Singapore

The widespread adoption of artificial intelligence(AI) in
cybersecurity has led to the emerging of intelligent
threats, such as Advanced Persistent Threats (APTs),
challenging the conventional deception defense mechanisms.
My work aims to fill this critical gap by developing a game
theoretic defense agent capable of confronting these
intelligent threats. In this proposal, we formalize the
attacker-defender interactions as a Bayesian game model
between AI agents so as to derive equilibrium defense
strategies. Simulation based experiments and real-world
implementations would be conducted to evaluate the proposed
framework. This study is potential to revolutionize cyber
defense methodologies by shifting from bilateral
decision-making to game theoretic strategy evolution.

AAAI 2026 Main Conference

When AI Meets AI: A Game-Theoretic Defense Framework Against AI Empowered Cyber Threats

poster

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.

RNA 3D structure prediction remains a fundamental challenge
due to limited experimental data, conformational
heterogeneity, and complex folding landscapes. Inspired by
breakthroughs in protein modeling, a data-efficient deep
learning framework that integrates RNA language embeddings,
geometric constraints, and physics-informed priors is
proposed. This approach leverages self-supervised
pretraining, contrastive learning, and SE(3)-equivariant
architectures to capture higher-order structural
relationships from scarce data. Predicted structures are
evaluated using benchmark datasets, thermodynamic
plausibility, and docking-based functional assessments,
ensuring both structural accuracy and biophysical
relevance. By advancing RNA structure prediction and
design, this work aims to accelerate the development of
RNA-based therapeutics, catalytic ribozymes, and precision
medicine applications, while providing an open-source
framework for the broader scientific community.

Towards Data-Efficient Deep Learning for RNA 3D Structure Prediction and Design

This research proposes an extension to the Program Lattice
Transformer (PLT) , a neuro-symbolic framework for program
induction that embeds programs into a structured latent
space. The current PLT model, which uses a flat lattice, is
computationally inefficient when modeling invariant
programs—operations that return to an initial state after a
set number of applications (e.g., a 360° rotation). To
address this, we propose embedding the program space onto a
cylindrical manifold instead of a plane. This approach is
grounded in the principle that only isometric
transformations preserve the lattice's compositional
structure, limiting valid manifolds to developable surfaces
like cylinders . A cylindrical geometry naturally
represents invariant programs as closed loops, enhancing
efficiency. The proposed method will be evaluated on
synthetic tasks like Rubik's Cube and the Abstraction and
Reasoning Corpus (ARC) to demonstrate improved performance
and efficiency. This work serves as a step toward models
that can autonomously configure their own geometric latent
spaces, connecting to future research in geometric deep
learning and meta-learning.

Cylindrical Lattice Embedding for Program Induction

With the increase in the number of open sourced models
available to the average consumer, Low-Rank Adaptation
(LoRA) have become essential tools for adapting large
language models with limited computational resources (Hu et
al., 2021). LoRA works by introducing trainable low-rank
matrices A and B to update pre-trained weights efficiently,
significantly reducing memory and compute requirements
compared to full fine-tuning. The original motivation was
that the weight update during fine-tuning could be
estimated by ∆W ≈ AB, and thus could drastically reduce the
number of trainable parameters while still capturing
task-specific adaptations, significantly reducing the
number of parameters that needs to be stored in memory.
While recent work has shown that LoRA is not strictly
equivalent to full fine-tuning (Shuttleworth et al., 2024),
it remains an efficient and practical method for adapting
models to specific tasks, playing a crucial role in the
democratization of AI. Despite being efficient, LoRA has
been shown to be suboptimal for finetuning models with
large embedding dimensions, due to differences in the
magnitudes of the values of A and B (Hayou et al., 2024;
Yen et al., 2024; Zhang & Pilanci, 2024). While there are
existing LoRA variants that claim to handle these problems
(Hayou et al., 2024; Yen et al., 2024; Bensaïd et al.,
2025; Zhang & Pilanci, 2024), we would like to rigorously
evaluate the performance of these LoRA variants on a
variety of tasks and models, and investigate an alternative
and novel approach, which introduces a penalty.

Scale Regularization for Stable Low-Rank Adaptation

With the development of Large Language Models (LLMs), there
is growing interest in how we can apply the knowledge of
LLMs to tasks beyond text generation and question
answering. Speech processing, a field of interest for
decades, has recently seen successful applications of LLM
architectures following the release of the Transformer
architecture such as in the Whisper models. While
significant development has occurred in improving LLM
capabilities through Supervised Fine-Tuning (SFT),
reasoning, and alignment with Reinforcement Learning (RL),
their application to the speech domain remain somewhat
underexplored. Recent work has demonstrated that fine
tuning LoRA (Low-Rank Adaptation) adapters for LLMs can
perform Automatic Speech Recognition (ASR) tasks natively,
leveraging existing LLM capabilities and bypassing the
pre-training stage. However, no approaches have yet to
successfully apply LLM knowledge in a similar fashion to
other speech processing tasks like speaker diarisation.
Current approaches utilise LLMs as a post-processing step
on the outputs of a speaker diarisation model, but no model
based on LLMs has yet to be able to natively perform
speaker diarisation. Therefore, this research proposal
explores how we can create LoRA adapters for LLMs to
perform speaker diarisation tasks natively, and explores
how we can also overcome the speech domain’s reliance on
annotated data.

Native Speech Processing with LLMs

This proposal aims to investigate epistemic uncertainty -
uncertainty about knowledge or truth, often conveyed by
modals like might or probably in Large Language Models
(LLMs). By probing how such cues affect reasoning, we seek
to achieve controllable epistemic sensitivity: enabling mod-
els to interpret and adapt to uncertainty. Using activation-
level analyses and multilingual benchmarks, this work ad-
vances transparent, context-aware, and trustworthy reasoning
in uncertainty-critical domains.

Controllable Epistemic Sensitivity in Large Language Models: Probing, Benchmarking, and Adaptive Reasoning

Post-training quantization is a widely adopted technique
for compressing large language models (LLMs), enabling
efficient deployment in resource-constrained environments.
However, recent studies have revealed that
quantization—especially aggressive methods such as 4-bit
QLoRA and Straight-Through Estimators (STE)—can
significantly degrade a model’s safety alignment,
increasing its susceptibility to harmful prompt
completions and jailbreak behaviors. This research
investigates the safety risks introduced by quantization
and proposes a novel mitigation strategy: projecting
quantized parameters back into safety-aligned subspaces.
Building on prior work such as SafeLoRA, the study aims to
empirically evaluate safety degradation across benchmark
datasets (PureBad, Dialog Summary, Alpaca) using metrics
like Harmfulness Score, Attack Success Rate (ASR), and
StrongReject Score. The second phase explores
projectionbased restoration techniques to recover
alignment-preserving directions in parameter space.
Finally, the effectiveness of these interventions will be
assessed through end-to-end evaluations. By addressing the
overlooked safety implications of model compression, this
research contributes toward the development of robust,
ethically aligned LLMs suitable for realworld deployment

Unveiling AI Safety in Fine-tuning Quantized Model

Bipolar disorder poses significant challenges due to
disruptive manic episodes often missed by traditional
clinical monitoring. This research proposes a multimodal AI
framework that integrates keystroke dynamics and circadian
rhythm analysis via smartphones to predict manic episodes 3
to 7 days prior to clinical onset. Combining fine-grained
cognitive-motor behavioral features from typing patterns
with physiological markers of circadian disruption, this
approach leverages temporal convolutional and LSTM networks
enhanced with attention mechanisms for robust prediction.
The model will be validated through longitudinal monitoring
to assess predictive accuracy and reliability. Such early
detection can enable timely interventions, reducing
personal and societal burdens while advancing digital
mental health methodologies rooted in precision psychiatry.

Multimodal Digital Phenotyping for Early Prediction of Manic Episodes Through Keystroke Dynamics and Circadian Pattern Analysis

Arbitrary-Oriented Object Detection (AOOD) has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. However, current AOOD frameworks face challenges in ineffective feature extraction and orientation regression inaccuracy. Inspired by Hilbert curve's intrinsic locality-preserving property, we propose a flexible \textbf{H}ilbert curve-\textbf{E}ncoded \textbf{R}otation-Equivariant \textbf{O}riented Object \textbf{Det}ector, termed \textbf{HERO-Det}. Our key innovations include: (i) a novel Hilbert curve traversal convolution paradigm with a dimensionality reduction scheme, which employs locality-preserving spatial filling curves for feature transformation, (ii) a Hilbert pyramid transformer enabling hierarchical construction of multi-scale feature sequences through space-folding operations, as well as (iii) an orientation-adaptive prediction head that decouples rotation-equivariant regression features from invariant classification cues to resolve orientation regression dilemmas in two-stage detectors. Extensive experiments show HERO-Det achieves state-of-the-art performance on AOOD benchmarks, with mAP of 79.56\%, 90.64\%, 90.10\%, and 80.47\% on DOTA, HRSC2016, SSDD, and HRSID, respectively. Consistent performance gains in cross-task validation further demonstrate the versatility of our method to diverse vision tasks, such as medical image segmentation and 3D object detection. Code is available at https://github.com/Qian-CV/HERO-Det.

Hilbert Curve-Encoded Rotation-Equivariant Oriented Object Detector with Locality-Preserving Spatial Mapping

3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models for high‑fidelity reconstruction. However, their multi‑step iterative sampling incurs significant computational overhead, limiting real‐time applicability. To address this, we propose LiNeXt—a lightweight, non‐diffusion network optimized for rapid and accurate point cloud completion. Specifically, LiNeXt first applies the Noise‑to‑Coarse (N2C) Module to denoise the input noisy point cloud in a single pass, thereby obviating the multi‑step iterative sampling of diffusion‑based methods. The Refine Module then takes the coarse point cloud and its intermediate features from N2C Module to perform more precise refinement, further enhancing structural completeness. Furthermore, we observe that LiDAR point clouds exhibit a distance‑dependent spatial distribution—densely sampled at proximal ranges and sparsely sampled at distal ranges. Accordingly, we propose the Distance‑aware Selected Repeat strategy to generate a more uniformly distributed noisy point cloud. On the SemanticKITTI dataset, LiNeXt achieves a 199.8$\times$ speedup in inference, reduces Chamfer Distance by 50.7\%, and uses only 6.1\% of the parameters compared with LiDiff. These results demonstrate the superior efficiency and effectiveness of LiNeXt for real-time scene completion.

LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures

Movie dubbing seeks to synthesize speech from a given script using a specific voice, while ensuring accurate lip synchronization and emotion-prosody alignment with the character’s visual performance. However, existing alignment approaches based on visual features face two key limitations: (1) they rely on complex, handcrafted visual preprocessing pipelines, including facial landmark detection and feature extraction; and (2) they generalize poorly to unseen visual domains, often resulting in degraded alignment and dubbing quality. To address these issues, we propose InstructDubber, a novel instruction-based alignment dubbing method for both robust in-domain and zero-shot movie dubbing. Specifically, we first feed the video, script, and corresponding prompts into a multimodal large language model to generate natural language dubbing instructions regarding the speaking rate and emotion state depicted in the video, which is robust to visual domain variations. Second, we design an instructed duration distilling module to mine discriminative duration cues from speaking rate instructions to predict lip-aligned phoneme-level pronunciation duration. Third, for emotion-prosody alignment, we devise an instructed emotion calibrating module, which fine-tunes an LLM-based instruction analyzer using ground truth dubbing emotion as supervision and predicts prosody based on the calibrated emotion analysis. Finally, the predicted duration and prosody, together with the script, are fed into the audio decoder to generate video-aligned dubbing. Extensive experiments on three major benchmarks demonstrate that InstructDubber outperforms state‑of‑the‑art approaches across both in‑domain and zero‑shot scenarios.

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Towards Data-Efficient Deep Learning for RNA 3D Structure Prediction and Design

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