OpenRaiser/Intern-Atlas huggingface dataset:
MVDRAM: Enabling GeMV Execution in Unmodified DRAM for Low-Bit LLM Acceleration
Venue: arXiv cs.AR 2025
Year: 2025

General matrix-vector multiplication (GeMV) remains a critical latency bottleneck in large language model (LLM) inference, even with quantized low-bit models. Processing-Using-DRAM (PUD), an analog in-DRAM computing technique, has the potential to repurpose on-device DRAM as a GeMV engine, offering additional high-throughput processing capabilities to widespread consumer devices without DRAM modifications. However, applying PUD to GeMV operations in the LLM inference pipeline incurs significant overheads $\textit{before}$ and $\textit{after}$ in-DRAM computation, diminishing the benefits of its high-throughput processing capabilities. This paper presents MVDRAM, the first practical system to accelerate GeMV operations for low-bit LLM inference using unmodified DRAM. By leveraging the data sharing patterns and mathematical linearity in GeMV operations, MVDRAM orchestrates the processor and DRAM to eliminate the costs associated with pre-arranging inputs and bit-transposition of outputs required in conventional PUD approaches. Our experimental evaluation with four DDR4 DRAM modules shows that MVDRAM achieves comparable or even better inference speed than the processor-based implementation for GeMV operations in low-bit (under 4-bit) LLM. In particular, MVDRAM achieves up to 7.29$\times$ speedup and 30.5$\times$ energy efficiency for low-bit GeMV operations. For end-to-end LLM inference, MVDRAM achieves 2.18$\times$ and 1.31$\times$ throughput improvements, along with 3.04$\times$ and 2.35$\times$ energy efficiency, for 2-bit and 4-bit quantized low-bit models, respectively. MVDRAM has the potential to redefine the AI hardware landscape by demonstrating the feasibility of standard DRAM as an LLM accelerator.
Blockchain for Federated Learning in the Internet of Things: Trustworthy Adaptation, Standards, and the Road Ahead
Venue: arXiv cs.NI 2025
Year: 2025

As edge computing gains prominence in Internet of Things (IoTs), smart cities, and autonomous systems, the demand for real-time machine intelligence with low latency and model reliability continues to grow. Federated Learning (FL) addresses these needs by enabling distributed model training without centralizing user data, yet it remains reliant on centralized servers and lacks built-in mechanisms for transparency and trust. Blockchain and Distributed Ledger Technologies (DLTs) can fill this gap by introducing immutability, decentralized coordination, and verifiability into FL workflows. This article presents current standardization efforts from 3GPP, ETSI, ITU-T, IEEE, and O-RAN that steer the integration of FL and blockchain in IoT ecosystems. We then propose a blockchain-based FL framework that replaces the centralized aggregator, incorporates reputation monitoring of IoT devices, and minimizes overhead via selective on-chain storage of model updates. We validate our approach with IOTA Tangle, demonstrating stable throughput and block confirmations, even under increasing FL workloads. Finally, we discuss architectural considerations and future directions for embedding trustworthy and resource-efficient FL in emerging 6G networks and vertical IoT applications. Our results underscore the potential of DLT-enhanced FL to meet stringent trust and energy requirements of next-generation IoT deployments.
Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language
Venue: arXiv cs.DB 2025
Year: 2025

People without a database background usually rely on file systems or tools such as Excel for data management, which often lead to redundancy and data inconsistency. Relational databases possess strong data management capabilities, but require a high level of professional expertise from users. Although there are already many works on Text2SQL to automate the translation of natural language into SQL queries for data manipulation, all of them presuppose that the database schema is pre-designed. In practice, schema design itself demands domain expertise, and research on directly generating schemas from textual requirements remains unexplored. In this paper, we systematically define a new problem, called Text2Schema, to convert a natural language text requirement into a relational database schema. With an effective Text2Schema technique, users can effortlessly create database table structures using natural language, and subsequently leverage existing Text2SQL techniques to perform data manipulations, which significantly narrows the gap between non-technical personnel and highly efficient, versatile relational database systems. We propose SchemaAgent, an LLM-based multi-agent framework for Text2Schema. We emulate the workflow of manual schema design by assigning specialized roles to agents and enabling effective collaboration to refine their respective subtasks. We also incorporate dedicated roles for reflection and inspection, along with an innovative error detection and correction mechanism to identify and rectify issues across various phases. Moreover, we build and open source a benchmark containing 381 pairs of requirement description and schema. Experimental results demonstrate the superiority of our approach over comparative work.
An End-to-End Comprehensive Gear Fault Diagnosis Method Based on Multi-Scale Feature-Level Fusion Strategy
Venue: arXiv cs.LG 2025
Year: 2025

To satisfy the requirements of the end-to-end fault diagnosis of gears, an integrated intelligent method of fault diagnosis for gears using acceleration signals was proposed, which was based on Gabor-based Adaptive Short-Time Fourier Transform (Gabor-ASTFT) and Dual-Tree Complex Wavelet Transform(DTCWT) algorithms, Dilated Residual structure and feature fusion layer, is proposed in this paper. Initially, the raw one-dimensional acceleration signals collected from the gearbox base using vibration sensors undergo pre-segmentation processing. The Gabor-ASTFT and DTCWT are then applied to convert the original one-dimensional time-domain signals into two-dimensional time-frequency representations, facilitating the preliminary extraction of fault features and obtaining weak feature maps.Subsequently, a dual-channel structure is established using deconvolution and dilated convolution to perform upsampling and downsampling on the feature maps, adjusting their sizes accordingly. A feature fusion layer is then constructed to integrate the dual-channel features, enabling multi-scale analysis of the extracted fault features.Finally, a convolutional neural network (CNN) model incorporating a residual structure is developed to conduct deep feature extraction from the fused feature maps. The extracted features are subsequently fed into a Global Average Pooling(GAP) and a classification function for fault classification. Conducting comparative experiments on different datasets, the proposed method is demonstrated to effectively meet the requirements of end-to-end fault diagnosis for gears.
Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset
Venue: arXiv cs.CL 2025
Year: 2025

The performance and usability of Large-Language Models (LLMs) are driving their use in explanation generation tasks. However, despite their widespread adoption, LLM explanations have been found to be unreliable, making it difficult for users to distinguish good from bad explanations. To address this issue, we present Rubrik's CUBE, an education-inspired rubric and a dataset of 26k explanations, written and later quality-annotated using the rubric by both humans and six open- and closed-source LLMs. The CUBE dataset focuses on two reasoning and two language tasks, providing the necessary diversity for us to effectively test our proposed rubric. Using Rubrik, we find that explanations are influenced by both task and perceived difficulty. Low quality stems primarily from a lack of conciseness in LLM-generated explanations, rather than cohesion and word choice. The full dataset, rubric, and code are available at https://github.com/RubriksCube/rubriks_cube.
System $F^ω$ with Coherent Implicit Resolution
Venue: arXiv cs.PL 2025
Year: 2025

We propose a calculus for modeling implicit programming that supports first-class, overlapping, locally scoped, and higher-order instances with higher-kinded types. We propose a straightforward generalization of the well-established System $F^ω$ to implicit parameters, with a uniform treatment of type and term abstractions. Unlike previous works, we give a declarative specification of unambiguous, and thus coherent, resolution without introducing restrictions motivated by an algorithmic formulation of resolution.
FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment
Venue: arXiv cs.CV 2025
Year: 2025

Action quality assessment (AQA) is critical for evaluating athletic performance, informing training strategies, and ensuring safety in competitive sports. However, existing deep learning approaches often operate as black boxes and are vulnerable to spurious correlations, limiting both their reliability and interpretability. In this paper, we introduce FineCausal, a novel causal-based framework that achieves state-of-the-art performance on the FineDiving-HM dataset. Our approach leverages a Graph Attention Network-based causal intervention module to disentangle human-centric foreground cues from background confounders, and incorporates a temporal causal attention module to capture fine-grained temporal dependencies across action stages. This dual-module strategy enables FineCausal to generate detailed spatio-temporal representations that not only achieve state-of-the-art scoring performance but also provide transparent, interpretable feedback on which features drive the assessment. Despite its strong performance, FineCausal requires extensive expert knowledge to define causal structures and depends on high-quality annotations, challenges that we discuss and address as future research directions. Code is available at https://github.com/Harrison21/FineCausal.
DOMAC: Differentiable Optimization for High-Speed Multipliers and Multiply-Accumulators
Venue: arXiv cs.AR 2025
Year: 2025

Multipliers and multiply-accumulators (MACs) are fundamental building blocks for compute-intensive applications such as artificial intelligence. With the diminishing returns of Moore's Law, optimizing multiplier performance now necessitates process-aware architectural innovations rather than relying solely on technology scaling. In this paper, we introduce DOMAC, a novel approach that employs differentiable optimization for designing multipliers and MACs at specific technology nodes. DOMAC establishes an analogy between optimizing multi-staged parallel compressor trees and training deep neural networks. Building on this insight, DOMAC reformulates the discrete optimization challenge into a continuous problem by incorporating differentiable timing and area objectives. This formulation enables us to utilize existing deep learning toolkit for highly efficient implementation of the differentiable solver. Experimental results demonstrate that DOMAC achieves significant enhancements in both performance and area efficiency compared to state-of-the-art baselines and commercial IPs in multiplier and MAC designs.
DiffuSE: Cross-Layer Design Space Exploration of DNN Accelerator via Diffusion-Driven Optimization
Venue: arXiv cs.AR 2025
Year: 2025

The proliferation of deep learning accelerators calls for efficient and cost-effective hardware design solutions, where parameterized modular hardware generator and electronic design automation (EDA) tools play crucial roles in improving productivity and final Quality-of-Results (QoR). To strike a good balance across multiple QoR of interest (e.g., performance, power, and area), the designers need to navigate a vast design space, encompassing tunable parameters for both hardware generator and EDA synthesis tools. However, the significant time for EDA tool invocations and complex interplay among numerous design parameters make this task extremely challenging, even for experienced designers. To address these challenges, we introduce DiffuSE, a diffusion-driven design space exploration framework for cross-layer optimization of DNN accelerators. DiffuSE leverages conditional diffusion models to capture the inverse, one-to-many mapping from QoR objectives to parameter combinations, allowing for targeted exploration within promising regions of the design space. By carefully selecting the conditioning QoR values, the framework facilitates an effective trade-off among multiple QoR metrics in a sample-efficient manner. Experimental results under 7nm technology demonstrate the superiority of the proposed framework compared to previous arts.
Spectral-Adaptive Modulation Networks for Visual Perception
Venue: arXiv cs.CV 2025
Year: 2025

Recent studies have shown that 2D convolution and self-attention exhibit distinct spectral behaviors, and optimizing their spectral properties can enhance vision model performance. However, theoretical analyses remain limited in explaining why 2D convolution is more effective in high-pass filtering than self-attention and why larger kernels favor shape bias, akin to self-attention. In this paper, we employ graph spectral analysis to theoretically simulate and compare the frequency responses of 2D convolution and self-attention within a unified framework. Our results corroborate previous empirical findings and reveal that node connectivity, modulated by window size, is a key factor in shaping spectral functions. Leveraging this insight, we introduce a \textit{spectral-adaptive modulation} (SPAM) mixer, which processes visual features in a spectral-adaptive manner using multi-scale convolutional kernels and a spectral re-scaling mechanism to refine spectral components. Based on SPAM, we develop SPANetV2 as a novel vision backbone. Extensive experiments demonstrate that SPANetV2 outperforms state-of-the-art models across multiple vision tasks, including ImageNet-1K classification, COCO object detection, and ADE20K semantic segmentation.
AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption
Venue: arXiv cs.CR 2025
Year: 2025

Biometric systems strive to balance security and usability. The use of multi-biometric systems combining multiple biometric modalities is usually recommended for high-security applications. However, the presentation of multiple biometric modalities can impair the user-friendliness of the overall system and might not be necessary in all cases. In this work, we present a simple but flexible approach to increase the privacy protection of homomorphically encrypted multi-biometric reference templates while enabling adaptation to security requirements at run-time: An adaptive multi-biometric fusion with fully homomorphic encryption (AMB-FHE). AMB-FHE is benchmarked against a bimodal biometric database consisting of the CASIA iris and MCYT fingerprint datasets using deep neural networks for feature extraction. Our contribution is easy to implement and increases the flexibility of biometric authentication while offering increased privacy protection through joint encryption of templates from multiple modalities.
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation
Venue: arXiv cs.CL 2025
Year: 2025

The goal of translation, be it by human or by machine, is, given some text in a source language, to produce text in a target language that simultaneously 1) preserves the meaning of the source text and 2) achieves natural expression in the target language. However, researchers in the machine translation community usually assess translations using a single score intended to capture semantic accuracy and the naturalness of the output simultaneously. In this paper, we build on recent advances in information theory to mathematically prove and empirically demonstrate that such single-score summaries do not and cannot give the complete picture of a system's true performance. Concretely, we prove that a tradeoff exists between accuracy and naturalness and demonstrate it by evaluating the submissions to the WMT24 shared task. Our findings help explain well-known empirical phenomena, such as the observation that optimizing translation systems for a specific accuracy metric (like BLEU) initially improves the system's naturalness, while ``overfitting'' the system to the metric can significantly degrade its naturalness. Thus, we advocate for a change in how translations are evaluated: rather than comparing systems using a single number, they should be compared on an accuracy-naturalness plane.
Bayesian Predictive Coding
Venue: arXiv cs.LG 2025
Year: 2025

Predictive coding (PC) is an influential theory of information processing in the brain, providing a biologically plausible alternative to backpropagation. It is motivated in terms of Bayesian inference, as hidden states and parameters are optimised via gradient descent on variational free energy. However, implementations of PC rely on maximum \textit{a posteriori} (MAP) estimates of hidden states and maximum likelihood (ML) estimates of parameters, limiting their ability to quantify epistemic uncertainty. In this work, we investigate a Bayesian extension to PC that estimates a posterior distribution over network parameters. This approach, termed Bayesian Predictive coding (BPC), preserves the locality of PC and results in closed-form Hebbian weight updates. Compared to PC, our BPC algorithm converges in fewer epochs in the full-batch setting and remains competitive in the mini-batch setting. Additionally, we demonstrate that BPC offers uncertainty quantification comparable to existing methods in Bayesian deep learning, while also improving convergence properties. Together, these results suggest that BPC provides a biologically plausible method for Bayesian learning in the brain, as well as an attractive approach to uncertainty quantification in deep learning.
IntelliCircos: A Data-driven and AI-powered Authoring Tool for Circos Plots
Venue: arXiv cs.HC 2025
Year: 2025

Genomics data is essential in biological and medical domains, and bioinformatics analysts often manually create circos plots to analyze the data and extract valuable insights. However, creating circos plots is complex, as it requires careful design for multiple track attributes and positional relationships between them. Typically, analysts often seek inspiration from existing circos plots, and they have to iteratively adjust and refine the plot to achieve a satisfactory final design, making the process both tedious and time-intensive. To address these challenges, we propose IntelliCircos, an AI-powered interactive authoring tool that streamlines the process from initial visual design to the final implementation of circos plots. Specifically, we build a new dataset containing 4396 circos plots with corresponding annotations and configurations, which are extracted and labeled from published papers. With the dataset, we further identify track combination patterns, and utilize Large Language Model (LLM) to provide domain-specific design recommendations and configuration references to navigate the design of circos plots. We conduct a user study with 8 bioinformatics analysts to evaluate IntelliCircos, and the results demonstrate its usability and effectiveness in authoring circos plots.
BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions
Venue: arXiv cs.CV 2025
Year: 2025

Wheat plays a critical role in global food security, making it one of the most extensively studied crops. Accurate identification and measurement of key characteristics of wheat heads are essential for breeders to select varieties for cross-breeding, with the goal of developing nutrient-dense, resilient, and sustainable cultivars. Traditionally, these measurements are performed manually, which is both time-consuming and inefficient. Advances in digital technologies have paved the way for automating this process. However, field conditions pose significant challenges, such as occlusions of leaves, overlapping wheat heads, varying lighting conditions, and motion blur. In this paper, we propose a novel data augmentation technique, BBoxCut, which uses random localized masking to simulate occlusions caused by leaves and neighboring wheat heads. We evaluated our approach using three state-of-the-art object detectors and observed mean average precision (mAP) gains of 2.76, 3.26, and 1.9 for Faster R-CNN, FCOS, and DETR, respectively. Our augmentation technique led to significant improvements both qualitatively and quantitatively. In particular, the improvements were particularly evident in scenarios involving occluded wheat heads, demonstrating the robustness of our method in challenging field conditions.
Simple general magnification of circuit lower bounds
Venue: arXiv cs.CC 2025
Year: 2025

We introduce a technically and conceptually simple approach to magnification of circuit and formula lower bounds. Central to the method are so-called distinguishers, sparse matrices that retain some of the key properties of error-correcting codes. As applications, we generalize and strengthen known general (not problem specific) magnification results and in particular achieve magnification thresholds below known lower bounds. For example, we show that fixed-polynomial formula-size lower bounds for NP are implied by slightly superlinear formula-size lower bounds for approximating any sufficiently sparse problem in NP. We also show that the thresholds achieved are sharp. Additionally, our approach yields a uniform magnification result for the Minimum Circuit Size Problem (MCSP). This seems to sidestep the localization barrier.
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
Venue: arXiv cs.CV 2025
Year: 2025

Image registration is fundamental in medical imaging, enabling precise alignment of anatomical structures for diagnosis, treatment planning, image-guided interventions, and longitudinal monitoring. This work introduces IMPACT (Image Metric with Pretrained model-Agnostic Comparison for Transmodality registration), a novel similarity metric designed for robust multimodal image registration. Rather than relying on raw intensities, handcrafted descriptors, or task-specific training, IMPACT defines a semantic similarity measure based on the comparison of deep features extracted from large-scale pretrained segmentation models. By leveraging representations from models such as TotalSegmentator, Segment Anything (SAM), and other foundation networks, IMPACT provides a task-agnostic, training-free solution that generalizes across imaging modalities. These features, originally trained for segmentation, offer strong spatial correspondence and semantic alignment capabilities, making them naturally suited for registration. The method integrates seamlessly into both algorithmic (Elastix) and learning-based (VoxelMorph) frameworks, leveraging the strengths of each. IMPACT was evaluated on five challenging 3D registration tasks involving thoracic CT/CBCT and pelvic MR/CT datasets. Quantitative metrics, including Target Registration Error and Dice Similarity Coefficient, demonstrated consistent improvements in anatomical alignment over baseline methods. Qualitative analyses further highlighted the robustness of the proposed metric in the presence of noise, artifacts, and modality variations. With its versatility, efficiency, and strong performance across diverse tasks, IMPACT offers a powerful solution for advancing multimodal image registration in both clinical and research settings.