GenAI4ELab/papercli-papers huggingface dataset:
SARA: Leveraging LLM Agents and Jurisprudential Ontologies for Automated Legal Reasoning
Authors: 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
Venue: AAAI 2026 (main)

Abstract: 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.
Optimizing Preferential Rate in Retail Lending with Causal Inference and Domain Adaptation
Authors: Jimyung Choi; Yujin Lee; Hyeryeong Oh; Sumin Shin; Jaehyun Kim; Wooyoung Kim; Kee-Eung Kim
Venue: AAAI 2026 (main)

Abstract: 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.
PRECISE: Reducing the Bias of LLM Evaluations Using Prediction-Powered Ranking Estimation
Authors: Abhishek Divekar; Anirban Majumder
Venue: AAAI 2026 (main)

Abstract: Evaluating the quality of search systems traditionally requires a significant number of human relevance annotations. In recent times, several systems have explored the usage of Large Language Models (LLMs) as automated judges for this task while their inherent biases prevent direct use for metric estimation. We present a statistical framework extending Prediction-Powered Inference (PPI) that combines minimal human annotations with LLM judgments to produce reliable estimates of metrics which require sub-instance annotations. Our method requires as few as 100 human-annotated queries and 10,000 unlabeled examples, reducing annotation requirements significantly compared to traditional approaches. We formulate our proposed framework (PRECISE) for inference of relevance uplift for an LLM-based query reformulation application, extending PPI to sub-instance annotations at the query-document level. By reformulating the metric-integration space, we reduced the computational complexity from O(2^|C|) to O(2^K), where |C| represents corpus size (in order of millions). Detailed experiments across prominent retrieval datasets demonstrate that our method reduces the variance of estimates for the business-critical Precision@K metric, while effectively correcting for LLM bias in low-resource settings.
Discovery of Feasible 3D Printing Configurations for Metal Alloys via AI-Driven Adaptive Experimental Design
Authors: Azza Fadhel; Nathaniel W. Zuckschwerdt; Aryan Deshwal; Susmita Bose; Amit Bandyopadhyay; Jana Doppa
Venue: AAAI 2026 (main)

Abstract: Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in terms of resources (physical and human labor) and the configuration space is very large. This paper combines the general principles of AI-driven adaptive experimental design with domain knowledge to address the challenging problem of discovering feasible configurations. The key idea is to build a surrogate model from past experiments to intelligently select a small batch of input configurations for validation in each iteration. To demonstrate the effectiveness of this methodology, we deploy it for Directed Energy Deposition process to print GRCop-42, a high-performance copper–chromium–niobium alloy developed by NASA for aerospace applications. Within three months, our approach yielded multiple defect-free outputs across a range of laser powers—dramatically reducing time-to-result and resource expenditure compared to several months of manual experimentation by domain scientists with no success. By enabling high-quality GRCop-42 fabrication on readily available infrared laser platforms for the first time, we democratize access to this critical alloy, paving the way for cost-effective, decentralized production for aerospace applications.
From Natural Language to Executable ETL Flows: The IBM DataStage Assistant
Authors: Nitin Gupta; Thomas Gschwind; Shramona Chakraborty; Sameep Mehta; Tristan Tyler; Shreya Sisodia; Ben Clermont
Venue: AAAI 2026 (main)

Abstract: Modern ETL (Extract, Transform, Load) tools offer graphical, no-code interfaces for workflow creation but still require users to manually identify transformation functions and configure their properties, which is time-consuming and demands prior expertise. We present the research and engineering foundations of the IBM DataStage Assistant, a deployed capability that generates complete multi-stage ETL flows directly from natural language (NL) descriptions. Our framework infers transformation functions, their properties, and transformer expressions, enabling novices to discover relevant functions and allowing experts to bypass manual configuration. The proposed framework achieves a prediction accuracy of 96.4% for flow predictions, 87.0% for properties, and 83.6% for transformer expressions. We also show a document exploration module that uses retrieval-augmented generation (RAG) over product documentation to answer tool-specific questions in NL. Implemented in IBM DataStage, this approach supports iterative, in-environment workflow design and reduces context switching. In initial studies, it achieves up to 90% time savings for novices and 50% for experts.
Scalable and Efficient Large-Scale Log Analysis with LLMs: An IT Software Support Case Study
Authors: Pranjal Gupta; Karan Bhukar; Harshit Kumar; Seema Nagar; Prateeti Mohapatra; Debanjana Kar
Venue: AAAI 2026 (main)

Abstract: IT environments typically have logging mechanisms to monitor system health and detect issues. However, the huge volume of generated logs makes manual inspection impractical, highlighting the importance of automated log analysis in IT Software Support. In this paper, we propose a log analytics tool that leverages Large Language Models (LLMs) for log data processing and issue diagnosis, enabling the generation of automated insights and summaries. We further present a novel approach for efficiently running LLMs on CPUs to process massive log volumes in minimal time without compromising output quality. We share the insights and lessons learned from deployment of the tool - in production since March 2024 - scaled across 70 software products, processing over 2000 tickets for issue diagnosis, achieving a time savings of 300+ man hours and an estimated $15,444 per month in manpower costs compared to the traditional practices.
MAFA: A Multi-Agent Framework for Enterprise-Scale Annotation with Configurable Task Adaptation
Authors: Mahmood Hegazy; Aaron Rodrigues; Azzam Naeem
Venue: AAAI 2026 (main)

Abstract: We present MAFA (Multi-Agent Framework for Annotation), a production-deployed system that transforms enterprise-scale annotation workflows through configurable multi-agent collaboration. Addressing the critical challenge of annotation backlogs in financial services, where millions of customer utterances require accurate categorization, MAFA combines specialized agents with structured reasoning and a judge-based consensus mechanism. Our framework uniquely supports dynamic task adaptation, allowing organizations to define custom annotation types (FAQs, intents, entities, or domain-specific categories) through configuration rather than code changes. Deployed at JP Morgan Chase, MAFA has eliminated a 1 million utterance backlog while achieving, on average, 86% agreement with human annotators, annually saving over 5,000 hours of manual annotation work. The system processes utterances with annotation confidence classifications, which are typically 85% high, 10% medium, and 5% low across all datasets we tested. This enables human annotators to focus exclusively on ambiguous and low-coverage cases. We demonstrate MAFA's effectiveness across multiple datasets and languages, showing consistent improvements over traditional and single-agent annotation baselines: 13.8% higher Top-1 accuracy, 15.1% improvement in Top-5 accuracy, and 16.9% better F1 in our internal intent classification dataset and similar gains on public benchmarks. This work bridges the gap between theoretical multi-agent systems and practical enterprise deployment, providing a blueprint for organizations facing similar annotation challenges.
Human-in-the-Loop Eider Duck Counting in Arctic Canada with an Open-Vocabulary Multi-Species Wildlife Detector
Authors: Jayden Hsiao; Aryan Kalia; Zhonghao Zhang; Hudson Sun; Muhammed Patel; David A. Clausi; Lincoln Linlin Xu; Becky Segal; Joel Heath
Venue: AAAI 2026 (main)

Abstract: Accurate monitoring of eider duck populations in Arctic Canada is essential for understanding ecosystem health and supporting conservation efforts in a rapidly changing climate. Traditional manual counting from aerial imagery is time-consuming, labor-intensive, and prone to observer bias. In this work, we present a human-in-the-loop wildlife counting system that integrates an open-vocabulary multi-species object detector to streamline and enhance the accuracy of eider duck surveys. The system leverages a pre-trained open-vocabulary model, enabling the identification of both target and incidental species without retraining, and employs human validation to correct and refine automated detections. This collaborative workflow combines the scalability of machine learning with expert ecological knowledge, reducing annotation effort while maintaining high accuracy. Field validation using aerial imagery from Arctic Canada demonstrates that our approach can significantly accelerate population assessments, improve consistency across surveys, and facilitate adaptive monitoring in remote environments.
DCRNet: Delayed Conversion Modeling Based Personalized Flight Itinerary Ranking Network
Authors: Maolei Huang; Zhuangzhuoran; Detao Lv; YuanTong Li; Shuhan Song; Yao Yu
Venue: AAAI 2026 (main)

Abstract: Over recent decades, the tourism industry has demonstrated progressive expansion, driven by advancements in aviation technologies and shifting consumer interests. In this context, online flight itinerary ranking has become a pivotal business for Online Travel Platforms (OTPs), which aim to rank flight itineraries by synthesizing real-time flight data provided by airlines with users' individual travel preferences.
Currently, most OTPs rely on rule-based methodologies or rudimentary user preference-driven models to address this task. However, these methods are inherently limited by their insufficient consideration of delayed booking behaviors and their neglect of dynamic contextual attributes associated with flight itineraries, thereby undermining their ability to effectively handle the intricacies of flight ranking.
To address these shortcomings, this paper introduces the  Delayed Conversion Modeling based Personalized Flight Itinerary Ranking  Network (DCRNet), designed to improve ranking accuracy by integrating delayed booking patterns and contextual dependencies into the modeling framework. 
Specifically, DCRNet explores the dynamic associations between users' current contextual information and their historical travel records, and models users' delayed booking behaviors via a masked attention mechanism. Moreover, an enhanced multi-task learning framework is employed to effectively integrate traditional behavioral modeling with delay-aware modeling, thereby improving the overall prediction accuracy and enhancing the system's personalized recommendation capabilities.
Extensive offline experiments conducted on real-world datasets from Amadeus and Fliggy demonstrate the superior performance of DCRNet. Furthermore, its successful deployment on Fliggy's online itinerary search system has yielded significant improvements, underscoring its practical effectiveness and scalability.
Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems
Authors: Weijie Jiang; Armando Ordorica; Jaewon Yang; Olafur Gudmundsson; Yucheng Tu; Huizhong Duan
Venue: AAAI 2026 (main)

Abstract: 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.
Reducing Alert Fatigue Through AI Ranking: A Deployed Public Health Data Monitoring System
Authors: Ananya Joshi; Nolan Gormley; Richa Gadgil; Catalina Vajiac; Tina Townes; Roni Rosenfeld; Bryan Wilder
Venue: AAAI 2026 (main)

Abstract: Public health experts need scalable methods to monitor large volumes of health data (e.g., human-reported cases, hospitalizations, deaths). These methods must identify individual data points that may indicate significant events, such as outbreaks, or reveal data quality issues. Identifying, triaging, and analyzing these data points in real-time is critical for preventing downstream errors in forecasting or policy. Traditional alert-based data monitoring systems, used for decades in practice, fail to identify relevant data events for several reasons. For example, these systems may not output real-time results from large data volumes, or they may return tens of thousands of unhelpful alerts. 

We introduce a human-in-the-loop AI system for public health data monitoring that uses a ranking-based AI anomaly detection method. This system was developed through a multi-year interdisciplinary collaboration with participatory design from researchers, engineers, and public health data experts. From this process, we identified system goals, such as user control and efficiency and designed a system that balances these goals. This system has since been deployed at a national public health organization and analyzes up to 5 million data points daily. A three-month longitudinal deployment evaluation revealed a significant improvement in system goals, including a 54x increase in data reviewer efficiency and increased engagement compared to traditional alert-based methods.
A Deployed Investigative AI Search Engine for Combating Human Trafficking at Web Scale
Authors: Mayank Kejriwal
Venue: AAAI 2026 (main)

Abstract: Online human trafficking investigations generate vast amounts of noisy, heterogeneous, and deliberately obfuscated data, making traditional search and analytics tools ineffective for supporting law enforcement. This paper discusses the deployment of the Domain-Specific Insight Graphs (DIG) system, an AI-powered investigative search engine that was operationally used by over 200 U.S. law enforcement agencies for more than five years in the pre-COVID period. The system integrates advanced research conducted over the years in information extraction, knowledge graph construction, and entity-centric search to enable investigators to formulate queries without technical background, aggregate evidence, and uncover latent relationships among entities such as phone numbers, emails, and locations. Beyond technical innovation, the deployment required sustained attention to usability, explainability, and policy compliance, ensuring trust in high-stakes legal contexts. We report measurable benefits in investigative efficiency, case initiation, and prosecutorial support, as well as lessons learned from long-term maintenance and adaptation to evolving online platforms. Since 2020, work conducted in this domain has also had significant policy and advocacy ramifications. The system's generalized design has also allowed it to be prototyped for adjacent illicit domains, including securities fraud and illegal firearm sales, demonstrating the broader applicability of AI-driven investigative tools. We contribute a rare case study of an AI system that has transitioned from research to sustained real-world impact in a socially critical domain.
Human-in-the-Loop Bandwidth Estimation for Quality of Experience Optimization in Real-Time Video Communication
Authors: Sami Khairy; Gabriel Mittag; Vishak Gopal; Ross Cutler
Venue: AAAI 2026 (main)

Abstract: The quality of experience (QoE) delivered by video conferencing systems is significantly influenced by accurately estimating the time-varying available bandwidth between the sender and receiver. Bandwidth estimation for real-time communications remains an open challenge due to rapidly evolving network architectures, increasingly complex protocol stacks, and the difficulty of defining QoE metrics that reliably improve user experience. In this work, we propose a deployed, human-in-the-loop, data-driven framework for bandwidth estimation to address these challenges. Our approach begins with training objective QoE reward models derived from subjective user evaluations to measure audio and video quality in real-time video conferencing systems. Subsequently, we collect roughly 1M network traces with objective QoE rewards from real-world Microsoft Teams calls to curate a bandwidth estimation training dataset. We then introduce a novel distributional offline reinforcement learning (RL) algorithm to train a neural-network-based bandwidth estimator aimed at improving QoE for users. Our real-world A/B test demonstrates that the proposed approach reduces the subjective poor call ratio by 11.41% compared to the baseline bandwidth estimator. Furthermore, the proposed offline RL algorithm is benchmarked on D4RL tasks to demonstrate its generalization beyond bandwidth estimation.
Layout-Aware Document Parsing with Visual-Linguistic Fusion: The DATA-LUX with Academic Content Service Provider
Authors: Min Chan Kim; Yeonkyung Kim; Jae Won Lee; Ki Hwan Kim; Ji Woo Kwak; Jae Hong Park
Venue: AAAI 2026 (main)

Abstract: Many organizations are increasingly relying on unstructured documents such as PDFs and scanned forms to support downstream large language model (LLM) services, including search, summarization, and recommendation. However, traditional OCR systems struggle with diverse layouts of documents, leading to frequent errors and high costs of labor. So, this study developed DATALUX - a robust document layout system that trans-forms unstructured documents into structured, machine-readable data suitable for automation. Built on a trans-former-based detector, DATALUX incorporates several modules for layout refinement, text-visual fusion, and layer-wise optimization to improve coherence and generalization across diverse layouts. Around January 2025, we successfully deployed DATALUX into one of the largest academic content service firms (Nurimedia) in South Korea. This firm faced the challenge of extracting metadata and references from thousands of academic pa-pers submitted in various formats. Also, the existing LLM-based tools provided unreliable results. So, they needed to process them manually, creating bottlenecks in both labor and time. However, DATALUX enabled the automatic structuring of over 100,000 research papers a year, improving extraction accuracy to over 97%, reducing costs by more than USD 185K annually, and accelerating processing speed by 8.7 times. These deployment results suggest that DATALUX enables scalable and efficient document automation in complex and high-volume environments successfully. We thus believe that our DATALUX has a significant impact on both academia and industry practices.