Singapore

Image forgery localization aims to precisely identify tampered regions within images, but it commonly depends on costly pixel-level annotations. To alleviate this annotation burden, weakly supervised image forgery localization (WSIFL) has emerged, yet existing methods still achieve limited localization performance as they mainly exploit intra-image consistency clues and lack external semantic guidance to compensate for weak supervision. In this paper, we propose ViLaCo, a vision-language collaborative reasoning framework that introduces auxiliary semantic supervision distilled from pre-trained vision-language models (VLMs), enabling accurate pixel-level localization using only image-level labels. Specifically, ViLaCo first incorporates semantic knowledge through a vision-language feature modeling network, which jointly extracts textual and visual priors using pre-trained VLMs. Next, an adaptive vision-language reasoning network aligns textual semantics and visual features through mutual interactions, producing semantically aligned representations. Subsequently, these representations are passed into dual prediction heads, where the coarse head performs image-level classification and the fine head generates pixel-level localization masks, thereby bridging the gap between weak supervision and fine-grained localization. Moreover, a contrastive patch consistency module is introduced to cluster tampered features while separating authentic ones, facilitating more reliable forgery discrimination.
Extensive experiments on multiple public datasets demonstrate that ViLaCo substantially outperforms existing WSIFL methods, achieving state-of-the-art performance in both detection and localization accuracy.

AAAI 2026

Weakly-Supervised Image Forgery Localization via Vision-Language Collaborative Reasoning Framework

app: misinformation & fake news app: security cv: applications

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.

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.

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

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

In collaborative filtering, learning effective embeddings for users and items from interaction data remains a central challenge. While recent efforts leverage large language models (LLMs) to enhance collaborative filtering, two critical limitations persist: (1) Efficiency: LLM-based inference is significantly slower than traditional embedding-based search; and (2) Topological Modeling: LLMs struggle to capture graph structures, which are essential for modeling multi-order user-item interactions. To address these limitations, we propose New Language Collaborative Filtering (NLCF), a framework that aligns LLMs with collaborative filtering by conceptualizing user-item graphs as new languages. This approach is based on two key insights: (1) LLMs excel at mastering new languages when trained on suitable corpora, and (2) the empirical conditional probability between tokens in corpora converges to the transition probabilities between nodes in graphs. NLCF translates user-item graphs into corpora, where users and items are treated as tokens. These corpora are used to fine-tune LLMs, and the learned representations are aggregated to construct user and item embeddings that encode multi-order interactions. Unlike methods that deploy LLMs for inference, NLCF distills LLM knowledge learned from corpora into compact embeddings, enabling both efficient training and real-time inference. The framework has been deployed on a billion-scale e-commerce platform for several months. Extensive experiments demonstrate that NLCF outperforms traditional graph CF models and LLM-based baselines while achieving significant training and inference efficiency improvement over LLM-based baselines.

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