pwc-archive/papers-with-abstracts huggingface dataset:
"I Apologize For Not Understanding Your Policy": Exploring the Specification and Evaluation of User-Managed Access Control Policies by AI Virtual Assistants
Authors: Jennifer Mondragon, Carlos Rubio-Medrano, Gael Cruz, Dvijesh Shastri
Date: 2025-05-12 00:00:00

The rapid evolution of Artificial Intelligence (AI)-based Virtual Assistants (VAs) e.g., Google Gemini, ChatGPT, Microsoft Copilot, and High-Flyer Deepseek has turned them into convenient interfaces for managing emerging technologies such as Smart Homes, Smart Cars, Electronic Health Records, by means of explicit commands,e.g., prompts, which can be even launched via voice, thus providing a very convenient interface for end-users. However, the proper specification and evaluation of User-Managed Access Control Policies (U-MAPs), the rules issued and managed by end-users to govern access to sensitive data and device functionality - within these VAs presents significant challenges, since such a process is crucial for preventing security vulnerabilities and privacy leaks without impacting user experience. This study provides an initial exploratory investigation on whether current publicly-available VAs can manage U-MAPs effectively across differing scenarios. By conducting unstructured to structured tests, we evaluated the comprehension of such VAs, revealing a lack of understanding in varying U-MAP approaches. Our research not only identifies key limitations, but offers valuable insights into how VAs can be further improved to manage complex authorization rules and adapt to dynamic changes.
Emotion-Gradient Metacognitive RSI (Part I): Theoretical Foundations and Single-Agent Architecture
Authors: Rintaro Ando
Date: 2025-05-12 00:00:00
Tasks: Informativeness

We present the Emotion-Gradient Metacognitive Recursive Self-Improvement (EG-MRSI) framework, a novel architecture that integrates introspective metacognition, emotion-based intrinsic motivation, and recursive self-modification into a unified theoretical system. The framework is explicitly capable of overwriting its own learning algorithm under formally bounded risk. Building upon the Noise-to-Meaning RSI (N2M-RSI) foundation, EG-MRSI introduces a differentiable intrinsic reward function driven by confidence, error, novelty, and cumulative success. This signal regulates both a metacognitive mapping and a self-modification operator constrained by provable safety mechanisms. We formally define the initial agent configuration, emotion-gradient dynamics, and RSI trigger conditions, and derive a reinforcement-compatible optimization objective that guides the agent's development trajectory. Meaning Density and Meaning Conversion Efficiency are introduced as quantifiable metrics of semantic learning, closing the gap between internal structure and predictive informativeness. This Part I paper establishes the single-agent theoretical foundations of EG-MRSI. Future parts will extend this framework to include safety certificates and rollback protocols (Part II), collective intelligence mechanisms (Part III), and feasibility constraints including thermodynamic and computational limits (Part IV). Together, the EG-MRSI series provides a rigorous, extensible foundation for open-ended and safe AGI.
Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems
Authors: Tomasz Szydlo, Viacheslaw Horbanow, Dev Nandan Jha, Shashikant Ilager, Aleksander Slominski, Rajiv Ranjan
Date: 2025-05-12 00:00:00
Tasks: Benchmarking, Computational Efficiency, CPU, Edge-computing

Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastructure. These benefits are crucial for applications requiring real-time data processing or strict security measures. Despite these advantages, edge devices operating within edge clusters are often underutilized. This inefficiency is mainly due to the absence of a holistic performance profiling mechanism which can help dynamically adjust the desired system configuration for a given workload. Since edge computing environments involve a complex interplay between CPU frequency, power consumption, and application performance, a deeper understanding of these correlations is essential. By uncovering these relationships, it becomes possible to make informed decisions that enhance both computational efficiency and energy savings. To address this gap, this paper evaluates the power consumption and performance characteristics of a single processing node within an edge cluster using a synthetic microbenchmark by varying the workload size and CPU frequency. The results show how an optimal measure can lead to optimized usage of edge resources, given both performance and power consumption.
Guiding Data Collection via Factored Scaling Curves
Authors: Lihan Zha, Apurva Badithela, Michael Zhang, Justin Lidard, Jeremy Bao, Emily Zhou, David Snyder, Allen Z. Ren, Dhruv Shah, Anirudha Majumdar
Date: 2025-05-12 00:00:00
Tasks: Imitation Learning

Generalist imitation learning policies trained on large datasets show great promise for solving diverse manipulation tasks. However, to ensure generalization to different conditions, policies need to be trained with data collected across a large set of environmental factor variations (e.g., camera pose, table height, distractors) $-$ a prohibitively expensive undertaking, if done exhaustively. We introduce a principled method for deciding what data to collect and how much to collect for each factor by constructing factored scaling curves (FSC), which quantify how policy performance varies as data scales along individual or paired factors. These curves enable targeted data acquisition for the most influential factor combinations within a given budget. We evaluate the proposed method through extensive simulated and real-world experiments, across both training-from-scratch and fine-tuning settings, and show that it boosts success rates in real-world tasks in new environments by up to 26% over existing data-collection strategies. We further demonstrate how factored scaling curves can effectively guide data collection using an offline metric, without requiring real-world evaluation at scale.
Hybrid Spiking Vision Transformer for Object Detection with Event Cameras
Authors: Qi Xu, Jie Deng, Jiangrong Shen, Biwu Chen, Huajin Tang, Gang Pan
Date: 2025-05-12 00:00:00
Tasks: Event Detection, Object, object-detection, Object Detection

Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, Spiking Neural Networks (SNNs) have emerged as a promising approach, offering low energy consumption and rich spatiotemporal dynamics. To further enhance the performance of event-based object detection, this study proposes a novel hybrid spike vision Transformer (HsVT) model. The HsVT model integrates a spatial feature extraction module to capture local and global features, and a temporal feature extraction module to model time dependencies and long-term patterns in event sequences. This combination enables HsVT to capture spatiotemporal features, improving its capability to handle complex event-based object detection tasks. To support research in this area, we developed and publicly released The Fall Detection Dataset as a benchmark for event-based object detection tasks. This dataset, captured using an event-based camera, ensures facial privacy protection and reduces memory usage due to the event representation format. We evaluated the HsVT model on GEN1 and Fall Detection datasets across various model sizes. Experimental results demonstrate that HsVT achieves significant performance improvements in event detection with fewer parameters.
Circuit Partitioning Using Large Language Models for Quantum Compilation and Simulations
Authors: Pranav Sinha, Sumit Kumar Jha, Sunny Raj
Date: 2025-05-12 00:00:00

We are in the midst of the noisy intermediate-scale quantum (NISQ) era, where quantum computers are limited by noisy gates, some of which are more error-prone than others and can render the final computation incomprehensible. Quantum circuit compilation algorithms attempt to minimize these noisy gates when mapping quantum algorithms onto quantum hardware but face computational challenges that restrict their application to circuits with no more than 5-6 qubits, necessitating the need to partition large circuits before the application of noisy quantum gate minimization algorithms. The existing generation of these algorithms is heuristic in nature and does not account for downstream gate minimization tasks. Large language models (LLMs) have the potential to change this and help improve quantum circuit partitions. This paper investigates the use of LLMs, such as Llama and Mistral, for partitioning quantum circuits by capitalizing on their abilities to understand and generate code, including QASM. Specifically, we teach LLMs to partition circuits using the quick partition approach of the Berkeley Quantum Synthesis Toolkit. Through experimental evaluations, we show that careful fine-tuning of open source LLMs enables us to obtain an accuracy of 53.4% for the partition task while over-the-shelf LLMs are unable to correctly partition circuits, using standard 1-shot and few-shot training approaches.
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors
Authors: Juan D. Pinto, Luc Paquette
Date: 2025-04-10 00:00:00

The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a neural-network-based behavior detection model that is interpretable by design. Our model is fully interpretable, meaning that the parameters we extract for our explanations have a clear interpretation, fully capture the model's learned knowledge about the learner behavior of interest, and can be used to create explanations that are both faithful and intelligible. We achieve this by implementing a series of constraints to the model that both simplify its inference process and bring it closer to a human conception of the task at hand. We train the model to detect gaming-the-system behavior, evaluate its performance on this task, and compare its learned patterns to those identified by human experts. Our results show that the model is successfully able to learn patterns indicative of gaming-the-system behavior while providing evidence for fully interpretable explanations. We discuss the implications of our approach and suggest ways to evaluate explainability using a human-grounded approach.
Lightweight End-to-end Text-to-speech Synthesis for low resource on-device applications
Authors: Biel Tura Vecino, Adam Gabryś, Daniel Mątwicki, Andrzej Pomirski, Tom Iddon, Marius Cotescu, Jaime Lorenzo-Trueba
Date: 2025-05-12 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recent works have shown that modelling raw waveform directly from text in an end-to-end (E2E) fashion produces more natural-sounding speech than traditional neural text-to-speech (TTS) systems based on a cascade or two-stage approach. However, current E2E state-of-the-art models are computationally complex and memory-consuming, making them unsuitable for real-time offline on-device applications in low-resource scenarios. To address this issue, we propose a Lightweight E2E-TTS (LE2E) model that generates high-quality speech requiring minimal computational resources. We evaluate the proposed model on the LJSpeech dataset and show that it achieves state-of-the-art performance while being up to $90\%$ smaller in terms of model parameters and $10\times$ faster in real-time-factor. Furthermore, we demonstrate that the proposed E2E training paradigm achieves better quality compared to an equivalent architecture trained in a two-stage approach. Our results suggest that LE2E is a promising approach for developing real-time, high quality, low-resource TTS applications for on-device applications.
Belief Injection for Epistemic Control in Linguistic State Space
Authors: Sebastian Dumbrava
Date: 2025-05-12 00:00:00

This work introduces belief injection, a proactive epistemic control mechanism for artificial agents whose cognitive states are structured as dynamic ensembles of linguistic belief fragments. Grounded in the Semantic Manifold framework, belief injection directly incorporates targeted linguistic beliefs into an agent's internal cognitive state, influencing reasoning and alignment proactively rather than reactively. We delineate various injection strategies, such as direct, context-aware, goal-oriented, and reflective approaches, and contrast belief injection with related epistemic control mechanisms, notably belief filtering. Additionally, this work discusses practical applications, implementation considerations, ethical implications, and outlines promising directions for future research into cognitive governance using architecturally embedded belief injection.
S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models
Authors: Muzhi Dai, Chenxu Yang, Qingyi Si
Date: 2025-05-12 00:00:00
Tasks: GSM8K, Large Language Model, Math, reinforcement-learning, Reinforcement Learning

As Test-Time Scaling emerges as an active research focus in the large language model community, advanced post-training methods increasingly emphasize extending chain-of-thought (CoT) generation length, thereby enhancing reasoning capabilities to approach Deepseek R1-like reasoning models. However, recent studies reveal that reasoning models (even Qwen3) consistently exhibit excessive thought redundancy in CoT generation. This overthinking problem stems from conventional outcome-reward reinforcement learning's systematic neglect in regulating intermediate reasoning steps. This paper proposes Serial-Group Decaying-Reward Policy Optimization (namely S-GRPO), a novel reinforcement learning method that empowers models with the capability to determine the sufficiency of reasoning steps, subsequently triggering early exit of CoT generation. Specifically, unlike GRPO, which samples multiple possible completions (parallel group) in parallel, we select multiple temporal positions in the generation of one CoT to allow the model to exit thinking and instead generate answers (serial group), respectively. For the correct answers in a serial group, we assign rewards that decay according to positions, with lower rewards towards the later ones, thereby reinforcing the model's behavior to generate higher-quality answers at earlier phases with earlier exits of thinking. Empirical evaluations demonstrate compatibility with state-of-the-art reasoning models, including Qwen3 and Deepseek-distill models, achieving 35.4% ~ 61.1\% sequence length reduction with 0.72% ~ 6.08% accuracy improvements across GSM8K, AIME 2024, AMC 2023, MATH-500, and GPQA Diamond benchmarks.
Multimodal Survival Modeling in the Age of Foundation Models
Authors: Steven Song, Morgan Borjigin-Wang, Irene Madejski, Robert L. Grossman
Date: 2025-05-12 00:00:00
Tasks: Hallucination, Survival Prediction, Text Summarization

The Cancer Genome Atlas (TCGA) has enabled novel discoveries and served as a large-scale reference through its harmonized genomics, clinical, and image data. Prior studies have trained bespoke cancer survival prediction models from unimodal or multimodal TCGA data. A modern paradigm in biomedical deep learning is the development of foundation models (FMs) to derive meaningful feature embeddings, agnostic to a specific modeling task. Biomedical text especially has seen growing development of FMs. While TCGA contains free-text data as pathology reports, these have been historically underutilized. Here, we investigate the feasibility of training classical, multimodal survival models over zero-shot embeddings extracted by FMs. We show the ease and additive effect of multimodal fusion, outperforming unimodal models. We demonstrate the benefit of including pathology report text and rigorously evaluate the effect of model-based text summarization and hallucination. Overall, we modernize survival modeling by leveraging FMs and information extraction from pathology reports.
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization
Authors: Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang
Date: 2025-05-12 00:00:00
Tasks: Few-Shot Image Classification, Knowledge Distillation, Semi-Supervised Image Classification, Semi-Supervised Image Classification on ImageNet - 10% labeled data

Vision-language models (VLMs) have achieved remarkable success across diverse tasks by leveraging rich textual information with minimal labeled data. However, deploying such large models remains challenging, particularly in resource-constrained environments. Knowledge distillation (KD) offers a well-established solution to this problem; however, recent KD approaches from VLMs often involve multi-stage training or additional tuning, increasing computational overhead and optimization complexity. In this paper, we propose $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization ($\mathbf{\texttt{DHO}}$) -- a simple yet effective KD framework that transfers knowledge from VLMs to compact, task-specific models in semi-supervised settings. Specifically, we introduce dual prediction heads that independently learn from labeled data and teacher predictions, and propose to linearly combine their outputs during inference. We observe that $\texttt{DHO}$ mitigates gradient conflicts between supervised and distillation signals, enabling more effective feature learning than single-head KD baselines. As a result, extensive experiments show that $\texttt{DHO}$ consistently outperforms baselines across multiple domains and fine-grained datasets. Notably, on ImageNet, it achieves state-of-the-art performance, improving accuracy by 3% and 0.1% with 1% and 10% labeled data, respectively, while using fewer parameters.
Benchmarking Retrieval-Augmented Generation for Chemistry
Authors: Xianrui Zhong, Bowen Jin, Siru Ouyang, Yanzhen Shen, Qiao Jin, Yin Fang, Zhiyong Lu, Jiawei Han
Date: 2025-05-12 00:00:00
Tasks: Benchmarking, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high-quality, domain-specific corpora and well-curated evaluation benchmarks. In this work, we introduce ChemRAG-Bench, a comprehensive benchmark designed to systematically assess the effectiveness of RAG across a diverse set of chemistry-related tasks. The accompanying chemistry corpus integrates heterogeneous knowledge sources, including scientific literature, the PubChem database, PubMed abstracts, textbooks, and Wikipedia entries. In addition, we present ChemRAG-Toolkit, a modular and extensible RAG toolkit that supports five retrieval algorithms and eight LLMs. Using ChemRAG-Toolkit, we demonstrate that RAG yields a substantial performance gain -- achieving an average relative improvement of 17.4% over direct inference methods. We further conduct in-depth analyses on retriever architectures, corpus selection, and the number of retrieved passages, culminating in practical recommendations to guide future research and deployment of RAG systems in the chemistry domain. The code and data is available at https://chemrag.github.io.
A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development
Authors: Werner Geyer, Jessica He, Daita Sarkar, Michelle Brachman, Chris Hammond, Jennifer Heins, Zahra Ashktorab, Carlos Rosemberg, Charlie Hill
Date: 2025-05-12 00:00:00

The broad availability of generative AI offers new opportunities to support various work domains, including agile software development. Agile epics are a key artifact for product managers to communicate requirements to stakeholders. However, in practice, they are often poorly defined, leading to churn, delivery delays, and cost overruns. In this industry case study, we investigate opportunities for large language models (LLMs) to evaluate agile epic quality in a global company. Results from a user study with 17 product managers indicate how LLM evaluations could be integrated into their work practices, including perceived values and usage in improving their epics. High levels of satisfaction indicate that agile epics are a new, viable application of AI evaluations. However, our findings also outline challenges, limitations, and adoption barriers that can inform both practitioners and researchers on the integration of such evaluations into future agile work practices.
Chronocept: Instilling a Sense of Time in Machines
Authors: Krish Goel, Sanskar Pandey, KS Mahadevan, Harsh Kumar, Vishesh Khadaria
Date: 2025-05-12 00:00:00
Tasks: Fact Checking, RAG, Retrieval-augmented Generation, Sentence, valid

Human cognition is deeply intertwined with a sense of time, known as Chronoception. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. Despite progress in vision, language, and motor control, AI still struggles to reason about temporal validity. We introduce Chronocept, the first benchmark to model temporal validity as a continuous probability distribution over time. Using skew-normal curves fitted along semantically decomposed temporal axes, Chronocept captures nuanced patterns of emergence, decay, and peak relevance. It includes two datasets: Benchmark I (atomic facts) and Benchmark II (multi-sentence passages). Annotations show strong inter-annotator agreement (84% and 89%). Our baselines predict curve parameters - location, scale, and skewness - enabling interpretable, generalizable learning and outperforming classification-based approaches. Chronocept fills a foundational gap in AI's temporal reasoning, supporting applications in knowledge grounding, fact-checking, retrieval-augmented generation (RAG), and proactive agents. Code and data are publicly available.