pwc-archive/papers-with-abstracts huggingface dataset:
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models
Authors: Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li
Date: 2025-05-01 00:00:00
Tasks: Large Language Model

Assessing how well a large language model (LLM) understands human, rather than merely text, remains an open challenge. To bridge the gap, we introduce Sentient Agent as a Judge (SAGE), an automated evaluation framework that measures an LLM's higher-order social cognition. SAGE instantiates a Sentient Agent that simulates human-like emotional changes and inner thoughts during interaction, providing a more realistic evaluation of the tested model in multi-turn conversations. At every turn, the agent reasons about (i) how its emotion changes, (ii) how it feels, and (iii) how it should reply, yielding a numerical emotion trajectory and interpretable inner thoughts. Experiments on 100 supportive-dialogue scenarios show that the final Sentient emotion score correlates strongly with Barrett-Lennard Relationship Inventory (BLRI) ratings and utterance-level empathy metrics, validating psychological fidelity. We also build a public Sentient Leaderboard covering 18 commercial and open-source models that uncovers substantial gaps (up to 4x) between frontier systems (GPT-4o-Latest, Gemini2.5-Pro) and earlier baselines, gaps not reflected in conventional leaderboards (e.g., Arena). SAGE thus provides a principled, scalable and interpretable tool for tracking progress toward genuinely empathetic and socially adept language agents.
Explainability by design: an experimental analysis of the legal coding process
Authors: Matteo Cristani, Guido Governatori, Francesco Olivieri, Monica Palmirani, Gabriele Buriola
Date: 2025-05-03 00:00:00

Behind a set of rules in Deontic Defeasible Logic, there is a mapping process of normative background fragments. This process goes from text to rules and implicitly encompasses an explanation of the coded fragments. In this paper we deliver a methodology for \textit{legal coding} that starts with a fragment and goes onto a set of Deontic Defeasible Logic rules, involving a set of \textit{scenarios} to test the correctness of the coded fragments. The methodology is illustrated by the coding process of an example text. We then show the results of a series of experiments conducted with humans encoding a variety of normative backgrounds and corresponding cases in which we have measured the efforts made in the coding process, as related to some measurable features. To process these examples, a recently developed technology, Houdini, that allows reasoning in Deontic Defeasible Logic, has been employed. Finally we provide a technique to forecast time required in coding, that depends on factors such as knowledge of the legal domain, knowledge of the coding processes, length of the text, and a measure of \textit{depth} that refers to the length of the paths of legal references.
Semantic Intelligence: Integrating GPT-4 with A Planning in Low-Cost Robotics
Authors: Jesse Barkley, Abraham George, Amir Barati Farimani
Date: 2025-05-03 00:00:00
Tasks: Large Language Model, Robot Navigation

Classical robot navigation often relies on hardcoded state machines and purely geometric path planners, limiting a robot's ability to interpret high-level semantic instructions. In this paper, we first assess GPT-4's ability to act as a path planner compared to the A* algorithm, then present a hybrid planning framework that integrates GPT-4's semantic reasoning with A* on a low-cost robot platform operating on ROS2 Humble. Our approach eliminates explicit finite state machine (FSM) coding by using prompt-based GPT-4 reasoning to handle task logic while maintaining the accurate paths computed by A*. The GPT-4 module provides semantic understanding of instructions and environmental cues (e.g., recognizing toxic obstacles or crowded areas to avoid, or understanding low-battery situations requiring alternate route selection), and dynamically adjusts the robot's occupancy grid via obstacle buffering to enforce semantic constraints. We demonstrate multi-step reasoning for sequential tasks, such as first navigating to a resource goal and then reaching a final destination safely. Experiments on a Petoi Bittle robot with an overhead camera and Raspberry Pi Zero 2W compare classical A* against GPT-4-assisted planning. Results show that while A* is faster and more accurate for basic route generation and obstacle avoidance, the GPT-4-integrated system achieves high success rates (96-100%) on semantic tasks that are infeasible for pure geometric planners. This work highlights how affordable robots can exhibit intelligent, context-aware behaviors by leveraging large language model reasoning with minimal hardware and no fine-tuning.
The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?
Authors: Kim Kaivanto
Date: 2025-05-01 00:00:00

In policy debates concerning the governance and regulation of Artificial Intelligence (AI), both the Precautionary Principle (PP) and the Innovation Principle (IP) are advocated by their respective interest groups. Do these principles offer wholly incompatible and contradictory guidance? Does one necessarily negate the other? I argue here that provided attention is restricted to weak-form PP and IP, the answer to both of these questions is "No." The essence of these weak formulations is the requirement to fully account for type-I error costs arising from erroneously preventing the innovation's diffusion through society (i.e. mistaken regulatory red-lighting) as well as the type-II error costs arising from erroneously allowing the innovation to diffuse through society (i.e. mistaken regulatory green-lighting). Within the Signal Detection Theory (SDT) model developed here, weak-PP red-light (weak-IP green-light) determinations are optimal for sufficiently small (large) ratios of expected type-I to type-II error costs. For intermediate expected cost ratios, an amber-light 'wait-and-monitor' policy is optimal. Regulatory sandbox instruments allow AI testing and experimentation to take place within a structured environment of limited duration and societal scale, whereby the expected cost ratio falls within the 'wait-and-monitor' range. Through sandboxing regulators and innovating firms learn more about the expected cost ratio, and what respective adaptations -- of regulation, of technical solution, of business model, or combination thereof, if any -- are needed to keep the ratio out of the weak-PP red-light zone.
Emotions in Artificial Intelligence
Authors: Hermann Borotschnig
Date: 2025-05-01 00:00:00

This conceptual contribution offers a speculative account of how AI systems might emulate emotions as experienced by humans and animals. It presents a thought experiment grounded in the hypothesis that natural emotions evolved as heuristics for rapid situational appraisal and action selection, enabling biologically adaptive behaviour without requiring full deliberative modeling. The text examines whether artificial systems operating in complex action spaces could similarly benefit from these principles. It is proposed that affect be interwoven with episodic memory by storing corresponding affective tags alongside all events. This allows AIs to establish whether present situations resemble past events and project the associated emotional labels onto the current context. These emotional cues are then combined with need-driven emotional hints. The combined emotional state facilitates decision-making in the present by modulating action selection. The low complexity and experiential inertness of the proposed architecture are emphasized as evidence that emotional expression and consciousness are, in principle, orthogonal-permitting the theoretical possibility of affective zombies. On this basis, the moral status of AIs emulating affective states is critically examined. It is argued that neither the mere presence of internal representations of emotion nor consciousness alone suffices for moral standing; rather, the capacity for self-awareness of inner emotional states is posited as a necessary condition. A complexity-based criterion is proposed to exclude such awareness in the presented model. Additional thought experiments are presented to test the conceptual boundaries of this framework.
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Authors: Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun, Peggy Li, James Diffenderfer, Busra Demirci, Obadiah Smolenski, Tim Hsu, Anna M. Hiszpanski, Kenneth Chiu, Bhavya Kailkhura, Brian Van Essen
Date: 2025-05-03 00:00:00
Tasks: Benchmarking, Hyperparameter Optimization, In-Context Learning, Inductive Bias, Molecular Property Prediction, molecular representation, Property Prediction

Advances in deep learning and generative modeling have driven interest in data-driven molecule discovery pipelines, whereby machine learning (ML) models are used to filter and design novel molecules without requiring prohibitively expensive first-principles simulations. Although the discovery of novel molecules that extend the boundaries of known chemistry requires accurate out-of-distribution (OOD) predictions, ML models often struggle to generalize OOD. Furthermore, there are currently no systematic benchmarks for molecular OOD prediction tasks. We present BOOM, $\boldsymbol{b}$enchmarks for $\boldsymbol{o}$ut-$\boldsymbol{o}$f-distribution $\boldsymbol{m}$olecular property predictions -- a benchmark study of property-based out-of-distribution models for common molecular property prediction models. We evaluate more than 140 combinations of models and property prediction tasks to benchmark deep learning models on their OOD performance. Overall, we do not find any existing models that achieve strong OOD generalization across all tasks: even the top performing model exhibited an average OOD error 3x larger than in-distribution. We find that deep learning models with high inductive bias can perform well on OOD tasks with simple, specific properties. Although chemical foundation models with transfer and in-context learning offer a promising solution for limited training data scenarios, we find that current foundation models do not show strong OOD extrapolation capabilities. We perform extensive ablation experiments to highlight how OOD performance is impacted by data generation, pre-training, hyperparameter optimization, model architecture, and molecular representation. We propose that developing ML models with strong OOD generalization is a new frontier challenge in chemical ML model development. This open-source benchmark will be made available on Github.
How Real Are Synthetic Therapy Conversations? Evaluating Fidelity in Prolonged Exposure Dialogues
Authors: Suhas BN, Dominik Mattioli, Saeed Abdullah, Rosa I. Arriaga, Chris W. Wiese, Andrew M. Sherrill
Date: 2025-04-30 00:00:00

The growing adoption of synthetic data in healthcare is driven by privacy concerns, limited access to real-world data, and the high cost of annotation. This work explores the use of synthetic Prolonged Exposure (PE) therapeutic conversations for Post-Traumatic Stress Disorder (PTSD) as a scalable alternative for training and evaluating clinical models. We systematically compare real and synthetic dialogues using linguistic, structural, and protocol-specific metrics, including turn-taking patterns and treatment fidelity. We also introduce and evaluate PE-specific metrics derived from linguistic analysis and semantic modeling, offering a novel framework for assessing clinical fidelity beyond surface fluency. Our findings show that although synthetic data holds promise for mitigating data scarcity and protecting patient privacy, it can struggle to capture the subtle dynamics of therapeutic interactions. Synthetic therapy dialogues closely match structural features of real-world conversations (e.g., speaker switch ratio: 0.98 vs. 0.99); however, they may not adequately reflect key fidelity markers (e.g., distress monitoring). We highlight gaps in existing evaluation frameworks and advocate for fidelity-aware metrics that go beyond surface fluency to uncover clinically significant failures. Our findings clarify where synthetic data can effectively complement real-world datasets -- and where critical limitations remain.
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching
Authors: Kewen Peng, Yicheng Yang, Hao Zhuo
Date: 2025-04-23 00:00:00
Tasks: Fairness

Fairness-aware learning aims to mitigate discrimination against specific protected social groups (e.g., those categorized by gender, ethnicity, age) while minimizing predictive performance loss. Despite efforts to improve fairness in machine learning, prior studies have shown that many models remain unfair when measured against various fairness metrics. In this paper, we examine whether the way training and testing data are sampled affects the reliability of reported fairness metrics. Since training and test sets are often randomly sampled from the same population, bias present in the training data may still exist in the test data, potentially skewing fairness assessments. To address this, we propose FairMatch, a post-processing method that applies propensity score matching to evaluate and mitigate bias. FairMatch identifies control and treatment pairs with similar propensity scores in the test set and adjusts decision thresholds for different subgroups accordingly. For samples that cannot be matched, we perform probabilistic calibration using fairness-aware loss functions. Experimental results demonstrate that our approach can (a) precisely locate subsets of the test data where the model is unbiased, and (b) significantly reduce bias on the remaining data. Overall, propensity score matching offers a principled way to improve both fairness evaluation and mitigation, without sacrificing predictive performance.
A cross-regional review of AI safety regulations in the commercial aviation
Authors: Penny A. Barr, Sohel M. Imroz
Date: 2025-02-12 00:00:00

In this paper we examine the existing artificial intelligence (AI) policy documents in aviation for the following three regions: the United States, European Union, and China. The aviation industry has always been a first mover in adopting technological advancements. This early adoption offers valuable insights because of its stringent regulations and safety-critical procedures. As a result, the aviation industry provides an optimal platform to counter AI vulnerabilities through its tight regulations, standardization processes, and certification of new technologies. Keywords: AI in aviation; aviation safety; standardization; certifiable AI; regulations
LookAlike: Consistent Distractor Generation in Math MCQs
Authors: Nisarg Parikh, Nigel Fernandez, Alexander Scarlatos, Simon Woodhead, Andrew Lan
Date: 2025-05-03 00:00:00
Tasks: Distractor Generation, Math, Multiple-choice

Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approaches are limited in ensuring that the generated distractors are consistent with common student errors. We propose LookAlike, a method that improves error-distractor consistency via preference optimization. Our two main innovations are: (a) mining synthetic preference pairs from model inconsistencies, and (b) alternating supervised fine-tuning (SFT) with Direct Preference Optimization (DPO) to stabilize training. Unlike prior work that relies on heuristics or manually annotated preference data, LookAlike uses its own generation inconsistencies as dispreferred samples, thus enabling scalable and stable training. Evaluated on a real-world dataset of 1,400+ math MCQs, LookAlike achieves 51.6% accuracy in distractor generation and 57.2% in error generation under LLM-as-a-judge evaluation, outperforming an existing state-of-the-art method (45.6% / 47.7%). These improvements highlight the effectiveness of preference-based regularization and inconsistency mining for generating consistent math MCQ distractors at scale.
Algorithmic Collective Action with Two Collectives
Authors: Aditya Karan, Nicholas Vincent, Karrie Karahalios, Hari Sundaram
Date: 2025-04-30 00:00:00
Tasks: Recommendation Systems

Given that data-dependent algorithmic systems have become impactful in more domains of life, the need for individuals to promote their own interests and hold algorithms accountable has grown. To have meaningful influence, individuals must band together to engage in collective action. Groups that engage in such algorithmic collective action are likely to vary in size, membership characteristics, and crucially, objectives. In this work, we introduce a first of a kind framework for studying collective action with two or more collectives that strategically behave to manipulate data-driven systems. With more than one collective acting on a system, unexpected interactions may occur. We use this framework to conduct experiments with language model-based classifiers and recommender systems where two collectives each attempt to achieve their own individual objectives. We examine how differing objectives, strategies, sizes, and homogeneity can impact a collective's efficacy. We find that the unintentional interactions between collectives can be quite significant; a collective acting in isolation may be able to achieve their objective (e.g., improve classification outcomes for themselves or promote a particular item), but when a second collective acts simultaneously, the efficacy of the first group drops by as much as $75\%$. We find that, in the recommender system context, neither fully heterogeneous nor fully homogeneous collectives stand out as most efficacious and that heterogeneity's impact is secondary compared to collective size. Our results signal the need for more transparency in both the underlying algorithmic models and the different behaviors individuals or collectives may take on these systems. This approach also allows collectives to hold algorithmic system developers accountable and provides a framework for people to actively use their own data to promote their own interests.
OODTE: A Differential Testing Engine for the ONNX Optimizer
Authors: Nikolaos Louloudakis, Ajitha Rajan
Date: 2025-05-03 00:00:00
Tasks: object-detection, Object Detection, Question Answering, Semantic Segmentation, Sentiment Analysis, Text Summarization

With over 700 stars on GitHub and being part of the official ONNX repository, the ONNX Optimizer is the default tool for applying graph-based optimizations to ONNX models. Despite its widespread use, its ability to maintain model accuracy during optimization has not been thoroughly investigated. In this work, we present OODTE, a utility designed to automatically and comprehensively evaluate the correctness of the ONNX Optimizer. OODTE adopts a straightforward yet powerful differential testing and evaluation methodology, which can be readily adapted for use with other compiler optimizers. Specifically, OODTE takes a collection of ONNX models, applies optimizations, and executes both the original and optimized versions across a user-defined input set, automatically capturing any issues encountered during optimization. When discrepancies in accuracy arise, OODTE iteratively isolates the responsible optimization pass by repeating the process at a finer granularity. We applied OODTE to 130 well-known models from the official ONNX Model Hub, spanning diverse tasks including classification, object detection, semantic segmentation, text summarization, question answering, and sentiment analysis. Our evaluation revealed that 9.2% of the model instances either caused the optimizer to crash or led to the generation of invalid models using default optimization strategies. Additionally, 30% of classification models and 16.6% of object detection and segmentation models exhibited differing outputs across original and optimized versions, whereas models focused on text-related tasks were generally robust to optimization. OODTE uncovered 15 issues-14 previously unknown-affecting 9 of 47 optimization passes and the optimizer overall. All issues were reported to the ONNX Optimizer team. OODTE offers a simple but effective framework for validating AI model optimizers, applicable beyond the ONNX ecosystem.
Evaluating the AI-Lab Intervention: Impact on Student Perception and Use of Generative AI in Early Undergraduate Computer Science Courses
Authors: Ethan Dickey, Andres Bejarano, Rhianna Kuperus, Bárbara Fagundes
Date: 2025-04-30 00:00:00

Generative AI (GenAI) is rapidly entering computer science education, yet its effects on student learning, skill development, and perceptions remain underexplored. Concerns about overreliance coexist with a gap in research on structured scaffolding to guide tool use in formal courses. This study examines the impact of a dedicated "AI-Lab" intervention -- emphasizing guided scaffolding and mindful engagement -- on undergraduate students in Data Structures and Algorithms, Competitive Programming, and first-year engineering courses at Purdue University. Over three semesters, we integrated AI-Lab modules into four mandatory and elective courses, yielding 831 matched pre- and post-intervention survey responses, alongside focus group discussions. Employing a mixed-methods approach, we analyzed quantitative shifts in usage patterns and attitudes as well as qualitative narratives of student experiences. While the overall frequency of GenAI usage for homework or programming projects remained largely stable, we observed large effect sizes in comfort and openness across conceptual, debugging, and homework problems. Notably, usage patterns for debugging also shifted statistically significantly, reflecting students' more mindful and deliberate approach. Focus group discussions corroborated these results, suggesting that the intervention "bridged the gap" between naive GenAI usage and more nuanced, reflective integration of AI tools into coursework, ultimately heightening students' awareness of their own skill development. These findings suggest that structured, scaffolded interventions can enable students to harness GenAI's benefits without undermining essential competencies. We offer evidence-based recommendations for educators seeking to integrate GenAI responsibly into computing curricula and identify avenues for future research on GenAI-supported pedagogy.
PhysNav-DG: A Novel Adaptive Framework for Robust VLM-Sensor Fusion in Navigation Applications
Authors: Trisanth Srinivasan, Santosh Patapati
Date: 2025-05-03 00:00:00
Tasks: Autonomous Driving, Sensor Fusion, Social Navigation, State Estimation

Robust navigation in diverse environments and domains requires both accurate state estimation and transparent decision making. We present PhysNav-DG, a novel framework that integrates classical sensor fusion with the semantic power of vision-language models. Our dual-branch architecture predicts navigation actions from multi-sensor inputs while simultaneously generating detailed chain-of-thought explanations. A modified Adaptive Kalman Filter dynamically adjusts its noise parameters based on environmental context. It leverages several streams of raw sensor data along with semantic insights from models such as LLaMA 3.2 11B and BLIP-2. To evaluate our approach, we introduce the MD-NEX Benchmark, a novel multi-domain dataset that unifies indoor navigation, autonomous driving, and social navigation tasks with ground-truth actions and human-validated explanations. Extensive experiments and ablations show that PhysNav-DG improves navigation success rates by over 20% and achieves high efficiency, with explanations that are both highly grounded and clear. This work connects high-level semantic reasoning and geometric planning for safer and more trustworthy autonomous systems.