Retrieval-Augmented Generation (RAG) systems are widely adopted in question answering, yet they often fail to satisfy complex multi-constraint queries, leading to constraint violations, factual inconsistencies, or hallucinations. We present Structure-Driven RAG System for Multi-Constraint Queries(MC-RAG), a structure-driven RAG system that reformulates retrieval as a subgraph matching problem over a knowledge graph. By integrating semantic and structural embeddings with path-level indexing, MC-RAG performs interpretable, structure-aware, and constraint-consistent retrieval and generation. During the demonstration, participants can input medical or encyclopedic multi-constraint queries, visualize how the system parses constraints, performs structural matching, and generates answers, thereby experiencing an end-to-end, interactive, and explainable RAG pipeline. A demo video is available at https://youtu.be/J8kahzmAnu0.
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The Personal AI landscape is currently dominated by "Black Box" Retrieval-Augmented Generation. While standard vector databases offer statistical matching, they suffer from a fundamental lack of accountability: when an AI hallucinates or retrieves sensitive data, the user cannot inspect the cause nor correct the error. Worse, "deleting" a concept from a vector space is mathematically imprecise, leaving behind probabilistic "ghosts" that violate true privacy. We propose Ruva, the first "Glass Box" architecture designed for Human-in-the-Loop Memory Curation. Ruva grounds Personal AI in a Personal Knowledge Graph, enabling users to inspect what the AI knows and to perform precise redaction of specific facts. By shifting the paradigm from Vector Matching to Graph Reasoning, Ruva ensures the "Right to be Forgotten." Users are the editors of their own lives; Ruva hands them the pen.
Large Language Models can use logical deduction to answer natural language questions, but they remain black-boxes with potentially erroneous chains-of-thought. In this paper, we adapt VANESSA, a neuro-symbolic method for chain-of-thought verification, to reasoning-based question answering. VANESSA combines a logical reasoner with a neural textual entailment model to handle phrasing variations. Building on VANESSA, we develop a transparent, logic-based approach to answer natural language questions even with phrase variations. Our experiments across various datasets show our method is competitive with the state of the art, while also delivering proof trees for its answers. A demo interface allows users to interact with the system.
Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting the filling dynamics of overflow basins is critical for anticipating capacity exceedance and enabling timely preventive actions for CSO. We present a web-based demonstrator that integrates Deep Learning forecasting methods in both cloud and edge settings into an interactive monitoring dashboard for overflow monitoring, resilient to network outages.
Precise descriptions of soil horizons are required for policy makers, agriculture and many applications in civil engineering. Up to date correct soil horizon annotations require human experts as they follow complex hierarchical taxonomies. We present the SoilNet App, a web-based demonstrator that guides experts through relevant tasks for expert-level soil horizon annotations from soil profile images. To demonstrate the reliability of the SoilNet app we present results of a user study with soil horizon annotation experts, which highlights the difficulty of image-only-based annotation and suggests that collaborating with our model not only increases expert performance but also improves inter-annotator consistency. Our app is publicly accessible (https://soilnet.demo.calgo-lab.de).
AI's increasing role as a personal agent assisting knowledge workers in everyday tasks underscores the need to investigate how to help human–agent teams build a shared understanding. We extend the collaborative "mind-reading" game Wavelength to include an AI teammate, presenting the first demonstration of an LLM capable of playing this game. Based on our agent–agent play experiments, we developed Wavelength.AI, which implements two strategies to support shared understanding: an initial team grounding conversation and post-game reflective explanations. We interpret higher team scores as evidence for better shared understanding in a preliminary user study with 24 human–AI teams. Our findings reveal that Wavelength.AI can help researchers evaluate and design different strategies to shape human-agent teams' shared understanding. Human players can see if they are on the same wavelength with AI and view our demo video today at https://play-wavelength-ai.com.
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While we have witnessed extraordinary progress across various subfields of AI, the deployment of these systems in safety-critical and mission-critical domains has lagged behind. A key requirement for such deployments is the availability of AI systems capable of generating optimal behavior that can be formally verified and effectively used by non-AI experts from diverse backgrounds. While significant advances have been made toward building such systems, we still lack comprehensive formal frameworks to model and analyze the complex dynamics of human-AI interaction. In this talk, I will introduce the Human-Aware AI framework, a multi-agent planning framework specifically designed to support and reason about human-AI interaction. I will show how this framework provides novel solutions to challenges such as explainability, value alignment, and proactive assistance, and demonstrate its application in domains including intelligent tutoring systems, cybersecurity, and robotics.
Time series forecasting has become a central problem in modern machine learning, with applications spanning science, engineering, health, and finance. Yet in many domains, accurate prediction is only the starting point. This talk explores a broader view of time series learning, moving from forecasting toward discovery. I will first discuss our work on efficient time series forecasting, focusing on methods that improve scalability and practicality without sacrificing predictive performance. I will then turn to structured learning for high-dimensional temporal data, showing how modeling dependencies across variables can lead to more reliable and expressive forecasting systems. Finally, I will ask whether machine learning can move beyond forecasting to discover symbolic structure from time series data. I will present our recent work on benchmarking symbolic reasoning over time series and on hybrid LLM-based methods for scientific discovery. These directions point toward time series models that support both prediction and discovery.
Continual learning (CL) is a prerequisite for autonomous intelligence. Despite decades of progress, the field has operated largely without a mechanistic account of \emph{where} and \emph{why} forgetting occurs, treating it as a monolithic phenomenon to be suppressed rather than a structured process to be understood. We argue that three recent shifts are collectively redefining the field: (1) a mechanistic turn that localizes forgetting to specific computational circuits; (2) the emergence of foundation models that change the problem entirely; and (3) the imperative to move beyond task-bounded protocols toward genuinely autonomous, boundary-free learning. Grounded in Complementary Learning Systems (CLS) theory, this work identifies fast and slow learning pathways inside large language models, characterizes the structural geometry of forgetting, and constructs deployable CL pipelines with biologically motivated signatures. These directions chart a path toward autonomous learning systems that regulate their own consolidation.
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The rapid advancement of Artificial Intelligence (AI) has accelerated the development of personalised healthcare. However, the clinical adoption of deep learning remains constrained by a persistent âtrust gapâ surrounding model transparency, security, and data privacy. This talk presents a research vision for building trustworthy human-centred AI in computer audition and biosignal processing by integrating signal processing, machine learning, and healthcare. This vision is structured around three interconnected pillars: Sensing, Understanding, and Empowerment. The Sensing pillar focuses on enabling machines to perceive clinically relevant information from multimodal biosignals, transforming body sounds and physiological signals into non-invasive, accessible, and cost-effective windows into human health and well-being. The Understanding pillar addresses the black-box nature of modern AI systems, with the goal of ensuring transparency, efficiency, robustness, and security in clinical deployment. This is achieved through the development of explainable AI methods, knowledge distillation techniques, and defences against adversarial attacks, fostering AI systems that clinicians can trust and interpret. The Empowerment pillar seeks to restore natural communication for individuals with speech impairments, such as laryngectomy patients. By developing Silent Speech Interfaces (SSIs) that translate facial muscle activity (EMG) directly into audible speech, this research advances speech intelligibility, naturalness, real-time causal architectures, and multi-speaker communication scenarios. Collectively, these research directions aim to establish a foundation of trustworthy, human-centred AI that is not only accurate and secure, but also empathetic, accessible, and impactful, ultimately enhancing healthcare, communication, and quality of life.
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Recent progress in Large Language Models (LLMs) has transformed text and code generation, yet models still falter on Partial Differential Equations (PDEs) where correctness, constraints, and physical consequences are critical. We explore how formal LLM reasoning can advance symbolic PDE modeling. First, our PDE-Controller formalizes informal PDEs, synthesizes solver-ready code, and plans subgoals to tackle nonconvex control via interactions with external solvers. Second, our Lean Finder accelerates PDE formalization via a semantics-aware search engine for Lean/Mathlib that retrieves relevant theorems, outperforming GPT models and gaining significant traction in the AI-for-math community. Through these efforts, we aim to design a semantics-first LLM that autoformalizes informal PDE problems into machine-checked specifications and synthesizes solver-ready code. This closes the loop between formal analysis and LLM reasoning, ultimately surpassing human heuristics across PDEs.
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Human reasoning relies on abstraction and generalization, in order to make decisions flexible under changing conditions while ignoring irrelevant details and focusing on the essence. Developing AI systems with such abilities, while ensuring transparency and explainability on the reasoning behind the made decision, remains a central challenge. Symbolic AI has a strong position for explainable reasoning but existing generalization methods either suffer from not discovering the underlying patterns or are domain-dependent, due to a lack of theoretical characteristics for generalized reasoning. This paper presents an overview on the research towards filling this gap by developing formal and computational methods for abstraction in Answer Set Programming, one of the core formalisms in symbolic AI, and related logic-based frameworks. The contributions span from foundational abstraction techniques that simplify reasoning representations while preserving essential solution properties, to investigations on how such abstractions can both improve computational reasoning and support human understanding of AI decision processes.