pwc-archive/papers-with-abstracts huggingface dataset:
Learning-Augmented Hierarchical Clustering
Authors: Vladimir Braverman, Jon C. Ergun, Chen Wang, Samson Zhou
Date: 2025-06-05 00:00:00
Tasks: Clustering, Triplet

Hierarchical clustering (HC) is an important data analysis technique in which the goal is to recursively partition a dataset into a tree-like structure while grouping together similar data points at each level of granularity. Unfortunately, for many of the proposed HC objectives, there exist strong barriers to approximation algorithms with the hardness of approximation. Thus, we consider the problem of hierarchical clustering given auxiliary information from natural oracles. Specifically, we focus on a *splitting oracle* which, when provided with a triplet of vertices $(u,v,w)$, answers (possibly erroneously) the pairs of vertices whose lowest common ancestor includes all three vertices in an optimal tree, i.e., identifying which vertex ``splits away'' from the others. Using such an oracle, we obtain the following results: - A polynomial-time algorithm that outputs a hierarchical clustering tree with $O(1)$-approximation to the Dasgupta objective (Dasgupta [STOC'16]). - A near-linear time algorithm that outputs a hierarchical clustering tree with $(1-o(1))$-approximation to the Moseley-Wang objective (Moseley and Wang [NeurIPS'17]). Under the plausible Small Set Expansion Hypothesis, no polynomial-time algorithm can achieve any constant approximation for Dasgupta's objective or $(1-C)$-approximation for the Moseley-Wang objective for some constant $C>0$. As such, our results demonstrate that the splitting oracle enables algorithms to outperform standard HC approaches and overcome hardness constraints. Furthermore, our approaches extend to sublinear settings, in which we show new streaming and PRAM algorithms for HC with improved guarantees.
Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors
Authors: Matei Gabriel Coşa, Marek Eliáš
Date: 2025-06-05 00:00:00

We consider the following problem: We are given $\ell$ heuristics for Metrical Task Systems (MTS), where each might be tailored to a different type of input instances. While processing an input instance received online, we are allowed to query the action of only one of the heuristics at each time step. Our goal is to achieve performance comparable to the best of the given heuristics. The main difficulty of our setting comes from the fact that the cost paid by a heuristic at time $t$ cannot be estimated unless the same heuristic was also queried at time $t-1$. This is related to Bandit Learning against memory bounded adversaries (Arora et al., 2012). We show how to achieve regret of $O(\text{OPT}^{2/3})$ and prove a tight lower bound based on the construction of Dekel et al. (2013).
Receiving RISs: Enabling Channel Estimation and Autonomous Configuration
Authors: George C. Alexandropoulos, Konstantinos D. Katsanos, Evangelos Vlachos
Date: 2025-06-12 00:00:00

This chapter focuses on a hardware architecture for semi-passive Reconfigurable Intelligent Surfaces (RISs) and investigates its consideration for boosting the performance of Multiple-Input Multiple-Output (MIMO) communication systems. The architecture incorporates a single or multiple radio-frequency chains to receive pilot signals via tunable absorption phase profiles realized by the metasurface front end, as well as a controller encompassing a baseband processing unit to carry out channel estimation, and consequently, the optimization of the RIS reflection coefficients. A novel channel estimation protocol, according to which the RIS receives non-orthogonal training pilot sequences from two multi-antenna terminals via tunable absorption phase profiles, and then, estimates the respective channels via its signal processing unit, is presented. The channel estimates are particularly used by the RIS controller to design the capacity-achieving reflection phase configuration of the metasurface front end. The proposed channel estimation algorithm, which is based on the Alternating Direction Method of Multipliers (ADMM), profits from the RIS random spatial absorption sampling to capture the entire signal space, and exploits the beamspace sparsity and low-rank properties of extremely large MIMO channels, which is particularly relevant for communication systems at the FR3 band and above. Our extensive numerical investigations showcase the superiority of the proposed channel estimation technique over benchmark schemes for various system and RIS hardware configuration parameters, as well as the effectiveness of using channel estimates at the RIS side to dynamically optimize the possibly phase-quantized reflection coefficients of its unit elements.
Prompt to Protection: A Comparative Study of Multimodal LLMs in Construction Hazard Recognition
Authors: Nishi Chaudhary, S M Jamil Uddin, Sathvik Sharath Chandra, Anto Ovid, Alex Albert
Date: 2025-06-09 00:00:00
Tasks: Prompt Engineering

The recent emergence of multimodal large language models (LLMs) has introduced new opportunities for improving visual hazard recognition on construction sites. Unlike traditional computer vision models that rely on domain-specific training and extensive datasets, modern LLMs can interpret and describe complex visual scenes using simple natural language prompts. However, despite growing interest in their applications, there has been limited investigation into how different LLMs perform in safety-critical visual tasks within the construction domain. To address this gap, this study conducts a comparative evaluation of five state-of-the-art LLMs: Claude-3 Opus, GPT-4.5, GPT-4o, GPT-o3, and Gemini 2.0 Pro, to assess their ability to identify potential hazards from real-world construction images. Each model was tested under three prompting strategies: zero-shot, few-shot, and chain-of-thought (CoT). Zero-shot prompting involved minimal instruction, few-shot incorporated basic safety context and a hazard source mnemonic, and CoT provided step-by-step reasoning examples to scaffold model thinking. Quantitative analysis was performed using precision, recall, and F1-score metrics across all conditions. Results reveal that prompting strategy significantly influenced performance, with CoT prompting consistently producing higher accuracy across models. Additionally, LLM performance varied under different conditions, with GPT-4.5 and GPT-o3 outperforming others in most settings. The findings also demonstrate the critical role of prompt design in enhancing the accuracy and consistency of multimodal LLMs for construction safety applications. This study offers actionable insights into the integration of prompt engineering and LLMs for practical hazard recognition, contributing to the development of more reliable AI-assisted safety systems.
Taxonomy of migration scenarios for Qiskit refactoring using LLMs
Authors: José Manuel Suárez, Luís Mariano Bibbó, Joaquín Bogado, Alejandro Fernandez
Date: 2025-06-08 00:00:00

As quantum computing advances, quantum programming libraries' heterogeneity and steady evolution create new challenges for software developers. Frequent updates in software libraries break working code that needs to be refactored, thus adding complexity to an already complex landscape. These refactoring challenges are, in many cases, fundamentally different from those known in classical software engineering due to the nature of quantum computing software. This study addresses these challenges by developing a taxonomy of quantum circuit's refactoring problems, providing a structured framework to analyze and compare different refactoring approaches. Large Language Models (LLMs) have proven valuable tools for classic software development, yet their value in quantum software engineering remains unexplored. This study uses LLMs to categorize refactoring needs in migration scenarios between different Qiskit versions. Qiskit documentation and release notes were scrutinized to create an initial taxonomy of refactoring required for migrating between Qiskit releases. Two taxonomies were produced: one by expert developers and one by an LLM. These taxonomies were compared, analyzing differences and similarities, and were integrated into a unified taxonomy that reflects the findings of both methods. By systematically categorizing refactoring challenges in Qiskit, the unified taxonomy is a foundation for future research on AI-assisted migration while enabling a more rigorous evaluation of automated refactoring techniques. Additionally, this work contributes to quantum software engineering (QSE) by enhancing software development workflows, improving language compatibility, and promoting best practices in quantum programming.
AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
Authors: Xiaoran Liu, Istvan David
Date: 2025-06-06 00:00:00

Insufficient data volume and quality are particularly pressing challenges in the adoption of modern subsymbolic AI. To alleviate these challenges, AI simulation uses virtual training environments in which AI agents can be safely and efficiently developed with simulated, synthetic data. Digital twins open new avenues in AI simulation, as these high-fidelity virtual replicas of physical systems are equipped with state-of-the-art simulators and the ability to further interact with the physical system for additional data collection. In this article, we report on our systematic survey of digital twin-enabled AI simulation. By analyzing 22 primary studies, we identify technological trends and derive a reference framework to situate digital twins and AI components. Based on our findings, we derive a reference framework and provide architectural guidelines by mapping it onto the ISO 23247 reference architecture for digital twins. Finally, we identify challenges and research opportunities for prospective researchers.
Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control
Authors: Ruitao Chen, Mozhang Guo, Jinge Li
Date: 2025-06-06 00:00:00
Tasks: Reinforcement Learning (RL), Sleep Quality

Automated driving (AD) has substantially improved vehicle safety and driving comfort, but their impact on passenger well-being, particularly infant sleep, is not sufficiently studied. Sudden acceleration, abrupt braking, and sharp maneuvers can disrupt infant sleep, compromising both passenger comfort and parental convenience. To solve this problem, this paper explores the integration of reinforcement learning (RL) within AD to personalize driving behavior and optimally balance occupant comfort and travel efficiency. In particular, we propose an intelligent cruise control framework that adapts to varying driving conditions to enhance infant sleep quality by effectively synergizing wearable sensing and vehicle data. Long short-term memory (LSTM) and transformer-based neural networks are integrated with RL to model the relationship between driving behavior and infant sleep quality under diverse traffic and road conditions. Based on the sleep quality indicators from the wearable sensors, driving action data from vehicle controllers, and map data from map applications, the model dynamically computes the optimal driving aggressiveness level, which is subsequently translated into specific AD control strategies, e.g., the magnitude and frequency of acceleration, lane change, and overtaking. Simulation results demonstrate that the proposed solution significantly improves infant sleep quality compared to baseline methods, while preserving desirable travel efficiency.
Positional Encoding meets Persistent Homology on Graphs
Authors: Yogesh Verma, Amauri H. Souza, Vikas Garg
Date: 2025-06-06 00:00:00
Tasks: Graph Classification, Graph Representation Learning, Inductive Bias, Out-of-Distribution Generalization, Property Prediction, Representation Learning

The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) have emerged as two promising approaches to mitigate this issue. PE schemes endow GNNs with location-aware features, while PH methods enhance GNNs with multiresolution topological features. However, a rigorous theoretical characterization of the relative merits and shortcomings of PE and PH has remained elusive. We bridge this gap by establishing that neither paradigm is more expressive than the other, providing novel constructions where one approach fails but the other succeeds. Our insights inform the design of a novel learnable method, PiPE (Persistence-informed Positional Encoding), which is provably more expressive than both PH and PE. PiPE demonstrates strong performance across a variety of tasks (e.g., molecule property prediction, graph classification, and out-of-distribution generalization), thereby advancing the frontiers of graph representation learning. Code is available at https://github.com/Aalto-QuML/PIPE.
DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems
Authors: Trisanth Srinivasan, Santosh Patapati, Himani Musku, Idhant Gode, Aditya Arora, Samvit Bhattacharya, Abubakr Nazriev, Sanika Hirave, Zaryab Kanjiani, Srinjoy Ghose
Date: 2025-06-04 00:00:00

Cyber-Physical Systems (CPS) increasingly depend on advanced AI techniques to operate in critical applications. However, traditional verification and validation methods often struggle to handle the unpredictable and dynamic nature of AI components. In this paper, we introduce DURA-CPS, a novel framework that employs multi-role orchestration to automate the iterative assurance process for AI-powered CPS. By assigning specialized roles (e.g., safety monitoring, security assessment, fault injection, and recovery planning) to dedicated agents within a simulated environment, DURA-CPS continuously evaluates and refines AI behavior against a range of dependability requirements. We demonstrate the framework through a case study involving an autonomous vehicle navigating an intersection with an AI-based planner. Our results show that DURA-CPS effectively detects vulnerabilities, manages performance impacts, and supports adaptive recovery strategies, thereby offering a structured and extensible solution for rigorous V&V in safety- and security-critical systems.
Contextual Memory Intelligence -- A Foundational Paradigm for Human-AI Collaboration and Reflective Generative AI Systems
Authors: Kristy Wedel
Date: 2025-05-28 00:00:00
Tasks: Drift Detection, RAG

A critical challenge remains unresolved as generative AI systems are quickly implemented in various organizational settings. Despite significant advances in memory components such as RAG, vector stores, and LLM agents, these systems still have substantial memory limitations. Gen AI workflows rarely store or reflect on the full context in which decisions are made. This leads to repeated errors and a general lack of clarity. This paper introduces Contextual Memory Intelligence (CMI) as a new foundational paradigm for building intelligent systems. It repositions memory as an adaptive infrastructure necessary for longitudinal coherence, explainability, and responsible decision-making rather than passive data. Drawing on cognitive science, organizational theory, human-computer interaction, and AI governance, CMI formalizes the structured capture, inference, and regeneration of context as a fundamental system capability. The Insight Layer is presented in this paper to operationalize this vision. This modular architecture uses human-in-the-loop reflection, drift detection, and rationale preservation to incorporate contextual memory into systems. The paper argues that CMI allows systems to reason with data, history, judgment, and changing context, thereby addressing a foundational blind spot in current AI architectures and governance efforts. A framework for creating intelligent systems that are effective, reflective, auditable, and socially responsible is presented through CMI. This enhances human-AI collaboration, generative AI design, and the resilience of the institutions.
VideoMat: Extracting PBR Materials from Video Diffusion Models
Authors: Jacob Munkberg, Zian Wang, Ruofan Liang, Tianchang Shen, Jon Hasselgren
Date: 2025-06-11 00:00:00

We leverage finetuned video diffusion models, intrinsic decomposition of videos, and physically-based differentiable rendering to generate high quality materials for 3D models given a text prompt or a single image. We condition a video diffusion model to respect the input geometry and lighting condition. This model produces multiple views of a given 3D model with coherent material properties. Secondly, we use a recent model to extract intrinsics (base color, roughness, metallic) from the generated video. Finally, we use the intrinsics alongside the generated video in a differentiable path tracer to robustly extract PBR materials directly compatible with common content creation tools.
Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation
Authors: Yuxin Liu, Zhenghao Peng, Xuanhao Cui, Bolei Zhou
Date: 2025-06-11 00:00:00
Tasks: Autonomous Driving

Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-critical scenarios in existing datasets collected in the real world. To tackle the data issue, we propose the Adv-BMT framework, which augments real-world scenarios with diverse and realistic adversarial interactions. The core component of Adv-BMT is a bidirectional motion transformer (BMT) model to perform inverse traffic motion predictions, which takes agent information in the last time step of the scenario as input, and reconstruct the traffic in the inverse of chronological order until the initial time step. The Adv-BMT framework is a two-staged pipeline: it first conducts adversarial initializations and then inverse motion predictions. Different from previous work, we do not need any collision data for pretraining, and are able to generate realistic and diverse collision interactions. Our experimental results validate the quality of generated collision scenarios by Adv-BMT: training in our augmented dataset would reduce episode collision rates by 20\% compared to previous work.
Token Perturbation Guidance for Diffusion Models
Authors: Javad Rajabi, Soroush Mehraban, Seyedmorteza Sadat, Babak Taati
Date: 2025-06-10 00:00:00

Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG requires specific training procedures and is limited to conditional generation. To address these limitations, we propose Token Perturbation Guidance (TPG), a novel method that applies perturbation matrices directly to intermediate token representations within the diffusion network. TPG employs a norm-preserving shuffling operation to provide effective and stable guidance signals that improve generation quality without architectural changes. As a result, TPG is training-free and agnostic to input conditions, making it readily applicable to both conditional and unconditional generation. We further analyze the guidance term provided by TPG and show that its effect on sampling more closely resembles CFG compared to existing training-free guidance techniques. Extensive experiments on SDXL and Stable Diffusion 2.1 show that TPG achieves nearly a 2$\times$ improvement in FID for unconditional generation over the SDXL baseline, while closely matching CFG in prompt alignment. These results establish TPG as a general, condition-agnostic guidance method that brings CFG-like benefits to a broader class of diffusion models. The code is available at https://github.com/TaatiTeam/Token-Perturbation-Guidance
SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach
Authors: Elly Akhoundi, Hung Yu Ling, Anup Anand Deshmukh, Judith Butepage
Date: 2025-06-09 00:00:00
Tasks: motion in-betweening, Motion Interpolation

Motion in-betweening is a crucial tool for animators, enabling intricate control over pose-level details in each keyframe. Recent machine learning solutions for motion in-betweening rely on complex models, incorporating skeleton-aware architectures or requiring multiple modules and training steps. In this work, we introduce a simple yet effective Transformer-based framework, employing a single Transformer encoder to synthesize realistic motions for motion in-betweening tasks. We find that data modeling choices play a significant role in improving in-betweening performance. Among others, we show that increasing data volume can yield equivalent or improved motion transitions, that the choice of pose representation is vital for achieving high-quality results, and that incorporating velocity input features enhances animation performance. These findings challenge the assumption that model complexity is the primary determinant of animation quality and provide insights into a more data-centric approach to motion interpolation. Additional videos and supplementary material are available at https://silk-paper.github.io.
STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support
Authors: Chenqi Zhang, Yu Feng, Jieru Zhao, Guangda Liu, Wenchao Ding, Chentao Wu, Minyi Guo
Date: 2025-06-09 00:00:00
Tasks: 3DGS

3D Gaussian Splatting (3DGS) has gained popularity for its efficiency and sparse Gaussian-based representation. However, 3DGS struggles to meet the real-time requirement of 90 frames per second (FPS) on resource-constrained mobile devices, achieving only 2 to 9 FPS.Existing accelerators focus on compute efficiency but overlook memory efficiency, leading to redundant DRAM traffic. We introduce STREAMINGGS, a fully streaming 3DGS algorithm-architecture co-design that achieves fine-grained pipelining and reduces DRAM traffic by transforming from a tile-centric rendering to a memory-centric rendering. Results show that our design achieves up to 45.7 $\times$ speedup and 62.9 $\times$ energy savings over mobile Ampere GPUs.
Noise Consistency Regularization for Improved Subject-Driven Image Synthesis
Authors: Yao Ni, Song Wen, Piotr Koniusz, Anoop Cherian
Date: 2025-06-06 00:00:00
Tasks: Diversity, Image Generation

Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, we propose two auxiliary consistency losses for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non-subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model's robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate that incorporating these losses into fine-tuning not only preserves subject identity but also enhances image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.