Singapore

The pursuit of high-efficiency Artificial General Intelligence (AGI) requires more than brute-force scaling of model size and data. While scaling remains a key driver of capability, equally important are scalable architectural and principles—designs that continue to work, improve, and remain controllable as we vary model scale, domains, and modalities. Central to our approach is the concept of the “Specialized Generalist” – a pathway that achieves deep expertise across multiple domains without sacrificing broad generalization capabilities. In this talk, we introduce the “Specialized Generalist” paradigm and our implementation of it, SAGE (Synergistic Architecture for Generalized Expertise), a three-layer architecture designed to balance specialization and generalization in a systematic way. We will describe how SAGE’s Base Model, Synergy Fusion, and Exploration-Evolution layers interact in practice, focusing on concrete mechanisms for coordinating domain-specific expertise with broad general reasoning. We will share empirical results and recent advances in large reasoning models, embodied AI, and scientific applications to further illustrate the approach. A central motivation is to support “AGI for Science” by building a stable plateau of capabilities that can reliably assist with complex scientific workflows rather than isolated demos. Finally, we will outline the safety and governance questions that arise when deploying Specialized Generalist systems in high-impact settings, and discuss what we have learned so far about monitoring, alignment, and operational safeguards.

AAAI 2026

Quest of AI towards Specializable Generalist: From Reasoning to Scientific Discovery

The AAAI 2025 presidential panel on the future of AI research aims to help all AI stakeholders navigate the recent significant transformations in AI capabilities, as well as AI research methodologies, environments, and communities. It includes 17 chapters, each covering one topic related to AI research, and sketching its history, current trends and open challenges. The study has been conducted by 25 AI researchers and supported by 15 additional contributors and 475 respondents to a community survey.

The Future of AI Research - Webinar Series

invited talk

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.

The identification of unique traits and behavior is
essential to providing personalized intervention in
individuals with Autism Spectrum Disorder. However, the
limited personalized quantitative data with experts'
annotations in autism research pose a fundamental challenge
to train AI models for unique behavioral patten discovery.
Multiple Instance Learning (MIL) has demonstrated promising
results in medical domains, where annotations are only
needed at the group (i.e., bag) level instead of individual
data instances. It provides a cost-effective way to train
statistical models with limited labeled data. Additionally,
the rise of pretrained models have shown great success in
improving the performance in few-shot learning scenarios.
In this work, we propose a novel framework that integrates
a transformer encoder pre-trained on large-scale
spatiotemporal data with Multiple Instance Learning (MIL),
for unique behavioral pattern detection from autistic
individuals. Our results demonstrated the discrimination of
individual-level autistic behavioral differences and the
accurate classification of behaviors across distinct
groups: typically developing (TD) and autistic (ASD). These
results show promising progress towards tools that can be
used for personalized intervention for autistic
individuals, and more interpretable AI diagnostics.

Spatiotemporal Transformers with Multiple Instance Learning for Label-Efficient Behavioral Analysis in Autism (Student Abstract)

Regional disaster resilience quantifies the changing nature of physical risks to inform policy instruments ranging from local immediate recovery to international sustainable development. While many existing state-of-practice methods have greatly advanced the dynamic mapping of exposure and hazard, our understanding of large-scale physical vulnerability has remained static, costly, limited, region-specific, coarse-grained, overly aggregated, and inadequately calibrated. With the significant growth in the availability of time-series satellite imagery and derived products for exposure and hazard, we focus our work on the equally important yet challenging element of the risk equation: physical vulnerability. Given this unique problem, we leverage machine learning methods that flexibly capture spatial contextual relationships, limited temporal observations, and uncertainty in a unified probabilistic spatiotemporal inference framework. We therefore introduce Graph Variational State-Space Model ($\textbf{GraphVSSM}$), a novel modular spatiotemporal approach that uniquely integrates graph deep learning, state-space modeling, and variational inference using time-series data and prior expert belief systems in a weakly supervised or coarse-to-fine-grained manner. We present three major results: a city-wide demonstration in Quezon City, Philippines; an investigation of sudden changes in the cyclone-impacted coastal Khurushkul community (Bangladesh) and the mudslide-affected Freetown (Sierra Leone); and an open geospatial dataset, $\textbf{METEOR 2.5D}$, that spatiotemporally enhances the existing global static dataset for 46 UN-recognized Least Developed Countries (as of 2020). Beyond advancing the practice of regional disaster resilience assessment and improving our understanding of global progress in disaster risk reduction, our method also offers a probabilistic deep learning approach, contributing to broader urban studies that require compositional data analysis in weakly supervised settings.

GraphVSSM: Graph Variational State-Space Model for Probabilistic Spatiotemporal Inference of Dynamic Exposure and Vulnerability for Regional Disaster Resilience Assessment

Differentially private (DP) image synthesis enables the generation of realistic images while bounding privacy leakage, facilitating secure data sharing across organizations. However, the Gaussian noise injected during DP training, such as via DP-SGD, often severely degrades synthesis quality by disrupting model convergence. To address this, we introduce RPGen, a novel framework that enhances diffusion models' parameter robustness to mitigate DP noise effects without compromising privacy guarantees. At its core, RPGen employs adversarial model perturbation (AMP) during public pre-training to build resilience against perturbations, but we identify and tackle the critical issue of robustness transferability across domains.
RPGen achieves this through a three-step process: (1) A pre-trained classifier infers labels for private images, aggregated into a class distribution noised with Gaussian mechanism for DP, and public samples are selected to match this privatized distribution for domain alignment; (2) The diffusion model is pre-trained on this curated subset with adversarial model perturbation to foster robustness; (3) The model undergoes fine-tuning on private data using DP-SGD. This synergy of robustness augmentation and transferability optimization yields high-fidelity synthesis.
Extensive evaluations on ImageNet for pre-training, with CelebA and CIFAR-10 for synthesis, show RPGen outperforming state-of-the-art baselines across $\varepsilon \in \{1, 5, 10\}$. On average, it achieves 20.18\% lower FID and 5.45\% higher classification accuracy. Ablations confirm the efficacy of domain curation and modest perturbations, establishing RPGen as a new benchmark for privacy-utility trade-offs in image generation.

RPGen: Robust and Differentially Private Synthetic Image Generation

Robust causal discovery from observational data under imperfect prior knowledge remains a significant and largely unresolved challenge. Existing methods typically presuppose perfect priors or can only handle specific, pre-identified error types. And their performance degrades substantially when confronted with flawed constraints of unknown location and type. This decline arises because most of them rely on inflexible and biased thresholding strategies that may conflict with the data distribution. To overcome these limitations, we propose to harmonizes knowledge and data through prior alignment and conflict resolution. First, we assess the credibility of imperfect structural constraints through a surrogate model, which then guides a sparse penalization term measuring the loss between the learned and constrained adjacency matrices. We theoretically prove that, under ideal assumption, the knowledge-driven objective aligns with the data-driven objective. Furthermore, to resolve conflicts when this assumption is violated, we introduce a multi-task learning framework optimized via multi-gradient descent, jointly minimizing both objectives. Our proposed method is robust to both linear and nonlinear settings. Extensive experiments, conducted under diverse noise conditions and structural equation model types, demonstrate the effectiveness and efficiency of our method under imperfect structural constraints.

Robust Causal Discovery Under Imperfect Structural Constraints

Spiking neural networks (SNNs) promise highly energy-efficient computing, but their adoption is hindered by a critical scarcity of event-stream data. This work introduces I2E, an algorithmic framework that resolves this bottleneck by converting static images into high-fidelity event streams. By simulating microsaccadic eye movements with a highly parallelized convolution, I2E achieves a conversion speed over 300x faster than prior methods, uniquely enabling on-the-fly data augmentation for SNN training. The framework's effectiveness is demonstrated on large-scale benchmarks. An SNN trained on the generated I2E-ImageNet dataset achieves a state-of-the-art accuracy of 60.50%. Critically, this work establishes a powerful sim-to-real paradigm where pre-training on synthetic I2E data and fine-tuning on the real-world CIFAR10-DVS dataset yields an unprecedented accuracy of 92.5%. This result validates that synthetic event data can serve as a high-fidelity proxy for real sensor data, bridging a long-standing gap in neuromorphic engineering. By providing a scalable solution to the data problem, I2E offers a foundational toolkit for developing high-performance neuromorphic systems. The open-source algorithm and all generated datasets are provided to accelerate research in the field.

I2E: Real-Time Image-to-Event Conversion for High-Performance Spiking Neural Networks

User sequential behaviors are driven by a variety of complex and evolving intents. Capturing the dynamic change of user intents has become critical yet challenging in the next-item recommendation. Existing studies usually model the transition relationships among multiple intents within a session or integrate temporal information to capture the dynamic evolution of user intents. However, they struggle to identify the precise timing and magnitudes of intent changes, leading to ambiguity in providing consistent or violated recommendations and ultimately yielding subpar performance. To this end, we propose a novel framework called Dual Fluctuation Modeling of Multi-level Intent Evolution for Next-Item Recommendation (DFRec) in this paper. DFRec explicitly identifies the user intent changes and further quantifies the magnitude of the changes. Specifically, we assume that a user's intent fluctuates around an inherent intent, with the magnitude of fluctuations indicating the extent of changes in user intents. Thus, we design an Emerging Intent Generation Module that employs a normal distribution with dynamic variance to capture intent fluctuations at each time step. Furthermore, we introduce a dual-layer dynamic variance update mechanism to capture fluctuation characteristics at different temporal levels, enhancing the representation of possible emergent intents. Extensive experiments on three real-world datasets verify DFRec's superiority over state-of-the-art baselines.

DFRec: Dual Fluctuation Modeling of Multi-level Intent Evolution for Next-Item Recommendation

Pre-trained diffusion models have shown great potential in real-world image super-resolution (Real-ISR) tasks by enabling high-resolution reconstructions. While one-step diffusion (OSD) methods significantly improve efficiency compared to traditional multi-step approaches, they still have limitations in balancing fidelity and realism across diverse scenarios. Since the OSDs for SR are usually trained or distilled by a single timestep, they lack flexible control mechanisms to adaptively prioritize these competing objectives, which are inherently manageable in multi-step methods through adjusting sampling steps. To address this challenge, we propose a Realism Controlled One-step Diffusion (RCOD) framework for Real-ISR. RCOD provides a latent domain grouping strategy that enables explicit control over fidelity-realism trade-offs during the noise prediction phase with minimal training paradigm modifications and original training data. A degradation-aware sampling strategy is also introduced to align distillation regularization with the grouping strategy and enhance the controlling of trade-offs. Moreover, a visual prompt injection module is used to replace conventional text prompts with degradation-aware visual tokens, enhancing both restoration accuracy and semantic consistency. Our method achieves superior fidelity and perceptual quality while maintaining computational efficiency. Extensive experiments demonstrate that RCOD outperforms state-of-the-art OSD methods in both quantitative metrics and visual qualities, with flexible realism control capabilities in the inference stage. The code will be released.

Realism Control One-step Diffusion for Real-world Image Super Resolution

While advancements in the reasoning abilities of LLMs have significantly enhanced their performance in solving mathematical problems, coding tasks, and general puzzles, their effectiveness in accurately adhering to instructions remains inconsistent, particularly with more complex directives. Our investigation identifies lazy reasoning during the thinking stage as the primary factor contributing to poor instruction adherence. To mitigate this issue, we propose a comprehensive framework designed to enable rigorous reasoning processes involving preview and self-checking, essential for satisfying strict instruction constraints. 
Specifically, we first generate instructions with complex constraints and apply a filtering process to obtain valid prompts, resulting in three distinct prompt datasets categorized as hard, easy, and pass. Then, we employ rejection sampling on the pass prompts to curate a small yet high-quality dataset, enabling a cold-start initialization of the model and facilitating its adaptation to effective reasoning patterns.
Subsequently, we employ an entropy-preserving supervised fine-tuning (Entropy-SFT) strategy coupled with token-wise entropy-adaptive (TEA-RL) reinforcement learning guided by rule-based dense rewards. This approach encourages the model to transform its reasoning mechanism, ultimately fostering generalizable reasoning abilities that encompass preview and performing self-checking. Extensive experiments conducted on instruction-following benchmarks demonstrate remarkable performance improvements across various model scales. Notably, our Light-IF-32B model surpasses both larger open-source models such as DeepSeek-R1 and closed-source models like Doubao-1.6.

Light-IF: Endowing LLMs with Generalizable Reasoning via Preview and Self-Checking for Complex Instruction Following

We introduce LLA, an effective intellectual property (IP) protection scheme for generative AI models. LLA leverages the synergy between hardware and software to defend against various supply chain threats, including model theft, model corruption, and information leakage. On the software side, it embeds key bits into neurons that can trigger outliers to degrade performance and applies invariance transformations to obscure the key values. On the hardware side, it integrates a lightweight locking module into the AI accelerator while maintaining compatibility with various dataflow patterns and toolchains. An accelerator with a pre-stored secret key acts as a license to access the model services provided by the IP owner. The evaluation results show that LLA can withstand a broad range of oracle-guided key optimization attacks, while incurring a minimal computational overhead of less than 0.1\% for 7,168 key bits.

LLA: Enhancing Security and Privacy for Generative Models with Logic-Locked Accelerators

Physicists aim to explain the Universe in terms of a compact, interpretable set of principles. Deducing those principles from experiments poses many challenging and problems which are ripe for application of AI and present opportunities to develop new AI techniques. I will describe how AI has changed the way particle physicists work and speculate about the role of AI in the future of fundamental physics. Finally, I will describe my experience in science communication, as an author, podcaster and television producer.

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