GenAI4ELab/papercli-papers huggingface dataset:
NovPhy: A Physical Reasoning Benchmark for Open-World AI Systems Author Links Open Overlay Panel (Abstract Reprint)
Authors: Vimukthini Pinto; Chathura Gamage; Cheng Xue; Peng Zhang; Ekaterina Nikonova; Matthew Stephenson; Jochen Renz
Venue: IJCAI 2025 (main)

Abstract: Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI systems. But is it enough to only have physical reasoning capabilities to operate in a real physical environment? In the real world, we constantly face novel situations we have not encountered before. As humans, we are competent at successfully adapting to those situations. Similarly, an agent needs to have the ability to function under the impact of novelties in order to properly operate in an open-world physical environment. To facilitate the development of such AI systems, we propose a new benchmark, NovPhy, that requires an agent to reason about physical scenarios in the presence of novelties and take actions accordingly. The benchmark consists of tasks that require agents to detect and adapt to novelties in physical scenarios. To create tasks in the benchmark, we develop eight novelties representing a diverse novelty space and apply them to five commonly encountered scenarios in a physical environment, related to applying forces and motions such as rolling, falling, and sliding of objects. According to our benchmark design, we evaluate two capabilities of an agent: the performance on a novelty when it is applied to different physical scenarios and the performance on a physical scenario when different novelties are applied to it. We conduct a thorough evaluation with human players, learning agents, and heuristic agents. Our evaluation shows that humans' performance is far beyond the agents' performance. Some agents, even with good normal task performance, perform significantly worse when there is a novelty, and the agents that can adapt to novelties typically adapt slower than humans. We promote the development of intelligent agents capable of performing at the human level or above when operating in open-world physical environments. Benchmark website: https://github.com/phy-q/novphy
Human Activity Recognition in an Open World (Abstract Reprint)
Authors: Derek Prijatelj; Samuel Grieggs; Jin Huang; Dawei Du; Ameya Shringi; Christopher Funk; Adam Kaufman; Eric Robertson; Walter Scheirer
Venue: IJCAI 2025 (main)

Abstract: Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current stateof-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released.
Explain It as Simple as Possible, but No Simpler – Explanation via Model Simplification for Addressing Inferential Gap (Abstract Reprint)
Authors: Sarath Sreedharan; Siddharth Srivastava; Subbarao Kambhampati
Venue: IJCAI 2025 (main)

Abstract: One of the core challenges of explaining decisions made by modern AI systems is the need to address the potential gap in the inferential capabilities of the system generating the decision and the user trying to make sense of it. This inferential capability gap becomes even more critical when it comes to explaining sequential decisions. While there have been some isolated efforts at developing explanation methods suited for complex decision-making settings, most of these current efforts are limited in scope. In this paper, we introduce a general framework for generating explanations in the presence of inferential capability gaps. A framework that is grounded in the generation of simplified representations of the agent model through the application of a sequence of model simplifying transformations. This framework not only allows us to develop an extremely general explanation generation algorithm, but we see that many of the existing works in this direction could be seen as specific instantiations of our more general method. While the ideas presented in this paper are general enough to be applied to any decision-making framework, we will focus on instantiating the framework in the context of stochastic planning problems. As a part of this instantiation, we will also provide an exhaustive characterization of explanatory queries and an analysis of various classes of applicable transformations. We will evaluate the effectiveness of transformation-based explanations through both synthetic experiments and user studies.
Out-of-Distribution Detection by Regaining Lost Clues (Abstract Reprint)
Authors: Zhilin Zhao; Longbing Cao; Philip S. Yu
Venue: IJCAI 2025 (main)

Abstract: Out-of-distribution (OOD) detection identifies samples in the test phase that are drawn from distributions distinct from that of training in-distribution (ID) samples for a trained network. According to the information bottleneck, networks that classify tabular data tend to extract labeling information from features with strong associations to ground-truth labels, discarding less relevant labeling cues. This behavior leads to a predicament in which OOD samples with limited labeling information receive high-confidence predictions, rendering the network incapable of distinguishing between ID and OOD samples. Hence, exploring more labeling information from ID samples, which makes it harder for an OOD sample to obtain high-confidence predictions, can address this over-confidence issue on tabular data. Accordingly, we propose a novel transformer chain (TC), which comprises a sequence of dependent transformers that iteratively regain discarded labeling information and integrate all the labeling information to enhance OOD detection. The generalization bound theoretically reveals that TC can balance ID generalization and OOD detection capabilities. Experimental results demonstrate that TC significantly surpasses state-of-the-art methods for OOD detection in tabular data.
Agent-based Modeling for Policy-Making in Inequity Contexts
Authors: Alba Aguilera
Venue: IJCAI 2025 (main)

Abstract: Recent advancements in AI have allowed for more nuanced agent-based models (ABMs). These simulations offer a non-invasive way to evaluate policies in diverse and complex social contexts. Our project focuses on developing ABMs to examine the impact of legal and social norms on inequity, particularly in scenarios where systemic discrimination affects human well-being. Our research is grounded on the Capability Approach, which provides a comprehensive framework to assess inequity in terms of real opportunities, underpinning the United Nations Sustainable Development Goals (SDGs). By defining representative (i) agent profiles, (ii) agent decision-making, and (iii) agent environment, this work aims to enhance the realism of ABMs and provide valuable insights for policy-making. To achieve this, we are in the process of developing (i) a novel population synthesis method to generate agent profiles that include motivators of behaviour, (ii) a decision-making model based on Markov decision processes (MDPs) that integrates values and needs in short-term and long-term rewards, and (iii) an ABM simulation to assess the impact of norms on inequity in terms of the real opportunities of individuals, among other indicators.
Data-Centric AI for Chest X-Ray Analysis in Resource-Constrained Settings
Authors: Yasmeena Akhter
Venue: IJCAI 2025 (main)

Abstract: With approximately 2 billion chest X-ray examinations conducted globally each year, the demand for radiological interpretation far surpasses the available expertise, particularly in resource-constrained regions. Recent advancements in artificial intelligence and computer vision present promising solutions for automated chest X-ray analysis. Nevertheless, integrating AI-driven diagnostics into clinical practice encounters several challenges, including data-centric issues, implementation barriers, deployment complexities, and the need for trustworthy AI. This dissertation focuses on the data-centric aspect, making significant contributions through enhanced data collection, the creation of novel datasets, algorithm development, privacy-preserving collaborative learning, and modelling for low-resolution data. It offers practical methodologies for embedding AI into chest radiology workflows, with a particular emphasis on addressing underserved conditions and healthcare settings with limited data availability. Furthermore, this work illustrates how tailored AI solutions can democratize access to high-quality radiological care while balancing privacy considerations and operational constraints across diverse environments.
A Formal Theory of Optimal Learning with Experimental Results
Authors: Michael Timothy Bennett
Venue: IJCAI 2025 (main)

Abstract: Here I summarise some results from my thesis which are relevant to artificial intelligence (AI) and machine learning (ML). The key contribution is a theory of optimally sample and energy efficient learning, which is supported by mathematical proofs and experimental results.
Enhancing the Logical Reasoning Abilities of Large Language Models
Authors: Fengxiang Cheng
Venue: IJCAI 2025 (main)

Abstract: Large language models (LLMs) have demonstrated impressive progress in various natural language progress tasks. However, it has been observed that LLMs still struggle with complex causal and logical reasoning. To facilitate this research direction, we first proposed a training method to distinguish causal relationships from spurious correlations in sentiment classification tasks. Then we conducted a comprehensive survey categorizing existing approaches, firstly identifying the main challenges of complex logical question-answering tasks and logical inconsistency across different questions. Our ongoing projects mainly focus on two points: (1) incorporating modal and epistemic logic to evaluate and enhance LLMs’ reasoning ability to handle more complex and diverse reasoning tasks, and (2) phased training LLMs with curriculum learning to improve their logical reasoning performance.
Ensuring Reliable and Transparent Algorithmic Fairness Through Optimal Transport and Uncertainty Quantification
Authors: Agathe Fernandes Machado
Venue: IJCAI 2025 (main)

Abstract: Machine learning (ML) models are increasingly used in high-stakes decisions, such as insurance pricing and pretrial detention, but often reproduce or amplify biases present in data. To mitigate discrimination, optimal transport (OT) offers a principled way to transform unfair model predictions into fair ones while minimizing performance loss. Moreover, uncertainty-based methods like calibration help assess fairness across sensitive groups, while uncertainty attribution helps identify sources of bias. This research aims to address algorithmic fairness challenges by developing evaluation and mitigation techniques with theoretical guarantees from OT, easily deployable in practice, while integrating fairness into the broader framework of trustworthy AI—enhancing calibration and uncertainty attribution methods to ensure ethical use of ML models by transparency and reliability.
Measuring and Mitigating Homelessness Bias:  Leveraging AI for Social Impact
Authors: Jonathan A. Karr Jr.
Venue: IJCAI 2025 (main)

Abstract: Bias towards people experiencing homelessness (PEH) is prevalent in online spaces. I leverage natural language processing (NLP) and large language models (LLMs) to identify, classify, and measure bias using geolocalized data collected from X (formerly Twitter), Reddit, meeting minutes, and news articles across the United States. The results of the study aim to provide a new path to alleviate homelessness by unveiling the intersectional bias that affects PEH. My research delivers a lexicon on homelessness, compiles an annotated database for homelessness and homelessness-racism intersectional (HRI) bias, evaluates LLMs as classifiers against these biases, and audits existing LLMs on HRI. My goal is to contribute to homelessness alleviation by counteracting social stigma and restoring the human dignity of the persons affected.
Rating AI Models for Robustness Through a Causal Lens
Authors: Kausik Lakkaraju
Venue: IJCAI 2025 (main)

Abstract: AI models are increasingly accessible through chatbots and other applications, but their black-box nature and sensitivity to small changes in the input make them hard to interpret and trust. Existing correlation-based robustness metrics fail to explain model errors or isolate causal effects. To address this, I propose ARC (AI Rating through Causality), a causally-grounded framework for rating AI models based on their robustness. ARC evaluates robustness by quantifying statistical and confounding biases, as well as the impact of perturbations on model performance across diverse tasks. ARC produces interpretable raw scores and ratings, helping developers and users make informed decisions about model robustness. Two future directions include: (1) deriving raw scores for composite models from their component scores, and (2) combining ratings with traditional explainable AI approaches to provide a more holistic view of model behavior.
Visual Analytics for Guiding Feature Attribution Method Selection
Authors: Priscylla Silva
Venue: IJCAI 2025 (main)

Abstract: Feature attribution methods explain model predictions by assigning importance to input features. Choosing the most suitable method is difficult due to inconsistencies and a lack of universal solutions. Our research focuses on proposing tools and methodologies for evaluating, comparing, and selecting these methods.
Reward Adaptation via Q-Manipulation: Provably Beneficial Reward Function Transfer in Reinforcement Learning
Authors: Kevin Jatin Vora
Venue: IJCAI 2025 (main)

Abstract: Reinforcement Learning has made great strides in game playing and robotics but faces challenges with sample complexity and generalization. Transfer learning, which allows agents to reuse knowledge from prior tasks, offers a promising solution. My current research focuses on Reward Adaptation, where agents adjust to new reward functions while leveraging knowledge from tasks with different reward functions. I propose Q-Manipulation (Q-M), a method that adapts Q-functions to new rewards by computing and iteratively tightening bounds, akin to value iteration. This allows for action pruning before learning begins, enhancing sample efficiency without compromising policy optimality. Through empirical comparisons I demonstrate its effectiveness, generalizability, and practicality. Future work will handle changes in transition dynamics and continuous MDPs.
Toward Interpretable Time Series Modeling: A Kernel Representation Perspective
Authors: Kunpeng Xu
Venue: IJCAI 2025 (main)

Abstract: Time series modeling is essential in finance, healthcare, and environmental science, yet nonlinear patterns, noise, and concept drift pose challenges. Although deep learning models, such as Transformer-based and recent pre-trained models, have achieved good performance across various time series tasks, they often lack interpretability, especially in co-evolving time series. This work introduces a kernel representation learning (KRL) perspective, rethinking time series modeling through kernel-induced self-representation to effectively capture temporal structures and dynamic transitions. Additionally, we establish theoretical connections between KRL and advanced deep-network models, demonstrating how kernel methods provide a principled approach to capturing complex time series behaviors.
DAVE: A Framework for Assisted Analysis of Document Collections in Knowledge-Intensive Domains
Authors: Ruben Agazzi; Renzo Alva Principe; Riccardo Pozzi; Marco Ripamonti; Matteo Palmonari
Venue: IJCAI 2025 (main)

Abstract: DAVE is a framework for assisting the analysis of documents in knowledge-intensive domains, based on an entity-centric approach supported by annotations of named entities in the documents. DAVE supports search & filtering, document exploration, question answering, and knowledge refinement. It is released as an open-source project that the community can further develop. DAVE’s distinguishing features are: the integration of a chatbot interface based on recent RAG solutions into well-established entity-powered faceted search, the fusion of search and filtering features provided by entity-level annotations with the capability to ask questions on annotated documents; human-in-the-loop functions to consolidate knowledge while exploring information, allowing users to improve annotations from NLP algorithms.
ASP Chef Chats with Large Language Models
Authors: Mario Alviano; Pietro Macrì; Luis Angel  Rodriguez Reiners
Venue: IJCAI 2025 (main)

Abstract: ASP Chef enriches Answer Set Programming (ASP) with the notion of recipe, that is, a sequence of operations on answer sets. Recipes are designed and executed in modern browsers, and further improve the fast prototyping capabilities of ASP. This paper introduces new operations designed to integrate Large Language Models (LLMs) in recipe, with the aim of combining the reasoning strength of ASP with the natural language capabilities of LLMs, to enable more interactive and adaptive problem-solving workflows. In a nutshell, answer sets in input are transformed into prompts for LLMs, whose responses are processed to extract facts for subsequent operations within the recipe.
Taking STEPS Forward: Enhancing Online Peer-Counseling with Schema Therapy via Socratic Questioning
Authors: Beng Heng Ang; Sujatha Das Gollapalli; See-Kiong Ng
Venue: IJCAI 2025 (main)

Abstract: Peer-counseling is essential in online mental health communities to provide relatable support to those seeking help, but the peer-counselors often lack professional training in therapeutic counseling to produce the desired cognitive changes. In this paper, we present STEPS, an AI-powered assistive dialog tool for peer-counseling. Unlike other existing tools, STEPS assists peer-counselors in facilitating cognitive change in online counseling settings. Towards this goal, we emulate two key phases in a Schema Therapy-based in-person counseling session–(1) Schema Assessment to uncover the deep-seated irrational beliefs underlying an individual’s mental health problems, and (2) Cognitive Change to reframe these beliefs into healthier alternatives. In both phases, we employ Socratic questioning techniques to effectively elicit critical introspection and guide cognitive change. We describe STEPS and present expert evaluation studies on its counseling conversations on real-world mental health forum posts. Our results indicate that STEPS significantly outperforms competitive baselines on all key metrics related to schema assessment, cognitive change strategies, and critical thinking, achieving an impressive average rating of 5 out of 6, highlighting its strong potential as a transformative tool for online peer-counseling.
Combining Code Generating Large Language Models and Self-Play to Iteratively Refine Strategies in Games
Authors: Yoram Bachrach; Edan Toledo; Karen Hambardzumyan; Despoina Magka; Martin Josifoski; Minqi Jiang; Jakob Foerster; Roberta Raileanu; Tatiana Shavrina; Nicola Cancedda; Avraham Ruderman; Katie Millican; Andrei Lupu; Rishi Hazra
Venue: IJCAI 2025 (main)

Abstract: We propose a self-play approach to generating strategies for playing in multi-player games, where strategies are represented as computer code. We use large language models (LLMs) to generate pieces of code to play in the game, which we refer to as generated bots. We engage the LLM generated bots in competitions, designed to generate increasingly stronger strategies. We follow game theoretic principles in organizing these tournaments, and use a Policy Space Response Oracle (PSRO) approach. We start with an initial set of LLM generated bots, and continue in rounds for adding new bots into the population. Each round adds a bot to the population by asking the LLM to produce code for playing against a bot representing the Nash equilibrium mixture over the current population. Our analysis shows that even a few rounds are sufficient to produces strong bots for playing the game. Our demo shows the process for the game of Checkers. We allow users to select initial bots in the population, run the process, inspect how the bots evolve over time, and play against the generated bots.
Using Planning for Automated Testing of Video Games
Authors: Tomáš Balyo; Roman Barták; Lukáš Chrpa; Michal Červenka; Filip Dvořák; Stephan Gocht; Lukáš Lipčák; Viktor Macek; Dominik Roháček; Josef Ryzí; Martin Suda; Dominik Šafránek; Slavomír Švancar; G. Michael Youngblood
Venue: IJCAI 2025 (main)

Abstract: In this demonstration, we present a system that automates regression testing for video games using automated planning techniques. Traditional test scripts are a common method for testing both video games and software in general. While effective, they require manual creation and frequent updates throughout development, making the process labor-intensive. Our system eliminates this burden by automatically generating and maintaining test scripts. The test engineer only needs to define the game’s rules using the Planning Domain Definition Language (PDDL) and specify initial states and goals for individual test cases. This significantly reduces human effort while ensuring test scripts remain up to date. Additionally, our system integrates with game engine editors—supporting both Unity and Unreal to execute and evaluate test cases directly within the game. It collects detailed logs, telemetry data, and video recordings, allowing users to review test results efficiently.
Aerial Coverage Path Planning in Nuclear Emergencies
Authors: Johann Blake; Matthias Schubert
Venue: IJCAI 2025 (main)

Abstract: We formulate a Coverage Path Planning (CPP) problem for a helicopter or a UAV tasked with mapping ground-level radiation while avoiding radiation that is too strong. We introduce a simulation environment that incorporates digital elevation models, altitude-dependent measurement footprints and realistic flight constraints, as well as state-of-the-art radiation scenario simulations, such as nuclear explosions, provided by the German Federal Office for Radiation Protection. We highlight the complexity of radiological survey missions and demonstrate the necessity for new CPP approaches that address these unique challenges. The code to our simulation environment can be found under https://github.com/JohannBlake/Aerial-Coverage-Path-Planning-in-Nuclear-Emergencies.