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Zekun Wu, Seonglae Cho, Cristian Enrique Munoz Villalobos, Theo King, Umar Mohammed, Emre Kazim, Maria Perez-Ortiz, Sahan Bulathwela, Adriano Koshiyama

Modern Agentic AI systems plan, reason, and act across multiple steps, creating execution patterns that are difficult to interpret. Existing observability platforms track prompt I/O and operational metrics but require manual inspection of traces to reconstruct structure and reasoning. We present AgentGraph, which converts execution logs into interactive knowledge graphs and actionable insights. Nodes represent agents, tasks, tools, data inputs/outputs, and humans, while typed edges capture relations such as inputs consumed, tasks delegated or sequenced, tools required or used, outputs produced and delivered, and interventions from agents or humans. Each graph element links to its exact trace span, ensuring verifiability. Building on this representation, AgentGraph enables two analyses: qualitative trace-grounded failure detection and optimisation recommendations, and quantitative robustness evaluation via perturbation testing and causal attribution.

Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson, Amit Sheth

Modern manufacturing systems demand real-time, trustworthy, and interpretable insights into anomalies and their underlying causes. However, conventional pipelines treat anomaly detection, causal inference, and decision-making as siloed tasks, lacking integration, explainability, and adaptability. We present CausalPulse, an intelligent, multi-agent copilot for automated Root Cause Analysis (RCA) in industrial settings. Built on a modular and extensible architecture, the system leverages standard agentic protocols, including Model Context Protocol (MCP), Agent2Agent (A2A), and LangGraph for dynamic tool and agent discovery and seamless orchestration of tasks. Agents dynamically interact to perform data preprocessing, anomaly detection, causal discovery, and root cause analysis through a neurosymbolic workflow that combines symbolic reasoning with neural methods. Intelligent postprocessing pipelines enable automatic chaining of agent tasks, enhancing contextual awareness and adaptability. CausalPulse is evaluated using both an academic public dataset (i.e., Future Factories) and an industrial proprietary dataset (i.e., Planar Oxygen Sensor Element) and shows that the system outperforms traditional baselines in interpretability, trustworthiness, and operational utility.

Zhiqing Cui

Spatiotemporal forecasting has seen remarkable progress with the advent of deep learning, particularly with Spatiotemporal Graph Neural Networks (STGNNs). These models excel at answering the what question: predicting future numerical values with high accuracy. However, they fail to answer the crucial why question. In high-stakes domains such as meteorology, urban planning, and public health, this opacity creates a critical bottleneck for adoption. A model that predicts a severe pollution event without explaining its atmospheric drivers is a black box, limiting its trustworthiness and utility for decision-makers who need actionable, causal insights. To address this critical gap, I propose a long-term research project to develop Causal-LLM, a new class of foundation models for spatiotemporal data that are both predictively powerful and causally interpretable. My central thesis is that genuine interpretability cannot be an afterthought; it must be designed into the model's core learning process. By adapting the powerful Time-LLM reprogramming framework and introducing a novel training methodology I term causal data synthesis, Causal-LLM will learn to not only forecast future states but also to articulate the human-understandable causal narratives behind them. This research will make two primary contributions: (1) a novel hybrid architecture that synergizes the perceptual power of GNNs with the reasoning capabilities of LLMs for complex physical systems, and (2) a new training paradigm that explicitly teaches this mapping. A successful project would provide a blueprint for a new class of trustworthy foundation models for science, enabling applications such as a climate model that not only predicts a flood but also explains the atmospheric river causing it, empowering authorities to make more informed and trusted decisions.

Hanzhang Yuan, Sheng Li

Estimating causal effects under network interference is challenging especially when edges are heterogeneous and nodes share latent dependencies. We study this realistic setting and propose MVDR, a targeted maximum likelihood (TMLE) framework that learns multi-view representations of covariates and exposure on heterogeneous networks while achieving double robustness: consistency holds if either the outcome model or the exposure density is correctly specified. MVDR supports multiple network interventions using only the observed network structure. On three semi-synthetic datasets, MVDR reduces intervention-level prediction error against baselines, and remains stable under misspecification.

Taqiya Ehsan, Shuren Xia, Jorge Ortiz

Embodied agents must reason causally, as correlation-based models fail under intervention and distribution shift. This challenge arises in domains like robotics and cyber-physical systems, where agents balance efficiency and comfort under uncertainty. We introduce POLICYGRID, unifying causal discovery and control by treating each action as both decision and experiment. Leveraging constraint-based search, neural causal models, and language model priors with interventional validation, POLICYGRID yields adaptive, interpretable policies. Across synthetic, real-world, and live deployments, it achieves superior causal recovery (F1 = 0.89) and 2.8× better multi-objective performance than correlation-based baselines, demonstrating safe, generalizable decision-making.

Ashim Dhor, Smily Bharadwaj

Automated cancer segmentation in Whole Slide Images (WSIs) has been dominated by a paradigm of static pattern recognition, where even advanced methods leveraging Transformers, Multiple Instance Learning, or topology-aware losses remain fundamentally descriptive and correlational. To address this limitation, we reframe WSI segmentation from a descriptive task to one of causal process modeling. We introduce Topo-GraT, a novel framework featuring a Causal Growth Field (CGF) to model tumor invasion dynamics and a Causal Flow Attention (CFA) mechanism that embeds this field as an architectural prior. This causal engine is integrated within an iterative graph refinement loop that uses segmentation uncertainty to dynamically focus computational resources on the most ambiguous tissue regions. Our comprehensive experiments on multiple WSI datasets demonstrate that Topo-GraT establishes a new state-of-the-art, significantly outperforming existing methods and reducing the 95% Hausdorff Distance, a key boundary metric, by over 15%. Crucially, our framework yields the CGF as a rich, interpretable output whose structure correlates with tumor aggressiveness, positioning it as a novel biomarker for downstream prognostic tasks. By shifting the paradigm from static recognition to causal reasoning, Topo-GraT offers a more robust, efficient, and clinically insightful approach, setting a new direction for the causally-aware medical image analysis.

Vashu Chauhan, Avinash Anand, Manisha Luthra, Uelison Jean Lopes dos Santos, Carsten Binnig, Rajiv Ratn Shah

Traffic accidents pose a significant societal challenge, with many fatalities being avoidable through timely emergency response. We introduce IMPACT (Integrated Multimodal Pipeline for Rapid Accident Causality Tracking), a scalable AI framework designed for autonomous, rapid traffic incident analysis using existing urban CCTV infrastructure. IMPACT combines a low-latency CPU-based vision module for real-time key-frame filtering (24 FPS) with the causal reasoning capabilities of MLLMs, reducing costly MLLM calls by over 92% compared to naive sparse sampling. We further present TRACE10K, a dataset featuring three-tier textual annotations that describe accident dynamics at the frame-sequence level.

Matteo Ceriscioli, Shohei Shimizu, Karthika Mohan

Causal discovery is the task of learning causal models, encoding causal relationships, from a source of information, such as a dataset containing observational data. While many algorithms have been developed to discover causal models under varied sets of assumptions, the case in which the dataset is affected by missing data remains significantly underexplored. Naively applying standard causal discovery algorithms to listwise, test-wise, or regression-wise deleted datasets, or imputing the missing data, can introduce spurious associations between variables and bias function estimation in functional causal models. This issue arises when the data is missing at random or not at random. It ultimately invalidates the theoretical guarantees of these algorithms and prevents finding the true underlying causal model, even in the large-sample limit. An established family of causal models is the Linear Non-Gaussian Acyclic Model (LiNGAM), which assumes linear functional relationships and non-Gaussian independent noise terms. We propose a new causal discovery algorithm for LiNGAM, capable of recovering the underlying causal structure and providing unbiased estimates of the model’s parameters, even when the data is affected by MNAR missingness.

Fengxiang Cheng

Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous neuro-symbolic methods can be summarized into a two-stage framework: first translating natural language (NL) problems into symbolic language (SL) representation, and then performing the symbolic reasoning process. To facilitate this direction, we provide a comprehensive survey, summarizing two main challenges including complex logical question-answering (QA) and cross-question logical consistency, and further propose a new taxonomy. To achieve precise symbolic representation and enhance the accuracy of LLMs’ logical reasoning, we propose several effective and efficient approaches, including adaptively selecting the most suitable SL for each QA problem, a data-driven approach to determine the fine-tuning samples order, and an efficient multi-agent debate framework with sparse communication. Our future research will focus on theoretical analysis for optimal SL selection, translation refinement and robust neuro-symbolic approach to improve LLMs' reasoning.

Matteo Ceriscioli

Causal discovery is the task of learning a causal model from a source of information. Traditionally, the community has focused on algorithms that infer causal models from observational and/or interventional data, while alternative approaches have been only marginally explored. The proposed work aims to contribute to the theoretical foundations connecting agent-based systems with causal modeling, and to identify conditions under which newly developed causal discovery algorithms can be applied to elicit causal knowledge from agents.

Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach, Utkarshani Jaimini, Cory Henson, Ramy Harik, Amit Sheth

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal reasoning required for a unified decision-support solution. This fragmentation limits their trustworthiness and practical utility in high-stakes industrial environments. In this work, we present CausalTrace, a neurosymbolic causal analysis module integrated into the SmartPilot industrial CoPilot. CausalTrace performs data-driven causal analysis enriched by industrial ontologies and knowledge graphs, including advanced functions such as causal discovery, counterfactual reasoning, and root cause analysis (RCA). It supports real-time operator interaction and is designed to complement existing agents by offering transparent, explainable decision support. We conducted a comprehensive evaluation of CausalTrace using multiple causal assessment methods and the C3AN framework (i.e. Custom, Compact, Composite AI with Neurosymbolic Integration), which spans principles of robustness, intelligence, and trustworthiness. In an academic rocket assembly testbed, CausalTrace achieved substantial agreement with domain experts (ROUGE-1: 0.91 in ontology QA) and strong RCA performance (MAP@3: 94%, PR@2: 97%, MRR: 0.92, Jaccard: 0.92). It also attained 4.59/5 in the C3AN evaluation, demonstrating precision and reliability for live deployment.

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.

Rituparna Datta, Methun Kamruzzaman, Eili Y. Klein, Gregory R. Madden, Xinwei Deng, Anil Vullikanti, Parantapa Bhattacharya

The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets.

Sudarshan Srinivasa Ramanujam, Antonio Alonso, Saurabh Kataria, Siddharth Dangi, Akhilesh Gupta, Birjodh Singh Tiwana, Manas Haribhai Somaiya, Luke Simon, David Byrne, Sojeong Ha 等

In large-scale recommendation systems like LinkedIn’s, the retrieval stage is critical for narrowing billions of potential candidates to a manageable subset for ranking. LinkedIn's feed now serves suggested content based on the topical interests of members, where 2000 candidates are retrieved from several million candidates with a latency budget of a few milliseconds and inbound QPS of several thousand per second. This paper presents a novel retrieval approach that fine tunes a large causal language model (Meta’s LLaMA 3) as a dual encoder to generate high quality embeddings for both users (members) and content (items), using only textual input. We describe the end to end pipeline, including prompt design for embedding generation, techniques for fine tuning at LinkedIn scale, and infrastructure for low latency, cost effective online serving. We share our findings on how quantizing numerical features in the prompt enables the information getting encoded in the embedding facilitating greater alignment between the retrieval and ranking layer. The system was evaluated using offline metrics and an online A/B test, which showed substantial improvements in member engagement. We observed significant gains among newer members, who often lack strong network connections, indicating that high-quality suggested content aids retention. This work demonstrates how generative language models can be effectively adapted for real time, high throughput retrieval in industrial applications.

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.

Jimyung Choi, Yujin Lee, Hyeryeong Oh, Sumin Shin, Jaehyun Kim, Wooyoung Kim, Kee-Eung Kim

In retail lending, offering preferential interest rates is a core marketing instrument for balancing customer acquisition with portfolio profitability. Accurately predicting the effect of interest-rate discounts for each customer is pivotal for optimizing the discount strategy: offering overly generous discounts erodes margins, while insufficient discounts drive price-sensitive customers to defect. Off‑the‑shelf machine learning uplift models rarely respect the complex operational constraints of financial business, such as tiered rate grids, regulatory guard‑rails, and marketing budget ceilings. We propose an integrated system that fuses causal inference and domain adaptation to produce constraint‑aware, customer‑specific discount recommendations. To further enhance practitioner adoption, a large language model layer translates model outputs into actionable narratives. Developed in Hyundai Capital Services, the system boosted transaction volume by 13%, demonstrating both technical soundness and material business impact.

Samer B. Nashed, Saaduddin Mahmud, Claudia V. Goldman, Shlomo Zilberstein

Stochastic sequential decision-making systems — such as Markov decision processes and their variants — are increasingly used in areas such as transportation, healthcare, and communication. However, the ability to explain these systems’ outputs to non-technical end users has not kept pace with their widespread adoption. This paper addresses that gap by extending prior work and presenting a unified framework for generating causal explanations of agent behavior in sequential decision-making settings, grounded in the structural causal model (SCM) paradigm. Our framework supports the generation of multiple, semantically distinct explanations for agent actions — capabilities that were previously unattainable. In addition to introducing a novel taxonomy of explanations for MDPs to guide empirical investigation, we develop both exact and approximate causal inference methods within the SCM framework. We analyze their applicability and derive run-time bounds for each. This leads to the proposed algorithm, MeanRESP, which operates flexibly across a spectrum of approximations tailored to external constraints. We further analyze the sample complexity and error rates of approximate MeanRESP, and provide a detailed comparison of its outputs — under varying definitions of responsibility — with popular Shapley-value-based methods. Empirically, we performed a series of experiments to evaluate the practicality and effectiveness of the proposed system, focusing on real-world computational demands and the validity and reliability of metrics for comparing approximate and exact causal methods. Finally, we present two user studies that reveal user preferences for certain types of explanations and demonstrate a strong preference for explanations generated by our framework compared to those from other state-of-the-art systems.

Ehsan Mokhtarian, Sepehr Elahi, Sina Akbari, Negar Kiyavash

Causal discovery from observational data, i.e., learning the causal graph from a finite set of samples from the joint distribution of the variables, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is daunting. This paper builds upon and extends four of our prior publications (Mokhtarian et al., 2021; Akbari et al., 2021; Mokhtarian et al., 2022, 2023a). These works introduced the concept of removable variables, which are the only variables that can be removed recursively for the purpose of causal discovery. Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively. This reduction not only minimizes conditioning sets in each conditional independence (CI) test, leading to fewer errors but also significantly decreases the number of required CI tests. The worst-case performances of these methods nearly match the lower bound. In this paper, we present a unified framework for the proposed algorithms, refined with additional details and enhancements for a coherent presentation. A comprehensive literature review is also included, comparing the computational complexity of our methods with existing approaches, showcasing their state-of-the-art efficiency. Another contribution of this paper is the release of RCD, a Python package that efficiently implements these algorithms. This package is designed for practitioners and researchers interested in applying these methods in practical scenarios. The package is available at github.com/ban-epfl/rcd, with comprehensive documentation provided at rcdpackage.com.

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.

Alexander Rodríguez

AI and population health are becoming increasingly intertwined, driven by the growing availability of multimodal data and rapid advances in AI. At the AAAI-26 New Faculty Highlights, I present our efforts to harness these trends to enhance our capacity to model, simulate, and adapt to complex dynamical processes. I first introduce our robust deep learning architectures for real-time outbreak response, highlighting how our frameworks capture uncertainty and dynamics across shifting distributions, multimodal data, hierarchical structures, and relational dependencies. I will then introduce our hybrid approaches that integrate machine learning with science-based mechanistic epidemiological models, including physics-informed neural networks, expert-guided generative models for causal inference, and differentiable agent-based models. Together, these advances illustrate how combining data-driven AI with domain knowledge can enable more reliable, adaptive, and actionable solutions to inform decision making in population health.