论文检索

Enrico Scala, Francesco Percassi, Mauro Vallati

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.

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

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.

Balaji Rao, Naveen Mathews Renji, Elena Korshakova, Carlo Lipizzi

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.

Bharath Muppasani, Ritirupa Dey, Biplav Srivastava, Vignesh Narayanan

Multi-Agent Path Finding (MAPF) algorithms provide highly optimized solutions for coordinating multiple agents in shared environments, yet their outputs lack explainability to human stakeholders. Existing explanation approaches, such as visual trace segmentation or logic-based reasoning, remain fragmented. In this demo, we present OMEGA, an interactive explanation platform that generates Natural Language (NL) explanations using the novel Multi-Agent Planning Ontology (maPO). Our framework transforms raw MAPF planner execution logs into a semantic knowledge graph, enabling SPARQL-based explanations of collision events, replanning strategies, and efficiency trade-offs. A lightweight web interface allows users to query, visualize, and interpret planner decisions, thereby making MAPF solutions transparent and auditable. We conducted a user study that confirms the ontology-driven explanations are significantly clearer and more preferred than raw logs, underscoring the potential of semantic technologies for explainable multi-agent systems.

Erol Medenčević, Jakub Med, Lukáš Chrpa

The demo presents a tool that visualizes the acting of planning agents in dynamic environments that might be modified by "acts of nature'', The purpose of this tool is to better understand the behavior of the agent, debug agent's behavior, and for making the underlying planning concepts accessible to wider audience.

Pallav Koppisetti, Nitin Gupta, Kausik Lakkaraju, Biplav Srivastava

The rapid evolution of Generative AI, yielding outputs across text, structured data, images, and audio, has outpaced the development of standardized evaluation tools, leading to fragmented and non-reproducible practices. GAICo (Generative AI Comparator) offers a solution: a deployed, open-source Python library that provides a unified, extensible, and reproducible framework for multi-modal GenAI evaluation. Our demonstration highlights GAICo’s utility through a practical case study: evaluating and debugging composite AI Travel Assistant pipelines. We show how GAICo facilitates isolating performance issues, for instance, distinguishing orchestrator LLM planning deficiencies from specialist image model generation flaws, by consistently comparing diverse outputs against tailored references. This framework streamlines development, improves system reliability, and promotes reproducible evaluation, making it a critical tool for building safer and more effective AI. Its rapid adoption, evidenced by over 16,000 downloads in the first 6 months, underscores its relevance and impact within the AI community.

Francesco Doria, Francesco Percassi, Marco Maratea, Mauro Vallati

We present the Traffic Signal Plans Explorer, a framework for visualising and exploring traffic signal plans generated via PDDL+ planning. Designed to support both traffic experts and non-specialists, the tool offers a web-based interface for high-level network analysis and a SUMO-based adapter for detailed simulation. Users can inspect junction settings and link dynamics, and simulate plan execution step by step. The system bridges planning technology with practical traffic control, enhancing the transparency and usability of automatically generated solutions.

Saumya Chauhan, Mila Hong

Supporting children with Autism Spectrum Disorder (ASD) requires highly individualized knowledge. However, critical information is often dispersed across documents such as Individualized Education Plans (IEPs), diagnostic assessments, and caregiver notes. Thus, we propose SHARE (Synthesizing Heterogeneous Autism-support Records into Evidence-based Recommendations), a framework that combines diverse autism-related documents into a concise, actionable set of recommendations for caregivers of children with ASD. Feedback is generated using OpenAI’s large language model API, grounded in user-provided evidence with optional web-based extensions for missing details, and citation-linked. After caregivers attempt and then rate recommendations, SHARE uses a Bayesian bandit algorithm with Upper Confidence Bound (UCB) re-ranking to refine future advice. While previous work mostly focuses on drafting static goals, SHARE additionally combines LLM-generated recommendations, caregiver feedback, and interpretable ranking into a pipeline that can adapt over time.

Haritha Ananthakrishnan, Harsha Kokel, Kelsey Sikes, Debarun Bhattacharjya, Michael Katz, Shirin Sohrabi, Kavitha Srinivas

We introduce QueryGym, an interactive environment for building, testing, and evaluating LLM-based query planning agents. Existing frameworks often tie agents to specific query language dialects or obscure their reasoning; QueryGym instead requires agents to construct explicit sequences of relational algebra operations, ensuring engine-agnostic evaluation and transparent step-by-step planning. The environment is implemented as a Gymnasium interface that supplies observations---including schema details, intermediate results, and execution feedback---and receives actions that represent database exploration (e.g., previewing tables, sampling column values, retrieving unique values) as well as relational algebra operations (e.g., filter, project, join).We detail the motivation and the design of the environment. In the demo, we showcase the utility of the environment by contrasting it with contemporary LLMs that query databases. QueryGym serves as a practical testbed for research in error remediation, transparency, and reinforcement learning for query generation.

Tianruo Rose Xu

Reasoning-based large language models now often produce natural-language thinking traces alongside their answers, but it remains unclear whether these verbalized uncertainties faithfully reflect their knowledge or can be used to improve factuality. We study this question for long-form, knowledge-intensive biography generation. Our pipeline decomposes thinking traces and responses into atomic facts, filters out planning-style content, labels factual reasoning by certainty, and aligns response facts to their supporting reasoning, enabling plan-based filtering, self-verification, and a classifier that predicts factuality from facts and associated reasoning. Preliminary results suggest that high-certainty reasoning is more likely to be included and correct and that structured use of these signals can improve factual precision, though broader validation across models and dataset will be needed.

Jiaen Sun

This research statement proposes to measure and mitigate speaker entanglement, where accent features inadvertently encode who is speaking in accented automatic speech recognition (ASR). We argue that entanglement inflates scores under lenient split for the same speaker and worsens fairness gaps across accents, and we outline a parameter-efficient mitigation that combines adversarial de-speakerization with safe conditioning. The plan is grounded in established results in accented ASR, domain-adversarial learning, and parameter-efficient fine-tuning; it is feasible with public datasets and a frozen Whisper backbone, and can potentially guide low-resource data collection.

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.

Ryan Zhang, Herbert Woisetschläger

Real-world AI systems are tackling increasingly complex problems, often through interactions among Large Language Model (LLM) agents. When these agents develop inconsistent conventions, coordination can break down. Applications such as collaborative coding and distributed planning therefore require reliable, consistent communication, and scalability is a central concern as systems grow. We introduce Schema-Induced Games for Naming (SIGN), a naming game that examines how lightweight structure can steer convention formation. We compare schema-induced communication to unconstrained natural language and find faster convergence with up to 5.8× higher agreement. These results suggest that minimal structure can act as a simple control knob for efficient multi-agent coordination, pointing toward broader applications beyond the naming game.

Roman Sultimov, Mikhail Mozikov, Dmitrii Abramov, Mariia Kovalchuk, Maksim Malykh, Aleksandr Volkov, Ilya Makarov, Andrei Osiptsov, Yury Maximov

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.

Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Shah, Isabelle Diana May-Xin Ng, Christopher M. Homan, Wei Wei

Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications. These workflows depend critically on the prompts used to provide the roles models play in such workflows. Poorly designed prompts that fail even slightly to guide individual agents can lead to sub-optimal performance that may snowball within a system of agents, limiting their reliability and scalability. To address this important problem of inference-time prompt optimization, we introduce ProRefine, an innovative inference-time optimization method that uses an agentic loop of LLMs to generate and apply textual feedback. ProRefine dynamically refines prompts for multi-step reasoning tasks without additional training or ground truth labels. Evaluated on five benchmark mathematical reasoning datasets, ProRefine significantly surpasses zero-shot Chain-of-Thought baselines by 3 to 37 percentage points. This approach not only boosts accuracy but also allows smaller models to approach the performance of their larger counterparts. This highlights its potential for building cost-effective and powerful hybrid AI systems, thereby democratizing access to high-performing AI.

Chun-Wei Kang, Chung-Chi Chen, An-Zi Yen

Large Language Models (LLMs) demonstrate strong capabilities in code generation but often lack adaptability in planning and refinement. We propose Self-PR, a framework that integrates adaptive plan selection and iterative repair to improve correctness and generalization. Self-PR constructs a reusable plan database via task clustering and trains a selector to choose task-specific strategies. Incorrect outputs are refined through multi-round feedback until correctness. Trained only on HumanEval, Self-PR generalizes well to out-of-distribution tasks (MBPP), improving pass@1 by +4.9% on HumanEval and +5.5% on MBPP compared to Modularization-of-Thought prompting. Experiments across Llama-3 (8B, 70B) and GPT-4o-mini confirm robustness and scalability. These findings suggest that adaptive planning and feedback-driven repair are essential for reliable LLM-based code generation.

Jun Jang, Thai Le, Bo Wang

With the rise of generative artificial intelligence (GenAI), academic dishonesty in classrooms has skyrocketed, yet the existing solutions for detecting such dishonesty often fall short. Standard "AI detectors" merely analyze one text at a time, failing to account for students' previous writings, which risks erroneous predictions. Meanwhile, existing token-based authorship verification (AV) models fail to analyze the nuances in writing styles that truly distinguish authorship. To fill this existing gap, we propose a novel AV framework that combines token-level stylometric features (e.g., POS tag patterns) with handcrafted stylistic features (e.g., sentence structure variation) to construct a comprehensive feature set. Using both benchmark corpora and real-world high school student essays, we trained multiple machine learning classifiers using the proposed feature set. Our initial experiments show that our approach outperforms the standard token-only baselines by over 25%, while offering interpretable, style-based insights. These preliminary results highlight the importance of nuanced stylistic features and suggest that a holistic AV system can provide educators with more reliable and transparent detection tools. Looking ahead, we plan to extend this work with large language models and multi-agent approaches to further enhance robustness and adaptability.

Yao Gao, Feng Li, Jeroen Van Dessel, Yi Sun, Robin Willaert

What if the next generation of human-computer interaction is not a screen... but a conversation? Large Language Models (LLMs) offer a new paradigm for interacting with computers through text, but they lack shape reasoning capabilities. We introduce Textual Anatomy Encoding (TAE), a workflow that connects LLMs with 3D anatomies. TAE employs clinician-validated semantic annotations and rule-based prompts to achieve deterministic and interpretable landmark localization. The results indicate that TAE enables LLMs to move beyond textual knowledge, achieving an accurate understanding of anatomical localization. This framework opens opportunities for diagnosis, surgical planning, and scalable medical annotation, positioning LLMs as a foundation for next-generation human–computer interaction in healthcare.

Jungho An, Taeyoung Kim, Haeun Kim, Dongsoo Har

Offline Zero-Shot Reinforcement Learning requires an agent to solve unseen tasks using only a fixed offline dataset without explicit rewards. A central challenge is learning representations that capture both high-level long-term planning and low-level physical dynamics. We propose a novel framework, Dynamics-Aware Planning Representation (DAPR), which disentangles these two aspects via complementary contrastive objectives. Specifically, DAPR learns goal-oriented planning directions and local dynamics-consistent directions in the latent space. By jointly enforcing these constraints, DAPR yields representations that balance “where to go” with “how to move.” Experiments on standard locomotion benchmarks (Walker, Cheetah, Quadruped) demonstrate that DAPR consistently improves performance and generalization over strong baselines, achieving substantial gains on precision demanding tasks.

Fanmeng Wang

Molecular conformations, the stable three-dimensional structures corresponding to local minima on the potential energy surface, govern key molecular properties and consequently underpin a wide range of downstream tasks. However, contemporary learning-based methods often lack scalability, interpretability, and robustness, thereby significantly constraining their practical effectiveness and reliability. In this context, I will introduce my ongoing explorations and the proposed research plan to address these challenges, with the ultimate objective of developing conformation‑centric universal foundation models to accelerate scientific discovery.