimodal framework that introduces a memory alignment learning mechanism to integrate geostationary satellite data and historical irradiance observations. 
By preserving and recalling long-term spatiotemporal patterns from a specialized satellite memory bank, SatSolarCast enables effective guidance for both short- and long-term prediction.
Additionally, SatSolarCast offers plug-and-play compatibility and can be incorporated into various forecasting architectures. 
Extensive experiments across four ground stations demonstrate that SatSolarCast substantially improves forecasting performance compared to prior methods with much lower computational costs.

AAAI 2026

SatSolarCast: A Flexible Framework for Multimodal Solar Irradiance Forecasting via Memory-Alignment Learning

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

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.<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.

Multi-annotator learning traditionally aggregates diverse annotations to approximate a single “ground truth”, treating disagreements as noise. However, this paradigm faces fundamental challenges: subjective tasks often lack absolute ground truth, and sparse annotation coverage makes aggregation statistically unreliable. We introduce a paradigm shift from sample-wise aggregation to annotator-wise behavior modeling. By treating annotator disagreements as valuable information rather than noise, modeling annotator-specific behavior patterns can reconstruct unlabeled data to reduce annotation cost, enhance aggregation reliability, and explain annotator decision behavior. To this end, we propose QuMAB (Query-based Multi-Annotator Behavior Pattern Learning), which uses lightweight queries to model individual annotators while capturing inter-annotator correlations as implicit regularization, preventing overfitting to sparse individual data while maintaining individualization and improving generalization, with a visualization of annotator focus regions offering an explainable analysis of behavior understanding. We contribute two large-scale datasets with dense per-annotator labels: STREET (4,300 labels/annotator) and AMER (average 3,118 labels/annotator), the first multimodal multi-annotator dataset. Extensive experiments demonstrate the superiority of our QuMAB in modeling individual annotators’ behavior patterns, their utility for consensus prediction, and applicability under sparse annotations.

QuMAB: Query-based Multi-annotator Behavior Pattern Learning

Recent advances in graph OOD detection have focused on test-time training techniques that facilitate OOD detection without accessing potential supervisory information (e.g., training data). However, most of these methods employ a one-pass inference paradigm, which prevents them from progressively correcting erroneous predictions to amplify OOD signals. To this end, we propose a Self-Improving Graph Out-of-Distribution detector (SIGOOD), which is an unsupervised framework that integrates continuous self-learning with test-time training for effective graph OOD detection. Specifically, SIGOOD generates a prompt to construct a prompt-enhanced graph that amplifies potential OOD signals. To optimize prompts, SIGOOD introduces an Energy Preference Optimization (EPO) loss, which leverages energy variations between the original test graph and the prompt-enhanced graph. By iteratively optimizing the prompt by involving it into the detection model in a self-improving loop, the resulting optimal prompt-enhanced graph is ultimately used for OOD detection. Comprehensive evaluations on 21 real-world datasets confirm the effectiveness and outperformance of our SIGOOD method.

From Subtle to Significant: Prompt-Driven Self-Improving Optimization in Test-Time Graph OOD Detection

High-dimensional mediation analysis (HMA) seeks to uncover complex causal mechanisms involving numerous mediators and plays a crucial role in scientific and social science domains. In this work, we introduce the Generative Adversarial High-dimensional Mediation Network (GAHMN), a novel, scalable structured generative modeling framework designed for causal analysis in high-dimensional settings.
GAHMN formulates mediation analysis as dual conditional generative blocks, explicitly capturing mediators' dual roles as outcomes influenced by treatments and as predictors affecting outcomes. Each block integrates a high-dimensional partially linear structure with multi-channel convolutional layers, promoting effective parameter sharing and enhanced representation learning.
To induce sparsity and accurate mediator selection, GAHMN employs customized min-max optimization problems with $\ell_1$ penalties on generator parameters, alongside specially designed optimization algorithms for efficient computation.
Unlike existing benchmark methods relying on restrictive parametric assumptions or random-effect specifications, GAHMN flexibly captures heterogeneity, complex distributions, and inter-mediator correlations. Through our carefully design, the complexity of GAHMN is $O(p)$ rather than $O(p^2)$ ($p$ is the number of mediators) in conventional approach. Theoretical results rigorously ensures estimation consistency, convergence rate, and accurate sparse recovery. GAHMN also serves as a structured generative causal modeling framework, extending to causal decomposition, structural equation modeling, and counterfactual policy evaluation.
Extensive numerical experiments confirm GAHMN's superior performance and robustness in synthetic and real-world scenarios.

GAHMN: A Generative Approach for High-Dimensional Mediation Analysis

Dynamic retrieval-augmented generation (RAG) enables large language models (LLMs) to fetch external knowledge on demand, improving adaptability over static RAG. A key challenge in this setting is determining when retrieval should occur. Prior methods typically trigger retrieval based on low confidence in individual tokens, which can result in delayed intervention after errors have already occurred. We propose the Entropy-Trend Constraint (ETC), a training-free method that selects optimal retrieval timing by modeling the dynamics of token-level uncertainty. Specifically, ETC leverages first- and second-order differences of the entropy sequence to capture emerging uncertainty trends, enabling earlier and more precise retrieval. Experiments across six QA benchmarks and three LLM backbones show that ETC consistently outperforms strong baselines while reducing retrieval frequency. It is especially effective in domain-specific settings, demonstrating robust generalization. Further ablation studies and qualitative analysis confirm that trend-aware uncertainty modeling leads to more effective retrieval timing. Our approach is plug-and-play, model-agnostic, and easy to integrate into existing decoding pipelines. Code is provided in the supplementary materials.

Modeling Uncertainty Trends for Timely Retrieval in Dynamic RAG

We present a polynomial-time algorithm for exactly computing second-price pacing equilibria (SPPE) in multi-buyer auction markets with a constant number of buyers. In addition, our algorithm can efficiently find a pacing equilibrium which optimizes key metrics such as revenue or social welfare. SPPEs are central to modern advertising auctions, yet computing or even approximating them is PPAD-hard in general. To overcome this challenge in the restricted setting, we apply the cell-decomposition method. Specifically, we partition the solution space into polynomially many cells defined by hyperplanes, each corresponding to a fixed ordering of buyers' scaled valuations across goods. Within each cell, finding an equilibrium can be reduced to solving a linear program. To the best of our knowledge, our work identifies the first class of second-price pacing games for which an exact SPPE can be computed efficiently.

Pacing Equilibria in Second-Price Auctions with Few Buyers

3D teeth segmentation, involving the localization of tooth instances and their semantic categorization in 3D dental models, is a critical yet challenging task in digital dentistry due to the complexity of real-world dentition. In this paper, we propose 3DTeethSAM, an adaptation of the Segment Anything Model 2 (SAM2) for 3D teeth segmentation. SAM2 is a pretrained foundation model for image and video segmentation, demonstrating a strong backbone in various downstream scenarios. To adapt SAM2 for 3D teeth data, we render images of 3D teeth models from predefined views, apply SAM2 for 2D segmentation, and reconstruct 3D results using 2D-3D projections. Since SAM2's performance depends on input prompts and its initial outputs often have deficiencies, and given its class-agnostic nature, we introduce three light-weight learnable modules: (1) a prompt embedding generator to derive prompt embeddings from image embeddings for accurate mask decoding, (2) a mask refiner to enhance SAM2's initial segmentation results, and (3) a mask classifier to categorize the generated masks. Additionally, we incorporate Deformable Global Attention Plugins (DGAP) into SAM2's image encoder. The DGAP enhances both the segmentation accuracy and the speed of the training process. Our method has been validated on the 3DTeethSeg benchmark, achieving an IoU of 91.90\% on high-resolution 3D teeth meshes, establishing a new state-of-the-art in the field.

3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation

Multimodal in-context learning (ICL) is emerging as a key capability that enables large vision-language models (LVLMs) to adapt to novel tasks without parameter updates, expanding their utility across various real-world applications. However, ICL remains unstable, even with well-matched in-context demonstrations (ICDs), suggesting that LVLMs struggle to fully utilize the provided context. While existing efforts focus on prompt engineering or post‑hoc logit calibration, we instead investigate the underlying attention dynamics to overcome LVLMs' inherent limitations. We identify two critical deficits in their self-attention that impair effective ICL. To bridge the gap, we propose \textbf{Context-Aware Modulated Attention} (CAMA), a plug-and-play and training-free method that dynamically modulates LVLM's attention logits based on the input in‑context sequence. CAMA employs a two-stage attention modulation to address both identified deficits, enhancing the focus on semantically significant tokens, particularly visual ones. Across four LVLMs and seven benchmarks, CAMA consistently outperforms vanilla models and baselines, demonstrating great effectiveness and generalization. It can also activate the desired effects of prompt engineering methods and remains robust under diverse sequence configurations. Thus, CAMA paves the way for deeper explorations of attention dynamics to advance multimodal reasoning.

Make LVLMs Focus: Context-Aware Attention Modulation for Better Multimodal In-Context Learning

We clarify the complexity of answering unions of conjunctive queries over knowledge bases formulated in the description logic $\mathcal{S}$, the extension of $\mathcal{ALC}$ with transitive roles. Contrary to what existing partial results suggested, we show that the problem is, in fact, 2ExpTime-complete; hardness already holds in the presence of two transitive roles and for Boolean conjunctive queries. We complement this result by showing that the problem remains in coNExpTime when the input query is rooted or is restricted to use at most one transitive role (but may use arbitrarily many non-transitive roles).

Revisiting Conjunctive Query Entailment for S

Despite the rapid progress of Vision Language Models (VLMs), existing benchmarks still concentrate on coarse-grained object recognition or simple relational reasoning, leaving the fine-grained and higher-order reasoning abilities of these systems largely unexamined. 
To bridge this critical evaluation gap, we introduce EmojiGrid, a novel diagnostic benchmark specifically designed to probe these fine-grained and higher-order skills. 
Leveraging the universal and semantically rich nature of emojis, we synthesize a grid‑based visual dataset paired with 29,000+ QA pairs.
Each pair is explicitly anchored in a three-level cognitive taxonomy comprising (i) Perception and Information Extraction, (ii) Relational and Structural Reasoning, and (iii) Abstraction and Advanced Cognition.
These dimensions further decompose into nine categories covering a broad range of cognitive skills, including counting, spatial relations, compositional logic, semantic sentiment, and related higher-order reasoning tasks.
Our extensive evaluation of 25 state-of-the-art open-source and proprietary VLMs reveals a significant performance gap between foundational perceptual tasks and higher-level cognitive abilities, particularly in abstraction and advanced emotional reasoning.
Notably, all models struggle with compositional logic, spatial consistency, and especially emotional and semantic understanding. 
EmojiGrid provides a quantifiable, fine-grained benchmark to diagnose VLM limitations and guides future progress toward models that can truly perceive, reason about, and interpret complex, symbol-rich visual scenes. The benchmark will be publicly released.

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