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Segev Shlomov, Alon Oved, Sami Marreed, Ido Levy, Offer Akrabi, Avi Yaeli, Łukasz Strąk, Elizabeth Koumpan, Yinon Goldshtein, Eilam Shapira 等

Agents are rapidly advancing in automating digital work, but enterprises face a harder challenge: moving beyond prototypes to deployed systems that deliver measurable business value. This path is complicated by fragmented frameworks, slow development, and the absence of standardized evaluation practices. Generalist agents have emerged as a promising direction, excelling on academic benchmarks and offering flexibility across tasks, applications, and modalities. Yet, evidence of their use in enterprise settings remains limited. This paper reports IBM’s experience developing and piloting the Computer Using Generalist Agent (CUGA). CUGA adopts a hierarchical planner--executor architecture with strong analytical foundations, achieving state-of-the-art performance on AppWorld and WebArena. Beyond benchmarks, it was evaluated in a Business-Process-Outsourcing talent acquisition pilot, addressing enterprise requirements for scalability, auditability, safety, and governance. In preliminary evaluations, CUGA approached the accuracy of specialized agents while suggesting reductions in development time and cost. We provide early evidence that generalist agents can operate at enterprise scale, distill key technical and organizational lessons, and outline requirements for transitioning research-grade architectures like CUGA into enterprise-ready systems.

Sel Ly, Rufan Yang, Ninad Dixit, Hung Dinh Nguyen

Lithium-ion (Li-ion) batteries are the major type of battery used in a variety of everyday applications, including electric vehicles (EVs), mobile devices, and energy storage systems. Predicting the Remaining Useful Life (RUL) of Li-ion batteries is crucial for ensuring their reliability, safety, and cost-effectiveness in battery-powered systems. The materials used for the battery cathodes and their designs play a significant role in determining the degradation rates and RUL, as they lead to distinct electrochemical reactions. Unfortunately, RUL prediction models often overlook the cathode materials and designs to simplify the model-building process, ignoring the effects of these electrochemical reactions. Other reasons are that specifications related to battery materials may not always be readily available, and a battery might consist of a mix of different materials. As a result, the predictive models that are developed often lack generalizability. To tackle these challenges, this paper proposes a novel material-based Mixture-of-Experts (MoE) approach for predicting the RUL of batteries, specifically addressing the complexities associated with heterogeneous battery chemistries. The MoE is integrated into a probabilistic framework, called Multiple Non-crossing Quantile Mixture-of-Experts for Probabilistic Prediction (RUL-QMoE), which accommodates battery operational conditions and enables uncertainty quantification. The RUL-QMoE model integrates specialized expert networks for five battery types: LFP, NCA, NMC, LCO, and NMC-LCO, within a gating mechanism that dynamically assigns relevance based on the battery's input features. Furthermore, by leveraging non-crossing quantile regression, the proposed RUL-QMoE produces coherent and interpretable predictive distributions of the battery's RUL, enabling robust uncertainty quantification in the battery's RUL prediction. Trained on seven real-world datasets, the proposed RUL-QMoE achieves strong predictive performance across all battery types, with MAE = 65 (cycles), MAPE = 9.59%, RMSE = 100 (cycles), and R2=96.84%. Compared to traditional models like XGBoost, Random Forest, CNN, and LSTM, the proposed RUL-QMoE model consistently delivers lower RMSE and superior probabilistic insights, including survival probabilities and prediction intervals. The model has been integrated into our Battery AI platform in collaboration with Toyota Motor Engineering & Manufacturing North America, Inc., as part of a broader Battery Foundation Model initiative. This RUL-QMoE model will serve future Toyota EVs' users and battery system designers.

Qiming Guo, Bishal Khatri, Wenbo Sun, Jinwen Tang, Hua Zhang, Wenlu Wang

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Federico Cabitza, Andrea Campagner, Gian Eugenio Tontini

AI systems are widely proposed as second-opinion advisors in clinical diagnosis, offering the promise of enhancing decision accuracy and clinician confidence while preserving human oversight. However, successful deployment in real-world practice faces a critical barrier: clinicians' reliance on AI is often miscalibrated, manifesting as misuse (over-reliance driven by automation bias) and disuse (under-utilization driven by self-anchoring bias). This paper addresses these deployment challenges by systematically analyzing how such reliance patterns affect diagnostic accuracy, confidence, and decision-making across diverse medical specialties. We report results from controlled simulations involving over 300 medical professionals across six diagnostic settings—including knee MRI analysis, spinal X-rays, cardiac ECG evaluation, and gastrointestinal endoscopy—using a human-first, AI-second workflow. Although AI advice improved average diagnostic accuracy (+2 percentage points) and clinician confidence (+3 points on a normalized scale), overall levels of appropriate reliance remained well below 50%, with disuse emerging as the more prevalent and consequential barrier. We introduce and validate Appropriate Reliance as an actionable metric for assessing and improving human-AI collaboration, providing practical guidance for developers, healthcare institutions, and policymakers seeking to deploy second-opinion AI systems safely and effectively. By identifying the sociotechnical barriers and offering evidence-based design insights, this work supports the emerging application of AI as a collaborative advisor in clinical workflows, charting a clear path toward deployment that enhances diagnostic safety, accountability, and patient care. Specifically, we propose integrating the Appropriate Reliance metric into system development workflows, clinician training, and regulatory evaluations to enable safe and effective deployment of second-opinion AI systems.

Raphael Anaadumba, Nazim A.Belabbaci, Connor Sullivan, Anton Kovalev, Yidong Zhu, Pradeep Kurup, Mohammad Arif Ul Alam

Lead contamination in urban water systems remains a prevalent public health threat, affecting millions of American households and disproportionately endangering vulnerable population groups. Current municipal risk assessment and inspection strategies are overwhelmingly based on random sampling and complaint-driven protocols that overlook spatial complexity, reinforce inequities, and squander limited resources, leaving critical exposure areas unidentified. This paper presents a lead contamination risk prediction framework from socio-demographic housing features analytics, first of its kind, by drawing on partially anonymized residential testing data as ground truth and applying graph neural networks alongside gradient-boosted ensembles. Specifically, our method integrates spatial Deep Graph Attention Networks classifiers to capture inter-neighborhood contamination dependencies, fuse demographic and spatial evidence, and produce interpretable risk scores. Those scores are actionable by municipal water authorities at the intra-neighborhood level. Through extensive experiments on newly constructed Chicago block-group level datasets, our framework achieves a balanced accuracy of 84.8% and reduces false positive lead contamination by up to 44% versus spatial-only baselines and 21% over current practice, without sacrificing recall on contaminated blocks. Our approach not only extends technical boundaries in spatial-ensemble learning and privacy-preserving urban health modeling, but also provides policymakers and public health officials with a means to assess and address contamination risks, supporting efforts to protect community health and safety.

Gaowei Zhang, Wei Wang, Tiong Lee Kong, Kai Xing, Huan Li, Yi Wang

Ensuring safety in power grid construction remains a critical yet challenging task, as existing monitoring approaches often lack scalability, timeliness, and adaptability to diverse on-site conditions. To address these limitations, we present ConstructAI, a deployed AI-driven safety management system that integrates multi-source image and video acquisition devices with advanced multimodal large model reasoning. The system combines text, image, and video prompts through an efficient workflow powered by LLaMA3 and Meta SAM2 backbones, enhanced with LoRA and adaptor modules for multimodal fusion. Once deployed, ConstructAI continuously processes real-time construction footage to identify violations, assess risk levels, and generate standardized rectification requirements. The deployment has demonstrated measurable benefits across multiple sites, including a >70% increase in violation rectification rates, reduction of average rectification delays from hours to minutes, and a 45% decline in repeat violations. Beyond technical gains, ConstructAI has delivered significant business impacts, such as reduced safety incidents, improved compliance with national regulations, and higher operational efficiency. By enabling proactive risk management and structured safety feedback loops, our system exemplifies how innovative use of AI can translate into tangible improvements for industrial safety. The lessons learned from deployment highlight the importance of balancing algorithmic advances with practical integration into organizational workflows.

Zhao Li, Zixin Lin, Donghui Ding, Yichen Zhong, Biao Wang, Haitao Xu, Peng Cai

The rapid proliferation of smart-city ecosystems has significantly amplified the demand for Li-ion batteries, which now serve as the primary energy source for sustainable transportation systems such as e-bikes. Ensuring battery safety and optimal performance is crucial, yet challenging due to complex intrinsic dynamics and extrinsic operating conditions. This paper presents LiBrain, an innovative LLM-powered, time-series-aware retrieval-augmented framework designed to simultaneously address both safety and performance challenges through three synergistic components: (1) a distributed IoT-enabled edge network for continuous real-time battery monitoring and data acquisition, (2) a pretrained deep multi-task diagnostic engine capable of comprehensive battery performance forecasting, and (3) a knowledge-base augmentation module that transforms technical diagnostics into clear, actionable guidance tailored for e-bike users. Functioning as an intelligent battery management assistant, LiBrain effectively bridges the gap between expert-level real-time analytics and practical, user-friendly instructions. Extensive validation across a real-world operational e-bike battery-swap network demonstrates LiBrain's exceptional capabilities, achieving a 95% adoption rate in hazardous alarm detection and 92% in battery-status prediction. In real application, Li-Brain has processed over 500 million battery events, managed almost 10 million inquiries and 1 million alarms annually, and identified 10% of on-site batteries daily for proactive replacement, thereby maintaining operational safety and reliability.

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.

Yujie Yang, Hanjiang Hu, Tianhao Wei, Shengbo Eben Li, Changliu Liu

Hamilton-Jacobi (HJ) reachability analysis provides a formal method for guaranteeing safety in constrained control problems. It synthesizes a value function to represent a long-term safe set called feasible region. Early synthesis methods based on state space discretization cannot scale to high-dimensional problems, while recent methods that use neural networks to approximate value functions result in unverifiable feasible regions. To achieve both scalability and verifiability, we propose a framework for synthesizing verified neural value functions for HJ reachability analysis. Our framework consists of three stages: pre-training, adversarial training, and verification-guided training. We design three techniques to address three challenges to improve scalability respectively: boundary-guided backtracking (BGB) to improve counterexample search efficiency, entering state regularization (ESR) to enlarge feasible region, and activation pattern alignment (APA) to accelerate neural network verification. We also provide a neural safety certificate synthesis and verification benchmark called Cersyve-9, which includes nine commonly used safe control tasks and supplements existing neural network verification benchmarks. Our framework successfully synthesizes verified neural value functions on all tasks, and our proposed three techniques exhibit superior scalability and efficiency compared with existing methods.

Sepehr Samavi, James R. Han, Florian Shkurti, Angela P. Schoellig

Robots need to predict and react to human motions to navigate through a crowd without collisions. Many existing methods decouple prediction from planning, which does not account for the interaction between robot and human motions and can lead to the robot getting stuck. We propose SICNav, a Model Predictive Control (MPC) method that jointly solves for robot motion and predicted crowd motion in closed-loop. We model each human in the crowd to be following an Optimal Reciprocal Collision Avoidance (ORCA) scheme and embed that model as a constraint in the robot's local planner, resulting in a bilevel nonlinear MPC optimization problem. We use a KKT-reformulation to cast the bilevel problem as a single level and use a nonlinear solver to optimize. Our MPC method can influence pedestrian motion while explicitly satisfying safety constraints in a single-robot multi-human environment. We analyze the performance of SICNav in two simulation environments and indoor experiments with a real robot to demonstrate safe robot motion that can influence the surrounding humans. We also validate the trajectory forecasting performance of ORCA on a human trajectory dataset.

Dejan Milojevic, Gioele Zardini, Miriam Elser, Andrea Censi, Emilio Frazzoli

This article discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using false negative rate and false positive rate across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an integer linear programming approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a co-design optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the co-design problem of mobile robots as CODEI, short for co-design of embodied intelligence. A case study on developing an autonomous vehicle for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.

Pit Henrich, Franziska Mathis-Ullrich, Paul Maria Scheikl

Accurately determining the shape of deformable objects and the location of their internal structures is crucial for medical tasks that require precise targeting, such as robotic biopsies. We introduce LUDO, a method for accurate low-latency understanding of deformable objects. LUDO reconstructs objects in their deformed state, including their internal structures, from a single-view point cloud observation in under 30 ms using occupancy networks. LUDO provides uncertainty estimates for its predictions. Additionally, it provides explainability by highlighting key features in its input observations. Both uncertainty and explainability are important for safety-critical applications such as surgery. We evaluate LUDO in real-world robotic experiments, achieving a success rate of 98.9% for puncturing various regions of interest (ROIs) inside deformable objects. We compare LUDO to a popular baseline and show its superior ROI localization accuracy, training time, and memory requirements. LUDO demonstrates the potential to interact with deformable objects without the need for deformable registration methods.

Wenxuan Zhang

Large language models (LLMs) have advanced rapidly, yet their development remains disproportionately focused on a few high-resource languages, leaving fundamental scientific and societal questions about multilingual capability, safety, and equity unresolved. This talk examines multilingual LLMs as a lens for understanding these challenges. I will first discuss observations from large-scale evaluations with real-world natural data, which reveal substantial performance gaps and highlight the need to treat multilingualism as a multidimensional construct. I then turn to safety, presenting work that uncovers multilingual jailbreak vulnerabilities and introduces frameworks for achieving more consistent cross-lingual alignment. Building on analyses of language-specific internal mechanisms, I will outline new strategies for enhancing multilingual systems and describe open-source efforts such as the SeaLLMs and Babel projects that aim to broaden linguistic and cultural inclusivity. Finally, I will discuss emerging directions beyond language, including recent findings on abstract thought in LLMs, which point toward the development of models that are not only multilingual but genuinely multicultural and contextually grounded.

Chenyu You

Artificial intelligence is rapidly transforming biomedical imaging, from tumor segmentation to multimodal diagnosis, but reliable deployment in hospitals remains a pressing challenge. Models often struggle with limited labeled data, fail to generalize across scanners and institutions, and lack theoretical guarantees needed for safety-critical decisions. In this talk, I will present our research on overcoming these challenges. I will discuss label-efficient and class-imbalanced learning methods that make effective use of large-scale unlabeled data, theoretical frameworks that embed statistical principles into deep learning for stability and reliability, and medical foundation models that provide universal anatomical priors with lightweight refinement for diverse clinical settings. Together, these advances move biomedical AI beyond benchmark performance toward systems that are robust, generalizable, and clinically actionable.

Yu Meng

Large language models (LLMs) have rapidly transformed the landscape of AI, demonstrating remarkable capabilities across reasoning, communication, and problem-solving. Yet, realizing their full potential requires addressing two critical challenges. First, their behavior must be steered and refined after training to ensure reliability, safety, and alignment with human values and intentions. Second, their large scale comes with substantial costs in training and deployment, necessitating research into more efficient methods. My research centers on advancing both of these fronts—making LLMs both aligned and efficient. On one side, I investigate post-training techniques that allow models to better reflect human preferences, demonstrate strong reasoning capabilities, and mitigate hallucination. On the other side, I study methods for improving data efficiency in training and inference efficiency in deployment. Together, these thrusts highlight a broader vision of enabling LLMs that are not only powerful, but also trustworthy and accessible at scale.

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.

Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh, Chuan Li, Kenneth Kwok, Ziwei Wang, Cheston Tan, Jiajun Wu, David Hsu

Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly preva- lent in the embodied AI arena, following the widespread suc- cess of their precursors—LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing develop- ment of VLA models—multimodality, reasoning, data, eval- uation, cross-robkot action generalization, efficiency, whole- body coordination, safety, agents, and coordination with hu- mans. Furthermore, we discuss the emerging trends of us- ing spatial understanding, modeling world dynamics, post training, and data synthesis—all aiming to reach these mile- stones. Through these discussions, we hope to bring attention to the research avenues that may accelerate the development of VLA models into wider acceptability.

Xiaocai Zhang, Zhe Xiao, Maohan Liang, Tao Liu, Haijiang Li, Wenbin Zhang

Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

Yicheng Zhan, Fahim Ahmed, Amy Burrell, Matthew Tonkin, Sarah Galambos, Jessica Woodhams, Dalal Alrajeh

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.