pwc-archive/papers-with-abstracts huggingface dataset:
Enter: Graduated Realism: A Pedagogical Framework for AI-Powered Avatars in Virtual Reality Teacher Training
Authors: Judson Leroy Dean Haynes IV
Date: 2025-06-13 00:00:00
Tasks: Retrieval-augmented Generation

Virtual Reality simulators offer a powerful tool for teacher training, yet the integration of AI-powered student avatars presents a critical challenge: determining the optimal level of avatar realism for effective pedagogy. This literature review examines the evolution of avatar realism in VR teacher training, synthesizes its theoretical implications, and proposes a new pedagogical framework to guide future design. Through a systematic review, this paper traces the progression from human-controlled avatars to generative AI prototypes. Applying learning theories like Cognitive Load Theory, we argue that hyper-realism is not always optimal, as high-fidelity avatars can impose excessive extraneous cognitive load on novices, a stance supported by recent empirical findings. A significant gap exists between the technological drive for photorealism and the pedagogical need for scaffolded learning. To address this gap, we propose Graduated Realism, a framework advocating for starting trainees with lower-fidelity avatars and progressively increasing behavioral complexity as skills develop. To make this computationally feasible, we outline a novel single-call architecture, Crazy Slots, which uses a probabilistic engine and a Retrieval-Augmented Generation database to generate authentic, real-time responses without the latency and cost of multi-step reasoning models. This review provides evidence-based principles for designing the next generation of AI simulators, arguing that a pedagogically grounded approach to realism is essential for creating scalable and effective teacher education tools.
Real-Time Feedback and Benchmark Dataset for Isometric Pose Evaluation
Authors: Abhishek Jaiswal, Armeet Singh Luthra, Purav Jangir, Bhavya Garg, Nisheeth Srivastava
Date: 2025-06-13 00:00:00

Isometric exercises appeal to individuals seeking convenience, privacy, and minimal dependence on equipments. However, such fitness training is often overdependent on unreliable digital media content instead of expert supervision, introducing serious risks, including incorrect posture, injury, and disengagement due to lack of corrective feedback. To address these challenges, we present a real-time feedback system for assessing isometric poses. Our contributions include the release of the largest multiclass isometric exercise video dataset to date, comprising over 3,600 clips across six poses with correct and incorrect variations. To support robust evaluation, we benchmark state-of-the-art models-including graph-based networks-on this dataset and introduce a novel three-part metric that captures classification accuracy, mistake localization, and model confidence. Our results enhance the feasibility of intelligent and personalized exercise training systems for home workouts. This expert-level diagnosis, delivered directly to the users, also expands the potential applications of these systems to rehabilitation, physiotherapy, and various other fitness disciplines that involve physical motion.
Forgetful by Design? A Critical Audit of YouTube's Search API for Academic Research
Authors: Bernhard Rieder, Adrian Padilla, Oscar Coromina
Date: 2025-06-13 00:00:00

This paper critically audits the search endpoint of YouTube's Data API (v3), a common tool for academic research. Through systematic weekly searches over six months using eleven queries, we identify major limitations regarding completeness, representativeness, consistency, and bias. Our findings reveal substantial differences between ranking parameters like relevance and date in terms of video recall and precision, with relevance often retrieving numerous off-topic videos. We also find severe temporal decay, as the number of findable videos for a specific period dramatically decreases after just 20-60 days from the publication date, potentially hampering many different research designs. Furthermore, search results lack consistency, with identical queries yielding different video sets over time, compromising replicability. A case study on the European Parliament elections highlights how these issues impact research outcomes. While the paper offers several mitigation strategies, it concludes that the API's search function, potentially prioritizing "freshness" over comprehensive retrieval, is not adequate for robust academic research, especially concerning Digital Services Act requirements.
Interaction, Process, Infrastructure: A Unified Architecture for Human-Agent Collaboration
Authors: Yun Wang, Yan Lu
Date: 2025-06-13 00:00:00
Tasks: Human Agent Collaboration

As AI tools proliferate across domains, from chatbots and copilots to emerging agents, they increasingly support professional knowledge work. Yet despite their growing capabilities, these systems remain fragmented: they assist with isolated tasks but lack the architectural scaffolding for sustained, adaptive collaboration. We propose a layered framework for human-agent systems that integrates three interdependent dimensions: interaction, process, and infrastructure. Crucially, our architecture elevates process to a primary focus by making it explicit, inspectable, and adaptable, enabling humans and agents to align with evolving goals and coordinate over time. This model clarifies limitations of current tools, unifies emerging system design approaches, and reveals new opportunities for researchers and AI system builders. By grounding intelligent behavior in structured collaboration, we reimagine human-agent collaboration not as task-specific augmentation, but as a form of coherent and aligned system for real-world work.
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Authors: Liying Wang, Daffodil Carrington, Daniil Filienko, Caroline El Jazmi, Serena Jinchen Xie, M. S., Martine De Cock, Sarah Iribarren, Ph. D., Weichao Yuwen, Ph. D
Date: 2025-06-13 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model, RAG, Retrieval-augmented Generation

Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support for caregivers, specifically Problem-Solving Therapy (PST) integrated with Motivational Interviewing (MI) and Behavioral Chain Analysis (BCA). A within-subject experiment was conducted with 28 caregivers interacting with four LLM configurations to evaluate empathy and therapeutic alliance. The best-performing models incorporated Few-Shot and Retrieval-Augmented Generation (RAG) prompting techniques, alongside clinician-curated examples. The models showed improved contextual understanding and personalized support, as reflected by qualitative responses and quantitative ratings on perceived empathy and therapeutic alliances. Participants valued the model's ability to validate emotions, explore unexpressed feelings, and provide actionable strategies. However, balancing thorough assessment with efficient advice delivery remains a challenge. This work highlights the potential of LLMs in delivering empathetic and tailored support for family caregivers.
Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention
Authors: Md Mynoddin, Troyee Dev, Rishita Chakma
Date: 2025-06-12 00:00:00
Tasks: Diagnostic, EEG, Electroencephalogram (EEG)

Mental stress has become a pervasive factor affecting cognitive health and overall well-being, necessitating the development of robust, non-invasive diagnostic tools. Electroencephalogram (EEG) signals provide a direct window into neural activity, yet their non-stationary and high-dimensional nature poses significant modeling challenges. Here we introduce Brain2Vec, a new deep learning tool that classifies stress states from raw EEG recordings using a hybrid architecture of convolutional, recurrent, and attention mechanisms. The model begins with a series of convolutional layers to capture localized spatial dependencies, followed by an LSTM layer to model sequential temporal patterns, and concludes with an attention mechanism to emphasize informative temporal regions. We evaluate Brain2Vec on the DEAP dataset, applying bandpass filtering, z-score normalization, and epoch segmentation as part of a comprehensive preprocessing pipeline. Compared to traditional CNN-LSTM baselines, our proposed model achieves an AUC score of 0.68 and a validation accuracy of 81.25%. These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.
Self-Calibrating BCIs: Ranking and Recovery of Mental Targets Without Labels
Authors: Jonathan Grizou, Carlos de la Torre-Ortiz, Tuukka Ruotsalo
Date: 2025-06-11 00:00:00
Tasks: EEG

We consider the problem of recovering a mental target (e.g., an image of a face) that a participant has in mind from paired EEG (i.e., brain responses) and image (i.e., perceived faces) data collected during interactive sessions without access to labeled information. The problem has been previously explored with labeled data but not via self-calibration, where labeled data is unavailable. Here, we present the first framework and an algorithm, CURSOR, that learns to recover unknown mental targets without access to labeled data or pre-trained decoders. Our experiments on naturalistic images of faces demonstrate that CURSOR can (1) predict image similarity scores that correlate with human perceptual judgments without any label information, (2) use these scores to rank stimuli against an unknown mental target, and (3) generate new stimuli indistinguishable from the unknown mental target (validated via a user study, N=53).
Manifesto from Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE)
Authors: Christine Bauer, Li Chen, Nicola Ferro, Norbert Fuhr, Avishek Anand, Timo Breuer, Guglielmo Faggioli, Ophir Frieder, Hideo Joho, Jussi Karlgren, Johannes Kiesel, Bart P. Knijnenburg, Aldo Lipani, Lien Michiels, Andrea Papenmeier, Maria Soledad Pera, Mark Sanderson, Scott Sanner, Benno Stein, Johanne R. Trippas, Karin Verspoor, Martijn C Willemsen
Date: 2025-06-08 00:00:00
Tasks: Conversational Information Access

During the workshop, we deeply discussed what CONversational Information ACcess (CONIAC) is and its unique features, proposing a world model abstracting it, and defined the Conversational Agents Framework for Evaluation (CAFE) for the evaluation of CONIAC systems, consisting of six major components: 1) goals of the system's stakeholders, 2) user tasks to be studied in the evaluation, 3) aspects of the users carrying out the tasks, 4) evaluation criteria to be considered, 5) evaluation methodology to be applied, and 6) measures for the quantitative criteria chosen.
Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience
Authors: Andrew Chang, Chenkai Hu, Ji Qi, Zhuojian Wei, Kexin Zhang, Viswadruth Akkaraju, David Poeppel, Dustin Freeman
Date: 2025-06-01 00:00:00

Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment remain understudied. These moments are infrequent in naturalistic data, and thus training a supervised learning (SL) model requires costly manual data annotation. We applied semi-supervised learning (SSL) to leverage targeted labeled and unlabeled clips for training multimodal (audio, facial, text) deep features to predict non-fluid or unenjoyable moments in holdout videoconference sessions. The modality-fused co-training SSL achieved an ROC-AUC of 0.9 and an F1 score of 0.6, outperforming SL models by up to 4% with the same amount of labeled data. Remarkably, the best SSL model with just 8% labeled data matched 96% of the SL model's full-data performance. This shows an annotation-efficient framework for modeling videoconference experience.
A stochastic noise model based excess noise factor expressions for staircase avalanche photodiodes
Authors: Ankitha E Bangera
Date: 2025-06-17 00:00:00

Multistep staircase avalanche photodiodes (APDs) are the solid-state analogue of photomultiplier tubes, owing to their deterministic amplification with twofold stepwise gain via impact ionization. Yet, the stepwise impact ionization irregularities worsen with increasing step counts, which are a major source of internal noise in these APDs. Some noise models for staircase APDs have been previously reported, where the excess noise factor expressions are based on Friis' noise factor formula for cascade networks, erroneously considering the power gains as the gains. Excess noise factor being a key component in staircase APDs' noise models, we formulate generalized excess noise factor expressions for multilayer graded-bandgap APDs in terms of their layer-wise ionization probabilities, applicable for all operating biases, which include the sub-threshold, staircase, and tunnelling breakdown regimes. We further derive simplified expressions for staircase APDs and prove that these expressions match Bangera's corrections to Friis' noise factor formulas for cascade networks.
Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders
Authors: Siyu Chen, Heejune Sheen, Xuyuan Xiong, Tianhao Wang, Zhuoran Yang
Date: 2025-06-16 00:00:00

We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations such as hyperparameter sensitivity and instability. To address these issues, we first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability by modeling polysemantic features as sparse mixtures of underlying monosemantic concepts. Building on this framework, we introduce a new SAE training algorithm based on ``bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity. We theoretically \highlight{prove that this algorithm correctly recovers all monosemantic features} when input data is sampled from our proposed statistical model. Furthermore, we develop an improved empirical variant, Group Bias Adaptation (GBA), and \highlight{demonstrate its superior performance against benchmark methods when applied to LLMs with up to 1.5 billion parameters}. This work represents a foundational step in demystifying SAE training by providing the first SAE algorithm with theoretical recovery guarantees, thereby advancing the development of more transparent and trustworthy AI systems through enhanced mechanistic interpretability.
The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints
Authors: Hadi Kazemi, Ankit Pensia, Varun Jog
Date: 2025-06-16 00:00:00

This paper resolves two open problems from a recent paper, arXiv:2403.16981, concerning the sample complexity of distributed simple binary hypothesis testing under information constraints. The first open problem asks whether interaction reduces the sample complexity of distributed simple binary hypothesis testing. In this paper, we show that sequential interaction does not help. The second problem suggests tightening existing sample complexity bounds for communication-constrained simple binary hypothesis testing. We derive optimally tight bounds for this setting and resolve this problem. Our main technical contributions are: (i) a one-shot lower bound on the Bayes error in simple binary hypothesis testing that satisfies a crucial tensorisation property; (ii) a streamlined proof of the formula for the sample complexity of simple binary hypothesis testing without constraints, first established in arXiv:2403.16981; and (iii) a reverse data-processing inequality for Hellinger-$\lambda$ divergences, generalising the results from arXiv:1812.03031 and arXiv:2206.02765.
Digital Transformation of Urban Planning in Australia: Influencing Factors and Key Challenges
Authors: Soheil Sabri, Sherah Kurnia
Date: 2025-06-16 00:00:00

Over the past two decades, several governments in developing and developed countries have started their journey toward digital transformation. However, the pace and maturity of digital technologies and strategies are different between public services. Current literature indicates that research on the digital transformation of urban planning is still developing. Therefore, the aim of this study is to understand the influencing factors and key challenges for the digital transformation of urban planning in Australia. The study adopts the inter-organisational theory and Planning Support Science (PSScience) under the Technological, Organisational, and External Environmental (TOE) framework. It involves a multiple case study, administered semi-structured interviews with thirteen IT and urban planning experts across Victoria and New South Wales governments and private industries. The study findings indicate that the main challenges for digital transformation of the Australian urban planning system are related to organisational and external environmental factors. Furthermore, a digital maturity model is absent in the Australian urban planning industry. This study offers important implications to research and practice related to digital transformation in urban planning.
DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar
Authors: Murat Babek Salman, Emil Björnson
Date: 2025-06-16 00:00:00
Tasks: parameter estimation, Super-Resolution

Sensing emerges as a critical challenge in 6G networks, which require simultaneous communication and target sensing capabilities. State-of-the-art super-resolution techniques for the direction of arrival (DoA) estimation encounter significant performance limitations when the number of targets exceeds antenna array dimensions. This paper introduces a novel sensing parameter estimation algorithm for orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar systems. The proposed approach implements a strategic two-stage methodology: first, discriminating targets through delay and Doppler domain filtering to reduce the number of effective targets for super-resolution DoA estimation, and second, introducing a fusion technique to mitigate sidelobe interferences. The algorithm enables robust DoA estimation, particularly in high-density target environments with limited-size antenna arrays. Numerical simulations validate the superior performance of the proposed method compared to conventional DoA estimation approaches.
Zero-shot denoising via neural compression: Theoretical and algorithmic framework
Authors: Ali Zafari, Xi Chen, Shirin Jalali
Date: 2025-06-15 00:00:00
Tasks: Denoising

Zero-shot denoising aims to denoise observations without access to training samples or clean reference images. This setting is particularly relevant in practical imaging scenarios involving specialized domains such as medical imaging or biology. In this work, we propose the Zero-Shot Neural Compression Denoiser (ZS-NCD), a novel denoising framework based on neural compression. ZS-NCD treats a neural compression network as an untrained model, optimized directly on patches extracted from a single noisy image. The final reconstruction is then obtained by aggregating the outputs of the trained model over overlapping patches. Thanks to the built-in entropy constraints of compression architectures, our method naturally avoids overfitting and does not require manual regularization or early stopping. Through extensive experiments, we show that ZS-NCD achieves state-of-the-art performance among zero-shot denoisers for both Gaussian and Poisson noise, and generalizes well to both natural and non-natural images. Additionally, we provide new finite-sample theoretical results that characterize upper bounds on the achievable reconstruction error of general maximum-likelihood compression-based denoisers. These results further establish the theoretical foundations of compression-based denoising. Our code is available at: github.com/Computational-Imaging-RU/ZS-NCDenoiser.
Deep Learning-based mmWave MIMO Channel Estimation using sub-6 GHz Channel Information: CNN and UNet Approaches
Authors: Faruk Pasic, Lukas Eller, Stefan Schwarz, Markus Rupp, Christoph F. Mecklenbräuker
Date: 2025-06-13 00:00:00
Tasks: Deep Learning

Future wireless multiple-input multiple-output (MIMO) systems will integrate both sub-6 GHz and millimeter wave (mmWave) frequency bands to meet the growing demands for high data rates. MIMO link establishment typically requires accurate channel estimation, which is particularly challenging at mmWave frequencies due to the low signal-to-noise ratio (SNR). In this paper, we propose two novel deep learning-based methods for estimating mmWave MIMO channels by leveraging out-of-band information from the sub-6 GHz band. The first method employs a convolutional neural network (CNN), while the second method utilizes a UNet architecture. We compare these proposed methods against deep-learning methods that rely solely on in-band information and with other state-of-the-art out-of-band aided methods. Simulation results show that our proposed out-of-band aided deep-learning methods outperform existing alternatives in terms of achievable spectral efficiency.
Enhancing Symbolic Machine Learning by Subsymbolic Representations
Authors: Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
Date: 2025-06-17 00:00:00
Tasks: Tensor Networks

The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach: We propose to enhance symbolic machine learning schemes by giving them access to neural embeddings. In the present paper, we show this for TILDE and embeddings of constants used by TILDE in similarity predicates. The approach can be fine-tuned by further refining the embeddings depending on the symbolic theory. In experiments in three real-world domain, we show that this simple, yet effective, approach outperforms all other baseline methods in terms of the F1 score. The approach could be useful beyond this setting: Enhancing symbolic learners in this way could be extended to similarities between instances (effectively working like kernels within a logical language), for analogical reasoning, or for propositionalization.