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Sampling algorithms play a pivotal role in probabilistic AI. However, verifying if a sampler program indeed samples from the claimed distribution is a notoriously hard problem. Provably correct testers like Barbarik,Teq,Flash, Cubeprobe for testing of different kinds of samplers were proposed only in the last few years. All these testers focus on the worst-case efficiency, and do not support verification of samplers over infinite domains, a case occurring frequently in Astronomy, Finance, Network Security etc.

In this work, we design the first tester of samplers with instance-dependent efficiency, allowing us to test samplers over natural numbers. Our tests are developed via a novel distance estimation algorithm between an unknown and a known probability distribution using an &#39;interval conditioning&#39; framework. The core technical contribution is a new connection with probability mass estimation of a continuous distribution. The practical gains are also substantial—our experiments establish up to 1000× speedup over state-of-the-art testers.

AAAI 2026

Instance Dependent Testing of Samplers Using Interval Conditioning

Sampling algorithms play a pivotal role in probabilistic AI. However, verifying if a sampler program indeed samples from the claimed distribution is a notoriously hard problem. Provably correct testers like Barbarik,Teq,Flash, Cubeprobe for testing of different kinds of samplers were proposed only in the last few years. All these testers focus on the worst-case efficiency, and do not support verification of samplers over infinite domains, a case occurring frequently in Astronomy, Finance, Network Security etc.

In this work, we design the first tester of samplers with instance-dependent efficiency, allowing us to test samplers over natural numbers. Our tests are developed via a novel distance estimation algorithm between an unknown and a known probability distribution using an 'interval conditioning' framework. The core technical contribution is a new connection with probability mass estimation of a continuous distribution. The practical gains are also substantial—our experiments establish up to 1000× speedup over state-of-the-art testers.

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.<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.

Self-supervised 3D point cloud understanding is crucial for scene understanding, where Masked Autoencoders (MAE) have achieved excellent performance in point cloud representation learning. However, existing MAE-style methods fail to consider spatial-semantic variations in masking strategies, and joint learning with multi-view images often overlooks view redundancy. To address these challenges, we propose an MAE framework enhanced with reliable multi-view 2D-3D \textbf{K}ey-part alignment and \textbf{R}einforced masking, named as \textbf{KR-MAE}. Our approach comprises three key innovations: Reinforced Masking (RM) strategically samples visible tokens based on semantic saliency to enhance reconstruction fidelity; Reliable Multi-View Selector (RVS) dynamically refines the most informative image subset by filtering occluded or low-texture views, mitigating detrimental redundancy; Reliable-view 2D-3D Key-part Aligned Transformer (KAT) establishes semantic-aligned correspondence between salient 3D point cloud parts and reliable multi-view 2D image patches, leveraging rich texture cues from 2D images to compensate for sparse geometry in point cloud. Extensive experiments on 3D classification and segmentation benchmarks demonstrate that KR-MAE achieves state-of-the-art performance, surpassing prior multi-modal methods.

Reliable-View 2D-3D Key-Part Aligned Transformer with Reinforced Masking for 3D Point Cloud Understanding

Spatial multi-modal omics technologies have transformed biological research by enabling the simultaneous profiling of gene expression, protein abundance, and chromatin accessibility within their native spatial contexts. Despite these advances, accurately clustering rare cell types remains a major challenge due to data sparsity, high dimensionality, and limited annotated samples. While Graph Neural Networks (GNNs) have shown potential in modeling spatial omics data, their effectiveness is often constrained by the use of fixed K-nearest neighbor (KNN) graph structures, which fail to capture latent semantic relationships masked by sequencing noise. To overcome these limitations, we propose CRCT (Clustering Rare Cell Types): a novel framework that combines Implicit Semantic Data Augmentation (ISDA) with adaptive graph learning for spatial multi-modal omics analysis. Unlike traditional augmentation strategies that generate explicit synthetic samples, CRCT operates in the deep feature space by dynamically estimating intra-class covariance matrices and implicitly perturbing features along semantically meaningful directions. This enables effective augmentation for rare cell populations while preserving biological fidelity. Extensive experiments across four real-world datasets (HLN, MB, Stereo‑CITE‑seq, and SPOTS) and one synthetic benchmark demonstrate the state-of-the-art performance of CRCT, achieving improvements of up to +1.7 NMI and +7.8 ARI over strong baseline methods.

Learning to Cluster Rare Cell Types: Implicit Semantic Data Augmentation for Spatial Multi-modal Omics Analysis

Clustering approaches that utilize convex loss functions have recently attracted growing interest in the formation of compact data clusters. Although classical methods like $k-$means and its wide family of variants are still widely used, all of them require the number of clusters ($k$) to be supplied as input and many are notably sensitive to initialization. Convex clustering provides a more stable alternative by formulating the clustering task as a convex optimization problem, ensuring a unique global solution. However, it faces challenges in handling high-dimensional data, especially in the presence of noise and outliers. Additionally, strong fusion regularization, controlled by the tuning parameter, can hinder effective cluster formation within a convex clustering framework. To overcome these challenges, we introduce a robust approach that integrates convex clustering with the Median of Means (MoM) estimator, thus developing an outlier-resistant and efficient clustering framework that does not necessitate a prior knowledge of the number of clusters. By leveraging the robustness of MoM alongside the stability of convex clustering, our method enhances both performance and efficiency, especially on large-scale datasets. Theoretical analysis demonstrates weak consistency under specific conditions, while experiments on synthetic and real-world datasets validate the method’s superior performance compared to existing approaches.

Convex Clustering Redefined: Robust Learning with the Median of Means Estimator

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities, yet aligning their outputs with human preferences typically requires expensive supervised fine-tuning. In this paper, we introduce a novel paradigm—Textual Network—which enables test-time preference optimization (TPO) without any parameter updates. Unlike traditional numerical or gradient-based alignment methods, our approach operates entirely in the space of natural language, where both the attention mechanism and output refinement are realized through LLM-interpretable textual modules. Our proposed Textual Self-Attention Network (TSAN) emulates the core principles of self-attention by constructing a latent Q-K-V-style Textual Network: (1) candidate responses are scored and formatted as textual keys and values, (2) an LLM-based attention module interprets their relevance to the user query in natural language, and (3) a textual aggregator synthesizes a new, preference-aligned response guided by the learned attention. All components are running in the textual gradient space, enabling iterative optimization with interpretable updates and no gradient backpropagation through model weights. Empirical evaluations of instruction following, alignment, security, and mathematical reasoning tasks show that TSAN equipped with TSAN outperforms supervised models such as Llama-3.1-70B-Instruct and outperforms the state-of-the-art reasoning alignment method, TPO, after just a few test time iterations on the base SFT model.

Textual Self-Attention Network: Test-Time Preference Optimization Through Textual Gradient-Based Attention

Privacy concerns have long been a critical issue in AI models. With the rapid advancement of generative AI, the privacy awareness of models has drawn attention, raising new challenges for privacy protection that is independent of data and tasks. This paper introduces a novel framework for enhancing privacy protection through directional steering in representation space, which seamlessly integrates with both language and vision-language models. Specifically, we first construct a comprehensive privacy-related dataset based on the Solove taxonomy of privacy. Then, we leverage this dataset to enhance model privacy awareness in the representation space, steering the model to protect privacy during inference. Experiments on 12 models validate the effectiveness and generalization of our method. Moreover, we demonstrate the transferability of privacy-enhanced representations between same-source large language models (LLMs) and vision-language models (VLMs), offering a scalable solution for privacy protection in frontier AI models.

Steering Representations, Safeguarding Privacy: A Cross-Modal Privacy Protection Method for Generative AI

Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency. Existing efficient reasoning methods typically require accurate task assessment to preset token budgets or select reasoning modes, which limits their flexibility and reliability. In this work, we revisit the essence of overthinking and identify that encouraging effective steps while penalizing ineffective ones is key to its solution. To this end, we propose a novel rule-based verifiable stepwise reward mechanism (VSRM), which assigns rewards based on the performance of intermediate states in the reasoning trajectory. This approach is intuitive and naturally fits the step-by-step nature of reasoning tasks. We conduct extensive experiments on standard mathematical reasoning benchmarks, including AIME24 and AIME25, by integrating VSRM with PPO and Reinforce++. Results show that our method achieves substantial output length reduction while maintaining original reasoning performance, striking an optimal balance between efficiency and accuracy. Further analysis of overthinking frequency and pass@k score before and after training demonstrates that our approach indeed effectively suppresses ineffective steps and encourages effective reasoning, fundamentally alleviating the overthinking problem.

Promoting Efficient Reasoning with Verifiable Stepwise Reward

Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles. Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions. We validated our framework using the D$^4$ dataset. First, we created a public ECAs dataset through batch simulations based on D$^4$. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines.

An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling

Understanding human emotions from an image is a challenging yet essential task for vision-language models. While recent efforts have fine-tuned vision-language models to enhance emotional awareness, most approaches rely on global visual representations and fail to capture the nuanced, multi-faceted nature of emotional cues. Furthermore, most existing approaches adopt instruction tuning, which requires costly dataset construction and involves training a large number of parameters, thereby limiting scalability and efficiency. To address these challenges, we propose MASP, a novel framework for Multi-Aspect guided emotion reasoning with Soft Prompt tuning in vision-language models. MASP explicitly separates emotion-relevant visual cues via multi-aspect cross-attention modules and guides the language model using soft prompts, enabling efficient and scalable task adaptation without modifying the base model. Our method achieves state-of-the-art performance on various emotion recognition benchmarks, demonstrating that explicit modeling of multi-aspect emotional cues with soft prompt tuning leads to more accurate and interpretable emotion reasoning in vision-language models.

MASP: Multi-Aspect Guided Emotion Reasoning with Soft Prompt Tuning In Vision-Language Models

Centralized training with decentralized execution (CTDE) is a framework for MARL with wide applications.
In CTDE, agents leverage global state information during training to mitigate the non-stationarity of the MARL environment, but rely solely on partial observations during execution. 
Recent work has highlighted the growing importance of inter-agent communication for more effective learning and coordination. 
However, most existing methods overlook the fact that real-world communication channels are often bandwidth-constrained and imperfectly reliable.
Toward more communication-efficient and robust MARL, we extend the conventional CTDE framework with an information hub.
The hub collects local observations from the agents to restore the global states, which are then delivered to the agents on demand.
To this end, technical mechanisms are designed to enable effective global reconstruction with incomplete observations, as well as agent-specific attention to the reconstructed global information.
Experiments on multiple cooperative MARL benchmarks demonstrate that our method achieves state-of-the-art performance compared to popular MARL algorithms while substantially reducing communication overhead and exhibiting strong robustness under imperfect communication channels.

Communication-efficient Multi-Agent Reinforcement Learning with Spatiotemporal Information Hub

We study online interval scheduling in the irrevocable setting, where each interval must be immediately accepted or rejected upon arrival. The objective is to maximize the total length of accepted intervals while ensuring that no two accepted intervals overlap. We consider this problem in a learning-augmented setting, where the algorithm has access to (machine-learned) predictions. The goal is to design algorithms that leverage these predictions to improve performance while maintaining robust guarantees in the presence of prediction errors.

Our main contribution is the SemiTrust-and-Switch framework, which provides a unified approach for combining prediction-based and classical interval scheduling algorithms. This framework applies to both deterministic and randomized algorithms and captures the trade-off between consistency (performance under accurate predictions) and robustness (performance under adversarial inputs). Moreover, we provide lower bounds, proving the tightness of this framework in particular settings.

We further design a randomized algorithm that smoothly interpolates between prediction-based and robust algorithms. This algorithm achieves both robustness and smoothness—its performance degrades gracefully with the quality of the prediction.

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Reliable-View 2D-3D Key-Part Aligned Transformer with Reinforced Masking for 3D Point Cloud Understanding

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Next from AAAI 2026

Reliable-View 2D-3D Key-Part Aligned Transformer with Reinforced Masking for 3D Point Cloud Understanding

Stay up to date with the latest Underline news!

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CONFERENCES

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RESOURCES

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