GenAI4ELab/papercli-papers huggingface dataset:
GenMatLab: A Generative Platform for Inverse Materials Design
Authors: Hangwei Qian; Yang He; Yaxin Shi; Ivor Tsang
Venue: AAAI 2026 (main)

Abstract: In this demo, we present GenMatLab, a user-friendly web platform that makes latest AI techniques accessible for inverse materials design. The platform integrates data analysis and generative modeling into an easy-to-use interface, enabling researchers, material domain experts, and practitioners to explore and apply AI techniques without requiring advanced coding expertise. At its core are generative AI models that support interactive operations, allowing users to conduct inverse design and investigate generated candidates in an intuitive and exploratory way. By lowering technical barriers, GenMatLab empowers a broader community to leverage cutting-edge AI methods for accelerating materials discovery.
Wikatoni: An Agentic AI System for Energy Engineering Workflows
Authors: Sampath Rajapaksha; Nirmalie Wiratunga; Ikechukwu Nkisi-Orji; Tim Clarke; Fraser Kerr
Venue: AAAI 2026 (main)

Abstract: Capturing expertise and enabling efficient information retrieval are critical in the energy sector, where high staff turnover can lead to significant knowledge loss. Retrieval Augmented Generation (RAG) offers a solution by grounding Large Language Model (LLM) outputs in documented sources, but its effectiveness is limited by reliance on general-purpose embeddings. We present Wikatoni, an agentic AI system for energy engineering workflows that integrates a novel domain-specific embedding model. Wikatoni combines fine-tuned embeddings with agentic RAG, metadata filtering, and hybrid retrieval to improve document search, automated reporting, and workflow efficiency. Evaluation on internal enterprise offshore energy data shows that the domain-adapted embedding improves recall by 10%, and Wikatoni agentic RAG further increases answer accuracy by 14% compared to vanilla RAG with the base embedding model, achieving the best overall performance in context recall, faithfulness, and answer accuracy.
Steve: Your Personal AI Career Coach
Authors: Balaji Rao; Naveen Mathews Renji; Elena Korshakova; Carlo Lipizzi
Venue: AAAI 2026 (main)

Abstract: Steve is an AI career coaching platform that turns a resume and insights from an AI-enabled chat with a user into a personalized skill gap report and upskilling roadmap. The platform suggests a personalized course plan, supports continuous learning, and helps shape the user’s career trajectory. Steve is built around schema-constrained JSON artifacts and a configurable career-tree ontology. The system compares confirmed skills against role-specific requirements, prioritizes gaps (critical/important/beneficial), and translates the analysis into embedding-based queries over the course index. Steve has three personas (Interview Coach, Resume Evaluator, and Career Coach) that provide concise feedback tailored to the user’s goals and context. Steve also supports speech Input/Output (I/O) via Whisper-based speech-to-text and a dual-voice text-to-speech layer, enabling users to talk to Steve. The platform offers flexible adaptability across institutions, enabling them to configure deployments by substituting their own ontologies and course catalogs. Our demo uses STEM trajectories as a case study, but the pipeline is domain-agnostic by design. Users can edit inputs, check speech recognition accuracy, and observe consistent updates, illustrating a reproducible, human-in-the-loop pattern for deploying LLMs in career guidance. Steve is currently in its alpha stage and available for demonstration.
ProEthica: A Professional Role Based Ethical Analysis Tool Using LLM-Orchestrated, Ontology Supported Case Based Reasoning
Authors: Christopher B. Rauch; Rosina O. Weber
Venue: AAAI 2026 (main)

Abstract: Professional ethics committees currently lack structured tools to identify relevant ethical concepts from complex narratives and compare them against prior decisions. ProEthica analyzes professional ethical scenarios against established codes and precedent cases. The system uses large language models (LLMs), leveraging their natural language processing capabilities to extract nine types of components (Roles, Principles, Obligations, States, Resources, Actions, Events, Capabilities, and Constraints) from case text and scenario descriptions. Domain-specific ontologies provide precise definitions that constrain LLM output to match formal concept specifications, ensuring consistency across extraction and validation. Case-based reasoning identifies precedent cases and analogous situations relevant to the scenario under analysis. The current implementation processes engineering ethics cases with complete provenance tracking and ontology-driven validation. The framework supports extension to other professional domains with established codes and precedent systems.
Automated Multi-Camera Inspection System for Aircraft
Authors: Mark David Rice; Gu Ying; Kelvin Wei Lim; Qing Yu Hoo; Liyuan Li; Lee Jue Ying; Jacky Jie Wei Tan; Lai Xing Ng; Jamie Ng
Venue: AAAI 2026 (main)

Abstract: In this paper, we present the development of an automated visual inspection system designed to detect defects on the upper surface of an aircraft airframe. Specifically, the system employs a multi-camera PTZ (Pan-Tilt-Zoom) set-up to capture and process images from designated regions. Custom developed software manages path planning and camera localization, while a hybrid-AI framework is integrated to identify various defect types, including missing and damaged components. The demonstration highlights the system’s detection capabilities and prototype functionalities using a large aircraft model, supported by a user interface to monitor progress and visualize results. To help validate this work, performance evaluations were conducted using selected multimodal and object detection models.
City of Light (COL): A City-Scale, Geo-Anchored Urban Simulator with High-Throughput Multi-Sensor Streams
Authors: Ilias Sarbout; Mehdi Ounissi; Théo Cazenave-Coupet; Dan Milea; Daniel Racoceanu
Venue: AAAI 2026 (main)

Abstract: We present City Of Light, a Unity-based, city-scale 116 km² simulator of Paris for high-throughput embodied AI research. COL fuses open geographic information system sources into geo-anchored, per-tile meshes and provides a configurable, stochastic runtime with controllable traffic and pedestrians. Agents receive frame-synchronized multi-sensor observations (RGB, depth, normals, semantics) and execute step-synchronized actions to navigate the environment. To support high-rate vision pipelines, we introduce TURBO, a Unity-Python bridge that streams multi-camera observations and allows control at up to 1300 FPS, achieving higher throughput than ML-Agents in our benchmark. We also provide a Street View Digital Twin that aligns simulator viewpoints with corresponding real-world panoramas for frame-accurate visual comparison and quantitative matching. COL enables fast scripting, large-scale data collection, and reinforcement learning in geo-anchored urban settings.
PPS: An Efficient Java-based Simulator for Time-Discrete PDDL+
Authors: Enrico Scala; Francesco Percassi; Mauro Vallati
Venue: AAAI 2026 (main)

Abstract: The expressive power of PDDL+ is crucial in a wide range of real-world applications, where it is necessary to represent hybrid discrete-continuous changes and environmental dynamics. Given the complexity of the dynamics that can be modelled in PDDL+ and the scale of the problems involved, the ability to validate plans and simulate their trajectories is essential for assessing the accuracy of the models.

In this paper, we present PPS (PDDL Plus Simulator), a Java-based tool that enables seamless validation and simulation of PDDL+ plans under time-discrete semantics.
CausalPulse: Agentic Copilot for Root Cause Analysis in Smart Manufacturing
Authors: Chathurangi Shyalika; Utkarshani Jaimini; Cory Henson; Amit Sheth
Venue: AAAI 2026 (main)

Abstract: 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.
AuditAgent: LLM Agent for Risks Auditing in Recommender Systems
Authors: Du Su; Zhenxing Chen; Shilong Zhao; Yuanhao Liu; Fei Sun; Qi Cao; Huawei Shen
Venue: AAAI 2026 (main)

Abstract: Auditing recommendation systems has attracted growing attention due to increasing concerns over filter bubbles, unfairness, and data misuse. A common approach is sock-puppet auditing, where autonomous agents interact with platforms to reveal risks. However, existing approaches rely on hard-coded agents, lacking adaptability to dynamic GUI layouts and generating behaviors far from those of real users, limiting the comprehensiveness and representativeness of assessment. To address these issues, we introduce AuditAgent, an LLM-powered GUI-agent framework for risk auditing. AuditAgent simulates realistic user preferences and performs adaptive, human-like interactions on recommendation platforms. This design enables more thorough and faithful auditing, providing comprehensive assessments across multiple risk dimensions, including filter bubbles, unfairness, and data misuse.
AI for Memory Preservation: Automated Restoration of Photographs Damaged by Floods
Authors: Jose Daniel Galvan Suazo; Hugo Albert Bonet; Carlos Monserrat-Aranda; Cèsar Ferri
Venue: AAAI 2026 (main)

Abstract: The floods caused by the Isolated High-Level Depression (DANA) in the Valencian Community in October 2024 destroyed and damaged hundreds of thousands of personal photographs, erasing key pieces of collective and emotional memory. Within the project Recuperar las Memorias, we present an AI-based system for automated photo reconstruction, designed to support the recovery of more than 200,000 affected images. The system integrates YOLOv8 and SAM2 for automatic detection of damaged regions, followed by context-aware inpainting to restore visual coherence. Special modules are included for facial restoration, preserving identity in one of the most emotionally critical aspects of personal photographs. The tool is deployed as a web application that enables both single-image and batch restoration, making it accessible to non-expert users. Preliminary evaluation, combining human perceptual studies and automatic metrics (LPIPS), shows consistent alignment between subjective and objective assessments of quality. This demonstration highlights how advances in computer vision can be mobilised in real-world crisis contexts, placing AI at the service of cultural heritage, dignity, and memory preservation.
RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents
Authors: Roman Sultimov; Mikhail Mozikov; Dmitrii Abramov; Mariia Kovalchuk; Maksim Malykh; Ilya Makarov; Andrei Osiptsov; Aleksandr Volkov; Yury Maximov
Venue: AAAI 2026 (main)

Abstract: Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform that jointly simulates natural hazards and human response. RESPOND couples high-fidelity flood AI forecasting an agent-based model of human behavior. LLM modules improve each agent decision-making, enabling context-aware reasoning over alerts, road closures, social signals, and changing water levels. The system simulates evacuation flows, resource seeking, and communication patterns producing actionable outputs for emergency management, urban planning, and policy. In the live demo one can run what-if or predicted scenarios, adjust assumptions, and observe emergent population behavior and risk hot spots in real time. By tightly coupling dynamic hazards with LLM-driven multi-agent behavior, RESPOND moves beyond fragmented tools and offers a practical, integrated platform for disaster preparedness and response.
Caption Anything in Video: Fine-grained Object-centric Captioning via Spatiotemporal Multimodal Prompting
Authors: Yolo Yunlong Tang; Jing Bi; Chao Huang; Susan Liang; Daiki Shimada; Hang Hua; Yunzhong Xiao; Yizhi Song; Pinxin Liu; Mingqian Feng; Junjia Guo; Zhuo Liu; Luchuan Song; Ali Vosoughi; Jinxi He; Liu He; Zeliang Zhang; Jiebo Luo; Chenliang Xu
Venue: AAAI 2026 (main)

Abstract: In this work, we introduce CAT-V (Caption Anything in Video), a training-free framework for fine-grained object-centric video captioning of user-selected instances. CAT-V combines (i) a SAMURAI-based Segmenter for precise object masks across frames, (ii) a TRACE-Uni Temporal Analyzer for event boundary detection and coarse event descriptions, and (iii) an InternVL-2.5 Captioner that, conditioned on spatiotemporal visual prompts and chain-of-thought (CoT) guidance, produces detailed, temporally coherent captions about object attributes, actions, states, interactions, and context. The system supports point, box, and region prompts and maintains temporal sensitivity by tracking object states across segments. In contrast to vanilla video captioning that is overly abstract and dense video captioning that is often terse, CAT-V enables object-level specificity with spatial accuracy and temporal coherence, without additional training data.
A Visualized Framework for Event Cooperation with Generative Agents
Authors: Yuyang Tian; Shunqiang Mao; Wenchang Gao; Lanlan Qiu; Tianxing He
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frameworks often overlook systematic evaluations for event organization and lack visualized integration with physically grounded environments, limiting agents' ability to navigate spaces and interact with items realistically. We develop MiniAgentPro, a visualization platform featuring an intuitive map editor for customizing environments and a simulation player with smooth animations. Based on this tool, we introduce a comprehensive test set comprising eight diverse event scenarios with basic and hard variants to assess agents' ability. Evaluations using GPT-4o demonstrate strong performance in basic settings but highlight coordination challenges in hard variants.
Agentic Solutions for IT Financial Operations
Authors: Bekir O. Turkkan; Pavankumar Murali; Chandrasekhar Narayanaswami; Vadim Sheinin
Venue: AAAI 2026 (main)

Abstract: The dynamic nature of cloud spending and pricing structures
pose challenges for practitioners in IT Financial Operations
(FinOps). Recent advances in agentic systems enables them
to instead rely on agents for complex FinOps tasks such as
drawing insights from their data through natural language
queries. In this work, we present an IT FinOps Data Insights
Agent, that implements “chat with your data” approach to
support practitioners in their daily tasks. Our agent achieves
up to 90% accuracy across ITBench FinOps scenarios.
ToolSmith: A Multi-Agent Framework for Enterprise Tool Creation
Authors: Purna Chandra Sekhar Vakudavathu; Kushal Mukherjee; Jayachandu Bandlamudi; Renuka Sindhgatta; Sameep Mehta
Venue: AAAI 2026 (main)

Abstract: Although LLMs can generate tools for generic domains and tasks, they struggle with enterprise-related domains that involve proprietary APIs and data schemas. We present ToolSmith, a framework for autonomously generating and validating agent-compatible tools. Given an API specification and a Tool Specification Requirement (TSR), ToolSmith produces a tool function and verifies it through a closed-loop process: it creates natural language (NL) tests and executes the tool in a secure agent sandbox for validation. For state-changing tools, ToolSmith confirms outcomes by querying the API with parameters derived from the NL tests. If the tool fails to produce the desired output, ToolSmith generates diagnostic feedback to iteratively regenerate it. By ensuring both functional correctness and agent compatibility, ToolSmith enables reliable automation of enterprise workflows.
Risk Atlas Nexus: A System for Managing AI Risks
Authors: Inge Vejsbjerg; Rahul Nair; Elizabeth M. Daly; Dhaval Salwala; Seshu Tirupathi
Venue: AAAI 2026 (main)

Abstract: We present Risk Atlas Nexus, an open source system for governing AI risks. The system unifies several risk classification frameworks through a common ontology. Given an AI application use case (called an intent), the system estimates risks and associated mitigations that are linked to identified risks. The tool is designed to be incorporated in AI governance workflows where recommendations can be translated to business controls to cover risks arising from AI use in firms.
SafeLens: Segment-Level Hate Speech Detection in Online Videos
Authors: Zhuoran Wang; Dylan Raharja; Yujia Hu; Roy Ka-Wei Lee
Venue: AAAI 2026 (main)

Abstract: We present SafeLens, a lightweight segment-level video moderation system that fuses speech, text, and visual frames to produce hateful content detection for each segment. For every segment, SafeLens returns a structured prediction: label, prediction confidence, reasons for flag, harm categories. The structured predictions are optimized for triage, appeals, and downstream enforcement. The system is modular (pluggable speech, text, and visual processing modules back-ends and a mid-size policy Language Language Model (LLM) agent with parameter-efficient tuning). In the live demo, attendees can upload or select clips, scrub the timeline to flag hateful segments, inspect rationales, and vary the policy LLM agent to benchmark the hateful content moderation performance.
AutoTuneX: Interactive Automated Fine-Tuning for Large Language Models
Authors: Daniel Karl I. Weidele; Priyanshu Rai; Frederico Araujo; Teryl Taylor; Radu Marinescu
Venue: AAAI 2026 (main)

Abstract: We present AutoTuneX, a system architecture design and implementation for users to interactively fine-tune large language models (LLMs) based on automated hyperparameter optimization particularly built around Bandit Limited Discrepancy Search. Next to a classical Graphical User Interface (GUI) our system features an agentic runtime to facilitate automated fine-tuning via chat.
AgentSeer: Visualizing and Evaluating Temporal Actions in Agentic AI Systems
Authors: Ilham Wicaksono; Zekun Wu; Rahul Patel; Theo King; Adriano Koshiyama; Philip Colin Treleaven
Venue: AAAI 2026 (main)

Abstract: We present AgentSeer, an interactive observability framework for agentic AI systems. Unlike conventional tracing tools that expose raw spans or model-centric metrics, AgentSeer introduces a dual graph decomposition constructed through a deterministic rule-based parser: a temporal action graph, where each prompt or tool invocation is represented as a distinct action, and a component graph capturing architectural relations among agents, tools, and memory modules. Beyond visualization, AgentSeer enables action-level red teaming, where jailbreak payloads are systematically attached to every action node (including agent messages, tool calls, and memory retrievals) to uncover vulnerabilities invisible to model-level testing. Our demonstration features a six-agent hierarchical testbed with interactive visualization and deployment-oriented safety evaluation applied directly on the same prompts and contexts, systematically revealing high-risk interactions, context-dependent vulnerabilities, and emergent behaviors. By combining structured decomposition, automated red teaming, and rule-based reliability, AgentSeer establishes a safety-first methodology for observability in multi-agent AI.
AgentGraph: Trace-to-Graph Platform for Interactive Analysis and Robustness Testing in Agentic AI Systems
Authors: Zekun Wu; Seonglae Cho; Cristian Enrique Munoz Villalobos; Theo King; Umar Mohammed; Emre Kazim; Maria Perez-Ortiz; Sahan Bulathwela; Adriano Koshiyama
Venue: AAAI 2026 (main)

Abstract: 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.
KnowPilot: Your Knowledge-Driven Copilot for Domain Tasks
Authors: Zekun Xi; Ziyan Jiang; Yujie Bao; Zhenqian Xu; Shumin Deng
Venue: AAAI 2026 (main)

Abstract: Despite the rapid advancement of generative agents, their deployment in real world industry scenarios often encounters challenges due to a lack of domain-specific knowledge. 
To address this gap, we present KnowPilot: a Domain-Specific Knowledge Augmented Agent System. 
KnowPilot is an open-source framework that integrates task-specific priors, explicit knowledge, and experiential knowledge to enhance agent performance in specialized applications. 
It combines knowledge retrieval from structured repositories with a memory system capable of capturing expert experience through human–AI interaction.
VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management
Authors: Zhikai Xue; Tianqianjin Lin; Pengwei Yan; Ruichun Wang; Yuxin Liu; Zhuoren Jiang; Xiaozhong Liu
Venue: AAAI 2026 (main)

Abstract: Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this paper, we introduce VitalDiagnosis, an LLM-driven ecosystem designed to shift chronic disease management from passive monitoring to proactive, interactive engagement. By integrating continuous data from wearable devices with the reasoning capabilities of LLMs, the system addresses both acute health anomalies and routine adherence. It analyzes triggers through context-aware inquiries, produces provisional insights within a collaborative patient–clinician workflow, and offers personalized guidance. This approach aims to promote a more proactive and cooperative care paradigm, with the potential to enhance patient self-management and reduce avoidable clinical workload.