Singapore

Embodied agents must reason causally, as correlation-based
models fail under intervention and distribution shift. This
challenge arises in domains like robotics and cyber-physical
systems, where agents balance efficiency and comfort under
uncertainty. We introduce POLICYGRID, unifying causal
discovery and control by treating each action as both
decision
and experiment. Leveraging constraint-based search, neural
causal models, and language model priors with interventional
validation, POLICYGRID yields adaptive, interpretable
policies. Across synthetic, real-world, and live
deployments, it
achieves superior causal recovery (F1 = 0.89) and 2.8×
better multi-objective performance than correlation-based
baselines, demonstrating safe, generalizable
decision-making.

AAAI 2026

POLICYGRID: Causal Discovery for Adaptive Policy Optimization in Embodied Agents (Student Abstract)

and causality; causal reasoning

sensor networks & smart cities; action

change

internet of things

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.

poster

Well log datasets are often scarce, which hinders the
development of machine learning models for reservoir
analysis, a common challenge in the oil and gas industry.
We present VAEc-tMC, a Conditional Variational Autoencoder
designed to generate synthetic well log data conditioned on
rock type. By embedding geological context into the
generative process, our model addresses a critical gap
overlooked by existing methods. Our approach integrates a
Student’s t-distribution loss, a smoothed Kullback–Leibler
divergence, and low-variance Monte Carlo method sampling to
improve robustness and fidelity. When used for data
augmentation, the synthetic data preserve key statistical
properties of real logs and improve downstream lithology
classification by about 80% in AUC, 62% in accuracy, and
71% in F1. These findings validate the model’s ability to
generate geologically consistent synthetic data, extending
its applicability to reservoir modeling and downstream ML
workflows in data scarce environments.

Lithology-Aware Conditional Variational Autoencoder for Synthetic Well Log Generation in Petroleum Reservoirs (Student Abstract)

Ensuring safety in deep reinforcement learning is
challenging, as formal methods that provide strong
guarantees often fail to scale to complex, high-dimensional
systems. We introduce RAMPS, a scalable shielding framework
that pairs a general-purpose, learned linear dynamics model
with a robust, multi-step Control Barrier Function (CBF)
for real-time safety interventions. Experiments show RAMPS
significantly reduces safety violations in high-dimensional
environments compared to state-of-the-art methods, without
sacrificing task performance.

Robust Adaptive Multi-Step Predictive Shielding (Student Abstract)

We present iDT-diet, an intelligent digital twin prototype designed to model the long-term influence of diet quality on health biomarkers and chronic conditions. The system integrates three novel components: (i) a random forest learning model enhanced with Choquet LASSO feature selection for capturing complex, nonlinear interactions in temporal health data; (ii) a translation module that converts predictive outputs into natural language narratives of physical and biomarker states; and (iii) a generative 3D visualization engine that produces dynamic, personalized digital twins reflecting evolving health trajectories. This integration uniquely links advanced machine learning, interpretable communication, and immersive visualization within a single framework. While the current implementation focuses on retrospective digital twin generation, the system architecture supports real-time data integration, enabling continuous monitoring, predictive simulation, and personalized recommendation delivery for diet and lifestyle management.

iDT-diet: Toward Personalized Health Forecasting-An Intelligent Digital Twin Model for Diet-Influenced Biomarker Trajectories (Student Abstract)

Causal discovery is the task of learning causal models, encoding causal relationships, from a source of information, such as a dataset containing observational data. While many algorithms have been developed to discover causal models under varied sets of assumptions, the case in which the dataset is affected by missing data remains significantly underexplored. Naively applying standard causal discovery algorithms to listwise, test-wise, or regression-wise deleted datasets, or imputing the missing data, can introduce spurious associations between variables and bias function estimation in functional causal models. This issue arises when the data is missing at random or not at random. It ultimately invalidates the theoretical guarantees of these algorithms and prevents finding the true underlying causal model, even in the large-sample limit. An established family of causal models is the Linear Non-Gaussian Acyclic Model (LiNGAM), which assumes linear functional relationships and non-Gaussian independent noise terms. We propose a new causal discovery algorithm for LiNGAM, capable of recovering the underlying causal structure and providing unbiased estimates of the model’s parameters, even when the data is affected by MNAR missingness.

Discovering Linear Non-Gaussian Models for All Categories of Missing Data (Student Abstract)

Although deep networks excel on RGB images, their performance degrades sharply under severe domain shifts—such as sketch recognition, where color and texture cues are missing. In this work, we propose a novel pipeline that leverages semantic cues extracted from sketches to guide the synthesis of photorealistic RGB images using diffusion-based generative models. Our framework operates by extracting two crucial cues from the input sketch: semantic captions via the BLIP model and structural outlines via Canny edge detection. These cues are then integrated using ControlNet to guide a Stable Diffusion model, ensuring the synthesized RGB image is both semantically consistent with the content and structurally faithful to the original sketch. We evaluated our synthesized images by benchmarking classification performance. We trained standard architectures (from convolutional to transformer-based) on Tiny-ImageNet subsets and tested them on sketches, their synthesized counterparts, and the original RGB images. Experimental results demonstrate that our approach produces realistic, identity-preserving images, which significantly improve classification accuracy and effectively bridge the semantic gap. While BLIP-based captioning and ControlNet-guided diffusion are established methods, our contribution lies in their integration into a unified, caption-guided pipeline that enhances sketch-to-RGB translation with improved semantic consistency. The proposed method generalizes well across architectures, providing a scalable and cost-efficient solution for sketch-based image synthesis.

Semantic-Guided Sketch-to-RGB Image Generation via Controlled Diffusion for Improved Sketch Recognition (Student Abstract)

Government verification systems are increasingly relying on internet-based platforms, where users authenticate their identities by uploading images captured with ordinary mobile devices. However, the rapid advancements in generative algorithms have enabled the creation of highly realistic forged ID cards that can easily bypass such verification pipelines. These forgeries are not restricted to a single modality; they may target facial imagery, textual content, or both, posing significant challenges to existing detection approaches. We present a framework that analyzes visual features for ID forgery detection by integrating feature fusion with attention mechanisms, leveraging both convolutional neural network (CNN) architectures, such as ResNet-50 and EfficientNet, and transformer-based models, including ViT-16 and Swin Transformer. This study emphasises the significance of feature fusion and attention-driven representation learning in developing robust and trustworthy ID forgery detection systems for real-world deployment.

Guarding Digital Identity: Attention-Guided Fusion for Detecting Forged ID Documents (Student Abstract)

We study when users end a session on X using high-resolution interaction logs from 215 US participants collected over four weeks. Sessions are defined via data-driven inter-activity gaps, and each session is encoded by fine-grained activity counts and duration (versus a simple activity ratio baseline). Fine-grained activity features substantially outperform the activity ratio baseline (C-index ≈ 0.76 vs. 0.62 for future sessions; 0.72 vs. 0.60 for unseen users), indicating that the composition of activity types is a strong predictor of disengagement. At the app level, we analyze retention over early adoption windows and find that the ratio of active activity in the first three days is most predictive of later usage. These results highlight session composition and early on-platform behavior as practical levers for forecasting and mitigating premature drop-off.

Predicting Session Termination and Retention on X from Fine-Grained Interaction Logs (Student Abstract)

“Refusals must be resilient, not brittle.” Yet guarding
refusals against adversarial phrasing and shifting user
contexts remains difficult: large language models (LLMs)
still yield to jailbreak prompts that evade safety filters
and surface harmful content. Despite gains from methods
like reinforcement learning from human feedback (RLHF) and
supervised fine-tuning (SFT), these global controls blur
refusal boundaries across domains such as violence, fraud,
and privacy, and frequently collapse under adversarial
variation. We propose Refusal Activation Steering (RAS), a
training-free, inference-time method that uses contrastive
activations to shift LLM responses, biasing generation
trajectories toward refusals without altering model
weights. The approach is modular and domain-targetable,
avoiding collateral refusals on benign queries while
strengthening activation-space boundaries for unsafe
content. On adversarial evaluations with an 8B
instruction-tuned model, we find that steering improves
refusal rate by 52% and reduces attack success rate by 40%,
establishing a lightweight and interpretable safety layer
for robust refusal consistency.

Always Refuse: Steering LLMs Against Jailbreaks with Contrastive Activations (Student Abstract)

Deep learning has advanced medical imaging, but limited
interpretability hinders clinical adoption. Class
activation maps (CAMs) provide visual explanations, yet
methods such as Score-CAM are computationally expensive,
requiring a forward pass for each activation map and
limiting real-time applicability despite their high
fidelity. To overcome this limitation, LowRank-CAM is
proposed, which aggregates activation maps into a global
matrix and applies singular value decomposition (SVD) to
extract dominant spatial modes. The resulting top-r
attention masks, with r much smaller than K, replace
per-channel perturbations and require only r forward passes
through the classifier head. This low-rank formulation
substantially reduces complexity while preserving
class-discriminatory importance. Experiments on
musculoskeletal radiographs with Inception-v3 demonstrate
that LowRank-CAM achieves a 4.73× speedup over Score-CAM
while maintaining comparable visual clarity and diagnostic
relevance.

LowRank-CAM: A Computationally Efficient and Interpretable Framework for Medical Image Analysis (Student Abstract)

Quantum machine learning (QML) has attracted growing
interest for their ability to achieve superior performance
with significantly fewer parameters.
However, the high cost and scarcity of current hardware
push inference to cloud-hosted quantum devices, creating a
tension between verifiability and confidentiality.
This work proposes a novel framework that converts quantum
neural network operations into classical arithmetic
circuits that faithfully approximate genuine quantum
computations. By encrypting these circuits with
zero-knowledge proofs, it ensures computational validity
while concealing internal parameters. Experimental results
show that our classical circuits achieve fidelity above
0.9996 and total variation distance below 1% compared to
actual quantum computations, verifying the practicality of
trustworthy and privacy-preserving quantum inference.

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