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Junhua Liu, Fanfan Lin, Xinze Li, Shuai Zhao, Kwan Hui Lim

LLM-based autonomous agents have recently shown strong capabilities in solving complex industrial design tasks. However, in domains aiming for carbon neutrality and high-performance renewable energy systems, current AI-assisted design automation methods face critical challenges in explainability, scalability, and practical usability. To address these limitations, we introduce PHIA (Physics-Informed Autonomous Agent), an LLM-driven system that automates modulation design for power converters in Power Electronics Systems with minimal human intervention. In contrast to traditional pipeline-based methods, PHIA incorporates an LLM-based planning module that interactively acquires and verifies design requirements via a user-friendly chat interface. This planner collaborates with physics-informed simulation and optimization components to autonomously generate and iteratively refine modulation designs. The interactive interface also supports interpretability by providing textual explanations and visual outputs throughout the design process. Experimental results show that PHIA reduces standard mean absolute error by 63.2% compared to the second-best benchmark and accelerates the overall design process by over 33 times. A user study involving 20 domain experts further confirms PHIA’s superior design efficiency and usability, highlighting its potential to transform industrial design workflows in power electronics.

Weijie Jiang, Armando Ordorica, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan

User retention is a critical objective for online platforms like Pinterest, as it strengthens user loyalty and drives growth through repeated engagement. A key indicator of retention is revisitation, i.e., when users return to view previously saved content, a behavior often sparked by personalized recommendations and user satisfaction. However, modeling and optimizing revisitation poses significant challenges. One core difficulty is accurate attribution: it is often unclear which specific user actions or content exposures trigger a revisit, since many confounding factors (e.g., content quality, user interface, notifications, or even changing user intent) can influence return behavior. Additionally, the scale and timing of revisitations introduce further complexity; users may revisit content days or even weeks after their initial interaction, requiring the system to maintain and associate extensive historical records across millions of users and sessions. These complexities render existing methods insufficient for robustly capturing and optimizing long-term revisitation. To address these gaps, we introduce a novel, lightweight, and interpretable framework for modeling revisitation behavior and optimizing long-term user retention in Pinterest’s search-based recommendation context. By defining a surrogate attribution process that links saves to subsequent revisitations, we reduce noise in the causal relationship between user actions and return visits. Our scalable event aggregation pipeline enables large-scale analysis of user revisitation patterns and enhances the ranking system’s ability to surface items with high retention value. Deployed on Pinterest’s Related Pins surface to serve 500+ million users, the framework led to a significant lift of 0.1% in active users without additional computational costs. Our data analysis reveals novel insights, such as the impact of content topics on revisitation rates; for example, users are more likely to revisit aesthetically pleasing topics.

Francisco C J Bonfim, Sara Pessoa SIlva, Alicia S Neves, Henrique Santos, Joao A Monteiro Neto, Jorge L B Araujo, Rilder S Pires, Ricardo Alexandre Da Silva Costa, Vasco Furtado, Vladia Pinheiro

Delivering judicial decisions requires interpreting complex legal texts, analyzing evidence, and reasoning over jurisprudence and legal principles. Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have shown potential to automate parts of this process; however, practical and measurable benefits in real-world judicial settings remain limited. This paper introduces SARA, an LLM-powered legal reasoning platform deployed in a regional Brazilian court, which demonstrates significant efficiency and quality gains through the integration of LLM agents with a Jurisprudential Knowledge Graph (Jur-KG). SARA automatically extracts and structures key elements from legal documents, including claims, requests, and evidence, and generates legal reasoning grounded in retrieved jurisprudential precedents. The Jur-KG is modeled through an ontology encompassing core legal concepts such as parties, facts, and legal claims, enabling semantic matching and retrieval of relevant case law. By representing cases according to the Legal Case Ontology for the Brazilian Judicial System, SARA supports traceable reasoning and addresses competence questions to assess the coverage, coherence, and justification of AI-generated outputs. Deployment results indicate measurable improvements in processing time, consistency, and explainability, while ensuring compliance with ethical and legal guidelines established by Brazil’s National Council of Justice. This work demonstrates that combining LLM-based agents with domain-specific knowledge graphs can deliver both innovative capabilities and proven impact in judicial decision-making.

Matthias Zeller, Vardeep Singh Sandhu, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

Wijnand Van Woerkom, Davide Grossi, Henry Prakken, Bart Verheij

The widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision-making process of these systems. These efforts have their background in many different disciplines, one of which is the field of AI & law. In particular, recent works have observed that machine learning training data can be interpreted as legal cases. Under this interpretation, the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data—or should draw on them—to make decisions. In the present work, we advance the theory underlying these explanation methods, by relating it to order theory and logic. This allows us to write a software implementation of the theory that can be used to compute with the definitions and give automatic proofs of the properties of the model. We use this implementation to evaluate the model on a series of datasets. Through this analysis, we characterize the types of datasets that are more, or less, suitable to be described by the theory.

Erfan Shahriari, Petr Svarny, Seyed Ali Baradaran Birjandi, Matej Hoffmann, Sami Haddadin

Robots have surpassed humans in terms of strength and precision, yet humans retain an unparalleled ability for decision-making in the face of unpredictable disturbances. This article aims to combine the strengths of both entities within a singular task: human motion guidance under strict geometric constraints, particularly adhering to predetermined paths. To tackle this challenge, a modular haptic guidance law is proposed that takes the human-applied wrench as an input. Using an auxiliary variable called phase, the generated desired motion is guaranteed to consistently adhere to the constraint path. The guidance policy can be generalized into physically interpretable terms, adjustable either prior to initiating the task or dynamically while the task is in progress. An illustrative guidance adaptation policy is showcased that takes into account the human's manipulability. Passivity analysis is used to ensure overall system stability. Experiments, including a 20-participant user study, explore various aspects of the approach in practice.

Mayra Russo, Maria-Esther Vidal

Machine learning (ML)-powered systems are capable of reproducing and often amplifying undesired biases embedded in society, emphasizing the importance of operating under practices that enable the study and understanding of the intrinsic characteristics of ML pipelines. This supports the emergence of documentation frameworks with the idea that “any remedy for bias starts with awareness of its existence.” However, a resource that can formally describe ML pipelines in terms of detected biases is still missing. To address this gap, we present the Doc-BiasO ontology, a resource that sets out to create an integrated vocabulary of biases defined in the Trustworthy AI literature and their measures, as well as to incorporate relevant domain terminology and relationships between them. Overseeing ontology engineering best practices, we reuse existing vocabularies on machine learning and AI to foster knowledge sharing and interoperability between the actors concerned with its research, development, regulation, and others. In addition, we demonstrate the potential of Doc-BiasO with an experiment on an existing benchmark and as part of a neuro-symbolic system. Overall, our main objective is to contribute towards clarifying existing terminology on bias research as it rapidly expands to all areas of AI and to improve the interpretation of bias in data and downstream impact through its documentation.

Hosein Hasanbeig, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham, Daniel Kroening

This paper proposes DeepSynth, a method for effective training of deep reinforcement learning agents when the reward is sparse or non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact finite state automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton, so that the generation of a control policy by deep reinforcement learning is guided by the discovered structure encoded in the automaton. The proposed approach is able to cope with both high-dimensional, low-level features and unknown sparse or non-Markovian rewards. We have evaluated DeepSynth’s performance in a set of experiments that includes the Atari game Montezuma’s Revenge, known to be challenging. Compared to approaches that rely solely on deep reinforcement learning, we obtain a reduction of two orders of magnitude in the iterations required for policy synthesis, and a significant improvement in scalability.

Ruihua Han, Shuai Wang, Shuaijun Wang, Zeqing Zhang, Jianjun Chen, Shijie Lin, Chengyang Li, Chengzhong Xu, Yonina C. Eldar, Qi Hao 等

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via back propagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

Amanjot Bhullar, Michael Czomko, R. Ayesha Ali, Douglas L. Welch

Artificial neural networks are popular for computer vision as they often give state-of-the-art performance, but are difficult to interpret because of their complexity. This black box modeling is especially troubling when the application concerns human well-being such as in medical image analysis or autonomous driving. In this work, we propose a technique called routing path visualization for capsule networks, which reveals how much of each region in an image is routed to each capsule. In turn, this technique can be used to interpret the entity that a given capsule detects, and speculate how the network makes a prediction. We demonstrate our new visualization technique on several real world datasets. Experimental results suggest that routing path visualization can precisely localize the predicted class from an image, even though the capsule networks are trained using just images and their respective class labels, without additional information defining the location of the class in the image.

Mengyue Yang

My research lies at the intersection of causality, reinforcement learning, world models, and multi-agent systems. I aim to develop causal foundation world models that enable agents to interpret the past, reason about the future, and act reliably in dynamic, non-stationary, and open-ended environments. My work spans causal representation learning (e.g., CausalVAE), causal reasoning in large language models, and causality-driven exploration in open-ended worlds. These contributions have appeared in leading venues such as NeurIPS, ICML, ICLR, CVPR, and KDD, and have been recognized through over 770 citations and the Rising Star in AI award (2024). Looking forward, my agenda focuses on scalable, trustworthy causal world models for healthcare, robotics, scientific discovery, and digital systems.

Grigorios Chrysos

As large language models (LLMs) scale, ensuring interpretability and privacy becomes critical. This talk addresses these interconnected challenges with novel approaches to model specialization and safety. First, we tackle the dense, distributed nature of LLM representations by casting Mixture-of-Experts (MoE) as a tensor decomposition, enabling specialized experts in a factorized space. Second, we argue that current neuron-level sparsity methods create a severe accuracy-sparsity trade-off, and we propose a paradigm shift to layer-level sparsity with the Mixture of Decoders (MxD). We explain how MxD uses tensor factorization to expand dense layers into thousands of specialized, full-rank sublayers, demonstrating how it significantly outperforms alternatives in preserving model faithfulness and performance across LLMs up to 3B parameters. Finally, we address privacy in open-weight models by proposing a scalable and certifiable algorithm that induces maximal uncertainty on protected instances, proving tight bounds that characterize the resulting privacy-utility tradeoff.

Chirag Agarwal

Machine learning models have become ubiquitous in the last decade, and with their increasing use in critical applications (e.g., healthcare, financial systems, and crime forecasting), it is vital to ensure that ML developers and practitioners understand and trust their decisions. This problem has become paramount in the era of frontier models, which are developed by training billion-parameter models on broad, uncurated datasets and extensive computing. In this talk, we will first explore the (un)reliability of existing multimodal explainability techniques in large language and multimodal models and understand the robustness and safety implications of Mechanistic Interpretability tools. Next, we will delve into two complementary threads: i) domain-specific safety and related trustworthy evaluation that surfaces risks missed by generic red-teaming, focusing on multilingual and distribution-shifted settings; and ii) methods that explicitly train and assess reasoning in medical LLMs.

David Poole

Artificial intelligence seems to be taking over the world with systems that model pixels, words, and phonemes. The world is arguably made up, not of pixels, words, and phonemes but of entities (objects, things, including events) with properties and relations among them. Surely we should model these, not the perception or description of them. You might suspect that concentrating on modeling words and pixels is because all of the (valuable) data in the world is in terms of text and images. If you look into almost any company you will find their most valuable data is in spreadsheets, databases and other relational formats. These are not the form that are studied in introductory machine learning, but are full of product numbers, student numbers, transaction numbers and other identifiers that can't be interpreted naively as numbers. The field that studies this sort of data has various names including relational learning, statistical relational AI, and many others. This paper explains why relational learning is not taking over the world -- except in a few cases with restricted relations -- and what needs to be done to bring it to it's rightful prominence.

Roberto Navigli

Word Sense Disambiguation (WSD) has been a central challenge since the earliest proposals for Machine Translation (MT), most famously Weaver's 1949 memorandum. Classical systems treated WSD as an explicit task, grounded in lexical resources and annotated data. Recently, however, Large Language Models (LLMs) have blurred the boundary between disambiguation and general language understanding, leading some to suggest that WSD might be obsolete. This paper surveys the role of WSD in the LLM era, drawing on recent studies of encoder-based sense separation and disambiguation, and decoder-based definition selection and generation, as well as multilingual evaluation. Closed-source instruction-tuned LLMs now achieve performance comparable to specialized WSD systems, yet systematic weaknesses remain: non-predominant senses are often misclassified and disambiguation biases in MT persist. We argue that WSD is not "dead" but redefined as a diagnostic lens for assessing lexical-semantic competence, robustness, and interpretability in LLMs.

Chuanyi Li, Yi Feng, Vincent Ng

Dual-system theory distinguishes between fast, intuitive System 1 and slow, deliberative System 2. While this dichotomy describes many forms of reasoning, it oversimplifies the reality of expert legal reasoning. Legal reasoning is not merely a process of slow, logical deliberation. It is intrinsically normative, embedding precedent analysis, statutory interpretation, policy balancing, and social values. This paper envisions a reasoning architecture for legal reasoning, System L (Legal System 2), which extends traditional System 2 by integrating domain-specific normative frameworks in a structured manner. Using the IRAC (Issue–Rule–Application–Conclusion) structure as a backbone model, System L represents a blueprint for the next generation of cognitive and AI systems capable of human-like legal reasoning.

Anuj Karpatne, Xiaowei Jia, Vipin Kumar

As advances in artificial intelligence (AI) and machine learning (ML) continue to transform commercial applications, the scientific community is increasingly eager to harness AI/ML’s power to accelerate modeling and discovery. However, purely data-driven AI methods often lack interpretability, generalizability, and consistency with established scientific principles. Conversely, traditional process-based models embody deep scientific knowledge but suffer from limited scalability or incomplete representation of complex systems. Knowledge-guided machine learning (KGML) offers a promising path forward by integrating scientific knowledge with data-driven approaches to produce AI models that are robust, trustworthy, and capable of advancing both AI and science. This talk summarizes the foundations of KGML, outlines a taxonomy for organizing research efforts, and highlights emerging opportunities for broad scientific impact.

XuWen Zhang, Xiao Xue, Xia Xie, Qun Ma

Crowdsourced delivery platforms (e.g., Meituan, Uber Eats, DoorDash) have become vital infrastructure in urban logistics, yet their competitive order-grabbing mechanisms often lead to strategy homogenization, inefficiency, and income inequality. This paper presents ARDE (Adaptive Regulation via Dual-layer Evolution), an evolutionary governance framework that integrates individual reinforcement learning with adaptive platform-level regulation. The outer agent dynamically generates governance signals based on system diagnostics (strategy entropy, Gini coefficient, completion rate), while inner agents employ Diffusion Q-Learning guided by a language-model-driven reward shaping module to promote fairness and strategy diversity. Experiments on real-world datasets show that ARDE achieves stable diversity (0.997 ± 0.184), reduces inequality (Gini change 1.3%), and maintains high efficiency. Further comparison (ARDE-PPO vs. MAPPO) confirms that its advantages stem from explicit hierarchical governance rather than algorithmic coincidence. Overall, ARDE offers a scalable and interpretable paradigm for reconciling individual rationality with collective welfare in gig economies and other multi-agent socio-technical systems.

Shanfan Zhang, Yongyi Lin, Xiaoting Shen, Zhan Bu, Yuan Rao

Understanding opinion evolution in complex social networks is crucial for modeling social influence and predicting collective behavior. Yet, most models overlook how community structures shape opinion updates, often assuming homogeneous influence. This abstraction neglects individuals’ stronger responsiveness to intra-community peers—an empirically observed driver of localized consensus and inter-group polarization. We propose GCAOFP, a co-evolutionary framework that jointly models opinion dynamics and community formation as an integrated process. In GCAOFP, agents strategically alternate between two coupled modules: (1) a Community Dynamics Module, where agents play a non-cooperative game to optimize community memberships based on opinion alignment and structural cohesion; and (2) an Opinion Adjustment Module, where agents revise opinions via a bounded-confidence mechanism modulated by community-induced influence weights. This dual-stage process captures the feedback loop between structure and opinion. We prove that GCAOFP converges to stable equilibria, ensuring intra-community consensus and inter-community diversity—dynamics that standard models fail to replicate. Experiments on real-world networks show that GCAOFP better reproduces localized opinion clusters, while offering strong scalability and interpretability, illuminating the strategic foundations of polarization.

Bowen Zhang, Hongda Tian, Adam Berry, A. Craig Roussac

Accurate forecasting of the grid carbon intensity factor (CIF) is critical for enabling demand-side management and reducing emissions in modern electricity systems. Leveraging multiple interrelated time series, CIF prediction is typically formulated as a multivariate time series forecasting problem. Despite advances in deep learning-based methods, it remains challenging to capture the fine-grained local-temporal dependencies, dynamic higher-order cross-variable dependencies, and complex multi-frequency patterns for CIF forecasting. To address these issues, we propose a novel model that integrates two parallel modules: 1) one enhances the extraction of local-temporal dependencies under multi-frequency by applying multiple wavelet-based convolutional kernels to overlapping patches of varying lengths; 2) the other captures dynamic cross-variable dependencies under multi-frequency to model how inter-variable relationships evolve across the time-frequency domain. Evaluations on four representative electricity markets from Australia, featuring varying levels of renewable penetration, demonstrate that the proposed method outperforms the state-of-the-art models. An ablation study further validates the complementary benefits of the two proposed modules. Designed with built-in interpretability, the proposed model also enables better understanding of its predictive behavior, as shown in a case study where it adaptively shifts attention to relevant variables and time intervals during a disruptive event.