pwc-archive/papers-with-abstracts huggingface dataset:
RAVEN: Query-Guided Representation Alignment for Question Answering over Audio, Video, Embedded Sensors, and Natural Language
Authors: Subrata Biswas, Mohammad Nur Hossain Khan, Bashima Islam
Date: 2025-05-21 00:00:00
Tasks: Question Answering

Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. We present RAVEN, a unified QA architecture whose core is QuART, a query-conditioned cross-modal gating module that assigns scalar relevance scores to each token across modalities, enabling the model to amplify informative signals and suppress distractors before fusion. RAVEN is trained through a three-stage pipeline comprising unimodal pretraining, query-aligned fusion, and disagreement-oriented fine-tuning -- each stage targeting a distinct challenge in multi-modal reasoning: representation quality, cross-modal relevance, and robustness to modality mismatch. To support training and evaluation, we release AVS-QA, a dataset of 300K synchronized Audio--Video-Sensor streams paired with automatically generated question-answer pairs. Experimental results on seven multi-modal QA benchmarks -- including egocentric and exocentric tasks -- show that RAVEN achieves up to 14.5\% and 8.0\% gains in accuracy compared to state-of-the-art multi-modal large language models, respectively. Incorporating sensor data provides an additional 16.4\% boost, and the model remains robust under modality corruption, outperforming SOTA baselines by 50.23\%. Our code and dataset are available at https://github.com/BASHLab/RAVEN.
P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark
Authors: Tao Sun, Enhao Pan, Zhengkai Yang, Kaixin Sui, Jiajun Shi, Xianfu Cheng, Tongliang Li, Wenhao Huang, Ge Zhang, Jian Yang, Zhoujun Li
Date: 2025-05-21 00:00:00

Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in preserving intricate scientific details and achieving effective visual-textual integration. Existing approaches often struggle with semantic richness and structural nuances, and lack standardized benchmarks for evaluating generated academic posters comprehensively. To address these limitations, we introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters directly from research papers, demonstrating strong potential for practical applications. P2P employs three specialized agents-for visual element processing, content generation, and final poster assembly-each integrated with dedicated checker modules to enable iterative refinement and ensure output quality. To foster advancements and rigorous evaluation in this domain, we construct and release P2PInstruct, the first large-scale instruction dataset comprising over 30,000 high-quality examples tailored for the academic paper-to-poster generation task. Furthermore, we establish P2PEval, a comprehensive benchmark featuring 121 paper-poster pairs and a dual evaluation methodology (Universal and Fine-Grained) that leverages LLM-as-a-Judge and detailed, human-annotated checklists. Our contributions aim to streamline research dissemination and provide the community with robust tools for developing and evaluating next-generation poster generation systems.
DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces
Authors: Romeo Valentin, Sydney M. Katz, Vincent Vanhoucke, Mykel J. Kochenderfer
Date: 2025-05-24 00:00:00
Tasks: Dictionary Learning

Dictionary learning has recently emerged as a promising approach for mechanistic interpretability of large transformer models. Disentangling high-dimensional transformer embeddings, however, requires algorithms that scale to high-dimensional data with large sample sizes. Recent work has explored sparse autoencoders (SAEs) for this problem. However, SAEs use a simple linear encoder to solve the sparse encoding subproblem, which is known to be NP-hard. It is therefore interesting to understand whether this structure is sufficient to find good solutions to the dictionary learning problem or if a more sophisticated algorithm could find better solutions. In this work, we propose Double-Batch KSVD (DB-KSVD), a scalable dictionary learning algorithm that adapts the classic KSVD algorithm. DB-KSVD is informed by the rich theoretical foundations of KSVD but scales to datasets with millions of samples and thousands of dimensions. We demonstrate the efficacy of DB-KSVD by disentangling embeddings of the Gemma-2-2B model and evaluating on six metrics from the SAEBench benchmark, where we achieve competitive results when compared to established approaches based on SAEs. By matching SAE performance with an entirely different optimization approach, our results suggest that (i) SAEs do find strong solutions to the dictionary learning problem and (ii) that traditional optimization approaches can be scaled to the required problem sizes, offering a promising avenue for further research. We provide an implementation of DB-KSVD at https://github.com/RomeoV/KSVD.jl.
SEvoBench : A C++ Framework For Evolutionary Single-Objective Optimization Benchmarking
Authors: Yongkang Yang, Jian Zhao, Tengfei Yang
Date: 2025-05-23 00:00:00
Tasks: Benchmarking, Computational Efficiency

We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular implementations of Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, organized around three core components: (1) algorithm construction with reusable modules, (2) efficient benchmark problem suites, and (3) parallel experimental analysis. Experimental evaluations demonstrate the framework's superior performance in benchmark testing and algorithm comparison. Case studies further validate its capabilities in algorithm hybridization and parameter analysis. Compared to existing frameworks, SEvoBench demonstrates three key advantages: (i) highly efficient and reusable modular implementations of PSO and DE algorithms, (ii) accelerated benchmarking through parallel execution, and (iii) enhanced computational efficiency via SIMD (Single Instruction Multiple Data) vectorization for large-scale problems.
Deep Spectral Prior
Authors: Yanqi Cheng, Tieyong Zeng, Pietro Lio, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero
Date: 2025-05-26 00:00:00
Tasks: Denoising, Image Reconstruction, Inductive Bias, Super-Resolution

We introduce Deep Spectral Prior (DSP), a new formulation of Deep Image Prior (DIP) that redefines image reconstruction as a frequency-domain alignment problem. Unlike traditional DIP, which relies on pixel-wise loss and early stopping to mitigate overfitting, DSP directly matches Fourier coefficients between the network output and observed measurements. This shift introduces an explicit inductive bias towards spectral coherence, aligning with the known frequency structure of images and the spectral bias of convolutional neural networks. We provide a rigorous theoretical framework demonstrating that DSP acts as an implicit spectral regulariser, suppressing high-frequency noise by design and eliminating the need for early stopping. Our analysis spans four core dimensions establishing smooth convergence dynamics, local stability, and favourable bias-variance tradeoffs. We further show that DSP naturally projects reconstructions onto a frequency-consistent manifold, enhancing interpretability and robustness. These theoretical guarantees are supported by empirical results across denoising, inpainting, and super-resolution tasks, where DSP consistently outperforms classical DIP and other unsupervised baselines.
Hamiltonian Theory and Computation of Optimal Probability Density Control in High Dimensions
Authors: Nathan Gaby, Xiaojing Ye
Date: 2025-05-23 00:00:00

We develop a general theoretical framework for optimal probability density control and propose a numerical algorithm that is scalable to solve the control problem in high dimensions. Specifically, we establish the Pontryagin Maximum Principle (PMP) for optimal density control and construct the Hamilton-Jacobi-Bellman (HJB) equation of the value functional through rigorous derivations without any concept from Wasserstein theory. To solve the density control problem numerically, we propose to use reduced-order models, such as deep neural networks (DNNs), to parameterize the control vector-field and the adjoint function, which allows us to tackle problems defined on high-dimensional state spaces. We also prove several convergence properties of the proposed algorithm. Numerical results demonstrate promising performances of our algorithm on a variety of density control problems with obstacles and nonlinear interaction challenges in high dimensions.
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Authors: Xinchen Du, Wanrong Zhu, Wei Biao Wu, Sen Na
Date: 2025-05-23 00:00:00
Tasks: Stochastic Optimization

Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biology. As datasets continue to grow, online inference methods have become crucial for enabling real-time decision-making without the need to store historical data. In this work, we develop an online inference procedure for constrained stochastic optimization by leveraging a method called Sketched Stochastic Sequential Quadratic Programming (SSQP). As a direct generalization of sketched Newton methods, SSQP approximates the objective with a quadratic model and the constraints with a linear model at each step, then applies a sketching solver to inexactly solve the resulting subproblem. Building on this design, we propose a new online inference procedure called random scaling. In particular, we construct a test statistic based on SSQP iterates whose limiting distribution is free of any unknown parameters. Compared to existing online inference procedures, our approach offers two key advantages: (i) it enables the construction of asymptotically valid confidence intervals; and (ii) it is matrix-free, i.e. the computation involves only primal-dual SSQP iterates $(\boldsymbol{x}_t, \boldsymbol{\lambda}_t)$ without requiring any matrix inversions. We validate our theory through numerical experiments on nonlinearly constrained regression problems and demonstrate the superior performance of our random scaling method over existing inference procedures.
A deep solver for backward stochastic Volterra integral equations
Authors: Kristoffer Andersson, Alessandro Gnoatto, Camilo Andrés García Trillos
Date: 2025-05-23 00:00:00
Tasks: GPU

We present the first deep-learning solver for backward stochastic Volterra integral equations (BSVIEs) and their fully-coupled forward-backward variants. The method trains a neural network to approximate the two solution fields in a single stage, avoiding the use of nested time-stepping cycles that limit classical algorithms. For the decoupled case we prove a non-asymptotic error bound composed of an a posteriori residual plus the familiar square root dependence on the time step. Numerical experiments confirm this rate and reveal two key properties: \emph{scalability}, in the sense that accuracy remains stable from low dimension up to 500 spatial variables while GPU batching keeps wall-clock time nearly constant; and \emph{generality}, since the same method handles coupled systems whose forward dynamics depend on the backward solution. These results open practical access to a family of high-dimensional, path-dependent problems in stochastic control and quantitative finance.
A DSP-Free Carrier Phase Recovery System using 16-Offset-QAM Laser Forwarded Links for 400Gb/s and Beyond
Authors: Marziyeh Rezaei, Dan Sturm, Pengyu Zeng, Sajjad Moazeni
Date: 2025-05-24 00:00:00
Tasks: GPU

Optical interconnects are becoming a major bottleneck in scaling up future GPU racks and network switches within data centers. Although 200 Gb/s optical transceivers using PAM-4 modulation have been demonstrated, achieving higher data rates and energy efficiencies requires high-order coherent modulations like 16-QAM. Current coherent links rely on energy-intensive digital signal processing (DSP) for channel impairment compensation and carrier phase recovery (CPR), which consumes approximately 50pJ/b - 10x higher than future intra-data center requirements. For shorter links, simpler or DSP-free CPR methods can significantly reduce power and complexity. While Costas loops enable CPR for QPSK, they face challenges in scaling to higher-order modulations (e.g., 16/64-QAM) due to varying symbol amplitudes. In this work, we propose an optical coherent link architecture using laser forwarding and a novel DSP-free CPR system using offset-QAM modulation. The proposed analog CPR feedback loop is highly scalable, capable of supporting arbitrary offset-QAM modulations without requiring architectural modifications. This scalability is achieved through its phase error detection mechanism, which operates independently of the data rate and modulation type. We validated this method using GlobalFoundry's monolithic 45nm silicon photonics PDK models, with circuit- and system-level implementation at 100GBaud in the O-band. We will investigate the feedback loop dynamics, circuit-level implementations, and phase-noise performance of the proposed CPR loop. Our method can be adopted to realize low-power QAM optical interconnects for future coherent-lite pluggable transceivers as well as co-packaged optics (CPO) applications.
SzCORE as a benchmark: report from the seizure detection challenge at the 2025 AI in Epilepsy and Neurological Disorders Conference
Authors: Jonathan Dan, Amirhossein Shahbazinia, Christodoulos Kechris, David Atienza
Date: 2025-05-19 00:00:00
Tasks: Benchmarking, EEG, Seizure Detection, Sensitivity

Reliable automatic seizure detection from long-term EEG remains a challenge, as current machine learning models often fail to generalize across patients or clinical settings. Manual EEG review remains the clinical standard, underscoring the need for robust models and standardized evaluation. To rigorously assess algorithm performance, we organized a challenge using a private dataset of continuous EEG recordings from 65 subjects (4,360 hours). Expert neurophysiologists annotated the data, providing ground truth for seizure events. Participants were required to detect seizure onset and duration, with evaluation based on event-based metrics, including sensitivity, precision, F1-score, and false positives per day. The SzCORE framework ensured standardized evaluation. The primary ranking criterion was the event-based F1-score, reflecting clinical relevance by balancing sensitivity and false positives. The challenge received 30 submissions from 19 teams, with 28 algorithms evaluated. Results revealed wide variability in performance, with a top F1-score of 43% (sensitivity 37%, precision 45%), highlighting the ongoing difficulty of seizure detection. The challenge also revealed a gap between reported performance and real-world evaluation, emphasizing the importance of rigorous benchmarking. Compared to previous challenges and commercial systems, the best-performing algorithm in this contest showed improved performance. Importantly, the challenge platform now supports continuous benchmarking, enabling reproducible research, integration of new datasets, and clinical evaluation of seizure detection algorithms using a standardized framework.
ZeroML: A Next Generation AutoML Language
Authors: Monirul Islam Mahmud
Date: 2025-05-23 00:00:00
Tasks: AutoML

ZeroML is a new generation programming language for AutoML to drive the ML pipeline in a compiled and multi-paradigm way, with a pure functional core. Meeting the shortcomings introduced by Python, R, or Julia such as slow-running time, brittle pipelines or high dependency cost ZeroML brings the Microservices-based architecture adding the modular, reusable pieces such as DataCleaner, FeatureEngineer or ModelSelector. As a native multithread and memory-aware search optimized toolkit, and with one command deployability ability, ZeroML ensures non-coders and ML professionals to create high-accuracy models super fast and in a more reproducible way. The verbosity of the language ensures that when it comes to dropping into the backend, the code we will be creating is extremely clear but the level of repetition and boilerplate required when developing on the front end is now removed.
Gradient-Based Program Repair: Fixing Bugs in Continuous Program Spaces
Authors: André Silva, Gustav Thorén, Martin Monperrus
Date: 2025-05-23 00:00:00
Tasks: Program Repair

Automatic program repair seeks to generate correct code from buggy programs, with most approaches searching the correct program in a discrete, symbolic space of source code tokens. This symbolic search is fundamentally limited by its inability to directly reason about program behavior. We introduce Gradient-Based Program Repair (GBPR), a new paradigm that reframes program repair as continuous optimization in a differentiable numerical program space. Our core insight is to compile symbolic programs into differentiable numerical representations, enabling search in the numerical program space directly guided by program behavior. To evaluate GBPR, we present RaspBugs, a new benchmark of 1,466 buggy symbolic RASP programs and their respective numerical representations. Our experiments demonstrate that GBPR can effectively repair buggy symbolic programs by gradient-based optimization in the numerical program space, with convincing repair trajectories. To our knowledge, we are the first to state program repair as continuous optimization in a numerical program space. Our work establishes a new direction for program repair research, bridging two rich worlds: continuous optimization and program behavior.
DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration
Authors: Haotian Liu, Yuchuang Tong, Zhengtao Zhang
Date: 2025-05-23 00:00:00
Tasks: Human Dynamics

In physical Human-Robot Collaboration (pHRC), accurate human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance. Existing methods that rely on short-term motion data for intention estimation lack multi-step prediction capabilities, hindering their ability to sense intent changes and adjust human-robot assignments autonomously, resulting in potential discrepancies. To address these issues, we propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data to rapidly capture human intent changes, enabling accurate trajectory predictions and dynamic robot behavior adjustments for effective collaboration. Specifically, human intent estimation in DTRT uses two Transformer-based Conditional Variational Autoencoders (CVAEs), incorporating robot motion data in obstacle-free case with human-guided trajectory and force for obstacle avoidance. Additionally, Differential Cooperative Game Theory (DCGT) is employed to synthesize predictions based on human-applied forces, ensuring robot behavior align with human intention. Compared to state-of-the-art (SOTA) methods, DTRT incorporates human dynamics into long-term prediction, providing an accurate understanding of intention and enabling rational role allocation, achieving robot autonomy and maneuverability. Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers
Authors: Fotios Lygerakis, Ozan Özdenizci, Elmar Rückert
Date: 2025-05-26 00:00:00
Tasks: cross-modal alignment, Position, Representation Learning, Robotic Grasping, Spatial Reasoning, Transfer Learning, Zero-shot Generalization

Tactile sensing provides local essential information that is complementary to visual perception, such as texture, compliance, and force. Despite recent advances in visuotactile representation learning, challenges remain in fusing these modalities and generalizing across tasks and environments without heavy reliance on pre-trained vision-language models. Moreover, existing methods do not study positional encodings, thereby overlooking the multi-scale spatial reasoning needed to capture fine-grained visuotactile correlations. We introduce ViTaPEs, a transformer-based framework that robustly integrates visual and tactile input data to learn task-agnostic representations for visuotactile perception. Our approach exploits a novel multi-scale positional encoding scheme to capture intra-modal structures, while simultaneously modeling cross-modal cues. Unlike prior work, we provide provable guarantees in visuotactile fusion, showing that our encodings are injective, rigid-motion-equivariant, and information-preserving, validating these properties empirically. Experiments on multiple large-scale real-world datasets show that ViTaPEs not only surpasses state-of-the-art baselines across various recognition tasks but also demonstrates zero-shot generalization to unseen, out-of-domain scenarios. We further demonstrate the transfer-learning strength of ViTaPEs in a robotic grasping task, where it outperforms state-of-the-art baselines in predicting grasp success. Project page: https://sites.google.com/view/vitapes
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
Authors: Xueyi Liu, Zuodong Zhong, Yuxin Guo, Yun-Fu Liu, Zhiguo Su, Qichao Zhang, Junli Wang, Yinfeng Gao, Yupeng Zheng, Qiao Lin, Huiyong Chen, Dongbin Zhao
Date: 2025-05-26 00:00:00
Tasks: Autonomous Driving, Bench2Drive, Imitation Learning, Zero-shot Generalization

Due to the powerful vision-language reasoning and generalization abilities, multimodal large language models (MLLMs) have garnered significant attention in the field of end-to-end (E2E) autonomous driving. However, their application to closed-loop systems remains underexplored, and current MLLM-based methods have not shown clear superiority to mainstream E2E imitation learning approaches. In this work, we propose ReasonPlan, a novel MLLM fine-tuning framework designed for closed-loop driving through holistic reasoning with a self-supervised Next Scene Prediction task and supervised Decision Chain-of-Thought process. This dual mechanism encourages the model to align visual representations with actionable driving context, while promoting interpretable and causally grounded decision making. We curate a planning-oriented decision reasoning dataset, namely PDR, comprising 210k diverse and high-quality samples. Our method outperforms the mainstream E2E imitation learning method by a large margin of 19% L2 and 16.1 driving score on Bench2Drive benchmark. Furthermore, ReasonPlan demonstrates strong zero-shot generalization on unseen DOS benchmark, highlighting its adaptability in handling zero-shot corner cases. Code and dataset will be found in https://github.com/Liuxueyi/ReasonPlan.