Singapore

The long-term progression of neurodegenerative diseases is commonly conceptualized as a spatiotemporal diffusion process that consists of a graph diffusion process across the structural brain connectome and a localized reaction process within brain regions. However, modeling this progression remains challenging due to 1) the scarcity of longitudinal data obtained through irregular and infrequent subject visits and 2) the complex interplay of pathological mechanisms across brain regions and disease stages, where traditional models assume fixed mechanisms throughout disease progression. To address these limitations, we propose a novel stage-aware Mixture of Experts (MoE) framework that explicitly models how different contributing mechanisms dominate at different disease stages through time-dependent expert weighting. This architecture is a key innovation designed to maximize the utility of small datasets and provide interpretable insights into disease etiology. Data-wise, we utilize an iterative dual optimization method to properly estimate the temporal position of individual observations, constructing a cohort-level progression trajectory from irregular snapshots. Model-wise, we enhance the spatial component with an inhomogeneous graph neural diffusion model (IGND) that allows diffusivity to vary based on node states and time, providing more flexible representations of brain networks. We also introduce a localized neural reaction module to capture complex dynamics beyond standard processes.The resulting IGND-MoE model dynamically integrates these components across temporal states, offering a principled way to understand how stage-specific pathological mechanisms contribute to progression. When used to model tau pathology propagation in human brains, IGND-MoE outperforms purely pathophysiological and purely neural baselines in long-term prediction accuracy. Moreover, its stage-wise weights yield novel clinical insights that align with literature, suggesting that graph-related processes are more influential at early stages, while other unknown physical processes become dominant later on. Our findings highlight the necessity of designing hybrid and expert-constrained models that account for the evolving nature of neurodegenerative processes.

AAAI 2026

A Stage-Aware Mixture of Experts Framework for Neurodegenerative Disease Progression Modelling

ml: time-series/data streams

cv: medical and biological imaging

ml: graph-based machine learning

ml: applications

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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.

Large language models (LLMs) are transforming the field of
natural language processing, yet their development remains
concentrated on a handful of high-resource languages,
raising fundamental questions of inclusivity, trust, and
global accessibility. My research addresses these
challenges by advancing multilingual and trustworthy AI. On
the multilingual front, I have analyzed how LLMs internally
process diverse languages, introduced benchmarks such as
M3Exam and SeaBench to reveal performance gaps, and led
large-scale open-source initiatives including SeaLLMs and
Babel that extend strong model support to underrepresented
languages worldwide. Complementing inclusivity, my work
also uncovers vulnerabilities in LLMs (e.g., multilingual
jailbreaks) and introduces neuron-level interpretability
and automated evaluation frameworks (e.g., Auto-Arena) for
trustworthy deployment. Looking ahead, I aim to build AI
systems that are linguistically inclusive, culturally
aware, and inherently safe, bridging foundational advances
with real-world applications in diverse global contexts.

Towards Inclusive AI: Advancing Multilingual Large Language Models

Slum segmentation from satellite imagery holds significant promise in generating consistent global estimates of urban poverty. However, the morphological heterogeneity of informal settlements presents a major challenge, limiting the generalization of models trained on specific regions to unseen locations. To address this, we introduce a large-scale high-resolution dataset and propose GRAM (Generalized Region-Aware Mixture-of-Experts), a two-phase test-time adaptation framework that enables robust slum segmentation without labeled data from target regions. We compile a million-scale dataset of preprocessed satellite imagery from 12 cities across four continents for source training. Using this data set, GRAM employs a mixture-of-experts architecture to capture region-specific slum characteristics while learning universal features through a shared backbone. During adaptation, prediction consistency across experts filters unreliable pseudo-labels, allowing the model to generalize effectively to previously unseen regions. When tested on three African cities, GRAM outperforms state-of-the-art baselines in low-resource settings, offering a scalable and label-efficient solution for global slum mapping and data-driven urban planning.

Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts

This paper presents an eXplainable AI (XAI)-based class-
room game “Breakable Machine” for teaching critical, trans-
formative AI literacy through adversarial play and interroga-
tion of AI systems. Designed for learners aged 10–15, the
game invites students to spoof an image classifier by manip-
ulating their appearance or environment in order to trigger
high-confidence misclassifications. Rather than focusing on
building AI models, this activity centers on breaking them—
exposing their brittleness, bias, and vulnerability through
hands-on, embodied experimentation. The game includes an
XAI view to help students visualize feature saliency, reveal-
ing how models attend to specific visual cues. A shared class-
room leaderboard fosters collaborative inquiry and compar-
ison of strategies, turning the classroom into a site for col-
lective sensemaking. This approach repositions AI education
by treating model failure and misclassification not as prob-
lems to be debugged, but as pedagogically rich opportuni-
ties to interrogate AI as a sociotechnical system. In doing so,
the game supports students in developing data agency, ethical
awareness, and a critical stance toward AI systems increas-
ingly embedded in everyday life. The game and its source
code are freely available.

Breakable Machine: A K–12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)

Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies induce biased model representations and corrupt statistical estimates such as Batch Normalization (BN) statistics. In this paper, we rethink long-tailed dataset distillation by revisiting the limitations of trajectory-based methods, and instead adopt the statistical alignment perspective to jointly mitigate model bias and restore fair supervision. To this end, we introduce three dedicated components that enable unbiased recovery of distilled images and soft relabeling: (1) enhancing expert models (an observer model for recovery and a teacher model for relabeling) to enable reliable statistics estimation and soft-label generation; (2) recalibrating BN statistics via a full forward pass with dynamically adjusted momentum to reduce representation skew; (3) initializing synthetic images by incrementally selecting high-confidence and diverse augmentations via a multi-round mechanism that promotes coverage and diversity. Extensive experiments on four long-tailed benchmarks show consistent improvements over state-of-the-art methods across varying degrees of class imbalance.Notably, our approach improves top-1 accuracy by 15.6% on CIFAR-100-LT and 11.8% on Tiny-ImageNet-LT under IPC=10 and IF=10.

Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling

3D Gaussian Splatting (3DGS) achieves impressive rendering fidelity and speed for novel view synthesis. However, its substantial data size poses a significant challenge for practical applications. While many compression techniques have been proposed, they fail to efficiently utilize existing bitstreams in on-demand applications due to their lack of progressivity, leading to a waste of resource.
To address this issue, we propose __PCGS__ (Progressive Compression of 3D Gaussian Splatting), which adaptively controls __both the quantity and quality__ of Gaussians (or anchors) to enable effective progressivity for on-demand applications. For quantity, we introduce a progressive masking strategy that incrementally incorporates new anchors while refining existing ones to enhance fidelity. For quality, we propose a progressive quantization approach that gradually reduces quantization step sizes to achieve finer modeling of Gaussian attributes. Furthermore, to compact the incremental bitstreams, we leverage existing quantization results to refine probability prediction, improving entropy coding efficiency across progressive levels.
Overall, PCGS achieves progressivity while maintaining compression performance comparable to SoTA non-progressive methods. Our code will be made publicly available.

PCGS: Progressive Compression of 3D Gaussian Splatting

Large Language Models (LLMs) are increasingly used as scalable tools for pilot testing, predicting public opinion distributions before deploying costly surveys. However, the prevailing paradigm for evaluating these models relies on traditional structured surveys—a methodology misaligned with the more realistic scenarios like social media where opinions are rich in digital contexts. By design, surveys strip away the social and cultural context that shapes public opinion, and LLM benchmarks built on this paradigm inherit these critical limitations. To bridge this gap, we introduce MindVote, the first benchmark for public opinion prediction grounded in authentic social media discourse. MindVote is constructed from 3,918 naturalistic polls sourced from Reddit and Weibo, spanning 23 topics and enriched with detailed annotations for platform and topical context. Using this benchmark, we conduct a comprehensive evaluation of 15 LLMs, revealing a critical "survey-based specialization pitfall" where models fine-tuned on traditional surveys underperform their general-purpose counterparts and demonstrating the necessity of context in social media. MindVote provides a robust, ecologically valid framework to move beyond survey-based evaluations and advance the development of social intelligent AI systems.

MindVote: When AI Meets the Wild West of Social Media Opinion

Recent self-supervised image segmentation models have achieved promising performance on semantic segmentation and class-agnostic instance segmentation. 
However, their pretraining schedule is multi-stage, requiring a time-consuming pseudo-masks generation process between each training epoch. 
This time-consuming offline process not only makes it difficult to scale with training dataset size, but also leads to sub-optimal solutions due to its discontinuous optimization routine. 
To solve these, we first present a novel pseudo-mask algorithm, Fast Universal Agglomerative Pooling (UniAP). Each layer of UniAP can identify groups of similar nodes in parallel, allowing to generate both semantic-level and instance-level and multi-granular pseudo-masks within ens of milliseconds for one image. 
Based on the fast UniAP, we propose the Scalable Self-Supervised Universal Segmentation (S2-UniSeg), which employs a student and a momentum teacher for continuous pretraining. 
A novel segmentation-oriented pretext task, Query-wise Self-Distillation (QuerySD), is proposed to pretrain S2-UniSeg to learn the local-to-global correspondences. 
Under the same setting, S2-UniSeg outperforms the SOTA UnSAM model, achieving notable improvements of AP+6.9 on COCO, AR+11.1 on UVO, PixelAcc+4.5 on COCOStuff-27, RQ+8.0 on Cityscapes. After scaling up to a larger 2M-image subset of SA-1B, S2-UniSeg further achieves performance gains on all four benchmarks. 
Our code and pretrained models shall be released upon the acceptance of this work.

S2-UniSeg: Fast Universal Agglomerative Pooling for Scalable Segment Anything Without Supervision

State-of-the-art fact-checking systems combat misinformation at scale by employing autonomous LLM-based agents to decompose complex claims into smaller sub-claims, verify each sub-claim individually, and aggregate the partial results to produce verdicts with justifications (explanatory rationales for the verdicts).
The security of these systems is crucial, as compromised fact-checkers, which tend to be easily underexplored, can amplify misinformation.
This work introduces Fact2Fiction, the first poisoning attack framework targeting such agentic fact-checking systems.
Fact2Fiction mirrors the decomposition strategy and exploits system-generated justifications to craft tailored malicious evidences that compromise sub-claim verification.
Extensive experiments demonstrate that Fact2Fiction achieves 8.9\%--21.2\% higher attack success rates than state-of-the-art attacks across various poisoning budgets. Fact2Fiction exposes security weaknesses in current fact-checking systems and highlights the need for defensive countermeasures.

Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System

Quantum computing is finding promising applications in optimization, machine learning and physics, leading to the development of various models for representing quantum information classically.
Because these representations are often studied in different contexts (many-body physics, machine learning, formal verification, simulation), little is known about fundamental trade-offs between their succinctness and the runtime of operations to update them.
We therefore analytically investigate widely-used quantum state representations: matrix product states (MPS), several decision diagram (DD) variants, and restricted Boltzmann machines (RBMs).
We map the relative succinctness of these data structures and provide the complexity for relevant query and manipulation operations. Further, we introduce ``rapidity'' for non-canonical representations: representation $A$ is at least as rapid as $B$, if it can polynomially simulate an operation performed on $B$ \emph{taking the size of $B$ as a unit}.
We then provide a framework for deriving rapidity relations for almost all operations in one stroke.
Our results show inter alia 
(1) that most DD variants are redundant with respect to MPS in the strongest asymptotic sense
(MPS is at least as rapid as those DD variants for almost all operations),
(2) that only one DD variant called LIMDD and RBM have succinctness incomparable to MPS, and
(3) that LIMDD and RBM seem to achieve this by sacrificing tractability of counting queries through a metatheorem on conditional hardness of these queries.

A Knowledge Compilation Map for Quantum Information

We train Transformer-based language models on ten foundational algorithmic tasks and observe pronounced phase transitions in their loss curves that deviate from established power-law scaling trends. Over large ranges of compute, the validation loss barely improves, then abruptly decreases. Probing the models’ internal representations reveals that quiet features are learned prior to any decrease in task loss. These quiet features represent intermediate algorithmic computations that do not by themselves improve the output loss. Ablation experiments demonstrate that individual quiet features are causally necessary for task performance. Our results demonstrate that substantial representational progress can remain hidden beneath an apparently flat loss curve, challenging the prevailing use of cross‑entropy as a proxy for learning and motivating richer diagnostics for monitoring model training.

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