Singapore

Self-supervised learning (SSL) is an emerging paradigm that
exploits supervisory signals generated from the data
itself, and many recent studies have leveraged SSL to
conduct graph anomaly detection. However, we empirically
found that three important factors can substantially impact
detection performance across datasets: (1) the specific SSL
strategy employed; (2) the tuning of the strategy’s
hyperparameters; and (3) the allocation of combination
weights when using multiple strategies. Most SSL-based
graph anomaly detection methods circumvent these issues by
arbitrarily or selectively (i.e., guided by label
information) choosing SSL strategies, hyperparameter
settings, and combination weights. While an arbitrary
choice may lead to subpar performance, using label
information in an unsupervised setting is label information
leakage and leads to severe overestimation of a method’s
performance. Leakage has been criticized as “one of the top
ten data mining mistakes&quot;, yet many recent studies on
SSL-based graph anomaly detection have been using label
information to select hyperparameters. To mitigate this
issue, we propose to use an internal evaluation strategy
(with theoretical analysis) to select hyperparameters in
SSL for unsupervised anomaly detection. We perform
extensive experiments using 10 recent SSL-based graph
anomaly detection algorithms on various benchmark datasets,
demonstrating both the prior issues with hyperparameter
selection and the effectiveness of our proposed strategy.

AAAI 2026

Towards automated self-supervised learning for truly
unsupervised graph anomaly detection

graph anomaly detection

automated machine learning

self-supervised learning

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

Self-supervised learning (SSL) is an emerging paradigm that
exploits supervisory signals generated from the data
itself, and many recent studies have leveraged SSL to
conduct graph anomaly detection. However, we empirically
found that three important factors can substantially impact
detection performance across datasets: (1) the specific SSL
strategy employed; (2) the tuning of the strategy’s
hyperparameters; and (3) the allocation of combination
weights when using multiple strategies. Most SSL-based
graph anomaly detection methods circumvent these issues by
arbitrarily or selectively (i.e., guided by label
information) choosing SSL strategies, hyperparameter
settings, and combination weights. While an arbitrary
choice may lead to subpar performance, using label
information in an unsupervised setting is label information
leakage and leads to severe overestimation of a method’s
performance. Leakage has been criticized as “one of the top
ten data mining mistakes", yet many recent studies on
SSL-based graph anomaly detection have been using label
information to select hyperparameters. To mitigate this
issue, we propose to use an internal evaluation strategy
(with theoretical analysis) to select hyperparameters in
SSL for unsupervised anomaly detection. We perform
extensive experiments using 10 recent SSL-based graph
anomaly detection algorithms on various benchmark datasets,
demonstrating both the prior issues with hyperparameter
selection and the effectiveness of our proposed strategy.

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.

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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.

With the rise of generative artificial intelligence
(GenAI), academic dishonesty in classrooms has skyrocketed,
yet the existing solutions for detecting such dishonesty
often fall short. Standard "AI detectors" merely analyze
one text at a time, failing to account for students'
previous writings, which risks erroneous predictions.
Meanwhile, existing token-based authorship verification
(AV) models fail to analyze the nuances in writing styles
that truly distinguish authorship. To fill this existing
gap, we propose a novel AV framework that combines
token-level stylometric features (e.g., POS tag patterns)
with handcrafted stylistic features (e.g., sentence
structure variation) to construct a comprehensive feature
set. Using both benchmark corpora and real-world high
school student essays, we trained multiple machine learning
classifiers using the proposed feature set. Our initial
experiments show that our approach outperforms the standard
token-only baselines by over 25%, while offering
interpretable, style-based insights. These preliminary
results highlight the importance of nuanced stylistic
features and suggest that a holistic AV system can provide
educators with more reliable and transparent detection
tools. Looking ahead, we plan to extend this work with
large language models and multi-agent approaches to further
enhance robustness and adaptability.

Style-First Authorship Verification for Academic Integrity in the Generative AI Era (Student Abstract)

Real-world AI systems are tackling increasingly complex
problems, often through interactions among Large Language
Model (LLM) agents. When these agents develop inconsistent
conventions, coordination can break down. Applications such
as collaborative coding and distributed planning therefore
require reliable, consistent communication, and scalability
is a central concern as systems grow. We introduce
Schema-Induced Games for Naming (SIGN), a naming game that
examines how lightweight structure can steer convention
formation. We compare schema-induced communication to
unconstrained natural language and find faster convergence
with up to 5.8× higher agreement. These results suggest
that minimal structure can act as a simple control knob for
efficient multi-agent coordination, pointing toward broader
applications beyond the naming game.

SIGN: Schema Induced Games for Naming (Student Abstract)

Automated news summarization using large language models
(LLMs) offers great potential to enhance information
accessibility. However, critical challenges, such as
hallucinations, bias, and toxicity, threaten their
reliability and societal acceptance. In this paper, we
present NewsLensAI, a novel summarization framework
explicitly designed to address these trustworthiness
concerns through Named Entity Recognition (NER)-guided
prompting. By anchoring summaries in key factual entities
extracted from source articles, our method significantly
reduces factual inaccuracies without altering model weights
or architectures. We evaluate NewsLensAI on a dataset of
1,500 real-world news articles using both open- source
(LLaMA3) and proprietary (Gemini1.5) LLMs. Our analysis
encompasses factual consistency, political bias shifts,
sentiment preservation, and toxicity moderation. Our
results indicate substantial improvements in factual align-
ment, demonstrated by an average BERTScore increase from
0.80 (baseline) to 0.88 (NER-enhanced), and a marked 70%
relative reduction in hallucinated entities. Furthermore,
we identify and characterize a notable “centrist drift,”
wherein summaries tend to moderate extreme biases present
in source articles, along with a measurable reduction in
toxic or emotionally charged language. Complementing our
empirical findings, we introduce a real-time NewsLensAI
demo that summarizes live news feeds from the Guardian API,
providing dynamic bias and sentiment analysis. This
practical implementation underscores the real-world
applicability and potential societal benefit of our
approach. Finally, we dis- cuss critical ethical
implications, including potential impacts on media literacy
and information diversity. Our interdisciplinary approach,
linking NLP, journalism, and ethical analysis, positions
NewsLensAI as a meaningful step toward safer, fairer and
more trustworthy AI-generated news consumption.

NewsLensAI: NER-Guided Summarization for Mitigating Hallucination and Bias in LLM-Based News Summaries (Student Abstract)

We present APEX-Q, a flexible product quantization framework for compressing large language models. Unlike prior multi-codebook quantization methods with fixed partitions, APEX-Q supports arbitrary-dimensional tensor quantization, better capturing weight redundancy. It achieves performance on par with 4-bit and 8-bit baselines, enables post-training quantization without retraining, and reveals key trade-offs across subvector dimensions, codebook sizes, and hardware efficiency. APEX-Q thus provides a unified, hardware-friendly approach to scalable LLM deployment.

APEX-Q: Arbitrary-dimension Product-EXtension Quantization for Accelerated LLM Deployment (Student Abstract)

Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.

Aligning Generative Music AI with Human Preferences: Methods and Challenges

As tropical cyclones intensify and track forecasts become increasingly uncertain, U.S. ports face heightened supply-chain risk under extreme weather conditions. Port operators need to rapidly synthesize diverse multimodal forecast products, such as probabilistic wind maps, track cones, and official advisories, into clear, actionable guidance as cyclones approach. Multimodal large language models (MLLMs) offer a powerful means to integrate these heterogeneous data sources alongside broader contextual knowledge, yet their accuracy and reliability in the specific context of port cyclone preparedness have not been rigorously evaluated. To fill this gap, we introduce CyPortQA, the first multimodal benchmark tailored to port operations under cyclone threat. CyPortQA assembles 2,917 real-world disruption scenarios from 2015 through 2023, spanning 145 U.S. principal ports and 90 named storms. Each scenario fuses multi-source data (i.e., tropical cyclone products, port operational impact records, and port condition bulletins) and is expanded through an automated pipeline into 117,178 structured question–answer pairs. Using this benchmark, we conduct extensive experiments on diverse MLLMs, including both open-source and proprietary model. MLLMs demonstrate great potential in situation understanding but still face considerable challenges in reasoning tasks, including potential impact estimation and decision reasoning.

CyPortQA: Benchmarking Multimodal Large Language Models for Cyclone Preparedness in Port Operation

Defocus blur, common in shallow depth-of-field photography, varies across image regions and is challenging to accurately estimate and restore. Existing deblurring methods often struggle to capture fine structural textures and do not effectively adapt to regional differences in blur. We propose Multi-Level Blur-Aware Stable Diffusion (MBSD), a novel framework that explicitly integrates regional blur recognition into a diffusion-based image restoration process. MBSD assigns blur-level labels to image patches using a Patch Blur Annotator (PBA), guiding a Multi-Scale Blur Estimator (MSBE) to predict soft blur probabilities and generate routing weights. These weights control a Blur-Adaptive Expert Mixer (BAEM), which adaptively combines features based on local blur severity. The features are then passed to a text-to-image diffusion model via a cross-attention mechanism, enabling region-specific restoration. Extensive experiments on public benchmarks demonstrate that MBSD delivers superior perceptual quality while maintaining competitive PSNR and SSIM, consistently outperforming state-of-the-art methods.

Multi-Level Blur-Aware Stable Diffusion for Region-Adaptive Defocus Deblurring

Millimeter-wave radar offers a promising sensing modality for autonomous systems thanks to its robustness in adverse conditions and low cost. However, its utility is significantly limited by the sparsity and low resolution of radar point clouds, which poses challenges for tasks requiring dense and accurate 3D perception. Despite that recent efforts have shown great potential by exploring generative approaches to address this issue, they often rely on dense voxel representations that are inefficient and struggle to preserve structural detail. To fill this gap, we make the key observation that latent diffusion models (LDMs), though successful in other modalities, have not been effectively leveraged for radar-based 3D generation due to a lack of compatible representations and conditioning strategies. We introduce RaLD, a framework that bridges this gap by integrating scene-level frustum-based LiDAR autoencoding, order-invariant latent representations, and direct radar spectrum conditioning. These insights lead to a more compact and expressive generation process. Experiments show that RaLD produces dense and accurate 3D point clouds from raw radar spectrums, offering a promising solution for robust perception in challenging environments.

RaLD: Generating High-Resolution 3D Radar Point Clouds with Latent Diffusion

Multi-modal imbalanced cross-source entity alignment aims to identify equivalent entity pairs across multi-modal knowledge graphs (MMKGs) that encompass diverse data sources with imbalanced modality, which poses significant challenges due to the non-uniform distribution of information across different modalities. Existing methods encounter major limitations in aligning entities across MMKGs, where missing data and modality-specific inconsistencies thus create information gaps. These gaps, stemming from disparities in neighborhood structure and attribute availability, result in reduced alignment performance. To address these challenges, we propose a novel multi-modal fact knowledge generation framework to advance imbalanced cross-source entity alignment. Utilizing large language models (LLMs) for comprehensive knowledge completion, our framework enriches MMKGs by synthesizing missing neighboring entities and relational attributes, enabling precise one-to-one similarity comparisons across all relations and attributes. Specifically, neighbor entity completion generates probable neighboring entities to fill structural gaps, while attribute completion synthesizes missing relational attributes to improve alignment. The facts evaluation module assesses generated triples, ensuring that only high-quality information supports the alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms strong competitors, achieving superior entity alignment performance.

Multi-Modal Fact Knowledge Generation for Imbalanced Cross-Source Entity Alignment

Dense retrieval models commonly use flat indexes to achieve high-precision retrieval by computing exact distances between embedding vectors. However, flat indexes are memory-intensive and inefficient, limiting their scalability in large-scale retrieval tasks. In contrast, quantized indexes enable faster retrieval with significantly lower memory usage, but their accuracy tends to decrease. Therefore, we propose a scalable and efficient training method for the dual-encoder models to improves the retrieval accuracy on quantized indexes. Our approach combines the direct gradient update to the cached target embeddings with large scale negative sampling based on similarity, significantly reducing computational overhead and GPU memory usage. Target embeddings are initialized with a pre-trained encoder and stored in a memory buffer, which is directly updated via backpropagation, thus avoiding the repeated re-encoding of the full corpus. To build a rich set of negatives, we retrieve the top-$k$ most similar targets for each query from cached embeddings using the quantized index, including both query-specific and cross-batch top-$k$ results. This design effectively approximates the truncated softmax distribution. The experiments show that our method achieves performs exceptionally well on quantized indexes, providing a practical and scalable solution for real-world retrieval systems.

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