pwc-archive/papers-with-abstracts huggingface dataset:
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
Authors: Zhiyao Zhang, Myeung Suk Oh, FNU Hairi, Ziyue Luo, Alvaro Velasquez, Jia Liu
Date: 2025-05-24 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning

Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a wide range of applications in practice. To date, however, most theoretical convergence studies for existing actor-critic decentralized MARL methods are limited to the guarantee of a stationary solution under the linear function approximation. This leaves a significant gap between the highly successful use of deep neural actor-critic for decentralized MARL in practice and the current theoretical understanding. To bridge this gap, in this paper, we make the first attempt to develop a deep neural actor-critic method for decentralized MARL, where both the actor and critic components are inherently non-linear. We show that our proposed method enjoys a global optimality guarantee with a finite-time convergence rate of O(1/T), where T is the total iteration times. This marks the first global convergence result for deep neural actor-critic methods in the MARL literature. We also conduct extensive numerical experiments, which verify our theoretical results.
PolyPose: Localizing Deformable Anatomy in 3D from Sparse 2D X-ray Images using Polyrigid Transforms
Authors: Vivek Gopalakrishnan, Neel Dey, Polina Golland
Date: 2025-05-25 00:00:00
Tasks: Anatomy, Hyperparameter Optimization, Inductive Bias

Determining the 3D pose of a patient from a limited set of 2D X-ray images is a critical task in interventional settings. While preoperative volumetric imaging (e.g., CT and MRI) provides precise 3D localization and visualization of anatomical targets, these modalities cannot be acquired during procedures, where fast 2D imaging (X-ray) is used instead. To integrate volumetric guidance into intraoperative procedures, we present PolyPose, a simple and robust method for deformable 2D/3D registration. PolyPose parameterizes complex 3D deformation fields as a composition of rigid transforms, leveraging the biological constraint that individual bones do not bend in typical motion. Unlike existing methods that either assume no inter-joint movement or fail outright in this under-determined setting, our polyrigid formulation enforces anatomically plausible priors that respect the piecewise rigid nature of human movement. This approach eliminates the need for expensive deformation regularizers that require patient- and procedure-specific hyperparameter optimization. Across extensive experiments on diverse datasets from orthopedic surgery and radiotherapy, we show that this strong inductive bias enables PolyPose to successfully align the patient's preoperative volume to as few as two X-ray images, thereby providing crucial 3D guidance in challenging sparse-view and limited-angle settings where current registration methods fail.
Hermes@DravidianLangTech 2025: Sentiment Analysis of Dravidian Languages using XLM-RoBERTa
Authors: Emmanuel George P, Ashiq Firoz, Madhav Murali, Siranjeevi Rajamanickam, Balasubramanian Palani
Date: 2025-05-25 00:00:00
Proceeding: Association for Computational Linguistics 2025 5
Tasks: Sentiment Analysis

Sentiment analysis, the task of identifying subjective opinions or emotional responses, has become increasingly significant with the rise of social media. However, analysing sentiment in Dravidian languages such as Tamil-English and Tulu-English presents unique challenges due to linguistic code-switching (where people tend to mix multiple languages) and non-native scripts. Traditional monolingual sentiment analysis models struggle to address these complexities effectively. This research explores a fine-tuned transformer model based on the XLM-RoBERTa model for sentiment detection. It utilizes the tokenizer from the XLM-RoBERTa model for text preprocessing. Additionally, the performance of the XLM-RoBERTa model was compared with traditional machine learning models such as Logistic Regression (LR) and Random Forest (RF), as well as other transformer-based models like BERT and RoBERTa. This research was based on our work for the Sentiment Analysis in Tamil and Tulu DravidianLangTech@NAACL 2025 competition, where we received a macro F1-score of 59% for the Tulu dataset and 49% for the Tamil dataset, placing third in the competition.
BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching
Authors: Zhihua Liu, Lei Tong, Xilin He, Che Liu, Rossella Arcucci, Chen Jin, Huiyu Zhou
Date: 2025-05-23 00:00:00
Tasks: Anatomy, Segmentation

Existed echocardiography segmentation methods often suffer from anatomical inconsistency challenge caused by shape variation, partial observation and region ambiguity with similar intensity across 2D echocardiographic sequences, resulting in false positive segmentation with anatomical defeated structures in challenging low signal-to-noise ratio conditions. To provide a strong anatomical guarantee across different echocardiographic frames, we propose a novel segmentation framework named BOTM (Bi-directional Optimal Token Matching) that performs echocardiography segmentation and optimal anatomy transportation simultaneously. Given paired echocardiographic images, BOTM learns to match two sets of discrete image tokens by finding optimal correspondences from a novel anatomical transportation perspective. We further extend the token matching into a bi-directional cross-transport attention proxy to regulate the preserved anatomical consistency within the cardiac cyclic deformation in temporal domain. Extensive experimental results show that BOTM can generate stable and accurate segmentation outcomes (e.g. -1.917 HD on CAMUS2H LV, +1.9% Dice on TED), and provide a better matching interpretation with anatomical consistency guarantee.
ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection
Authors: Juxin Niu, Xiangfeng Liu, Dan Niu, Xi Wang, Zhe Jiang, Nan Guan
Date: 2025-05-26 00:00:00
Tasks: Code Generation

Coding with hardware description languages (HDLs) such as Verilog is a time-intensive and laborious task. With the rapid advancement of large language models (LLMs), there is increasing interest in applying LLMs to assist with HDL coding. Recent efforts have demonstrated the potential of LLMs in translating natural language to traditional HDL Verilog. Chisel, a next-generation HDL based on Scala, introduces higher-level abstractions, facilitating more concise, maintainable, and scalable hardware designs. However, the potential of using LLMs for Chisel code generation remains largely unexplored. This work proposes ReChisel, an LLM-based agentic system designed to enhance the effectiveness of Chisel code generation. ReChisel incorporates a reflection mechanism to iteratively refine the quality of generated code using feedback from compilation and simulation processes, and introduces an escape mechanism to break free from non-progress loops. Experiments demonstrate that ReChisel significantly improves the success rate of Chisel code generation, achieving performance comparable to state-of-the-art LLM-based agentic systems for Verilog code generation.
Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
Authors: Qiong Li, Chao Fang, Longwei Huang, Jun Lin, Zhongfeng Wang
Date: 2025-05-25 00:00:00

While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for lightweight, precision-scalable, and IEEE-754 arithmetic compatible hardware implementation. To address these challenges, we enhance RISC-V processors by 1) integrating dedicated posit codecs into the original FPU for lightweight implementation, 2) incorporating multi/mixed-precision support with dynamic exponent size for precision-scalability, and 3) reusing and customizing ISA extensions for IEEE-754 compatible posit operations. Our comprehensive evaluation spans the modified FPU, RISC-V core, and SoC levels. It demonstrates that our implementation achieves 47.9% LUTs and 57.4% FFs reduction compared to state-of-the-art posit-enabled RISC-V processors, while achieving up to 2.54$\times$ throughput improvement in various GEMM kernels.
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
Authors: Aotao Wang, Haikuo Shao, Shaobo Ma, Zhongfeng Wang
Date: 2025-05-25 00:00:00
Tasks: Computational Efficiency, CPU, GPU, Mamba, Quantization, State Space Models

State Space Models (SSMs), like recent Mamba2, have achieved remarkable performance and received extensive attention. However, deploying Mamba2 on resource-constrained edge devices encounters many problems: severe outliers within the linear layer challenging the quantization, diverse and irregular element-wise tensor operations, and hardware-unfriendly nonlinear functions in the SSM block. To address these issues, this paper presents FastMamba, a dedicated accelerator on FPGA with hardware-algorithm co-design to promote the deployment efficiency of Mamba2. Specifically, we successfully achieve 8-bit quantization for linear layers through Hadamard transformation to eliminate outliers. Moreover, a hardware-friendly and fine-grained power-of-two quantization framework is presented for the SSM block and convolution layer, and a first-order linear approximation is developed to optimize the nonlinear functions. Based on the accurate algorithm quantization, we propose an accelerator that integrates parallel vector processing units, pipelined execution dataflow, and an efficient SSM Nonlinear Approximation Unit, which enhances computational efficiency and reduces hardware complexity. Finally, we evaluate FastMamba on Xilinx VC709 FPGA. For the input prefill task on Mamba2-130M, FastMamba achieves 68.80\times and 8.90\times speedup over Intel Xeon 4210R CPU and NVIDIA RTX 3090 GPU, respectively. In the output decode experiment with Mamba2-2.7B, FastMamba attains 6\times higher energy efficiency than RTX 3090 GPU.
Autocomp: LLM-Driven Code Optimization for Tensor Accelerators
Authors: Charles Hong, Sahil Bhatia, Alvin Cheung, Yakun Sophia Shao
Date: 2025-05-24 00:00:00
Tasks: Code Generation

Hardware accelerators, especially those designed for tensor processing, have become ubiquitous in today's computing landscape. However, even with significant efforts in building compilers, programming these tensor accelerators remains challenging, leaving much of their potential underutilized. Recently, large language models (LLMs), trained on large amounts of code, have shown significant promise in code generation and optimization tasks, but generating low-resource languages like specialized tensor accelerator code still poses a significant challenge. We tackle this challenge with Autocomp, an approach that empowers accelerator programmers to leverage domain knowledge and hardware feedback to optimize code via an automated LLM-driven search. We accomplish this by: 1) formulating each optimization pass as a structured two-phase prompt, divided into planning and code generation phases, 2) inserting domain knowledge during planning via a concise and adaptable optimization menu, and 3) integrating correctness and performance metrics from hardware as feedback at each search iteration. Across three categories of representative workloads and two different accelerators, we demonstrate that Autocomp-optimized code runs 5.6x (GEMM) and 2.7x (convolution) faster than the vendor-provided library, and outperforms expert-level hand-tuned code by 1.4x (GEMM), 1.1x (convolution), and 1.3x (fine-grained linear algebra). Additionally, we demonstrate that optimization schedules generated from Autocomp can be reused across similar tensor operations, improving speedups by up to 24% under a fixed sample budget.
Leveraging Stochastic Depth Training for Adaptive Inference
Authors: Guilherme Korol, Antonio Carlos Schneider Beck, Jeronimo Castrillon
Date: 2025-05-23 00:00:00

Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii) increased training complexity (e.g., with non-differentiable operations), and iii) less control over performance-quality trade-offs due to its inherent input-dependent execution. To approach these issues, we propose a simpler yet effective alternative for adaptive inference with a zero-overhead, single-model, and time-predictable inference. Central to our approach is the observation that models trained with Stochastic Depth -- a method for faster training of residual networks -- become more resilient to arbitrary layer-skipping at inference time. We propose a method to first select near Pareto-optimal skipping configurations from a stochastically-trained model to adapt the inference at runtime later. Compared to original ResNets, our method shows improvements of up to 2X in power efficiency at accuracy drops as low as 0.71%.
Ray Antenna Array: A Novel Cost-Effective Multi-Antenna Architecture for Enhanced Wireless Communication
Authors: Zhenjun Dong, Zhiwen Zhou, Yong Zeng
Date: 2025-05-13 00:00:00

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which aims to enhance wireless communication performance in a cost-effective manner. RAA is composed of massive cheap antenna elements and a few radio frequency (RF) chains. The massive antenna elements are arranged in a novel ray-like structure, with each ray corresponding to a simple uniform linear array (sULA) with a carefully designed orientation. The antenna elements of each sULA are directly connected to an RF combiner, so that the sULA in each ray is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. Compared to conventional multi-antenna architectures like hybrid analog/digital beamforming (HBF), the proposed RAA has two major advantages. First, it can significantly reduce hardware costs since no phase shifters, which are usually expensive especially in high-frequency systems, are required. Besides, RAA can greatly improve system performance by configuring antenna elements with higher directionality, as each sULA only needs to be responsible for a portion of the total coverage angle. To demonstrate such advantages, in this paper, we first present the input-output model for RAA-based wireless communications, based on which the ray orientations of the RAA are designed. Furthermore, efficient algorithms for joint ray selection and beamforming are proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate the superior performance of RAA compared to HBF while significantly reducing hardware cost.
Exact Expressive Power of Transformers with Padding
Authors: William Merrill, Ashish Sabharwal
Date: 2025-05-25 00:00:00
Tasks: Hard Attention

Chain of thought is a natural inference-time method for increasing the computational power of transformer-based large language models (LLMs), but comes at the cost of sequential decoding. Are there more efficient alternatives to expand a transformer's expressive power without adding parameters? We consider transformers with padding tokens as a form of parallelizable test-time compute. We show that averaging-hard-attention, masked-pre-norm transformers with polynomial padding converge to precisely the class $\mathsf{TC}^0$ of extremely parallelizable problems. While the $\mathsf{TC}^0$ upper bound was known, proving a matching lower bound had been elusive. Further, our novel analysis reveals the precise expanded power of padded transformers when coupled with another form of inference-time compute, namely dynamically increasing depth via looping. Our core technical contribution is to show how padding helps bring the notions of complete problems and reductions, which have been a cornerstone of classical complexity theory, to the formal study of transformers. Armed with this new tool, we prove that padded transformers with $O(\log^d n)$ looping on inputs of length $n$ recognize exactly the class $\mathsf{TC}^d$ of moderately parallelizable problems. Thus, padding and looping together systematically expand transformers' expressive power: with polylogarithmic looping, padded transformers converge to the class $\mathsf{NC}$, the best that could be expected without losing parallelism (unless $\mathsf{NC} = \mathsf{P}$). Our results thus motivate further exploration of padding and looping as parallelizable alternatives to chain of thought.
Marginal Fairness: Fair Decision-Making under Risk Measures
Authors: Fei Huang, Silvana M. Pesenti
Date: 2025-05-24 00:00:00
Tasks: Attribute, Decision Making, Fairness

This paper introduces marginal fairness, a new individual fairness notion for equitable decision-making in the presence of protected attributes such as gender, race, and religion. This criterion ensures that decisions based on generalized distortion risk measures are insensitive to distributional perturbations in protected attributes, regardless of whether these attributes are continuous, discrete, categorical, univariate, or multivariate. To operationalize this notion and reflect real-world regulatory environments (such as the EU gender-neutral pricing regulation), we model business decision-making in highly regulated industries (such as insurance and finance) as a two-step process: (i) a predictive modeling stage, in which a prediction function for the target variable (e.g., insurance losses) is estimated based on both protected and non-protected covariates; and (ii) a decision-making stage, in which a generalized distortion risk measure is applied to the target variable, conditional only on non-protected covariates, to determine the decision. In this second step, we modify the risk measure such that the decision becomes insensitive to the protected attribute, thus enforcing fairness to ensure equitable outcomes under risk-sensitive, regulatory constraints. Furthermore, by utilizing the concept of cascade sensitivity, we extend the marginal fairness framework to capture how dependencies between covariates propagate the influence of protected attributes through the modeling pipeline. A numerical study and an empirical implementation using an auto insurance dataset demonstrate how the framework can be applied in practice.
FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets
Authors: Dannong Wang, Jaisal Patel, Daochen Zha, Steve Y. Yang, Xiao-Yang Liu
Date: 2025-05-26 00:00:00
Tasks: Benchmarking, GPU

Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, their efficacy in high-stakes domains like finance is rarely explored, e.g., passing CFA exams and analyzing SEC filings. In this paper, we present the open-source FinLoRA project that benchmarks LoRA methods on both general and highly professional financial tasks. First, we curated 19 datasets covering diverse financial applications; in particular, we created four novel XBRL analysis datasets based on 150 SEC filings. Second, we evaluated five LoRA methods and five base LLMs. Finally, we provide extensive experimental results in terms of accuracy, F1, and BERTScore and report computational cost in terms of time and GPU memory during fine-tuning and inference stages. We find that LoRA methods achieved substantial performance gains of 36\% on average over base models. Our FinLoRA project provides an affordable and scalable approach to democratize financial intelligence to the general public. Datasets, LoRA adapters, code, and documentation are available at https://github.com/Open-Finance-Lab/FinLoRA
Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation
Authors: Alou Diakite, Cheng Li, Lei Xie, Yuanjing Feng, Ruoyou Wu, Jianzhong He, Hairong Zheng, Shanshan Wang
Date: 2025-05-26 00:00:00
Tasks: Diffusion  MRI

Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring multi-parametric MR imaging data has been identified as an important way to delineate VP. However, due to the complex cross-sequence relationships, existing methods cannot effectively model the complementary information from different MRI sequences. In addition, these existing methods heavily rely on large training data with labels, which is labor-intensive and time-consuming to obtain. In this work, we propose a novel semi-supervised multi-parametric feature decomposition framework for VP delineation. Specifically, a correlation-constrained feature decomposition (CFD) is designed to handle the complex cross-sequence relationships by capturing the unique characteristics of each MRI sequence and easing the multi-parametric information fusion process. Furthermore, a consistency-based sample enhancement (CSE) module is developed to address the limited labeled data issue, by generating and promoting meaningful edge information from unlabeled data. We validate our framework using two public datasets, and one in-house Multi-Shell Diffusion MRI (MDM) dataset. Experimental results demonstrate the superiority of our approach in terms of delineation performance when compared to seven state-of-the-art approaches.
DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradients
Authors: Yuxing Lu, Gecheng Fu, Wei Wu, Xukai Zhao, Sin Yee Goi, Jinzhuo Wang
Date: 2025-05-26 00:00:00
Tasks: Clinical Knowledge, RAG, Retrieval

Existing medical RAG systems mainly leverage knowledge from medical knowledge bases, neglecting the crucial role of experiential knowledge derived from similar patient cases -- a key component of human clinical reasoning. To bridge this gap, we propose DoctorRAG, a RAG framework that emulates doctor-like reasoning by integrating both explicit clinical knowledge and implicit case-based experience. DoctorRAG enhances retrieval precision by first allocating conceptual tags for queries and knowledge sources, together with a hybrid retrieval mechanism from both relevant knowledge and patient. In addition, a Med-TextGrad module using multi-agent textual gradients is integrated to ensure that the final output adheres to the retrieved knowledge and patient query. Comprehensive experiments on multilingual, multitask datasets demonstrate that DoctorRAG significantly outperforms strong baseline RAG models and gains improvements from iterative refinements. Our approach generates more accurate, relevant, and comprehensive responses, taking a step towards more doctor-like medical reasoning systems.