Singapore

In recommender systems, recent advances highlight the critical role of alignment and uniformity (AU) in representation learning. Specifically, AU-based methods pull positive user-item pairs closer (alignment) and spread the overall representation distribution (uniformity), typically relying on observed positive samples. Despite their effectiveness, exist methods face two limitations: (1) noise issues have a more severe impact on AU-based methods in the absence of negative samples, leading to the capture of spurious signals such as misclicks or non-preferential behaviors; (2) data sparsity weakens the alignment of user-item representations, hindering reliable representation learning and harming recommendations for sparse users. To tackle these issues, we propose a novel recommendation framework named Constrained Alignment and Filtered Uniformity (CAFU). CAFU enhances robustness through Filtered Uniformity (FU) and improves performance under data sparsity via Constrained Alignment (CA). Specifically, FU adopts a threshold-based strategy to eliminate unreliable samples that degrade embedding quality, thereby strengthening robustness. In parallel, CA mitigates the impact of sparsity by masking low-confidence user-item pairs based on angular distance, leading to better recommendation for sparse users. Extensive experiments on three datasets and three backbones demonstrate the effectiveness and generalization of the proposed framework.

AAAI 2026

CAFU: Constrained Alignment and Filtered Uniformity for Denoising Recommendation

implicit feedback

denoising

recommender systems

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.

Neuroscientific evidence reveals that human visual recognition is not an instantaneous event but a hierarchical process, where the brain constructs a holistic perception by progressively integrating simple features like edges or texture into complex scenes. Ensemble learning successfully operationalizes this principle, yet existing methods typically integrate models at the decision level, neglecting the rich, complementary information within the feature space itself and thus fundamentally limiting their potential. To address this, we introduce Synergistic Semantic Boosting (S²-Boosting), a framework that first employs a self-supervised hierarchical semantic learning module to decompose an image into complementary, semantically meaningful parts autonomously. These parts guide a boosting procedure where a sequence of specialized learners, each focusing on a specific partition, collaboratively corrects the ensemble's errors. We further present encouraging results on real-world image datasets, highlighting the intrinsic interpretability, paving the way for more robust and transparent models. The code will be available at https://anonymous.4open.science/r/S-2Boosting-0D47/.

S2-Boost: Synergistic Semantic Boosting for Coarse-to-Fine Ensemble Learning

Higher-order Graph Neural Networks (HOGNNs), particularly those based on the 2-Folklore Weisfeiler-Leman (2-FWL) framework, achieve superior expressive power by modeling 2- and 3-node interactions, enabling fine-grained capture of graph structure beyond standard GNNs. However, their $\mathcal{O}(n^3)$ computational and memory complexity limits scalability, prompting efficiency-focused methods that often sacrifice expressivity. We propose **Co-Sparsify**, a connectivity-aware sparsification framework that removes only computation redundant for expressivity, preserving full 2-FWL discriminative power while significantly improving efficiency. Our key insight is that 3-node interactions are necessary only within *biconnected components*---maximal subgraphs without cut nodes, where any two nodes are connected by at least two internally disjoint paths. Within such components, joint modeling of intermediate nodes provides additional expressive power. In contrast, when node pairs are connected only through a cut node or belong to different connected components, their structural relationships can be decomposed into 2-node interactions or captured globally via readout, rendering higher-order modeling redundant. By restricting 2-node message passing to connected components and 3-node interactions to biconnected components---using an efficient $O(n + m)$ block-cut tree decomposition---Co-Sparsify eliminates provably redundant computations without approximation or sampling. We prove that Co-Sparsified GNNs are as powerful as the 2-FWL test. Empirical evaluation on PPGN shows preserved or improved accuracy on synthetic substructure counting tasks and state-of-the-art performance on real-world graph benchmarks such as ZINC and QM9. This work demonstrates that high expressivity and efficiency can coexist through principled, topology-guided sparsification, offering a scalable path for powerful GNNs without compromising theoretical guarantees.

Connectivity-Guided Sparsification of 2-FWL GNNs: Preserving Full Expressivity with Improved Efficiency

The advancement of intelligent agents has revolutionized problem-solving across diverse domains, yet solutions for personalized fashion styling remain underexplored, which holds immense promise for promoting shopping experiences. In this work, we present StyleTailor, the first collaborative agent framework that seamlessly unifies personalized apparel design, shopping recommendation, virtual try-on, and systematic evaluation into a cohesive workflow. To this end, StyleTailor pioneers an iterative visual refinement paradigm driven by _multi-level negative feedback_, enabling adaptive and precise user alignment. Specifically, our framework features two core agents, _i.e.,_ _Designer_ for personalized garment selection and _Consultant_ for virtual try-on, whose outputs are progressively refined via hierarchical vision-language model feedback spanning individual items, complete outfits, and try-on efficacy. Counterexamples are aggregated into negative prompts, forming a closed-loop mechanism that enhances recommendation quality.To assess the performance, we introduce a _comprehensive evaluation suite_ encompassing style consistency, visual quality, face similarity, and artistic appraisal. Extensive experiments demonstrate StyleTailor's superior performance in delivering personalized designs and recommendations, outperforming strong baselines without negative feedback and establishing a new benchmark for intelligent fashion systems.

StyleTailor: Towards Personalized Fashion Styling via Hierarchical Negative Feedback

Teleoperation can enable human intervention to help handle
instances of failure in autonomy thus allowing for much
safer deployment of autonomous vehicle technology.
Successful teleoperation requires recreating the
environment around the remote vehicle using camera data
received over wireless communication channels. This paper
develops a new predictive display system to tackle the
significant time delays encountered in receiving camera
data over wireless networks. First, a new high gain
observer is developed for estimating the position and
orientation of the ego vehicle. The novel observer is shown
to perform accurate state estimation using only GNSS and
gyroscope sensor readings. A vector field method which
fuses the delayed camera and Lidar data is then presented.
This method uses sparse 3D points obtained from Lidar and
transforms them using the state estimates from the high
gain observer to generate a sparse vector field for the
camera image. Polynomial based interpolation is then
performed to obtain the vector field for the complete image
which is then remapped to synthesize images for accurate
predictive display. The method is evaluated on real-world
experimental data from the nuScenes and KITTI datasets. The
performance of the high gain observer is also evaluated and
compared with that of the EKF. The synthesized images using
the vector field based predictive display are compared with
ground truth images using various image metrics and offer
vastly improved performance compared to delayed images.

Predictive Display for Teleoperation Based on Vector Fields
Using Lidar-Camera Fusion

Large Language Models (LLMs) have rapidly advanced and are widely adopted across diverse fields. Due to the substantial computational cost and data requirements of training from scratch, many developers choose to fine-tune or modify existing open-source models. While most adhere to open-source licenses, some falsely claim original training despite clear derivation from public models. This raises pressing concerns about intellectual property protection and highlights the need for reliable methods to verify model provenance.
In this paper, we propose GhostSpec, a lightweight yet effective method for verifying LLM lineage without access to training data or modification of model behavior. Our approach constructs compact and robust fingerprints by applying singular value decomposition (SVD) to invariant products of internal attention weight matrices, effectively capturing the structural identity of a model. Unlike watermarking or output-based methods, GhostSpec is fully data-free, non-invasive, and computationally efficient. It demonstrates strong robustness to sequential fine-tuning, pruning, block expansion, and even adversarial transformations.
Extensive experiments show that GhostSpec can reliably trace the lineage of transformed models with minimal overhead. By offering a practical solution for model verification and reuse tracking, our method contributes to the protection of intellectual property and fosters a transparent, trustworthy ecosystem for large-scale language models.

Ghost in the Transformer: Detecting Model Reuse with Invariant Spectral Signatures

Recent advancements in Text-to-Speech (TTS) technology have been remarkable, enabling current models to clone arbitrary unseen speakers and synthesize high-quality, natural-sounding speech. However, corresponding evaluation techniques appear to be lagging: Existing Mean Opinion Score (MOS) estimation models typically perform regression-based scoring on entire speech segments—while a failed synthesized speech usually contains problematic elements in only a few isolated words rather than throughout the entire utterance. In this context, we present an intriguing finding: encoder-decoder ASR models, such as Whisper, leverage their extensive pre-training to precisely capture word-level mismatches between speech and text within their cross-attention mechanisms, thereby providing a fine-grained reward signal. Building upon this insight, we propose a novel TTS optimization method, which we term Word-level TTS Alignment by ASR-driven Attentive Reward (W3AR). Instead of relying on any explicit reward annotations, W3AR leverages the attention information within a pre-trained ASR model, enabling finer-grained alignment and optimization of the sequences predicted by the TTS model. Experimental results demonstrate that W3AR not only effectively improves the TTS generation quality of existing models but also further enhances zero-shot robustness on unseen speakers. Additionally, our findings and proposed methodology offer a new insight for generative tasks: understanding models can potentially serve as evaluators, providing highly fine-grained and valuable feedback for generative optimization.

Speech Recognition Model Improves Text-to-Speech Synthesis Using Fine-Grained Reward

Multivariable time series forecasting methods can integrate information from exogenous variables, leading to significant prediction accuracy gains. The transformer architecture has been widely applied in various time series forecasting models due to its ability to capture long-range sequential dependencies. However, a naïve application of transformers often struggles to effectively model complex relationships among variables over time. To mitigate against this, we propose a novel architecture, termed Spectral Operator Neural Network (Sonnet). Sonnet applies learnable wavelet transformations to the input and incorporates spectral analysis using the Koopman operator. Its predictive skill relies on the Multivariable Coherence Attention (MVCA), an operation that leverages spectral coherence to model variable dependencies. Our empirical analysis shows that Sonnet yields the best performance on 34 out of 47 forecasting tasks with an average mean absolute error (MAE) reduction of 2.2% against the most competitive baseline. We further show that MVCA can remedy the deficiencies of naïve attention in various deep learning models, reducing MAE by 10.7% on average in the most challenging forecasting tasks.

Sonnet: Spectral Operator Neural Network for Multivariable Time Series Forecasting

Time-inconsistent behavior, such as procrastination or abandonment of long-term goals, arises when agents evaluate immediate outcomes disproportionately higher than future ones. This leads to globally suboptimal behavior, where plans are frequently revised or abandoned entirely. In the influential model of Kleinberg and Oren (2014) such behavior is modeled by a present-biased agent navigating a task graph toward a goal, making locally optimal decisions at each step based on discounted future costs. As a result, the agent may repeatedly deviate from initially intended plans.

Recent work by Belova et al. (2024) introduced a two-agent extension of this model, where a fully-aware principal attempts to guide the present-biased agent through a specific set of critical tasks without causing abandonment. This captures a rich class of principal–agent dynamics in behavioral settings.

In this paper, we provide a comprehensive algorithmic characterization of this problem. We analyze its computational complexity through the framework of parameterized algorithms, focusing on graph parameters that naturally emerge in this setting, such as treewidth, vertex cover, and feedback vertex set. Our main result is a fixed-parameter tractable algorithm when parameterized by the treewidth of the task graph and the number of distinct $(v,t)$-path costs. Our algorithm encaptures several input settings, such as bounded edge costs and restricted task graph structure. We demonstrate that our main result yields efficient algorithms for a number of such configurations.

We complement this with tight hardness results, that highlight the extreme difficulty of the problem even on simplest graphs with bounded number of nodes and constant parameter values, and motivate our choice of parameters. We delineate tractable and intractable regions of the problem landscape, which include answers to open questions of Belova et al. (2024).

Structural Approach to Guiding a Present-Biased Agent

In this work, we argue that not all sequence-to-sequence tasks require the strong inductive biases of autoregressive (AR) models. Tasks like multilingual transliteration, code refactoring, grammatical correction or text normalization often rely on local dependencies where the full modeling capacity of AR models can be overkill, creating a trade-off between their high accuracy and high inference latency. While non-autoregressive (NAR) models offer speed, they typically suffer from hallucinations and poor length control. To explore this trade-off, we focus on the multilingual transliteration task in Indic languages and introduce NADIR, a novel NAR architecture designed to strike a balance between speed and accuracy. NADIR integrates a Differential Transformer and a Mixture-of-Experts mechanism, enabling it to robustly model complex character mappings without sequential dependencies. NADIR achieves over a 13× speed-up compared to the state-of-the-art AR baseline. It maintains a competitive mean Character Error Rate of 15.78%, compared to 14.44% for the AR model and 21.88% for a standard NAR equivalent. Importantly, NADIR reduces Repetition errors by 49.53%, Substitution errors by 24.45%, Omission errors by 32.92%, and Insertion errors by 16.87%. This work provides a practical blueprint for building fast and reliable NAR systems, effectively bridging the gap between AR accuracy and the demands of real-time, large-scale deployment.

NADIR: Differential Attention Flow for Non-Autoregressive Transliteration in Indic Languages

The deployment of large, black-box foundation models for
medical image classification is often hindered by the high
cost of acquiring large, task-specific labeled datasets for fine-
tuning. While active learning (AL) presents a promising solu-
tion, many state-of-the-art AL methods are computationally
expensive or require full access to internal model parame-
ters. We present VALIANT (Visual Adaptation and Learning
Integration for Active learNing Tasks), a new active learning
framework designed to efficiently adapt black-box foundation
models by overcoming these limitations. VALIANT intro-
duces a lightweight Visual Prompt Decoder (VIPD), trained
via unsupervised Zero-Order Optimization (ZOO), to gener-
ate task-specific visual prompts without internal model access.
Our core contribution is a perturbation-based ranking strat-
egy that leverages this VIPD to formulate a computationally
efficient, gradient-aware informativeness metric. This metric,
which we term prompt instability, identifies the most impactful
samples for the labeling budget. VALIANT further enhances
this process by incorporating anatomical information from
unsupervised segmentation maps to generate more discrimina-
tive visual prompts. Extensive evaluations on multiple medical
datasets demonstrate VALIANT’s superior performance and
significant reduction in labeling costs compared to a range of
existing active learning techniques, positioning it as a scalable
and practical solution for medical image analysis.

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S2-Boost: Synergistic Semantic Boosting for Coarse-to-Fine Ensemble Learning

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