Singapore

Sequential recommendation has garnered significant attention for its ability to capture dynamic preferences by mining users’ historical interaction data. Given that users’ complex and intertwined periodic preferences are difficult to disentangle in the time domain, recent research is exploring frequency domain analysis to identify these hidden patterns. However, current frequency-domain-based methods suffer from two key limitations: (i) They primarily employ static filters with fixed characteristics, overlooking the personalized nature of behavioral patterns; (ii) While the global discrete Fourier transform excels at modeling long-range dependencies, it can blur non-stationary signals and short-term fluctuations. To overcome these limitations, we propose a novel method called Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation (WEARec). Specifically, it consists of two vital modules: dynamic frequency-domain filtering and wavelet feature enhancement. The former is used to dynamically adjust filtering operations based on behavioral sequences to extract personalized global information, and the latter integrates wavelet transform to reconstruct sequences, enhancing blurred non-stationary signals and short-term fluctuations. Finally, these two modules work synergistically to achieve comprehensive performance and efficiency optimization in long sequential recommendation scenarios. Extensive experiments on four widely-used benchmark datasets demonstrate the superiority of WEARec.

AAAI 2026

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

dmkm: rule mining & pattern mining

temporal or spatio-temporal data

dmkm: recommender systems

dmkm: mining of spatial

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.

technical paper

The Pickup-and-Delivery Problem with Time Windows (PDPTW)
is a time-constrained variant of the vehicle-routing
problem (VRP). Complex time constraints make it difficult
to solve using existing NCO methods. In this paper, we
present the Feasibility-Aware Masked Transformer
(FAM-Trans) specialized for PDPTW. FAM-Trans integrates a
lightweight side encoder with a context-aware embedding
scheme that effectively captures temporal dependencies. A
dynamic key-value module continuously updates node
embeddings as the route progresses. During inference, a
feasibility-guided post-inference filtering strategy
suppresses constraint violations without post-hoc repair.
Experiments on standard PDPTW benchmarks show that
FAM-Trans outperforms NCO baselines by 20~35% in solution
quality and constraint satisfaction.

Feasibility-Aware Masked Transformer for the Pickup-and-Delivery Problem with Time Windows (Student Abstract)

Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references. This enforces global topology and improves segmentation accuracy. Experiments on GlaS and CRAG demonstrate that TGC achieves state-of-the-art performance under 5–10% supervision and significantly narrows the gap to full supervision.

Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation (Student Abstract)

Current mainstream AI, at least as presented in media and measured by the number of people involved and papers published, is mainly about big data, deep learning, and recently trendy large language models. All these are techniques that are data-driven, model-free, and number-crunching. Their immense success in some areas, such as computer vision and natural language processing, started the next hype in the era of AI, which brings a question whether neural approaches, after being dismissed at the beginning of the AI era, finally conquered the world of AI and proved applicable to every problem. A deeper look at these new techniques shows they have similar issues as the old-fashioned AI techniques in the past: brittleness, making strange mistakes, and being highly dependent on data used for training. Moreover, there are problems with the explainability of results and no guarantees provided, which is a crucial issue in some application areas.

In this paper, we look at core principles of the neural ML techniques, that is, being data-driven rather than knowledge-based and being model-free rather than model-based, and we argue that symbolic knowledge models can still contribute to the design of trustworthy and explainable AI systems. Specifically, we focus on hierarchical reasoning, namely hierarchical planning, which is useful for highly complex problems but is not addressed by current neural models. We propose a research plan consisting of solving specific problems in hierarchical planning as an example of a knowledge-intensive approach to problem-solving. We show close connections between these problems that allow a smooth transition between solving techniques used to solve these problems. We also propose an ultimate goal of this endeavor, that is, autonomous construction of hierarchical planning models, that addresses the crucial problem of knowledge-based approaches -- how to obtain a formal model (extract knowledge from data).

On Trustworthy, Explainable, and Verifiable High-Level Autonomy via Hierarchical Planning

Unplanned extubation (UE)—the unintended removal of an airway tube—remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. 
Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. 
We propose AURA (Augmented Unplanned Removal Alert), a vision-based risk detection system developed and validated entirely on a fully synthetic ICU video dataset. 
By leveraging text-to-video diffusion, we generated diverse and clinically realistic scenarios capturing a range of patient behaviors and care contexts. 
The system applies pose estimation to identify two high-risk movement patterns: collision, defined as hand entry into spatial zones near airway tubes, and agitation, quantified by the velocity of tracked anatomical keypoints. 
Expert assessment validated the realism of the synthetic data, while quantitative evaluation demonstrated high collision detection accuracy and moderate agitation recognition performance. 
This work demonstrates a novel pathway for developing privacy-preserving, reproducible patient safety monitoring systems with potential for deployment in intensive care settings.

AURA: Development and Validation of an Augmented Unplanned Removal Alert System Using Synthetic ICU Videos

Vision-Language-Action (VLA) models have advanced in robotic manipulation, yet practical deployment remains hindered by two key limitations: **1) perceptual redundancy**, where irrelevant visual inputs are processed inefficiently, and **2) superficial instruction-vision alignment**, which hampers semantic grounding of actions.
In this paper, we propose **SemanticVLA**, a novel VLA framework that performs Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation. Specifically:
**1)** To sparsify redundant perception while preserving semantic alignment, **Semantic-guided Dual Visual Pruner (SD-Pruner)** performs: Instruction-driven Pruner (ID-Pruner) extracts global action cues and local semantic anchors in SigLIP; Spatial-aggregation Pruner (SA-Pruner) compacts geometry-rich features into task-adaptive tokens in DINOv2.
**2)** To exploit sparsified features and integrate semantics with spatial geometry, **Semantic-complementary Hierarchical Fuser (SH-Fuser)** fuses dense patches and sparse tokens across SigLIP and DINOv2 for coherent representation.
**3)** To enhance the transformation from perception to action, **Semantic-conditioned Action Coupler (SA-Coupler)** replaces the conventional observation-to-DoF approach, yielding more efficient and interpretable behavior modeling for manipulation tasks.
Extensive experiments on simulation and real-world tasks show that SemanticVLA sets a new SOTA in both performance and efficiency. SemanticVLA surpasses OpenVLA on LIBERO benchmark by **21.1%** in success rate, while reducing training cost and inference latency by **3.0×** and **2.7×**.

SemanticVLA: Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation

Perceptual image compression has recently gained increasing attention, as it aims to reconstruct visually realistic images using generative models. Most existing methods adopt patch-based generative adversarial networks (PatchGAN) for one-step image generation, where adversarial training helps the decoder learn the distribution of natural images. However, this strategy is often coarse-grained, as it focuses mainly on patch-level consistency and overlooks global structural and semantic details. To address this limitation, we propose a simple yet effective Semantic and Spectral Consistency Learning (SSCL) strategy, which complements existing patch-based approaches for more accurate distribution alignment. For semantic consistency, we leverage semantic vision models to extract semantic features. The semantic discriminator, aware of the specific semantics of each image, provides more adaptive and precise feedback. This guides the encoder to retain meaningful information and helps the decoder synthesize detailed textures, without requiring explicit semantic transmission or additional modules. For spectral consistency, we introduce a frequency discriminator that focuses on high-frequency components, helping to reduce artifacts based on spectral priors. Experiments show that SSCL outperforms existing perceptual codecs in terms of visual quality. Compared to MS-ILLM, SSCL achieves 45% to 60% bit-rate savings on CLIC2020 and Kodak datasets, measured by FID and DISTS.

SSCL: Adversarially Guided Image Compression via Semantic and Spectral Consistency Learning

We introduce the notion of contrastive ABox explanations to answer questions of the type “Why is a an instance of C, but b is not?” While there are various approaches for explaining positive entailments (why is C(a) entailed by the knowledge base) as well as missing entailments (why is C(b) not entailed) in isolation, contrastive explanations consider both at the same time, which allows them to focus on the relevant similarities and differences between a and b. We discuss and develop an appropriate notion of contrastive explanations for the special case of ABox reasoning within description logics and then analyze the computational complexity for different variants under different optimality criteria, considering light-weight as well as more expressive description logics. We furthermore present a practical implementation for computing contrastive explanations and evaluate it on constructed problems with realistic ontologies.

Can You Tell the Difference? Contrastive Explanations for ABox Entailments

Blurry video super-resolution (BVSR) remains fundamentally ill-posed due to the simultaneous loss of high-frequency spatial details and reliable motion cues in blurry low-resolution frames. While cascade-based and joint BVSR methods struggle under severe blur, existing event-guided VSR approaches largely assume clean inputs and are ineffective against complex motion degradation. These methods fail to model blurry representations or leverage event signals for blur-aware motion cues, leading to sub-optimal performance. We propose BluR-EVSR, a unified framework that implicitly models Blurry Representations and leverages Event cameras to jointly address both blur and resolution degradation for VSR. The framework begins with a self-supervised degradation learning strategy guided by event streams and neighboring frames, enabling adaptive blur representation without requiring explicit supervision. A dynamic routing mechanism encodes spatially varying degradations, while a motion-saliency degradation-aware attention module injects motion saliency priors to facilitate efficient RGB-event fusion. Integrated into a bidirectional recurrent framework, BluR-EVSR enables temporally consistent and detail-preserving restoration with low computational cost. Extensive experiments across multiple benchmarks show that our method significantly outperforms prior BVSR and event-based approaches.

Exploiting Blurry Representations for Event-guided Video Super-Resolution

With the increasing commercialization of latent diffusio-based text-to-audio generation, model attribution has become a critical challenge. Embedding watermarks in generated audio is an effective way to distinguish synthetic audio from natural audio. However, existing watermarking methods often suffer from limited robustness or require additional training, restricting their scalability in practical applications. In this paper, we propose an anchor-based inversion optimization framework. The method embeds a watermark into the model's initial latent vector, designated as a pivotal anchor, and extracts the watermark through inversion. To mitigate error accumulation and enhance robustness during inversion, we exploit the temporal consistency and distributional similarity of diffusion models, formulating watermark extraction as a time-series optimization problem. Specifically, given a suspicious audio sample and a candidate model with a predefined anchor, we first perform unguided denoising diffusion on the anchor to generate an intermediate latent trajectory as the anchor sequence. Then, we optimize the inversion process to align the inverted trajectory with the anchor sequence, thereby reducing accumulated errors. During optimization, we adopt Soft Dynamic Time Warping as the loss function. Its flexible temporal alignment capability ensures that correct attribution can be achieved only when the anchor matches the target audio. Experimental results show that our method enables training-free attribution while preserving audio quality and achieves an average attribution accuracy of 99\%, demonstrating strong robustness.

Anchor Watermark: Robust Attribution for Diffusion-based Text-to-Audio Model

As large language models (LLMs) grow more capable, they face growing vulnerability to sophisticated jailbreak attacks. While developers invest heavily in alignment finetuning and safety guardrails, researchers continue publishing novel attacks—driving progress through adversarial iteration. This dynamic mirrors a strategic game of continual evolution. However, two major challenges hinder jailbreak development: the high cost of querying top-tier LLMs and the short lifespan of effective attacks due to frequent safety updates. These factors limit cost-efficiency and practical impact. To address this, we propose MetaCipher, a low-cost, multi-agent jailbreak framework that generalizes across LLMs with varying safety measures. Using reinforcement learning, MetaCipher is modular and adaptive, supporting extensibility to future strategies. Within as few as 10 queries, MetaCipher achieves state-of-the-art attack success rates on recent malicious prompt benchmarks, outperforming prior jailbreak methods. We conduct a large-scale empirical evaluation across diverse victim models, demonstrating its robustness and adaptability.

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Next from AAAI 2026

Feasibility-Aware Masked Transformer for the Pickup-and-Delivery Problem with Time Windows (Student Abstract)

Similar lecture

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation