GenAI4ELab/papercli-papers huggingface dataset:
Automatic Funny Scene Extraction from Long-form Cinematic Videos
Authors: Sibendu Paul; Haotian Jiang; Caren Chen
Venue: AAAI 2026 (main)

Abstract: Automatically extracting engaging and high-quality humorous scenes from cinematic titles is pivotal for creating captivating video previews and snackable content, boosting user engagement on streaming platforms. Long-form cinematic titles, with their extended duration and complex narratives, challenge scene localization, while humor’s reliance on diverse modalities and its nuanced style add further complexity. This paper introduces an end-to-end system for automatically identifying and ranking humorous scenes from long-form cinematic titles,
featuring shot detection, multimodal scene localization, and humor tagging optimized for cinematic content. Key innovations include a novel scene segmentation approach combining visual and textual cues, improved shot representations via guided triplet mining, and a multimodal humor tagging framework leveraging both audio and text modalities. Our system achieves an 18.3% AP improvement over state-of-the-art scene detection on the OVSD dataset and an F1 score of 0.834 for detecting humor in long text. Extensive evaluations across five cinematic titles demonstrate 87% of clips extracted by our pipeline are intended to be funny, while 98% of scenes are accurately localized. With successful generalization to trailers, these results showcase the pipeline’s potential to enhance content creation workflows, improve user engagement, and streamline snackable content generation for diverse cinematic media formats.
Lightweight Additive Blend Maps for Texture-Preserving Face Retouching: A Neural Approach to Traditional Photographic Techniques
Authors: Abinash Pegu
Venue: AAAI 2026 (main)

Abstract: Professional photography face retouching requires achieving a balance between texture preservation and quality increase, a problem that conventional automated methods find difficult to effectively handle.  We provide a new lightweight neural architecture that converts conventional dodge-and-burn photography methods into predictions for learnable additive blend maps.  Instead of rebuilding whole images, our method uses a small U-Net that predicts pixel-level changes, allowing for exact brightness adjustments while maintaining the original skin texture.  With a 6MB model that operates effectively on common hardware, the technique produces high-quality results while preserving texture fidelity, which is crucial for professional applications.  Experimental validation offers significant computing advantages while demonstrating competitive performance with current methods.
Automated Unified Reasoning with Vision-Language Models for Multi-modal Burn Assessment
Authors: Md Masudur Rahman; Mohamed El Masry; Gayle Gordillo; Juan Wachs
Venue: AAAI 2026 (main)

Abstract: In emerging clinical applications such as ultrasound-based burn assessment, the lack of domain-specific data presents a significant challenge for developing robust AI systems. Vision-language models (VLMs) have shown strong performance in general computer vision tasks, yet their application to medical imaging remains limited, particularly due to insufficient reasoning capabilities and the scarcity of high-quality training data. We introduce AURA (Automated Unified Reasoning for Burn Assessment), a multi-modal approach that integrates pre-trained VLMs with symbolic first-order logic (FOL) reasoning to improve diagnostic accuracy and interpretability in this data-limited setting. For this study, we collected real-patient data over a one-year period at a U.S. burn center, performing all experiments in a real clinical setting to ensure practical relevance. The dataset includes both conventional B-Mode ultrasound and Tissue Doppler Imaging (TDI), with TDI introduced here for the first time in burn assessment, underscoring the emerging nature of this work. Beyond burn severity classification, we assess the system’s ability to produce expert-level surgical insight directly from imaging data. On the retrospective dataset, it achieves up to 93% accuracy in surgical classification and 87% in fine-grained burn depth prediction, comparable to expert-informed predictions and substantially exceeding the 70% accuracy of traditional visual inspection by human experts. These results, obtained from a novel multi-modal dataset collected in a real clinical burn center setting, highlight the potential of this approach to improve decision-making in burn care. To further support future deployment, we demonstrate a prototype integration with an Electronic Medical Record (EMR) system that aligns with clinical workflows and supports scalable, real-world implementation.
LLM-Enabled Scientific Knowledge Diffusion Analysis
Authors: Uttam Rao; Madhav Marathe
Venue: AAAI 2026 (main)

Abstract: Bibliometric and science-of-science studies have yielded valuable insights into co-authorship and citation networks, yet most analyses rely on static datasets and limited relation types. We introduce a multi-agent AI architecture that orchestrates specialized large language model (LLM) agents (ingestion, extraction, disambiguation, integration, and analysis) to build and query a comprehensive knowledge graph. Ingestion agents unify data from diverse sources such as OpenAlex, ORCID, ROR, USPTO, and custom web scrapers. Extraction agents harness LLMs to parse unstructured text. Disambiguation agents combine rule-based heuristics with LLM reasoning to resolve ambiguous authors and institutions. Integration agents assemble and cache a provenance-rich graph. An analysis agent translates natural language questions into graph queries and interprets results. This end-to-end pipeline produces a rich graph schema spanning authors, institutions, publications, patents, grants, topics, and temporal relations. Researcher mobility and knowledge diffusion are then modeled as timed automata, where each researcher node’s institutional transitions and accumulated attributes (such as publications, collaborators, and topic expertise) enable dynamic temporal reasoning. Results show that our multi-agent, graph-based system consistently outperforms standalone LLMs and research agents on complex temporal queries, entity disambiguation accuracy, and cross-entity reasoning while maintaining competitive efficiency. These capabilities position the system as a foundation for real-time, LLM-assisted knowledge analysis platforms that can support science policy, research evaluation, and meta-scientific inquiry.
Transferable RL for Real-World Navigation Using Semantic Segmentation and Bird’s-Eye View Abstraction
Authors: Benedikt Schlereth-Groh; Sakir Furkan Yöndem; Ramin Tavakoli Kolagari
Venue: AAAI 2026 (main)

Abstract: Reinforcement Learning (RL) has shown significant promise in developing autonomous navigation algorithms for complex environments. However, the direct application of RL policies trained in simulation to real-world scenarios often faces challenges due to the reality gap. This paper proposes a two-stage system incorporating a segmentation strategy and a bird’s-eye-view (BEV) representation to mitigate the domain gap between simulation and reality. In the first stage, the segmentation transforms sensor data into a simplified and interpretable representation of the surrounding area, facilitating transferability across different deployments. In the second stage, the agent navigates through the BEV map, which can be trained using a vectorized simulation environment---a setup that runs multiple parallel instances of the environment to provide a wide range of training scenarios. This vectorization enables rapid exposure to varied environmental conditions, thereby accelerating and diversifying the training of a deep RL agent to achieve optimal navigation behaviors while maintaining high-speed, in-bound trajectories. The segmentation is crucial because it supports generalization of the learned policy across different robotic platforms. The contribution of this paper lies in combining real-time semantic segmentation with a bird’s-eye-view navigation policy, resulting in a transferable and scalable framework for real-world deployment of RL-based navigation agents. Experimental results demonstrate that agents trained with this methodology exhibit robust navigation performance and adaptability in both simulated and real-world environments, validating the efficacy of combining vectorized simulation with real-world segmentation for practical robotic navigation.
From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production
Authors: Segev Shlomov; Alon Oved; Sami Marreed; Ido Levy; Offer Akrabi; Avi Yaeli; Łukasz Strąk; Elizabeth Koumpan; Yinon Goldshtein; Eilam Shapira; Nir Mashkif; Asaf Adi
Venue: AAAI 2026 (main)

Abstract: 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.
CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing
Authors: Chathurangi Shyalika; Aryaman Sharma; Fadi El Kalach; Utkarshani Jaimini; Cory Henson; Ramy Harik; Amit Sheth
Venue: AAAI 2026 (main)

Abstract: 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.
Interpretable Machine Learning for In-Home Mild Cognitive Impairment Detection
Authors: Budhitama Subagdja; Shanthoshigaa D; Ah-Hwee Tan; Iris Rawtaer
Venue: AAAI 2026 (main)

Abstract: This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can  explain the aspects of individuals' daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals' daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key domains: physical, daily activity, cognitive engagement, and sleep patterns. 
Based on a longitudinal study of over one hundred participants, deployed with non-intrusive sensors in their homes to undergo parallel clinical evaluation across a period of five years, our model successfully identified individuals with MCI, achieving high predictive accuracy regardless the noisy and sparse availability of data. As a transparent neural network, the learned model can also be interpreted as classification rules to distinguish MCI from normal cognition (NC) cases based on the digital biomarkers. These results demonstrate that passively collected, sensor-derived digital biomarkers can be leveraged to indicate cognitive status and potentially providing clinically meaningful insights on the impairment conditions. We also discuss the practical challenges and lessons learned from this real-world deployment to inform future large-scale implementations of such AI-driven health monitoring systems.
A Unified Geospatial Clustering Framework to Identify Varying Density Clusters in E-Commerce Logistics
Authors: Arpit Tiwari; Bhavuk Singhal; Anshu Aditya; Aryan Tiwari; Shubham Jain; Debashis Mukherjee; Debdoot Mukherjee
Venue: AAAI 2026 (main)

Abstract: In e-commerce logistics, accurate geospatial clustering is essential for optimizing resource allocation, manpower planning, and delivery network design. However, existing density-based clustering approaches, particularly their reliance on heuristic parameter tuning, have been underexplored in datasets with significant density variations, limiting robustness and scalability. This study presents an unsupervised framework that extends DBSCAN by leveraging Gaussian Mixture Models (GMM). First, we propose a method that systematically identifies suitable clustering scales through statistical modeling. Second, the approach iteratively applies DBSCAN to extract clusters from dense to sparse regions, overcoming single-parameter limitations. Finally, we validate the method through large-scale offline experiments using data from over 200 last-mile dispatch centers (LMDC). The results demonstrate the framework’s effectiveness in identifying heterogeneous geographic demand patterns and supporting workforce planning and operational benchmarking. This framework provides a scalable solution to a critical challenge in e-commerce logistics, offering a valuable reference for strategic and operational decision-making.
LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation
Authors: Gregory Hok Tjoan  Go; Khang Ly; Anders Søgaard; Seyed Amin Tabatabaei; Maarten de Rijke; Xinyi Chen
Venue: AAAI 2026 (main)

Abstract: The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing.
Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support
Authors: Chibuike E. Ugwu; Roschelle Fritz; Diane J. Cook; Jana Doppa
Venue: AAAI 2026 (main)

Abstract: Urinary tract infection (UTI) flare-ups pose a significant health risk for older adults with chronic conditions. These infections often go unnoticed until they become severe, making early detection through innovative smart home technologies crucial. Traditional machine learning (ML) approaches relying on simple binary classification for UTI detection offer limited utility to nurses and practitioners as they lack insight into prediction uncertainty, hindering informed clinical decision-making. This paper presents a clinician-in-the-loop (CIL) smart home system that leverages ambient sensor data to extract meaningful behavioral markers, train robust predictive ML models, and calibrate them to enable uncertainty-aware decision support. The system incorporates a statistically valid uncertainty quantification method called Conformal-Calibrated Interval (CCI), which quantifies uncertainty and abstains from making predictions ("I don’t know") when the ML model's confidence is low. Evaluated on real-world data from eight smart homes, our method outperforms baseline methods in recall and other classification metrics while maintaining the lowest abstention proportion and interval width. A survey of 42 nurses confirms that our system's outputs are valuable for guiding clinical decision-making, underscoring their practical utility in improving informed decisions and effectively managing UTIs and other condition flare-ups in older adults.
AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
Authors: Qixin Wang; Dawei Wang; Kun Chen; Yaowei Hu; Puneet Girdhar; Ruoteng Wang; Aadesh Gupta; Chaitanya Devella; Wenlai Guo; Shangwen Huang; Bachir Aoun; Greg Hayworth; Han Li; Xintao Wu
Venue: AAAI 2026 (main)

Abstract: In recent years, recommendation systems have evolved from providing a single list of recommendations to offering a comprehensive suite of topic-focused services. To better accomplish this task, conversational recommendation systems (CRS) have progressed from basic retrieval-augmented LLM generation to agentic systems with advanced reasoning and self-correction capabilities. However, agentic systems come with notable response latency—a longstanding challenge for conversational recommendation systems. To balance the trade-off between handling complex queries and minimizing latency, we propose AdaptJobRec, the first conversational job recommendation system that leverages autonomous agent to integrate personalized recommendation algorithm tools. The system employs a user query complexity identification  mechanism to minimize response latency. For straightforward queries, the agent directly selects the appropriate tool for rapid responses. For complex queries, the agent uses the memory processing module to filter chat history for relevant content, then passes the results to the intelligent task decomposition planner, and finally executes the tasks using personalized recommendation tools. Evaluation on Walmart’s real-world career recommendation scenarios demonstrates that AdaptJobRec reduces average response latency by up to 53.3\% compared to competitive baselines, while significantly improving recommendation accuracy.
Octopus: Entropy-Controlled Science Fiction Literature Generation with Persistent Memory-Context Binding
Authors: Xu Wang; Jiaju Kang; Puyu Han; Zeyu Ai; Luqi Gong
Venue: AAAI 2026 (main)

Abstract: Long-form science fiction generation demands rigorous maintenance of narrative coherence across evolving plots, character dynamics, and speculative world-building. We propose Octopus, an entropy-controlled neural framework with persistent memory-context binding that addresses these challenges through two key innovations: 1) dynamic entropy regulation balancing creativity and structural stability via narrative divergence thresholds, and 2) hierarchical memory architecture preserving character states, plot events, and scientific rules over 10K+ token spans. Evaluations across 12 sci-fi subgenres demonstrate Octopus's superiority over GPT-4 and ReAlign baselines, achieving 15.2% higher coherence scores (SciClarity) and 62% fewer contextual contradictions in extended narratives. Human evaluations confirm its effectiveness in maintaining speculative logic (4.7/5 vs. 3.1/5 baseline) while preserving creative diversity. The framework resolves the "hard sci-fi paradox" of enforcing scientific rigor without compromising narrative flexibility, establishing new capabilities for AI-assisted cross-media universe development.
An Interactive Simulation Framework by Ensemble Imitation Learning Agents for Training Robust Trading Policies
Authors: Julian Zhong-Nan Zhang; Yang Yu
Venue: AAAI 2026 (main)

Abstract: The reliable deployment of reinforcement learning (RL) for real-world algorithmic trading is critically hindered by the ``simulation-to-reality gap.'' Standard industry backtesting on static historical data ignores market impact—the feedback loop where an agent's trades influence price dynamics—leading to strategies that are fragile and untrustworthy in live markets. To solve this significant problem, we present a novel and emerging application of AI: a framework for building an interactive, responsive market simulator. Our system first uses imitation learning (IL) to automatically train an ensemble of agents, each learning a distinct trading strategy from a different historical market regime (e.g., bull, bear). This creates a data-driven proxy for a diverse population of real-world traders. We then deploy an innovative Action Synthesis Network to synthesize the actions of this ensemble, generating a realistic, synthetic price trajectory that endogenously models the market's reaction to trades. This interactive environment is then used to train a final RL policy. We evaluate our system on NASDAQ-100 (QQQ) data, and the results demonstrate strong potential for deployment. The RL policy trained in our responsive simulator achieves significantly more robust performance, exhibiting superior downside protection during market downturns compared to various traditional baselines. This application provides a scalable and technically sound methodology for building more realistic training environments, presenting a clear path toward the development and eventual deployment of more resilient and effective algorithmic trading strategies.