Singapore

Outlier detection (OD) aims to identify abnormal instances, known as outliers or anomalies, by learning typical patterns of normal data, or inliers. Performing OD under an unsupervised regime--without any information about anomalous instances in the training data--is challenging. A recently observed phenomenon, known as the $\textit{inlier-memorization (IM) effect}$, where deep generative models (DGMs) tend to memorize inlier patterns during early training, provides a promising signal for distinguishing outliers. However, existing unsupervised approaches that rely solely on the IM effect still struggle when inliers and outliers are not well-separated or when outliers form dense clusters. To address these limitations, we incorporate $\textit{active learning}$ to selectively acquire informative labels, and propose $\textit{IMBoost}$, a novel framework that explicitly reinforces the IM effect to improve outlier detection. Our method consists of two stages: 1) a $\textit{warm-up}$ phase that induces and promotes the IM effect, and 2) a $\textit{polarization}$ phase in which actively queried samples are used to maximize the discrepancy between inlier and outlier scores. In particular, we propose a novel query strategy and tailored loss function in the polarization phase to effectively identify informative samples and fully leverage the limited labeling budget. We provide a theoretical analysis showing that the IMBoost consistently decreases inlier risk while increasing outlier risk throughout training, thereby amplifying their separation. Extensive experiments on diverse benchmark datasets demonstrate that IMBoost not only significantly outperforms state-of-the-art active OD methods but also requires substantially less computational cost.

AAAI 2026

Memorize Early, Then Query: Inlier-Memorization-Guided Active Outlier Detection

im effect

outlier detection

deep generative models

active learning

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/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

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.



poster

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.

Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can represent task patterns and surface-level concepts, it remains unclear whether these models can encode high-level relational concepts and apply them to novel situations through structured comparisons. In this work, we explore this fundamental aspect using proportional and story analogies, and identify three key findings. First, LLMs effectively encode the underlying relationships between analogous entities; both attributive and relational information propagate through mid-upper layers in correct cases, whereas reasoning failures reflect missing relational information within these layers. Second, unlike humans, LLMs often struggle not only when relational information is missing, but also when attempting to apply it to new entities. In such cases, strategically patching hidden representations at critical token positions can facilitate information transfer to a certain extent. Lastly, successful analogical reasoning in LLMs is marked by strong structural alignment between analogous situations, whereas failures often reflect degraded or misplaced alignment. Overall, our findings reveal that LLMs exhibit emerging but limited capabilities in encoding and applying high-level relational concepts, highlighting both parallels and gaps with human cognition.

The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models

While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP ($\textbf{Path}$ology $\textbf{F}$ine-grained $\textbf{L}$anguage-$\textbf{I}$mage $\textbf{P}$retraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level sub-captions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in research and clinical practice.

PathFLIP: Fine-grained Language-Image Pretraining for Versatile Computational Pathology

Sequential recommendation models analyze user historical behavior sequences to capture temporal dependencies and the dynamic evolution of interests, enabling accurate predictions of future behaviors. However, there are still two critical challenges that remain unsolved: i) Inadequate temporal modeling of user intent, which fails to distinguish between global intent tendency and temporal contextual intent. ii) Noise in sequential interaction data may introduce bias into the model. To address these issues, we propose a Self-Supervised Hypergraph Sequential Recommendation Framework (S$^2$HyRec). This framework features the Global Intent Tendency module for capturing long-term preferences, the Temporal Contextual Intent module for modeling dynamic time-sensitive interests. Additionally, we develop the Sequence Dependency-Aware module that analyzes the chronological flow of interactions to uncover inherent behavioral dynamics, further enriching the comprehensive user intent representation. To mitigate noisy interactions, we employ a Cross-View Self-Supervised Learning module that enhances the model's ability to distinguish genuine preferences from noise. Extensive experiments on four benchmark datasets demonstrate the superiority of S$^2$HyRec over various state-of-the-art recommendation methods, especially achieving average improvements of 15.13\% and 14.03\% in NDCG@10 and NDCG@20, respectively, across the four datasets. The code is provided in the Appendix.

S²HyRec: Self-Supervised Hypergraph Sequential Recommendation

Vision-language models (VLMs) pre-trained on natural image and language data, such as CLIP, have exhibited significant potential in few-shot image recognition tasks, leading to development of various efficient transfer learning methods. These methods exploit inherent pre-learned knowledge in VLMs and have achieved strong performance on standard image datasets. However, their effectiveness is often limited when confronted with cross-domain tasks where imaging domains differ from natural images. To address this limitation, we propose Consistency-guided Multi-view Collaborative Optimization (CoMuCo), a novel fine-tuning strategy for VLMs. This strategy employs two functionally complementary expert modules to extract multi-view features, while incorporating prior knowledge-based consistency constraints and information geometry-based consensus mechanisms to enhance the robustness of feature learning. Additionally, a new cross-domain few-shot benchmark is established to help comprehensively evaluate methods on imaging domains distinct from natural images. Extensive empirical evaluations on both existing and newly proposed benchmarks suggest CoMuCo consistently outperforms current methods in few-shot tasks. The code and benchmark are available at https://github.com/kaderxon/CoMuCo.

Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models

We address the task of universal compressed image restoration, which involves recovering high-quality images degraded by a wide range of codecs and compression levels. While prior methods have made significant progress, they typically target specific degradation types and struggle to generalize across both traditional and learning-based codecs. To overcome this limitation, we propose a unified framework that leverages codec-aware conditioning and reinforcement learning-based fine-tuning. Specifically, we introduce a conditioning module that encodes both codec type and compression level, enabling the restoration network to adapt its behavior to diverse degradation settings. To further improve generalization, we incorporate reward-based objectives during fine-tuning, providing complementary signals that enhance training across both conventional and learned compression schemes. Experimental results demonstrate the effectiveness of our method in restoring images across a wide range of compression artifacts and scenarios.

Universal Compressed Image Restoration via Codec-Aware Conditioning with Reinforcement Learning

Large Language Models (LLMs) have shown remarkable success on a wide range of math and reasoning benchmarks. However, we observe that they often struggle when faced with unreasonable math problems. Instead of recognizing these issues, models frequently proceed as if the problem is well-posed, producing incorrect answers or falling into overthinking and verbose self-correction. To systematically investigate this overlooked vulnerability, we propose the Unreasonable Math Problems (UMP) benchmark, designed to evaluate LLMs' ability to detect and respond to unreasonable math problem statements. Based on extensive experiments covering 19 LLMs, we find that even state-of-the-art general models like GPT-4o struggle on UMP. While reasoning models such as DeepSeek-R1 demonstrate a higher sensitivity to unreasonable inputs, this often comes at the cost of generating overly long and meaningless responses that fail to converge. We further find that prompting and fine-tuning enhance the detection of unreasonable inputs, with minor and acceptable trade-offs, making them practical solutions in this challenging setting.

Large Language Models Struggle with Unreasonability in Math Problems

Multi-armed bandit algorithms are fundamental tools for sequential decision-making under uncertainty, with widespread applications across domains such as clinical trials and personalized decision-making. As bandit algorithms are increasingly deployed in these socially sensitive settings, it becomes critical to protect user data privacy and ensure fair treatment across decision rounds. While prior work has independently addressed privacy and fairness in bandit settings, the question of whether both objectives can be achieved simultaneously has remained largely open. Existing privacy-preserving bandit algorithms typically optimize average regret, a utilitarian measure, whereas fairness-aware approaches focus on minimizing Nash regret, which penalizes inequitable reward distributions, but often disregard privacy concerns.

To bridge this gap, we introduce Differentially Private Nash Confidence Bound (DP-NCB)—a novel and unified algorithmic framework that simultaneously ensures $\epsilon$-differential privacy and achieves order-optimal Nash regret, matching known lower bounds up to logarithmic factors. The framework is sufficiently general to operate under both global and local differential privacy models, and is anytime, requiring no prior knowledge of the time horizon. We support our theoretical guarantees with simulations on synthetic bandit instances, showing that DP-NCB incurs substantially lower Nash regret than state-of-the-art baselines. Our results offer a principled foundation for designing bandit algorithms that are both privacy-preserving and fair, making them suitable for high-stakes, socially impactful applications.

DP-NCB: Privacy Preserving Fair Bandits

We introduce SampurNER, a fine-grained named entity recognition (FgNER) dataset encompassing all 22 scheduled Indian languages spoken by more than two billion people across various countries. While manual annotation for FgNER resources is often labor-intensive and expensive, distant supervision methods have been employed as a viable solution. However, such datasets are often noisy, with entity mentions tagged with multiple types, requiring computationally intensive noise-aware models for effective FgNER. Moreover, resources for both coarse-grained and fine-grained named entity recognition tasks in Indian languages remain scarce. To address this, we propose an entity-anchored machine translation framework that leverages the largest manually annotated English FgNER dataset, FewNERD, to create a large-scale FgNER dataset in 22 languages. On average, the dataset comprises over 153k sentences, 354k entities, and 3.3M tokens in each language. The languages covered are: Assamese (as), Bengali (bn), Bodo (brx), Dogri (doi), Gujarati (gu), Hindi (hi), Kannada (kn), Kashmiri (ks), Konkani (gom), Maithili (mai), Malayalam (ml), Manipuri (mni), Marathi (mr), Nepali (ne), Odia (or), Punjabi (pa), Sanskrit (sa), Santali (sat), Sindhi (sd), Tamil (ta), Telugu (te), and Urdu (ur). Various rigorous analyses and human evaluations confirm the high quality of the dataset and demonstrate the effectiveness of the entity-anchored machine translation framework with up to 9% increase in F1-score against the current state-of-the-art. Additionally, we extend our analysis to zero-shot, multilingual, and cross-lingual settings, investigating the influence of language family and script similarity on cross-lingual FgNER performance.

SampurNER: Fine-Grained Named Entity Recognition Dataset for 22 Indian Languages

Integrating LiDAR and camera information in the bird’s eye view (BEV) representation has demonstrated its effectiveness in 3D object detection. However, owing to fundamental disparity in geometric and localization accuracy between these sensors, indiscriminate fusion in previous methods often leads to degraded performance. In this paper, we propose BEVDilation, a novel LiDAR-centric framework that prioritizes LiDAR information in the fusion. By formulating image BEV features as implicit guidance rather than naive concatenation, our strategy effectively alleviates the spatial misalignment caused by image depth estimation errors. Furthermore, the effective image guidance allows the LiDAR-centric paradigm to address the sparsity and semantic limitations of point clouds. Specifically, we propose a Sparse Voxel Dilation Block that mitigates the inherent point sparsity by densifying foreground voxel features through image priors. Moreover, we introduce a Semantic-Guided BEV Dilation Block to enhance the LiDAR feature diffusion processing with image semantic guidance and long-range context capture. On the challenging nuScenes benchmark, BEVDilation achieves better performance than state-of-the-art methods while maintaining competitive computational efficiency. Importantly, our LiDAR-centric strategy demonstrates greater robustness to depth noise compared to naive fusion. Our code will be released.

BEVDilation: LiDAR-Centric Multi-Modal Fusion for 3D Object Detection

Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficiency, existing methods remain unsuitable for satellite images due to incompatibility with rational polynomial coefficient (RPC) models and limited generalization capability. Recent advances in generalizable 3DGS approaches show potential, but they perform poorly on multi-temporal sparse satellite images due to limited geometric constraints, transient objects, and radiometric inconsistencies. To address these limitations, we propose SkySplat, a novel self-supervised framework that integrates the RPC model into the generalizable 3DGS pipeline, enabling more effective use of sparse geometric cues for improved reconstruction. SkySplat relies only on RGB images and radiometric-robust relative height supervision, thereby eliminating the need for ground-truth height maps. Key components include a Cross-Self Consistency Module (CSCM), which mitigates transient object interference via consistency-based masking, and a multi-view consistency aggregation strategy that refines reconstruction results. Compared to per-scene optimization methods, SkySplat achieves an 86 times speedup over EOGS with higher accuracy. It also outperforms generalizable 3DGS baselines, reducing MAE from 13.18 m to 1.80 m on the DFC19 dataset significantly, and demonstrates strong cross-dataset generalization on the MVS3D benchmark.

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