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Zero-shot classifier expansion aims to adapt existing model to new, unseen classes. It utilizes semantic descriptions of class attributes to learn a mapping from the semantic space to the classifier&#39;s weight space, without requiring new visual training data. However, the learning process for this mapping relies solely on the semantic-weight co-occurrence relationships observed on classes and lacks explicit modeling of inter-class differences, making it difficult for the model to capture the fundamental discriminative features required to define classification boundaries. To overcome this limitation, we reframe the problem from a causal perspective and introduce a novel framework driven by counterfactuals. Our method first generates factual descriptions alongside corresponding inter-class counterfactuals to pinpoint the causal attributes essential for classification, then refines these representations via a mutual purification process, and finally leverages a novel separation loss to explicitly push the factual and counterfactual classifier weights apart. This strategy compels the model to forge clearer and more discriminative classification boundaries. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods.

AAAI 2026

Counterfactual-Driven Zero-Shot Classifier Expansion

classifier expansion

counterfactual

zero-shot learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2021

Zero-shot classifier expansion aims to adapt existing model to new, unseen classes. It utilizes semantic descriptions of class attributes to learn a mapping from the semantic space to the classifier's weight space, without requiring new visual training data. However, the learning process for this mapping relies solely on the semantic-weight co-occurrence relationships observed on classes and lacks explicit modeling of inter-class differences, making it difficult for the model to capture the fundamental discriminative features required to define classification boundaries. To overcome this limitation, we reframe the problem from a causal perspective and introduce a novel framework driven by counterfactuals. Our method first generates factual descriptions alongside corresponding inter-class counterfactuals to pinpoint the causal attributes essential for classification, then refines these representations via a mutual purification process, and finally leverages a novel separation loss to explicitly push the factual and counterfactual classifier weights apart. This strategy compels the model to forge clearer and more discriminative classification boundaries. Extensive experiments on benchmark datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods.

technical paper

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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.

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)

Deep neural networks are increasingly vulnerable to physically deployable backdoor attacks, which manipulate real-world objects to induce targeted model failures. However, current physical backdoor attacks predominantly rely on perpetually visible triggers (e.g., glasses, stickers, mud) appended to target objects. These methods inevitably expose attack traces during the deployment phase, risking human suspicion prior to activation. In this paper, we propose a conditionally-visible physical backdoor attack, which can only be activated under specific optical conditions and thereby overcomes the risk of being detected after deployment and before the attack. Specifically, to ensure robust and reliable activation, we design irregular polygonal pattern as triggers to against across environmental variations (e.g., lighting, angles, and occlusions). Moreover, we introduce a dual-phase mechanism (dormant and activated) to enable stealthy deployment. Our trigger remains invisible and dormant under non-attack conditions, leaving no physical traces. It activates instantaneously under specific illumination, inducing the target model to perform the desired behavior. We conduct experiments on traffic sign recognition tasks to compare our attack with six digital backdoor attacks and seven physical attacks, and to evaluate its performance against ten potential defense methods. Extensive experimental results demonstrate the effectiveness, stealthiness, and robustness of our attack.

FRBAT: Conditionally-Visible Physical Backdoor Attack via Fluorescence

The proliferation of Large Language Models (LLMs) has established LLM routing as a standard service delivery mechanism, where users select models based on cost, Quality of Service (QoS), among other things.
However, optimal pricing in LLM routing platforms requires precise modeling for dynamic service markets, and solving this problem in real time at scale is computationally intractable.
In this paper, we propose \PriLLM, a novel practical and scalable solution for real-time dynamic pricing in competitive LLM routing.
\PriLLM models the service market as a Stackelberg game, where providers set prices and users select services based on multiple criteria.
To capture real-world market dynamics, we incorporate both objective factors (\eg~cost, QoS) and subjective user preferences into the model.
For scalability, we employ a deep aggregation network to learn provider abstraction that preserve user-side equilibrium behavior across pricing strategies.
Moreover, \PriLLM offers interpretability by explaining its pricing decisions.
Empirical evaluation on real-world data shows that \PriLLM achieves over 95\% of the optimal profit while only requiring less than 5\% of the optimal solution's computation time.

Pricing Online LLM Services with Data-Calibrated Stackelberg Routing Game

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically presuppose complete node attributes and structure information, a condition hardly satisfied in real-world scenarios owing to privacy, collection errors or dynamic node arrivals. Existing standard imputation schemes risk ``repairing'' rare anomalous nodes so that they appear normal, thereby introducing imputation bias into the detection process. In addition, when both node attributes and edges are missing simultaneously, estimation errors in one view can contaminate the other, causing cross-view interference that further undermines the detection performance. To overcome these challenges, we propose M$^2$V-UGAD, a multiple missing values–resistant unsupervised GAD framework on incomplete graphs. Specifically, a dual-pathway encoder is first proposed to independently reconstruct missing node attributes and graph structure, thereby preventing errors in one view from propagating to the other. The two pathways are then fused and regularized in a joint latent space so that normals occupy a compact inner manifold while anomalies reside on an outer shell. Lastly, to mitigate imputation bias, we sample latent codes just outside the normal region and decode them into realistic node features and subgraphs, providing hard negative examples that sharpen the decision boundary. Experiments on seven public benchmarks demonstrate that M$^2$V-UGAD consistently outperforms existing unsupervised GAD methods across varying missing rates.

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

The primate visual cortex exhibits topographic organization, where functionally similar neurons are spatially clustered, a structure widely believed to enhance neural processing efficiency. While prior works have demonstrated that conventional deep ANNs can develop topographic representations, these models largely neglect crucial temporal dynamics. This oversight often leads to significant performance degradation in tasks like object recognition and compromises their biological fidelity. To address this, we leverage spiking neural networks (SNNs), which inherently capture spike-based temporal dynamics and offer enhanced biological plausibility. We propose a novel Spatio-Temporal Constraints (STC) loss function for topographic deep spiking neural networks (TDSNNs), successfully replicating the hierarchical spatial functional organization observed in the primate visual cortex from low-level sensory input to high-level abstract representations. Our results show that STC effectively generates representative topographic features across simulated visual cortical areas. While introducing topography typically leads to significant performance degradation in ANNs, our spiking architecture exhibits a remarkably small performance drop (No drop in ImageNet top-1 accuracy, compared to a 3\% drop observed in TopoNet, which is the best-performing topographic ANN so far) and outperforms topographic ANNs in brain-likeness. We also reveal that topographic organization facilitates efficient and stable temporal information processing via the spike mechanism in TDSNNs, contributing to model robustness. These findings suggest that TDSNNs offer a compelling balance between computational performance and brain-like features, providing not only a framework for interpreting neural science phenomena but also novel insights for designing more efficient and robust deep learning models.

TDSNNs: Competitive Topographic Deep Spiking Neural Networks for Visual Cortex Modeling

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.

Wavelet Enhanced Adaptive Frequency Filter for Sequential Recommendation

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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