OpenRaiser/Intern-Atlas huggingface dataset:
Nishpaksh: TEC Standard-Compliant Framework for Fairness Auditing and Certification of AI Models
Venue: arXiv cs.CY 2026
Year: 2026

The growing reliance on Artificial Intelligence (AI) models in high-stakes decision-making systems, particularly within emerging telecom and 6G applications, underscores the urgent need for transparent and standardized fairness assessment frameworks. While global toolkits such as IBM AI Fairness 360 and Microsoft Fairlearn have advanced bias detection, they often lack alignment with region-specific regulatory requirements and national priorities. To address this gap, we propose Nishpaksh, an indigenous fairness evaluation tool that operationalizes the Telecommunication Engineering Centre (TEC) Standard for the Evaluation and Rating of Artificial Intelligence Systems. Nishpaksh integrates survey-based risk quantification, contextual threshold determination, and quantitative fairness evaluation into a unified, web-based dashboard. The tool employs vectorized computation, reactive state management, and certification-ready reporting to enable reproducible, audit-grade assessments, thereby addressing a critical post-standardization implementation need. Experimental validation on the COMPAS dataset demonstrates Nishpaksh's effectiveness in identifying attribute-specific bias and generating standardized fairness scores compliant with the TEC framework. The system bridges the gap between research-oriented fairness methodologies and regulatory AI governance in India, marking a significant step toward responsible and auditable AI deployment within critical infrastructure like telecommunications.
Reward-Forcing: Autoregressive Video Generation with Reward Feedback
Venue: arXiv cs.CV 2026
Year: 2026

While most prior work in video generation relies on bidirectional architectures, recent efforts have sought to adapt these models into autoregressive variants to support near real-time generation. However, such adaptations often depend heavily on teacher models, which can limit performance, particularly in the absence of a strong autoregressive teacher, resulting in output quality that typically lags behind their bidirectional counterparts. In this paper, we explore an alternative approach that uses reward signals to guide the generation process, enabling more efficient and scalable autoregressive generation. By using reward signals to guide the model, our method simplifies training while preserving high visual fidelity and temporal consistency. Through extensive experiments on standard benchmarks, we find that our approach performs comparably to existing autoregressive models and, in some cases, surpasses similarly sized bidirectional models by avoiding constraints imposed by teacher architectures. For example, on VBench, our method achieves a total score of 84.92, closely matching state-of-the-art autoregressive methods that score 84.31 but require significant heterogeneous distillation.
AERO: Adaptive and Efficient Runtime-Aware OTA Updates for Energy-Harvesting IoT
Venue: arXiv cs.AR 2026
Year: 2026

Energy-harvesting (EH) Internet of Things (IoT) devices operate under intermittent energy availability, which disrupts task execution and makes energy-intensive over-the-air (OTA) updates particularly challenging. Conventional OTA update mechanisms rely on reboots and incur significant overhead, rendering them unsuitable for intermittently powered systems. Recent live OTA update techniques reduce reboot overhead but still lack mechanisms to ensure consistency when updates interact with runtime execution. This paper presents AERO, an Adaptive and Efficient Runtime-Aware OTA update mechanism that integrates update tasks into the device's Directed Acyclic Graph (DAG) and schedules them alongside routine tasks under energy and timing constraints. By identifying update-affected execution regions and dynamically adjusting dependencies, AERO ensures consistent up date integration while adapting to intermittent energy availability. Experiments on representative workloads demonstrate improved update reliability and efficiency compared to existing live update approaches.
DataStates-LLM: Scalable Checkpointing for Transformer Models Using Composable State Providers
Venue: arXiv cs.DC 2026
Year: 2026

The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitated training across thousands of GPUs using complex hybrid parallelism strategies (e.g., data, tensor, and pipeline parallelism). Checkpointing this massive, distributed state is critical for a wide range of use cases, such as resilience, suspend-resume, investigating undesirable training trajectories, and explaining model evolution. However, existing checkpointing solutions typically treat model state as opaque binary blobs, ignoring the ``3D heterogeneity'' of the underlying data structures--varying by memory location (GPU vs. Host), number of ``logical'' objects sharded and split across multiple files, data types (tensors vs. Python objects), and their serialization requirements. This results in significant runtime overheads due to blocking device-to-host transfers, data-oblivious serialization, and storage I/O contention. In this paper, we introduce DataStates-LLM, a novel checkpointing architecture that leverages State Providers to decouple state abstraction from data movement. DataStates-LLM exploits the immutability of model parameters during the forward and backward passes to perform ``lazy'', non-blocking asynchronous snapshots. By introducing State Providers, we efficiently coalesce fragmented, heterogeneous shards and overlap the serialization of metadata with bulk tensor I/O. We evaluate DataStates-LLM on models up to 70B parameters on 256 A100-40GB GPUs. Our results demonstrate that DataStates-LLM achieves up to 4$\times$ higher checkpointing throughput and reduces end-to-end training time by up to 2.2$\times$ compared to state-of-the-art solutions, effectively mitigating the serialization and heterogeneity bottlenecks in extreme-scale LLM training.
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts
Venue: arXiv cs.AI 2026
Year: 2026

Geospatial reasoning is essential for real-world applications such as urban analytics, transportation planning, and disaster response. However, existing LLM-based agents often fail at genuine geospatial computation, relying instead on web search or pattern matching while hallucinating spatial relationships. We present Spatial-Agent, an AI agent grounded in foundational theories of spatial information science. Our approach formalizes geo-analytical question answering as a concept transformation problem, where natural-language questions are parsed into executable workflows represented as GeoFlow Graphs -- directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. Drawing on spatial information theory, Spatial-Agent extracts spatial concepts, assigns functional roles with principled ordering constraints, and composes transformation sequences through template-based generation. Extensive experiments on MapEval-API and MapQA benchmarks demonstrate that Spatial-Agent significantly outperforms existing baselines including ReAct and Reflexion, while producing interpretable and executable geospatial workflows.
Automated Classification of Research Papers Toward Sustainable Development Goals: A Boolean Query-Based Computational Framework
Venue: arXiv cs.DL 2026
Year: 2026

The rapid expansion of scholarly publications across diverse disciplines has made it increasingly difficult to systematically evaluate how research contributes to the United Nations Sustainable Development Goals (SDGs). Domain classification of research articles done manually through research experts is extremely impractical because of the number of publications, expensive in time and may not be consistent when done by human beings. This paper proposes an automated and rule-based computational model of classifying research papers based on SDGs with expert curated Boolean query mappings to overcome these challenges. The proposed system has a web-based interface to input data and display results, a backend application programming interface to do high throughput processing, and a Python-based classification engine which uses structured Boolean expressions to process bibliographic metadata (titles, abstracts, and keywords). The framework can be used to support single-paper-based classification and batch-based classification as well as offer clear and understandable outputs that clearly show what query parts motivated each SDG assignment. The experimental testing on massive bibliographic data sets has shown that the system can process thousands of research records in an hour with reproducible and consistent results. The proposed approach provides a viable solution to institutions, researchers and policymakers who are interested in analysis of research alignment with the goal of sustainability in a systematic fashion that would not involve the use of machine learning models whose inputs and outputs are not easily understandable.
pyBiblioNet: a Python library for a comprehensive network-based bibliometric analysis
Venue: arXiv cs.DL 2026
Year: 2026

Bibliometric analysis is a critical tool for understanding the structure, dynamics, and impact of scientific research. Traditional methods often fall short in capturing the intricate relationships and evolving trends within scientific literature. To address this gap, we present pyBiblioNet, a Python library designed to facilitate comprehensive network-based bibliometric analysis, providing insights into citation networks, co-authorship networks, and keyword co-occurrence networks. The library integrates with OpenAlex, a popular and open catalogue to the global research system, enabling users to easily preprocess, visualize, and analyse bibliometric data. Key features include topic selection, automatic data download via OpenAlex APIs, creation of the root and base sets of manuscripts to analyze, creation of the citation and co-authorship networks, network visualization tools, and a suite of algorithms for computing network centralities, clustering, and community detection, all of them tailored to the bibliometric domain. Additionally, it enables the analysis of key topics and concepts using NLP techniques. We showcase the main functions of the library by performing a bibliometric analysis on the multidisciplinary "15-minute city paradigm", demonstrating the utility of pyBiblioNet in uncovering hidden patterns and emerging trends in various scientific domains. pyBiblioNet can empower researchers, librarians, and policymakers with a powerful, user-friendly tool for enhancing their bibliometric analyses and making data-driven decisions.
Lex Reformatica: Five Principles of Policy Reform for the Technological Age
Venue: arXiv cs.CY 2026
Year: 2026

Twenty-five years ago, Joel Reidenberg argued that technology itself, not just law and regulation, imposes rules on communities in the Information Society. System design choices like network architecture and configurations create regulatory norms he termed "Lex Informatica"-referencing the merchant-driven medieval "Lex Mercatoria" that emerged independent of sovereign control. Today we face different challenges requiring us to revisit Reidenberg's insights and examine the consequences of that earlier era. While Lex Informatica provided a framework for analyzing the internet's birth, we now confront the aftereffects of decades of minimal or absent regulation. Critical questions emerge: When technological social norms develop outside clear legal restraints, who benefits and who suffers? This new era demands infrastructural reform focused on the interplay between public and private regulation and self-regulation, weighing both costs and benefits. Rather than showcasing the promise of yesterday's internet age, today's events reveal the pitfalls of information libertarianism and underscore the urgent need for new approaches to information regulation. This Issue presents articles from two symposiums-one on Lex Informatica and another on race and technology law. Their conversation is now essential. Together, these papers demonstrate what I call the "Lex Reformatica" of today's digital age. This collection shows why scholars, lawyers, and legislators must return to Reidenberg's foundational work and update its trajectory toward a reform-focused approach designed for our current era.
How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications
Venue: arXiv cs.DL 2026
Year: 2026

With the rising popularity of interdisciplinary work and increasing institutional incentives in this direction, there is a growing need to understand how resulting publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keywords, and citation patterns, do not account for how individual citations are used to advance the citing work. Although, in line with addressing this gap, prior studies have proposed taxonomies to classify citation purpose, these frameworks are not well-suited to interdisciplinary research and do not provide quantitative measures of citation engagement quality. To address these limitations, we propose a framework for the evaluation of citation engagement in interdisciplinary Natural Language Processing (NLP) publications. Our approach introduces a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study. We demonstrate the utility of this framework through a thorough analysis of publications at the intersection of NLP and Computational Social Science.
Ensuring Computer Science Learning in the AI Era: Open Generative AI Policies and Assignment-Driven Written Quizzes
Venue: arXiv cs.CY 2026
Year: 2026

The widespread availability of generative artificial intelligence (GenAI) has created a pressing challenge in computer science (CS) education: how to incorporate powerful AI tools into programming coursework without undermining student learning through cognitive offloading. This paper presents an assessment model that permits the use of generative AI for take-home programming assignments while enforcing individual mastery through immediate, assignment-driven written quizzes. To promote authentic learning, these in-class, closed-book assessments are weighted more heavily than the assignments themselves and are specifically designed to verify the student's comprehension of the algorithms, structure, and implementation details of their submitted code. Preliminary empirical data were collected from an upper-level computer science course to examine the relationship between self-reported GenAI usage and performance on AI-free quizzes, exams, and final course grades. Statistical analyses revealed no meaningful linear correlation between GenAI usage levels and assessment outcomes, with Pearson correlation coefficients consistently near zero. These preliminary results suggest that allowing GenAI for programming assignments does not diminish students' mastery of course concepts when learning is verified through targeted, assignment-driven quizzes. Although limited by a small sample size, this study provides preliminary evidence that the risks of cognitive offloading can be mitigated by allowing AI-assisted programming practice while verifying understanding through assignment-driven, AI-free quizzes. The findings support the responsible adoption of open GenAI policies in upper-level CS courses, when paired with rigorous, independent assessment mechanisms.
Parallel Algorithm For Finding The Minimum s/t Cut in a Structured 3-Dimensional Proper Order Graph
Venue: arXiv cs.DS 2026
Year: 2026

We present a parallel algorithm for computing the minimum s-t cut in structured 3-dimensional proper order graphs arising from image segmentation problems. Proper order graphs are multi-column structures where vertices are arranged in parallel columns, with each vertex connected to consecutive vertices in adjacent columns. This graph structure naturally arises in surface extraction problems for geological horizon segmentation in seismic imaging volumes. We develop two parallel approaches: a hierarchical merging variant of the Boykov-Kolmogorov algorithm, and a novel parallel push-relabel algorithm with level synchronized global relabeling. Our primary contribution is the push-relabel variant, which partitions the graph into segments along columns with processor affinity, eliminating the need for a global shared queue. We introduce level synchronized global relabeling that enables concurrent label updates while maintaining correctness through barriers at each frontier level.
PALMA: A Lightweight Tropical Algebra Library for ARM-Based Embedded Systems
Venue: arXiv cs.MS 2026
Year: 2026

Tropical algebra, including max-plus, min-plus, and related idempotent semirings, provides a unifying framework in which many optimization problems that are nonlinear in classical algebra become linear. This property makes tropical methods particularly well suited for shortest paths, scheduling, throughput analysis, and discrete event systems. Despite their theoretical maturity and practical relevance, existing tropical algebra implementations primarily target desktop or server environments and remain largely inaccessible on resource-constrained embedded platforms, where such optimization problems are most acute. We present PALMA (Parallel Algebra Library for Max-plus Applications), a lightweight, dependency-free C library that brings tropical linear algebra to ARM-based embedded systems. PALMA implements a generic semiring abstraction with SIMD-accelerated kernels, enabling a single computational framework to support shortest paths, bottleneck paths, reachability, scheduling, and throughput analysis. The library supports five tropical semirings, dense and sparse (CSR) representations, tropical closure, and spectral analysis via maximum cycle mean computation. We evaluate PALMA on a Raspberry Pi 4 and demonstrate peak performance of 2,274 MOPS, speedups of up to 11.9 times over classical Bellman-Ford for single-source shortest paths, and sub-10 microsecond scheduling solves for real-time control workloads. Case studies in UAV control, IoT routing, and manufacturing systems show that tropical algebra enables efficient, predictable, and unified optimization directly on embedded hardware. PALMA is released as open-source software under the MIT license.
Single-Pixel Vision-Language Model for Intrinsic Privacy-Preserving Behavioral Intelligence
Venue: arXiv cs.CV 2026
Year: 2026

Adverse social interactions, such as bullying, harassment, and other illicit activities, pose significant threats to individual well-being and public safety, leaving profound impacts on physical and mental health. However, these critical events frequently occur in privacy-sensitive environments like restrooms, and changing rooms, where conventional surveillance is prohibited or severely restricted by stringent privacy regulations and ethical concerns. Here, we propose the Single-Pixel Vision-Language Model (SP-VLM), a novel framework that reimagines secure environmental monitoring. It achieves intrinsic privacy-by-design by capturing human dynamics through inherently low-dimensional single-pixel modalities and inferring complex behavioral patterns via seamless vision-language integration. Building on this framework, we demonstrate that single-pixel sensing intrinsically suppresses identity recoverability, rendering state-of-the-art face recognition systems ineffective below a critical sampling rate. We further show that SP-VLM can nonetheless extract meaningful behavioral semantics, enabling robust anomaly detection, people counting, and activity understanding from severely degraded single-pixel observations. Combining these findings, we identify a practical sampling-rate regime in which behavioral intelligence emerges while personal identity remains strongly protected. Together, these results point to a human-rights-aligned pathway for safety monitoring that can support timely intervention without normalizing intrusive surveillance in privacy-sensitive spaces.
Synthetic Data Guided Feature Selection for Robust Activity Recognition in Older Adults
Venue: arXiv cs.CV 2026
Year: 2026

Physical activity during hip fracture rehabilitation is essential for mitigating long-term functional decline in geriatric patients. However, it is rarely quantified in clinical practice. Existing continuous monitoring systems with commercially available wearable activity trackers are typically developed in middle-aged adults and therefore perform unreliably in older adults with slower and more variable gait patterns. This study aimed to develop a robust human activity recognition (HAR) system to improve continuous physical activity recognition in the context of hip fracture rehabilitation. 24 healthy older adults aged over 80 years were included to perform activities of daily living (walking, standing, sitting, lying down, and postural transfers) under simulated free-living conditions for 75 minutes while wearing two accelerometers positioned on the lower back and anterior upper thigh. Model robustness was evaluated using leave-one-subject-out cross-validation. The synthetic data demonstrated potential to improve generalization across participants. The resulting feature intervention model (FIM), aided by synthetic data guidance, achieved reliable activity recognition with mean F1-scores of 0.896 for walking, 0.927 for standing, 0.997 for sitting, 0.937 for lying down, and 0.816 for postural transfers. Compared with a control condition model without synthetic data, the FIM significantly improved the postural transfer detection, i.e., an activity class of high clinical relevance that is often overlooked in existing HAR literature. In conclusion, these preliminary results demonstrate the feasibility of robust activity recognition in older adults. Further validation in hip fracture patient populations is required to assess the clinical utility of the proposed monitoring system.
Failing on Bias Mitigation: Investigating Why Predictive Models Struggle with Government Data
Venue: arXiv cs.CY 2026
Year: 2026

The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a case study, we examine how these issues persist. Rather than auditing real-world deployed systems, our goal is to understand why widely adopted bias mitigation techniques often fail when applied to government data. Our findings reveal that bias mitigation approaches applied to government data are not always effective -- not because of flaws in model architecture or metric selection, but due to the inherent properties of the data itself. Through comparing a set of comprehensive models and fairness methods, our experiments consistently show that the mitigation efforts cannot overcome the embedded unfairness in the data -- further reinforcing that the origin of bias lies in the structure and history of government datasets. We then explore the reasons for the mitigation failures in predictive models on government data and highlight the potential sources of unfairness posed by data distribution shifts, the accumulation of historical bias, and delays in data release. We also discover the limitations of the blind spots in fairness analysis and bias mitigation methods when only targeting a single sensitive feature through a set of intersectional fairness experiments. Although this study is limited to one city, the findings are highly suggestive, which can contribute to an early warning that biases in government data may persist even with standard mitigation methods.