Singapore

We present SafeLens, a lightweight segment-level video moderation system that fuses speech, text, and visual frames to produce hateful content detection for each segment. For every segment, SafeLens returns a structured prediction: label, prediction confidence, reasons for flag, harm categories. The structured predictions are optimized for triage, appeals, and downstream enforcement. The system is modular (pluggable speech, text, and visual processing modules back-ends and a mid-size policy Language Language Model (LLM) agent with parameter-efficient tuning). In the live demo, attendees can upload or select clips, scrub the timeline to flag hateful segments, inspect rationales, and vary the policy LLM agent to benchmark the hateful content moderation performance.

Video: https://www.youtube.com/watch?v=B1dYceLSnXA

AAAI 2026

SafeLens: Segment-Level Hate Speech Detection in Online Videos

demo

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.

Time series anomaly detection has received substantial attention over the past two decades, leading to the development of hundreds of algorithms. However, comprehensively understanding this vast landscape remains challenging, particularly for non-experts and novices. In this demonstration paper, we present \demonstratorname, an interactive web application that provides access to more than 30 state-of-the-art time series anomaly detection algorithms. \demonstratorname is intended to explore the performance of existing as well as custom anomaly detection models in an interactive, hands-on manner. By lowering the entry bar, we support practitioners overwhelmed by the large number of existing techniques, while providing a platform for researchers to rapidly analyze their novel anomaly detection algorithms.

InTimeAD: Interactive Time Series Anomaly Detection

We present ARGUS, an end-to-end Argument Mining (AM) tool that exploits Large Language Models (LLMs) to automatically perform all core AM tasks, i.e., Argument Component Segmentation, Classification, Relation Identification, and Relation Classification. Furthermore, ARGUS builds the corresponding argumentation framework (AF) and seamlessly integrates symbolic solvers to compute extensions and perform formal reasoning. ARGUS is designed to ensure broad flexibility and usability, supporting any open-source or commercial LLMs and symbolic solvers, providing a ready-to-use platform for exploring neuro-symbolic approaches to argumentation in both research and practical applications.

ARGUS: Towards End-to-End Argument Mining with Large Language Models

We present City of Light (COL), a Unity-based, city-scale (116 km2) simulator of Paris for high-throughput embodied-AI research. COL fuses open geographic information system (GIS) sources into geo-anchored, per-tile meshes and provides a configurable, stochastic runtime with controllable traffic and pedestrians. Agents receive frame-synchronized multi-sensor observations (RGB, depth, normals, semantics). To support high-rate vision pipelines, we introduce TURBO, a zero-copy Unity-Python bridge that streams multi-camera observations to Python and allows control at up to 1300 frames per second (FPS), achieving higher throughput than ML-Agents in our benchmark. We also provide a Street View Digital Twin that aligns simulator viewpoints with corresponding real-world panoramas for frame-accurate visual comparison and quantitative matching. COL enables fast scripting, large-scale data collection, and reinforcement-learning (RL) in geo-anchored urban settings.

City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams

Extended reality (XR) is well suited to support the situated learning of technical procedures. At the same time, AI-driven intelligent tutoring systems (ITS) can complement XR by providing adaptive pedagogical support. Many domains would benefit from this combination, especially when trainers, equipment, or team members are limited. We present a domain-agnostic XR-based ITS that integrates a training procedure representation (TPR), XR simulation, and an LLM-driven instructor. We demonstrate the tutor's use for tissue sample handling and engine repair, showing how it delivers adaptive feedback, collaborative roleplay, and dynamic scenario management to create realistic and pedagogically meaningful training experiences.

TPR: A Training Procedure Representation to Augment XR Simulations with LLMs

We present a practical system that supports in-depth analysis of cryptocurrency markets through timeline-based event detection and contextual summarization. Our framework processes continuous news streams, identifies price-relevant events, and organizes them into semantic timelines with concise background summaries generated by large language models (LLMs). This design allows traders and analysts to retrospectively explore events alongside price charts, facilitating a deeper understanding of how news developments relate to market fluctuations. By transforming unstructured news data into structured insights, the system provides a valuable tool for market analysis, risk evaluation, and behavioral studies in volatile trading environments.

Market-Aware Event Timeline Summarization: Integrating Price Signals to Improve Financial News Understanding

The rising demand for Trusted AI (TAI) underscores the need for interpretable and robust models, yet existing tools rarely support graph-structured data or integrate interpretability with security. At the same time, Graph Neural Networks (GNNs) deliver state-of-the-art performance on numerous graph tasks.

We present GNN-AID (Graph Neural Network Analysis, Interpretation, and Defense), an open-source Python framework for analyzing, interpreting, and defending GNNs, addressing this critical gap. Built on PyTorch-Geometric, GNN-AID offers preloaded datasets, model libraries, flexible APIs, and a web interface for visualization and no-code model design. MLOps features further support reproducibility and experiment tracking.

GitHub repo: https://github.com/ispras/GNN-AID.

YouTube video: https://youtu.be/uHxaxLSQ9JM.

Framework GNN-AID: Graph Neural Network Analysis, Interpretation and Defense

Supporting children with Autism Spectrum Disorder (ASD) requires highly individualized knowledge, but crucial information is often scattered across documents such as Individualized Education Plans (IEPs), diagnostic assessments, and caregiver notes. Thus, we propose SHARE (Synthesis of Heterogeneous Autism-support Records into Evidence-based Recommendations), a framework that transforms diverse autism-related documents into a concise, actionable set of recommendations directed towards caregivers of children with autism. Recommendations are generated with OpenAI’s large language model API, grounded in user-provided evidence with optional web-based augmentation for missing details, and each recommendation is citation-linked to ensure traceability. When caregivers rate attempted recommendations, SHARE applies a lightweight Bayesian bandit with Upper Confidence Bound (UCB) re-ranking to refine and personalize future outputs. This adaptive feedback loop sets SHARE apart from prior systems, which have focused on static goal drafting or summaries, by combining LLM-based generation, caregiver input, and interpretable ranking in a pipeline that evolves over time.

SHARE: Synthesizing Heterogeneous Autism-support Records into Evidence-based Recommendations

Ethical awareness is critical for robots operating in human environments, yet existing automated planning tools provide little support. Manually specifying ethical rules is labour-intensive and highly context-specific. We present Principles2Plan, an interactive research prototype demonstrating how a human and a Large Language Model (LLM) can collaborate to produce context-sensitive ethical rules and guide automated planning. A domain expert provides the planning domain, problem details, and relevant high-level principles such as beneficence and privacy. The system generates operationalisable ethical rules consistent with these principles, which the user can review, prioritise, and supply to a planner to produce ethically-informed plans. To our knowledge, no prior system supports users in generating principle-grounded rules for classical planning contexts. Principles2Plan showcases the potential of human-LLM collaboration for making ethical automated planning more practical and feasible.

Principles2Plan: LLM-Guided System for Operationalising Ethical Principles into Plans

Fine-grained action evaluation in medical vision faces unique challenges due to the unavailability of comprehensive datasets, stringent precision requirements, and insufficient spatiotemporal dynamic modeling of very rapid actions. 
To support development and evaluation, we introduce CPREval-6k, a multi-view, multi-label medical action benchmark containing 6,372 expert-annotated videos with 22 clinical labels.
Using this dataset, we present GaussMedAct, a multivariate Gaussian encoding framework, to advance medical motion analysis through adaptive spatiotemporal representation learning. Multivariate Gaussian Representation projects the joint motions to a temporally scaled multi-dimensional space, and decomposes actions into adaptive 3D Gaussians that serve as tokens. These tokens preserve motion semantics through anisotropic covariance modeling while maintaining robustness to spatiotemporal noise. Hybrid Spatial Encoding, employing a Cartesian and Vector dual-stream strategy, effectively utilizes skeletal information in the form of joint and bone features.
The proposed method achieves 92.1\% Top-1 accuracy with real-time inference on the benchmark, outperforming the ST-GCN baseline by +5.9\% accuracy with only 10\% FLOPs. Cross-dataset experiments confirm the superiority of our method in robustness.

Multivariate Gaussian Representation Learning for Medical Action Evaluation

Effective crime linkage analysis is crucial for identifying serial offenders and enhancing public safety. To address the limitations of traditional crime linkage methods when handling high-dimensional, sparse, and heterogeneous data, this paper proposes a Siamese Autoencoder framework to learn meaningful latent representations and uncover correlations in highly complex data. Using a dataset from the Violent Crime Linkage Analysis System—a database maintained by the Serious Crime Analysis Section of the UK’s National Crime Agency—our approach mitigates signal dilution in high-dimensional sparse data through decoder-stage integration of geographic-temporal features. This integration amplifies learned behavioral representations rather than allowing them to be overwhelmed at the input stage, leading to consistent improvements over baseline methods across multiple metrics. We further examine how different data reduction strategies based on domain-expert can impact model performance, offering practical insights into preprocessing for crime linkage. Our solution shows that advanced machine learning approaches can enhance linkage accuracy, improving AUC by up to 9% over traditional methods and providing insights to support human decision-making in crime investigation.

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InTimeAD: Interactive Time Series Anomaly Detection

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