pwc-archive/papers-with-abstracts huggingface dataset:
Hogwild! Inference: Parallel LLM Generation via Concurrent Attention
Authors: Gleb Rodionov, Roman Garipov, Alina Shutova, George Yakushev, Erik Schultheis, Vage Egiazarian, Anton Sinitsin, Denis Kuznedelev, Dan Alistarh
Date: 2025-04-08 00:00:00

Large Language Models (LLMs) have demonstrated the ability to tackle increasingly complex tasks through advanced reasoning, long-form content generation, and tool use. Solving these tasks often involves long inference-time computations. In human problem solving, a common strategy to expedite work is collaboration: by dividing the problem into sub-tasks, exploring different strategies concurrently, etc. Recent research has shown that LLMs can also operate in parallel by implementing explicit cooperation frameworks, such as voting mechanisms or the explicit creation of independent sub-tasks that can be executed in parallel. However, each of these frameworks may not be suitable for all types of tasks, which can hinder their applicability. In this work, we propose a different design approach: we run LLM "workers" in parallel , allowing them to synchronize via a concurrently-updated attention cache and prompt these workers to decide how best to collaborate. Our approach allows the LLM instances to come up with their own collaboration strategy for the problem at hand, all the while "seeing" each other's memory in the concurrent KV cache. We implement this approach via Hogwild! Inference: a parallel LLM inference engine where multiple instances of the same LLM run in parallel with the same attention cache, with "instant" access to each other's memory. Hogwild! Inference takes advantage of Rotary Position Embeddings (RoPE) to avoid recomputation while improving parallel hardware utilization. We find that modern reasoning-capable LLMs can perform inference with shared Key-Value cache out of the box, without additional fine-tuning.
Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification
Authors: Zehao Wu, Yanjie Zhao, Haoyu Wang
Date: 2025-06-02 00:00:00
Tasks: Clone Detection, Clustering

As Large Language Models (LLMs) become integral software components in modern applications, unauthorized model derivations through fine-tuning, merging, and redistribution have emerged as critical software engineering challenges. Unlike traditional software where clone detection and license compliance are well-established, the LLM ecosystem lacks effective mechanisms to detect model lineage and enforce licensing agreements. This gap is particularly problematic when open-source model creators, such as Meta's LLaMA, require derivative works to maintain naming conventions for attribution, yet no technical means exist to verify compliance. To fill this gap, treating LLMs as software artifacts requiring provenance tracking, we present TensorGuard, a gradient-based fingerprinting framework for LLM similarity detection and family classification. Our approach extracts model-intrinsic behavioral signatures by analyzing gradient responses to random input perturbations across tensor layers, operating independently of training data, watermarks, or specific model formats. TensorGuard supports the widely-adopted safetensors format and constructs high-dimensional fingerprints through statistical analysis of gradient features. These fingerprints enable two complementary capabilities: direct pairwise similarity assessment between arbitrary models through distance computation, and systematic family classification of unknown models via the K-Means clustering algorithm with domain-informed centroid initialization using known base models. Experimental evaluation on 58 models comprising 8 base models and 50 derivatives across five model families (Llama, Qwen, Gemma, Phi, Mistral) demonstrates 94% classification accuracy under our centroid-initialized K-Means clustering.
VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning
Authors: Qiuchen Wang, Ruixue Ding, Yu Zeng, Zehui Chen, Lin Chen, Shihang Wang, Pengjun Xie, Fei Huang, Feng Zhao
Date: 2025-05-28 00:00:00
Tasks: RAG

Effectively retrieving, reasoning and understanding visually rich information remains a challenge for RAG methods. Traditional text-based methods cannot handle visual-related information. On the other hand, current vision-based RAG approaches are often limited by fixed pipelines and frequently struggle to reason effectively due to the insufficient activation of the fundamental capabilities of models. As RL has been proven to be beneficial for model reasoning, we introduce VRAG-RL, a novel RL framework tailored for complex reasoning across visually rich information. With this framework, VLMs interact with search engines, autonomously sampling single-turn or multi-turn reasoning trajectories with the help of visual perception tokens and undergoing continual optimization based on these samples. Our approach highlights key limitations of RL in RAG domains: (i) Prior Multi-modal RAG approaches tend to merely incorporate images into the context, leading to insufficient reasoning token allocation and neglecting visual-specific perception; and (ii) When models interact with search engines, their queries often fail to retrieve relevant information due to the inability to articulate requirements, thereby leading to suboptimal performance. To address these challenges, we define an action space tailored for visually rich inputs, with actions including cropping and scaling, allowing the model to gather information from a coarse-to-fine perspective. Furthermore, to bridge the gap between users' original inquiries and the retriever, we employ a simple yet effective reward that integrates query rewriting and retrieval performance with a model-based reward. Our VRAG-RL optimizes VLMs for RAG tasks using specially designed RL strategies, aligning the model with real-world applications. The code is available at \hyperlink{https://github.com/Alibaba-NLP/VRAG}{https://github.com/Alibaba-NLP/VRAG}.
MedBookVQA: A Systematic and Comprehensive Medical Benchmark Derived from Open-Access Book
Authors: Sau Lai Yip, Sunan He, Yuxiang Nie, Shu Pui Chan, Yilin Ye, Sum Ying Lam, Hao Chen
Date: 2025-06-01 00:00:00
Tasks: Benchmarking

The accelerating development of general medical artificial intelligence (GMAI), powered by multimodal large language models (MLLMs), offers transformative potential for addressing persistent healthcare challenges, including workforce deficits and escalating costs. The parallel development of systematic evaluation benchmarks emerges as a critical imperative to enable performance assessment and provide technological guidance. Meanwhile, as an invaluable knowledge source, the potential of medical textbooks for benchmark development remains underexploited. Here, we present MedBookVQA, a systematic and comprehensive multimodal benchmark derived from open-access medical textbooks. To curate this benchmark, we propose a standardized pipeline for automated extraction of medical figures while contextually aligning them with corresponding medical narratives. Based on this curated data, we generate 5,000 clinically relevant questions spanning modality recognition, disease classification, anatomical identification, symptom diagnosis, and surgical procedures. A multi-tier annotation system categorizes queries through hierarchical taxonomies encompassing medical imaging modalities (42 categories), body anatomies (125 structures), and clinical specialties (31 departments), enabling nuanced analysis across medical subdomains. We evaluate a wide array of MLLMs, including proprietary, open-sourced, medical, and reasoning models, revealing significant performance disparities across task types and model categories. Our findings highlight critical capability gaps in current GMAI systems while establishing textbook-derived multimodal benchmarks as essential evaluation tools. MedBookVQA establishes textbook-derived benchmarking as a critical paradigm for advancing clinical AI, exposing limitations in GMAI systems while providing anatomically structured performance metrics across specialties.
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
Authors: Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jierui Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Kun Xie, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
Date: 2025-06-02 00:00:00
Tasks: Math

High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. To address these, we introduce $\textbf{STORM-BORN}$, an ultra-challenging dataset of mathematical derivations sourced from cutting-edge academic papers, which includes dense human-like approximations and heuristic cues. To ensure the reliability and quality, we propose a novel human-in-the-loop, multi-agent data generation framework, integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians' evaluations. We curated a set of 2,000 synthetic samples and deliberately selected the 100 most difficult problems. Even most advanced models like GPT-o1 solved fewer than $5\%$ of them. Fine-tuning on STORM-BORN boosts accuracy by $7.84\%$ (LLaMA3-8B) and $9.12\%$ (Qwen2.5-7B). As AI approaches mathematician-level reasoning, STORM-BORN provides both a high-difficulty benchmark and a human-like reasoning training resource. Our code and dataset are publicly available at https://github.com/lwhere/STORM-BORN.
No Soundness in the Real World: On the Challenges of the Verification of Deployed Neural Networks
Authors: Attila Szász, Balázs Bánhelyi, Márk Jelasity
Date: 2025-06-01 00:00:00

The ultimate goal of verification is to guarantee the safety of deployed neural networks. Here, we claim that all the state-of-the-art verifiers we are aware of fail to reach this goal. Our key insight is that theoretical soundness (bounding the full-precision output while computing with floating point) does not imply practical soundness (bounding the floating point output in a potentially stochastic environment). We prove this observation for the approaches that are currently used to achieve provable theoretical soundness, such as interval analysis and its variants. We also argue that achieving practical soundness is significantly harder computationally. We support our claims empirically as well by evaluating several well-known verification methods. To mislead the verifiers, we create adversarial networks that detect and exploit features of the deployment environment, such as the order and precision of floating point operations. We demonstrate that all the tested verifiers are vulnerable to our new deployment-specific attacks, which proves that they are not practically sound.
Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
Authors: Linzh Zhao, Aki Rehn, Mikko A. Heikkilä, Razane Tajeddine, Antti Honkela
Date: 2025-06-02 00:00:00
Tasks: Privacy Preserving

Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model predictions, e.g., for minority groups. Gradient clipping, which is often used in DP learning, can suppress larger gradients from challenging samples. We show that this problem is amplified by adaptive clipping, which will often shrink the clipping bound to tiny values to match a well-fitting majority, while significantly reducing the accuracy for others. We propose bounded adaptive clipping, which introduces a tunable lower bound to prevent excessive gradient suppression. Our method improves the accuracy of the worst-performing class on average over 10 percentage points on skewed MNIST and Fashion MNIST compared to the unbounded adaptive clipping, and over 5 percentage points over constant clipping.
Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol
Authors: Mohammad Saleh Hasankhani
Date: 2025-05-30 00:00:00
Tasks: Drug Discovery, Feature Compression

Accurate prediction of molecular solubility is a cornerstone of early-stage drug discovery, yet conventional machine learning models face significant challenges due to limited labeled data and the high-dimensional nature of molecular descriptors. To address these issues, we propose LatMixSol, a novel latent space augmentation framework that combines autoencoder-based feature compression with guided interpolation to enrich training data. Our approach first encodes molecular descriptors into a low-dimensional latent space using a two-layer autoencoder. Spectral clustering is then applied to group chemically similar molecules, enabling targeted MixUp-style interpolation within clusters. Synthetic samples are generated by blending latent vectors of cluster members and decoding them back to the original feature space. Evaluated on the Huuskonen solubility benchmark, LatMixSol demonstrates consistent improvements across three of four gradient-boosted regressors (CatBoost, LightGBM, HistGradientBoosting), achieving RMSE reductions of 3.2-7.6% and R-squared increases of 0.5-1.5%. Notably, HistGradientBoosting shows the most significant enhancement with a 7.6% RMSE improvement. Our analysis confirms that cluster-guided latent space augmentation preserves chemical validity while expanding dataset diversity, offering a computationally efficient strategy to enhance predictive models in resource-constrained drug discovery pipelines.
SOC-DGL: Social Interaction Behavior Inspired Dual Graph Learning Framework for Drug-Target Interaction Identification
Authors: Xiang Zhao, Ruijie Li, Qiao Ning, Shikai Guo, Hui Li, Qian Ma
Date: 2025-06-02 00:00:00
Tasks: Drug Discovery, Graph Learning

The identification of drug-target interactions (DTI) is crucial for drug discovery and repositioning, as it reveals potential uses of existing drugs, aiding in the acceleration of the drug development process and reducing associated costs. Despite the similarity information in DTI is important, most models are limited to mining direct similarity information within homogeneous graphs, overlooking the potential yet rich similarity information in heterogeneous graphs. Inspired by real-world social interaction behaviors, we propose SOC-DGL, which comprises two specialized modules: the Affinity-Driven Graph Learning (ADGL) module and the Equilibrium-Driven Graph Learning (EDGL) module. The ADGL module adopts a comprehensive social interaction strategy, leveraging an affinity-enhanced global drug-target graph to learn both global DTI and the individual similarity information of drugs and targets. In contrast, the EDGL module employs a higher-order social interaction strategy, amplifying the influence of even-hop neighbors through an even-polynomial graph filter grounded in balance theory, enabling the indirect mining of higher-order homogeneous information. This dual approach enables SOC-DGL to effectively and comprehensively capture similarity information across diverse interaction scales within the affinity matrices and drug-target association matrices, significantly enhancing the model's generalization capability and predictive accuracy in DTI tasks. To address the issue of imbalance in drug-target interaction datasets, this paper proposes an adjustable imbalance loss function that mitigates the impact of sample imbalance by adjusting the weight of negative samples and a parameter. Extensive experiments on four benchmark datasets demonstrate significant accuracy improvements achieved by SOC-DGL, particularly in scenarios involving data imbalance and unseen drugs or targets.
Compiler Optimization via LLM Reasoning for Efficient Model Serving
Authors: Sujun Tang, Christopher Priebe, Rohan Mahapatra, Lianhui Qin, Hadi Esmaeilzadeh
Date: 2025-06-02 00:00:00
Tasks: Compiler Optimization, Large Language Model

While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid innovation. Compiler optimizations have long driven substantial performance improvements, but existing compilers struggle with neural workloads due to the exponentially large and highly interdependent space of possible transformations. Although existing stochastic search techniques can be effective, they are often sample-inefficient and fail to leverage the structural context underlying compilation decisions. We set out to investigate the research question of whether reasoning with large language models (LLMs), without any retraining, can leverage the context-aware decision space of compiler optimization to significantly improve sample efficiency. To that end, we introduce a novel compilation framework (dubbed REASONING COMPILER) that formulates optimization as a sequential, context-aware decision process, guided by a large language model and structured Monte Carlo tree search (MCTS). The LLM acts as a proposal mechanism, suggesting hardware-aware transformations that reflect the current program state and accumulated performance feedback. Monte Carlo tree search (MCTS) incorporates the LLM-generated proposals to balance exploration and exploitation, facilitating structured, context-sensitive traversal of the expansive compiler optimization space. By achieving substantial speedups with markedly fewer samples than leading neural compilers, our approach demonstrates the potential of LLM-guided reasoning to transform the landscape of compiler optimization.
Demonstrating Periscope: A Robotic Camera System to Support Remote Physical Collaboration
Authors: Haoming Meng, Yeping Wang, Pragathi Praveena, Michael Gleicher, Bilge Mutlu
Date: 2023-10-14 00:00:00
Proceeding: Computer Supported Cooperative Work 2023 10

This paper proposes a demonstration of Periscope, a robotic camera system that allows two people to collaborate remotely on physical tasks. With Periscope, a worker co-located with a robotic arm can receive guidance from a remote helper who observes the workspace through a camera mounted on the robot. Periscope facilitates remote collaboration by providing the worker and the helper with shared visual information that enhances their verbal communication and coordination processes.
COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning
Authors: Mahbub Ul Alam, Jaakko Hollmén, Rahim Rahmani
Date: 2023-07-17 00:00:00
Proceeding: IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) 2023 7

COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. X G Boost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.
Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning
Authors: Mahbub Ul Alam, Rahim Rahmani
Date: 2021-03-30 00:00:00
Proceeding: Communications in Computer and Information Science (CCIS,volume 1400) 2021 3

The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.
Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
Authors: Florian Fürrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Muñoz-Gil
Date: 2025-06-02 00:00:00
Tasks: Denoising, Parameter Prediction

Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete gate sets. Here, we introduce a multimodal denoising diffusion model that simultaneously generates a circuit's structure and its continuous parameters for compiling a target unitary. It leverages two independent diffusion processes, one for discrete gate selection and one for parameter prediction. We benchmark the model over different experiments, analyzing the method's accuracy across varying qubit counts, circuit depths, and proportions of parameterized gates. Finally, by exploiting its rapid circuit generation, we create large datasets of circuits for particular operations and use these to extract valuable heuristics that can help us discover new insights into quantum circuit synthesis.