Singapore

LLMs are increasingly being deployed as chatbots, but today’s interfaces offer little to no friction: users interact through seamless conversations that conceal when the model is drifting, hallucinating or failing. This lack of transparency fosters blind trust, even as models produce unstable or repetitive outputs. We introduce an interactive demo that surfaces and mitigates cognitive fatigue, a failure mode where LLMs gradually lose coherence during auto-regressive generation. Our system, Chatsparent, instruments real-time, token-level signals of fatigue, including attention-to-prompt decay, embedding drift, and entropy collapse, and visualizes them as a unified fatigue index. When fatigue thresholds are crossed, the interface allows users to activate lightweight interventions such as attention resets, entropy-regularized decoding, and self-reflection checkpoints. The demo streams live text and fatigue signals, allowing users to observe when fatigue arises, how it affects output quality, and how interventions restore stability. By turning passive chatbot interaction into an interactive diagnostic experience, our system empowers users to better understand LLM behavior while improving reliability at inference time. The demo video is available at https://youtu.be/ktqkZyYWDDE.

AAAI 2026

Chatsparent: An Interactive System for Detecting and Mitigating Cognitive Fatigue in LLMs

machine learning systems

interpretability

natural language processing

robustness

[![](https://assets.underline.io/uploads/markdown_image/1/image/261902fdf187a88bfa74da226ff77186.png)](https://drive.google.com/file/d/1gUViQJJHCSgM38OhwpDoTdN89evCsZ-7/view?usp=sharing)
[![](https://assets.underline.io/uploads/markdown_image/1/image/1605292c232e6f185c7f87ee38e8502a.png)](https://drive.google.com/file/d/1kPkznVX_6cx-n846z_pUtyVDZql9KImc/view?usp=sharing)


### **Welcome Message**

Welcome to the inaugural **Dutch Lung Congress.** We have all missed our opportunities to network and interact with our colleagues in the last twelve months or more. While our desire was to meet face to face once again, when it became evident that this would not be possible until much later in 2021 (or even into 2022) the hard working committee organizing the Dutch Lung Congress found, what we hope will be, an excellent alternative to facilitate our annual meeting.

The Dutch Lung Congress aims to bring together everyone interested in research related to pulmonary diseases in the Netherlands. We hope this event will provide opportunities for multidisciplinary interactions to enhance our approaches for addressing these challenging diseases. Each session will include input from patients, clinical and basic and/or translational scientists discussing the latest information around the central themes chosen by the organizing committee. One advantage of holding our congress as a virtual event this year is that we will have the opportunity to hear from world leading experts from all over the globe. We hope you enjoy the programme the organizing committee have put together and will engage in dynamic discussions with all the speakers.

One highlight of our congress will be the Young Investigator Session. Remember to join us for this exciting session when we will hear from the presenters who submitted the top scoring abstracts. This promises to be a session full of exciting science that will display the strengths that constitute the future talent in our field.

When we are not listening to these great presentations we can all meet in Gathertown. Within this interactive platform you will be able to meet colleagues and friends for networking. Enjoy a private chat over a virtual coffee, visit the booths of our sponsors and other parties, chat with the Young Investigator Board members or take some timeout in the relaxation spaces. While in Gathertown take the opportunity to visit all the diverse posters on display. Chat live with the presenters or leave them a question on the discussion board for them to engage with you. Make sure you check for any messages each time you enter Gathertown to make the most of your networking opportunities.

We look forward to seeing you during the DLC and the great scientific (and other) discussions we are anticipating during our 2021 meeting.

On behalf of the DLC organizing Committee
 **Janette Burgess
 Chair, DLC Organizing Committee**
![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)



### Special Session: Publieksmiddag
![](https://assets.underline.io/uploads/markdown_image/1/image/77e768dd500e6205175e5cafba59abdc.png)






![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)
**More about the committee team:**:
[Barbro N. Melgert, PhD] (https://drive.google.com/file/d/1q_IIH3JvTivumCUzzsZGX7BgD41HxQmk/view?usp=sharing)
// [Bas Holverda, PhD] (https://drive.google.com/file/d/1QKXHmTTQjgs4OZVuM8doXU055S-fu_uU/view?usp=sharing)
// [Frits Franssen, PhD] (https://drive.google.com/file/d/1d7HRUJaqGuRFzkPN3SVgkbBfqFkDkWq2/view?usp=sharing)
// [Janette Burgess, PhD] (https://drive.google.com/file/d/1VHFlrEN6uCQGeeW3Lj4I8aTw7HsB0GFr/view?usp=sharing)
// [Michel van den Heuvel, PhD] (https://drive.google.com/file/d/1JeOEP8MHxiK_4lONr8UM2HaxdxpkAmA2/view?usp=sharing)
// [Patrick Bhairosing] (https://drive.google.com/file/d/1xI8ORq-g1bbVcrlRDviZ9NhAwanc4dFl/view?usp=sharing)
// [Gimano Amatngalim, PhD] (https://drive.google.com/file/d/1fyVDDKARLNjRk_JZk8_GyhGMjmp6DWHp/view?usp=sharing)
// [Peter Kunst, PhD] (https://drive.google.com/file/d/1QhtB4nQezpAjbfUAcBnINc-Bm3Shy2Lf/view?usp=sharing)
// [Dr. Michel van den Heuvel, Prof., MD, PhD] (https://drive.google.com/file/d/1JBdNEBXZCEikEZhJ6MoD5DhQfXMz3bP6/view?usp=sharing)
![](https://assets.underline.io/uploads/markdown_image/1/image/f461fb275d9667fb2a76a30813388e90.png)

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Dutch Lung Congress

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

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Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

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

SafeLens: Segment-Level Hate Speech Detection in Online Videos

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.

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