OpenRaiser/Intern-Atlas huggingface dataset:
Quantum Private Distributed Matrix Multiplication With Degree Tables
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we explore how quantum resources can be used to increase the rate of private distributed matrix multiplication (PDMM). In PDMM, a user who has two high-dimensional matrices, $A$ and $B$, and lacks the computational capabilities to apply matrix multiplication locally, divides the matrices $A$ and $B$ into $K$ and $L$ sub-blocks, respectively. Then, the user sends them to $N$ servers to apply the required multiplication privately from any $T$ servers. The goal is to reduce the number of servers needed to perform the required matrix multiplication. In the quantum setting, we allow the servers to share an entangled state and respond over quantum channels. Upon receiving the qudits, the user applies measurements to obtain the required multiplication. There are two main regimes in the PDMM literature: The high-privacy regime and the low-privacy regime where $T$ is less than $K$ and $L$. First, in the high-privacy regime, the state-of-the-art classical code is called the gap additive secure polynomial (GASP) code. We define a feasibility requirement in the quantum setting for the GASP code such that the highest performance is achieved when it is satisfied. When it is not satisfied, we address two main concerns. The first is to find a relation between the minimum privacy requirement and the dimensions of the two matrices needed for the feasibility condition to be satisfied. Second, we develop a new family of codes that can work in the quantum setting. Second, since GASP does not work efficiently in the low-privacy regimes compared to cyclic-addition degree tables (CAT) and discretely optimized GASP (DOG), we show that the feasibility condition developed for GASP can be adopted for both CAT and DOG codes as well. In addition, we propose another set of codes that can be used in the low privacy regime in the quantum setting when the feasibility requirement is not satisfied.
ML Researchers Support Openness in Peer Review But Are Concerned About Resubmission Bias
Venue: arXiv cs.DL 2025
Year: 2025

Peer-review venues have increasingly adopted open reviewing policies that publicly release anonymized reviews and permit public commenting. Venues have adopted a variety of policies, and there is still ongoing debate about the benefits and drawbacks of decisions. To inform this debate, we surveyed 2,385 reviewers, authors, and other peer-review participants in machine learning to understand their experiences and opinions. Our key findings are: (a) Preferences: Over 80% of respondents support releasing reviews for accepted papers and allowing public comments. However, only 27.1% support releasing rejected manuscripts. (b) Benefits: Respondents cite improved public understanding (75.3%) and reviewer education (57.8%), increased fairness (56.6%), and stronger incentives for high-quality reviews (48.0%). (c) Challenges: The top concern is resubmission bias, where rejection history biases future reviewers (ranked top impact of open reviewing by 41% of respondents, and mentioned in over 50% of free responses). Other challenges include fear of reviewer de-anonymization (33.2%) and potential commenting abuse. (d) AI and open peer review: Participants believe open policies deter "AI slop" submissions (71.9%) and AI-generated reviews (38.9%). Respondents are split regarding peer-review venues generating official AI reviews, with 56.0% opposed and 44.0% supportive. Finally, we use AI to annotate 4,244 reviews from ICLR (fully open) and NeurIPS (partially open). We find that the fully open venue (ICLR) has higher levels of correctness and completeness than the partially open venue (NeurIPS). The effect size is small for correctness and very small for completeness, and both are statistically significant. We also find that there is no statistically significant difference in the level of substantiation. We release the full dataset at https://github.com/justinpayan/OpenReviewAnalysis.
Object-Centric Data Synthesis for Category-level Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model's detection capability to new object classes requires large amounts of annotated training data, which is costly and time-consuming to acquire, especially for long-tailed classes with insufficient representation in existing datasets. Here, we introduce the object-centric data setting, when limited data is available in the form of object-centric data (multi-view images or 3D models), and systematically evaluate the performance of four different data synthesis methods to finetune object detection models on novel object categories in this setting. The approaches are based on simple image processing techniques, 3D rendering, and image diffusion models, and use object-centric data to synthesize realistic, cluttered images with varying contextual coherence and complexity. We assess how these methods enable models to achieve category-level generalization in real-world data, and demonstrate significant performance boosts within this data-constrained experimental setting.
The Impact of Concept Explanations and Interventions on Human-Machine Collaboration
Venue: arXiv cs.HC 2025
Year: 2025

Deep Neural Networks (DNNs) are often considered black boxes due to their opaque decision-making processes. To reduce their opacity Concept Models (CMs), such as Concept Bottleneck Models (CBMs), were introduced to predict human-defined concepts as an intermediate step before predicting task labels. This enhances the interpretability of DNNs. In a human-machine setting greater interpretability enables humans to improve their understanding and build trust in a DNN. In the introduction of CBMs, the models demonstrated increased task accuracy as incorrect concept predictions were replaced with their ground truth values, known as intervening on the concept predictions. In a collaborative setting, if the model task accuracy improves from interventions, trust in a model and the human-machine task accuracy may increase. However, the result showing an increase in model task accuracy was produced without human evaluation and thus it remains unknown if the findings can be applied in a collaborative setting. In this paper, we ran the first human studies using CBMs to evaluate their human interaction in collaborative task settings. Our findings show that CBMs improve interpretability compared to standard DNNs, leading to increased human-machine alignment. However, this increased alignment did not translate to a significant increase in task accuracy. Understanding the model's decision-making process required multiple interactions, and misalignment between the model's and human decision-making processes could undermine interpretability and model effectiveness.
Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges
Venue: arXiv cs.RO 2025
Year: 2025

The emergence of multi-modal foundation models has markedly transformed the technology for autonomous driving, shifting away from conventional and mostly hand-crafted design choices towards unified, foundation-model-based approaches, capable of directly inferring motion trajectories from raw sensory inputs. This new class of methods can also incorporate natural language as an additional modality, with Vision-Language-Action (VLA) models serving as a representative example. In this review, we provide a comprehensive examination of such methods through a unifying taxonomy to critically evaluate their architectural design choices, methodological strengths, and their inherent capabilities and limitations. Our survey covers 37 recently proposed approaches that span the landscape of trajectory planning with foundation models. Furthermore, we assess these approaches with respect to the openness of their source code and datasets, offering valuable information to practitioners and researchers. We provide an accompanying webpage that catalogs the methods based on our taxonomy, available at: https://github.com/fiveai/FMs-for-driving-trajectories
Analysis of Single Event Induced Bit Faults in a Deep Neural Network Accelerator Pipeline
Venue: arXiv cs.AR 2025
Year: 2025

In recent years, the increased interest and the growth in application domains of Artificial Intelligence (AI), and more specifically Deep Neural Networks (DNNs), has led to an extensive usage of domain specific DNN accelerator processors to improve the computational efficiency of DNN inference. However, like any digital circuit, these processors are prone to faults induced by radiation particles such as heavy ions, protons, etc., making their use in harsh radiation environments a challenge. This work presents an in-depth analysis of the impact of such faults on the computational pipeline of a Systolic Array based Deep Neural Network accelerator (SA-DNN accelerator) by means of a Register Transfer Level (RTL) Fault Injection (FI) simulation in order to improve the observability of each hardware block. From this analysis, we present the sensitivity to single bit faults of register groups in the pipeline for three different DNN workloads utilising two datasets, namely MNIST and CIFAR-10. These sensitivity figures are presented in terms of Fault Propagation Probability ($P(f_{non-crit})$) and False Classification Probability ($P(f_{crit})$) which respectively show the probability that an injected fault causes a non-critical error (numerical offset) or a critical error (classification fault). From these results, we devise a fault mitigation strategy to harden the SA-DNN accelerator in an efficient way, both in terms of area and power overhead.
An optimization framework for task allocation in the edge/hub/cloud paradigm
Venue: arXiv cs.NI 2025
Year: 2025

With the advent of the Internet of Things (IoT), novel critical applications have emerged that leverage the edge/hub/cloud paradigm, which diverges from the conventional edge computing perspective. A growing number of such applications require a streamlined architecture for their effective execution, often comprising a single edge device with sensing capabilities, a single hub device (e.g., a laptop or smartphone) for managing and assisting the edge device, and a more computationally capable cloud server. Typical examples include the utilization of an unmanned aerial vehicle (UAV) for critical infrastructure inspection or a wearable biomedical device (e.g., a smartwatch) for remote patient monitoring. Task allocation in this streamlined architecture is particularly challenging, due to the computational, communication, and energy limitations of the devices at the network edge. Consequently, there is a need for a comprehensive framework that can address the specific task allocation problem optimally and efficiently. To this end, we propose a complete, binary integer linear programming (BILP) based formulation for an application-driven design-time approach, capable of providing an optimal task allocation in the targeted edge/hub/cloud environment. The proposed method minimizes the desired objective, either the overall latency or overall energy consumption, while considering several crucial parameters and constraints often overlooked in related literature. We evaluate our framework using a real-world use-case scenario, as well as appropriate synthetic benchmarks. Our extensive experimentation reveals that the proposed approach yields optimal and scalable results, enabling efficient design space exploration for different applications and computational devices.
Decoupled Control Flow and Data Access in RISC-V GPGPUs
Venue: arXiv cs.AR 2025
Year: 2025

Vortex, a newly proposed open-source GPGPU platform based on the RISC-V ISA, offers a valid alternative for GPGPU research over the broadly-used modeling platforms based on commercial GPUs. Similarly to the push originating from the RISC-V movement for CPUs, Vortex can enable a myriad of fresh research directions for GPUs. However, as a young hardware platform, it currently lacks the performance competitiveness of commercial GPUs, which is crucial for widespread adoption. State-of-the-art GPUs, in fact, rely on complex architectural features, still unavailable in Vortex, to hide the micro-code overheads linked to control flow (CF) management and memory orchestration for data access. In particular, these components account for the majority of the dynamic instruction count in regular, memory-intensive kernels, such as linear algebra routines, which form the basis of many applications, including Machine Learning. To address these challenges with simple yet powerful micro-architecture modifications, this paper introduces decoupled CF and data access through 1.) a hardware CF manager to accelerate branching and predication in regular loop execution and 2.) decoupled memory streaming lanes to further hide memory latency with useful computation. The evaluation results for different kernels show 8$\times$ faster execution, 10$\times$ reduction in dynamic instruction count, and overall performance improvement from 0.35 to 1.63 $\mathrm{GFLOP/s/mm^2}$. Thanks to these enhancements, Vortex can become an ideal playground to enable GPGPU research for the next generation of Machine Learning.
Intelligent Systems and Robotics: Revolutionizing Engineering Industries
Venue: arXiv cs.RO 2025
Year: 2025

A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.
LM4Opt-RA: A Multi-Candidate LLM Framework with Structured Ranking for Automating Network Resource Allocation
Venue: arXiv cs.NI 2025
Year: 2025

Building on advancements in Large Language Models (LLMs), we can tackle complex analytical and mathematical reasoning tasks requiring nuanced contextual understanding. A prime example of such complex tasks is modelling resource allocation optimization in networks, which extends beyond translating natural language inputs into mathematical equations or Linear Programming (LP), Integer Linear Programming (ILP), and Mixed-Integer Linear Programming (MILP) models. However, existing benchmarks and datasets cannot address the complexities of such problems with dynamic environments, interdependent variables, and heterogeneous constraints. To address this gap, we introduce NL4RA, a curated dataset comprising 50 resource allocation optimization problems formulated as LP, ILP, and MILP. We then evaluate the performance of well-known open-source LLMs with varying parameter counts. To enhance existing LLM based methods, we introduce LM4Opt RA, a multi candidate framework that applies diverse prompting strategies such as direct, few shot, and chain of thought, combined with a structured ranking mechanism to improve accuracy. We identified discrepancies between human judgments and automated scoring such as ROUGE, BLEU, or BERT scores. However, human evaluation is time-consuming and requires specialized expertise, making it impractical for a fully automated end-to-end framework. To quantify the difference between LLM-generated responses and ground truth, we introduce LLM-Assisted Mathematical Evaluation (LAME), an automated metric designed for mathematical formulations. Using LM4Opt-RA, Llama-3.1-70B achieved a LAME score of 0.8007, outperforming other models by a significant margin, followed closely by Llama-3.1-8B. While baseline LLMs demonstrate considerable promise, they still lag behind human expertise; our proposed method surpasses these baselines regarding LAME and other metrics.
SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning
Venue: arXiv cs.RO 2025
Year: 2025

Recent advances in robotic policy learning have enabled complex manipulation in real-world environments, yet the execution speed of these policies often lags behind hardware capabilities due to the cost of collecting faster demonstrations. Existing works on policy acceleration reinterpret action sequence for unseen execution speed, thereby encountering distributional shifts from the original demonstrations. Reinforcement learning is a promising approach that adapts policies for faster execution without additional demonstration, but its unguided exploration is sample inefficient. We propose SpeedAug, an RL-based policy acceleration framework that efficiently adapts pre-trained policies for faster task execution. SpeedAug constructs behavior prior that encompasses diverse tempos of task execution by pre-training a policy on speed-augmented demonstrations. Empirical results on robotic manipulation benchmarks show that RL fine-tuning initialized from this tempo-enriched policy significantly improves the sample efficiency of existing RL and policy acceleration methods while maintaining high success rate.
A CNN-Based Technique to Assist Layout-to-Generator Conversion for Analog Circuits
Venue: arXiv cs.AR 2025
Year: 2025

We propose a technique to assist in converting a reference layout of an analog circuit into the procedural layout generator by efficiently reusing available generators for sub-cell creation. The proposed convolutional neural network (CNN) model automatically detects sub-cells that can be generated by available generator scripts in the library, and suggests using them in the hierarchically correct places of the generator software. In experiments, the CNN model examined sub-cells of a high-speed wireline receiver that has a total of 4,885 sub-cell instances including different 145 sub-cell designs. The CNN model classified the sub-cell instances into 51 generatable and one not-generatable classes. One not-generatable class indicates that no available generator can generate the classified sub-cell. The CNN model achieved 99.3% precision in examining the 145 different sub-cell designs. The CNN model greatly reduced the examination time to 18 seconds from 88 minutes required in manual examination. Also, the proposed CNN model could correctly classify unfamiliar sub-cells that are very different from the training dataset.
ProvRain: Rain-Adaptive Denoising and Vehicle Detection via MobileNet-UNet and Faster R-CNN
Venue: arXiv cs.CV 2025
Year: 2025

Provident vehicle detection has a lot of scope in the detection of vehicle during night time. The extraction of features other than the headlamps of vehicles allows us to detect oncoming vehicles before they appear directly on the camera. However, it faces multiple issues especially in the field of night vision, where a lot of noise caused due to weather conditions such as rain or snow as well as camera conditions. This paper focuses on creating a pipeline aimed at dealing with such noise while at the same time maintaining the accuracy of provident vehicular detection. The pipeline in this paper, ProvRain, uses a lightweight MobileNet-U-Net architecture tuned to generalize to robust weather conditions by using the concept of curricula training. A mix of synthetic as well as available data from the PVDN dataset is used for this. This pipeline is compared to the base Faster RCNN architecture trained on the PVDN dataset to see how much the addition of a denoising architecture helps increase the detection model's performance in rainy conditions. The system boasts an 8.94\% increase in accuracy and a 10.25\% increase in recall in the detection of vehicles in rainy night time frames. Similarly, the custom MobileNet-U-Net architecture that was trained also shows a 10-15\% improvement in PSNR, a 5-6\% increase in SSIM, and upto a 67\% reduction in perceptual error (LPIPS) compared to other transformer approaches.
Conceptual Evaluation of Deep Visual Stereo Odometry for the MARWIN Radiation Monitoring Robot in Accelerator Tunnels
Venue: arXiv cs.CV 2025
Year: 2025

The MARWIN robot operates at the European XFEL to perform autonomous radiation monitoring in long, monotonous accelerator tunnels where conventional localization approaches struggle. Its current navigation concept combines lidar-based edge detection, wheel/lidar odometry with periodic QR-code referencing, and fuzzy control of wall distance, rotation, and longitudinal position. While robust in predefined sections, this design lacks flexibility for unknown geometries and obstacles. This paper explores deep visual stereo odometry (DVSO) with 3D-geometric constraints as a focused alternative. DVSO is purely vision-based, leveraging stereo disparity, optical flow, and self-supervised learning to jointly estimate depth and ego-motion without labeled data. For global consistency, DVSO can subsequently be fused with absolute references (e.g., landmarks) or other sensors. We provide a conceptual evaluation for accelerator tunnel environments, using the European XFEL as a case study. Expected benefits include reduced scale drift via stereo, low-cost sensing, and scalable data collection, while challenges remain in low-texture surfaces, lighting variability, computational load, and robustness under radiation. The paper defines a research agenda toward enabling MARWIN to navigate more autonomously in constrained, safety-critical infrastructures.
A Fast and Efficient Modern BERT based Text-Conditioned Diffusion Model for Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

In recent times, denoising diffusion probabilistic models (DPMs) have proven effective for medical image generation and denoising, and as representation learners for downstream segmentation. However, segmentation performance is limited by the need for dense pixel-wise labels, which are expensive, time-consuming, and require expert knowledge. We propose FastTextDiff, a label-efficient diffusion-based segmentation model that integrates medical text annotations to enhance semantic representations. Our approach uses ModernBERT, a transformer capable of processing long clinical notes, to tightly link textual annotations with semantic content in medical images. Trained on MIMIC-III and MIMIC-IV, ModernBERT encodes clinical knowledge that guides cross-modal attention between visual and textual features. This study validates ModernBERT as a fast, scalable alternative to Clinical BioBERT in diffusion-based segmentation pipelines and highlights the promise of multi-modal techniques for medical image analysis. By replacing Clinical BioBERT with ModernBERT, FastTextDiff benefits from FlashAttention 2, an alternating attention mechanism, and a 2-trillion-token corpus, improving both segmentation accuracy and training efficiency over traditional diffusion-based models.