OpenRaiser/Intern-Atlas huggingface dataset:
Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions among various components. Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids. However, RL needs to iterate compulsively throughout a given environment to obtain the optimal policy. This means obtaining samples from a, most likely, costly simulator, which can lead to a sample efficiency problem. In this work, we address this problem by substituting costly smart grid simulators with surrogate models built using Phisics-informed Neural Networks (PINNs), optimizing the RL policy training process by arriving to convergent results in a fraction of the time employed by the original environment.
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students' grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce EduAdapt, a benchmark of nearly 48k grade-labeled QA pairs across nine science subjects, spanning Grades 1-12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs on EduAdapt and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1-5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt code and datasets are publicly available at https://github.com/NaumanNaeem/EduAdapt.
Process Automation Architecture Using RFID for Transparent Voting Systems
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents the development of a process automation architecture leveraging Radio Frequency Identification (RFID) technology for secure, transparent and efficient voting systems. The proposed architecture automates the voting workflow through RFID-enabled voter identification, encrypted vote casting, and secure data transmission. Each eligible voter receives a smart RFID card containing a uniquely encrypted identifier, which is verified using an RC522 reader interfaced with a microcontroller. Upon successful verification, the voter interacts with a touchscreen interface to cast a vote, which is then encrypted using AES-128 and securely stored on a local SD card or transmitted via GSM to a central server. A tamper-proof monitoring mechanism records each session with time-stamped digital signatures, ensuring auditability and data integrity. The architecture is designed to function in both online and offline modes, with an automated batch synchronization mechanism that updates vote records once network connectivity is restored. System testing in simulated environments confirmed 100% voter authentication accuracy, minimized latency (average voting time of 11.5 seconds), and robustness against cloning, double voting, and data interception. The integration of real-time monitoring and secure process control modules enables electoral authorities to automate data logging, detect anomalies, and validate system integrity dynamically. This work demonstrates a scalable, automation-driven solution for voting infrastructure, offering enhanced transparency, resilience, and deployment flexibility, especially in environments where digital transformation of electoral processes is critically needed.
Is It Worth to Use Feedback Channel in 5G V2X Platoon Scenarios?
Venue: arXiv cs.NI 2025
Year: 2025

5G Vehicle-to-Everything (V2X) is a new technology developed by 3GPP to support inter-vehicle communication. In contrast to 4G V2X which allows only broadcast communication, 5G V2X enables groupcast and unicast communication. Such types of communication are needed for new V2X scenarios: platooning, extended sensors, remote driving, etc. To improve the data transmission reliability and assist in the selection of the transmission parameters in these scenarios, 5G V2X introduces a feedback channel that allows receivers to send acknowledgments in response to data packets. However, some part of the overall resource shall be allocated for the feedback channel, which reduces the amount of channel resources available for data transmission. In this paper, we consider a scenario with a platoon, which generates groupcast traffic, and surrounding vehicles, which generate legacy broadcast traffic. Using extensive simulations in NS-3, we analyze how the usage of the feedback channel influences the overall system capacity. Our results show that depending on the platoon size, groupcast, and broadcast traffic intensities, and their quality of service requirements, the usage of the feedback channel can in some cases significantly increase the system capacity (up to 2x), while in other cases it almost halves the system capacity. We explain the reasons for such effects and discuss how to adaptively select the feedback channel parameters.
A Conditional Diffusion Model for Probabilistic Prediction of Battery Capacity Degradation
Venue: arXiv cs.LG 2025
Year: 2025

Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a novel method, termed the Condition Diffusion U-Net with Attention (CDUA), which integrates feature engineering and deep learning to address this challenge. The proposed approach employs a diffusion-based generative model for time-series forecasting and incorporates attention mechanisms to enhance predictive performance. Battery capacity is first derived from real-world vehicle operation data. The most relevant features are then identified using the Pearson correlation coefficient and the XGBoost algorithm. These features are used to train the CDUA model, which comprises two core components: (1) a contextual U-Net with self-attention to capture complex temporal dependencies, and (2) a denoising network to reconstruct accurate capacity values from noisy observations. Experimental validation on the real-world vehicle data demonstrates that the proposed CDUA model achieves a relative Mean Absolute Error (MAE) of 0.94% and a relative Root Mean Square Error (RMSE) of 1.14%, with a narrow 95% confidence interval of 3.74% in relative width. These results confirm that CDUA provides both accurate capacity estimation and reliable uncertainty quantification. Comparative experiments further verify its robustness and superior performance over existing mainstream approaches.
Scalable CI/CD for Legacy Modernization: An Industrial Experience Addressing Internal Challenges Related to the 2025 Japan Cliff
Venue: arXiv cs.SE 2025
Year: 2025

We have developed a Scalable CI/CD Pipeline to address internal challenges related to Japan 2025 cliff problem, a critical issue where the mass end of service life of legacy core IT systems threatens to significantly increase the maintenance cost and black box nature of these system also leads to difficult update moreover replace, which leads to lack of progress in Digital Transformation (DX). If not addressed, Japan could potentially lose up to 12 trillion yen per year after 2025, which is 3 times more than the cost in previous years. Asahi also faced the same internal challenges regarding legacy system, where manual maintenance workflows and limited QA environment have left critical systems outdated and difficult to update. Middleware and OS version have remained unchanged for years, leading to now its nearing end of service life which require huge maintenance cost and effort to continue its operation. To address this problem, we have developed and implemented a Scalable CI/CD Pipeline where isolated development environments can be created and deleted dynamically and is scalable as needed. This Scalable CI/CD Pipeline incorporate GitHub for source code control and branching, Jenkins for pipeline automation, Amazon Web Services for scalable environment, and Docker for environment containerization. This paper presents the design and architecture of the Scalable CI/CD Pipeline, with the implementation along with some use cases. Through Scalable CI/CD, developers can freely and safely test maintenance procedures and do experiments with new technology in their own environment, reducing maintenance cost and drive Digital Transformation (DX). key words: 2025 Japan Cliff, Scalable CI/CD, DevOps, Legacy IT Modernization.
Castor Ministerialis
Venue: arXiv cs.FL 2025
Year: 2025

The famous problem of Busy Beavers can be stated as the question on how long a $n$-state Turing machine (using a 2-symbol alphabet or -- in a generalization -- a $m$-symbol alphabet) can run if it is started on the blank tape before it holds. Thus, not halting Turing machines are excluded. For up to four states the answer to this question is well-known. Recently, it could be verified that the widely assumed candidate for five states is in fact the champion. And there is progress in searching for good candidates with six or more states. We investigate a variant of this problem: Additionally to the requirement that the Turing machines have to start from the blank tape we only consider such Turing machines that hold on the blank tape, too. For this variant we give definitive answers on how long such a Turing machine with up to five states can run, analyze the behavior of a six-states candidate and give some findings on the generalization of Turing-machines with $m$-symbol alphabet.
Adaptive Local Combining with Decentralized Decoding for Distributed Massive MIMO
Venue: arXiv cs.NI 2025
Year: 2025

A major bottleneck in uplink distributed massive multiple-input multiple-output networks is the sub-optimal performance of local combining schemes, coupled with high fronthaul load and computational cost inherent in centralized large scale fading decoding (LSFD) architectures. This paper introduces a decentralized decoding architecture that fundamentally breaks from the LSFD, by allowing each access point (AP) to calculate interference-suppressing local weights independently and apply them to its data estimates before transmission. Furthermore, two generalized local zero-forcing (ZF) frameworks, generalized partial full-pilot ZF (G-PFZF) and generalized protected weak PFZF (G-PWPFZF), are introduced, where each AP adaptively and independently determines its combining strategy through a local sum spectral efficiency (SE) optimization that classifies user equipments (UEs) as strong or weak, eliminating the fixed thresholds used in the PFZF and PWPFZF schemes. To enhance scalability, pilot-dependent combining vectors instead of user-dependent ones are introduced and are shared among users with the same pilot. The closed-form SE expressions corresponding to the proposed schemes are derived. Numerical results show that the proposed schemes consistently outperform fixed-threshold counterparts, while the introduction of local weights yields lower overheads and computation costs with lower performance penalty compared to them.
Initialize to Generalize: A Stronger Initialization Pipeline for Sparse-View 3DGS
Venue: arXiv cs.CV 2025
Year: 2025

Sparse-view 3D Gaussian Splatting (3DGS) often overfits to the training views, leading to artifacts like blurring in novel view rendering. Prior work addresses it either by enhancing the initialization (\emph{i.e.}, the point cloud from Structure-from-Motion (SfM)) or by adding training-time constraints (regularization) to the 3DGS optimization. Yet our controlled ablations reveal that initialization is the decisive factor: it determines the attainable performance band in sparse-view 3DGS, while training-time constraints yield only modest within-band improvements at extra cost. Given initialization's primacy, we focus our design there. Although SfM performs poorly under sparse views due to its reliance on feature matching, it still provides reliable seed points. Thus, building on SfM, our effort aims to supplement the regions it fails to cover as comprehensively as possible. Specifically, we design: (i) frequency-aware SfM that improves low-texture coverage via low-frequency view augmentation and relaxed multi-view correspondences; (ii) 3DGS self-initialization that lifts photometric supervision into additional points, compensating SfM-sparse regions with learned Gaussian centers; and (iii) point-cloud regularization that enforces multi-view consistency and uniform spatial coverage through simple geometric/visibility priors, yielding a clean and reliable point cloud. Our experiments on LLFF and Mip-NeRF360 demonstrate consistent gains in sparse-view settings, establishing our approach as a stronger initialization strategy. Code is available at https://github.com/zss171999645/ItG-GS.
Deep Self-Evolving Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprietary models to solve Olympiad-level problems, their effectiveness hinges on strong, reliable verification and correction capabilities, which remain fragile in open-weight, smaller-scale models. This work demonstrates that even with weak verification and refinement capabilities on hard tasks, the reasoning limits of such models can be substantially extended through a probabilistic paradigm we call Deep Self-Evolving Reasoning (DSER). We conceptualize iterative reasoning as a Markov chain, where each step represents a stochastic transition in the solution space. The key insight is that convergence to a correct solution is guaranteed as long as the probability of improvement marginally exceeds that of degradation. By running multiple long-horizon, self-evolving processes in parallel, DSER amplifies these small positive tendencies, enabling the model to asymptotically approach correct answers. Empirically, we apply DSER to the DeepSeek-R1-0528-Qwen3-8B model. On the challenging AIME 2024-2025 benchmark, DSER solves 5 out of 9 previously unsolvable problems and boosts overall performance, enabling this compact model to surpass the single-turn accuracy of its 600B-parameter teacher through majority voting. Beyond its immediate utility for test-time scaling, the DSER framework serves to diagnose the fundamental limitations of current open-weight reasoners. By clearly delineating their shortcomings in self-verification, refinement, and stability, our findings establish a clear research agenda for developing next-generation models with powerful, intrinsic self-evolving capabilities.
Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization
Venue: arXiv cs.CV 2025
Year: 2025

We propose a rubric-guided, pseudo-labeled, and prompt-driven zero-shot video summarization framework that bridges large language models with structured semantic reasoning. A small subset of human annotations is converted into high-confidence pseudo labels and organized into dataset-adaptive rubrics defining clear evaluation dimensions such as thematic relevance, action detail, and narrative progression. During inference, boundary scenes, including the opening and closing segments, are scored independently based on their own descriptions, while intermediate scenes incorporate concise summaries of adjacent segments to assess narrative continuity and redundancy. This design enables the language model to balance local salience with global coherence without any parameter tuning. Across three benchmarks, the proposed method achieves stable and competitive results, with F1 scores of 57.58 on SumMe, 63.05 on TVSum, and 53.79 on QFVS, surpassing zero-shot baselines by +0.85, +0.84, and +0.37, respectively. These outcomes demonstrate that rubric-guided pseudo labeling combined with contextual prompting effectively stabilizes LLM-based scoring and establishes a general, interpretable, and training-free paradigm for both generic and query-focused video summarization.
Insum: Sparse GPU Kernels Simplified and Optimized with Indirect Einsums
Venue: arXiv cs.PL 2025
Year: 2025

Programming high-performance sparse GPU kernels is notoriously difficult, requiring both substantial effort and deep expertise. Sparse compilers aim to simplify this process, but existing systems fall short in two key ways. First, they are primarily designed for CPUs and rarely produce high-performance GPU code. Second, when computations involve both sparse and dense regions, these compilers often fail to optimize the dense portions effectively. In this paper, we propose a new approach for expressing sparse computations. We start from format-agnostic Einsums over sparse tensors and rewrite them into format-conscious indirect Einsums, which explicitly encode format information by mapping sparse data and metadata onto dense tensor operations through indirect indexing. To execute indirect Einsums, we introduce the Insum compiler, which generates efficient GPU code for these Einsums by lowering to the PyTorch compiler, extended to better support Tensor Core-enabled indirect Einsums. We also present two fixed-length sparse formats, GroupCOO and BlockGroupCOO, designed to fit naturally with indirect Einsums. Our approach achieves 1.14x to 3.81x speedups across a range of sparse GPU applications while reducing lines of code by 202x to 4491x compared to hand-written implementations.
Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares
Venue: arXiv cs.LG 2025
Year: 2025

Classical optimisation theory guarantees monotonic objective decrease for gradient descent (GD) when employed in a small step size, or ``stable", regime. In contrast, gradient descent on neural networks is frequently performed in a large step size regime called the ``edge of stability", in which the objective decreases non-monotonically with an observed implicit bias towards flat minima. In this paper, we take a step toward quantifying this phenomenon by providing convergence rates for gradient descent with large learning rates in an overparametrised least squares setting. The key insight behind our analysis is that, as a consequence of overparametrisation, the set of global minimisers forms a Riemannian manifold $M$, which enables the decomposition of the GD dynamics into components parallel and orthogonal to $M$. The parallel component corresponds to Riemannian gradient descent on the objective sharpness, while the orthogonal component is a bifurcating dynamical system. This insight allows us to derive convergence rates in three regimes characterised by the learning rate size: (a) the subcritical regime, in which transient instability is overcome in finite time before linear convergence to a suboptimally flat global minimum; (b) the critical regime, in which instability persists for all time with a power-law convergence toward the optimally flat global minimum; and (c) the supercritical regime, in which instability persists for all time with linear convergence to an orbit of period two centred on the optimally flat global minimum.
SAFE-D: A Spatiotemporal Detection Framework for Abnormal Driving Among Parkinson's Disease-like Drivers
Venue: arXiv cs.LG 2025
Year: 2025

A driver's health state serves as a determinant factor in driving behavioral regulation. Subtle deviations from normalcy can lead to operational anomalies, posing risks to public transportation safety. While prior efforts have developed detection mechanisms for functionally-driven temporary anomalies such as drowsiness and distraction, limited research has addressed pathologically-triggered deviations, especially those stemming from chronic medical conditions. To bridge this gap, we investigate the driving behavior of Parkinson's disease patients and propose SAFE-D, a novel framework for detecting Parkinson-related behavioral anomalies to enhance driving safety. Our methodology starts by performing analysis of Parkinson's disease symptomatology, focusing on primary motor impairments, and establishes causal links to degraded driving performance. To represent the subclinical behavioral variations of early-stage Parkinson's disease, our framework integrates data from multiple vehicle control components to build a behavioral profile. We then design an attention-based network that adaptively prioritizes spatiotemporal features, enabling robust anomaly detection under physiological variability. Finally, we validate SAFE-D on the Logitech G29 platform and CARLA simulator, using data from three road maps to emulate real-world driving. Our results show SAFE-D achieves 96.8% average accuracy in distinguishing normal and Parkinson-affected driving patterns.
Mitigating Clever Hans Strategies in Image Classifiers through Generating Counterexamples
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning models remain vulnerable to spurious correlations, leading to so-called Clever Hans predictors that undermine robustness even in large-scale foundation and self-supervised models. Group distributional robustness methods, such as Deep Feature Reweighting (DFR) rely on explicit group labels to upweight underrepresented subgroups, but face key limitations: (1) group labels are often unavailable, (2) low within-group sample sizes hinder coverage of the subgroup distribution, and (3) performance degrades sharply when multiple spurious correlations fragment the data into even smaller groups. We propose Counterfactual Knowledge Distillation (CFKD), a framework that sidesteps these issues by generating diverse counterfactuals, enabling a human annotator to efficiently explore and correct the model's decision boundaries through a knowledge distillation step. Unlike DFR, our method not only reweights the undersampled groups, but it also enriches them with new data points. Our method does not require any confounder labels, achieves effective scaling to multiple confounders, and yields balanced generalization across groups. We demonstrate CFKD's efficacy across five datasets, spanning synthetic tasks to an industrial application, with particularly strong gains in low-data regimes with pronounced spurious correlations. Additionally, we provide an ablation study on the effect of the chosen counterfactual explainer and teacher model, highlighting their impact on robustness.