OpenRaiser/Intern-Atlas huggingface dataset:
AI-Assisted Authoring for Transparent, Data-Driven Documents
Venue: arXiv cs.HC 2025
Year: 2025

We introduce _transparent documents_, interactive web-based scholarly articles which allow readers to explore the relationship to the underlying data by hovering over fragments of text, and present an LLM-based tool for authoring transparent documents, building on recent developments in data provenance for general-purpose programming languages. As a target platform, our implementation uses Fluid, an open source programming language with a provenance-tracking runtime. Our agent-based tool supports a human author during the creation of transparent documents, identifying fragments of text which can be computed from data, such as numerical values selected from records or computed by aggregations like sum and mean, comparatives and superlatives like _better than_ and _largest_, trend-adjectives like _growing_, and similar quantitative or semi-quantitative phrases, and then attempts to synthesise a suitable Fluid query over the data which generates the target string. The resulting expression is inserted into the article's web page, turning the static text fragment into an interactable data-driven element able to reveal the data that underwrites the natural language claim. We evaluate our approach on a subset of SciGen, an open source dataset consisting of tables from scientific articles and their corresponding descriptions, which we extend with hand-generated counterfactual test cases to evaluate how well machine-generated expressions generalise. Our results show that gpt4o is often able to synthesise compound expressions extensionally compatible with our gold solutions.
Autonomous QA Agent: A Retrieval-Augmented Framework for Reliable Selenium Script Generation
Venue: arXiv cs.SE 2025
Year: 2025

Software testing is critical in the software development lifecycle, yet translating requirements into executable test scripts remains manual and error-prone. While Large Language Models (LLMs) can generate code, they often hallucinate non-existent UI elements. We present the Autonomous QA Agent, a Retrieval-Augmented Generation (RAG) system that grounds Selenium script generation in project-specific documentation and HTML structure. By ingesting diverse formats (Markdown, PDF, HTML) into a vector database, our system retrieves relevant context before generation. Evaluation on 20 e-commerce test scenarios shows our RAG approach achieves 100% (20/20) syntax validity and 90% (18/20, 95% CI: [85%, 95%], p < 0.001) execution success, compared to 30% for standard LLM generation. While our evaluation is limited to a single domain, our method significantly reduces hallucinations by grounding generation in actual DOM structure, demonstrating RAG's potential for automated UI testing.
Cognitive Sovereignty and the Neurosecurity Governance Gap: Evidence from Singapore
Venue: arXiv cs.CY 2025
Year: 2025

As brain computer interfaces (BCIs) transition from experimental medical systems to consumer and military adjacent technologies, they introduce a novel security domain in which the human nervous system becomes a networked and contestable substrate. Existing frameworks for cybersecurity, biomedical safety, and data protection were not designed to address adversarial threats to neural signal integrity, creating a governance gap characterized by systemic misclassification. This paper argues that cognition is becoming strategic infrastructure and is situated between the market driven diffusion of neurotechnology in the United States and the state integrated fusion of AI and brain science in China. Using Singapore as a critical stress test and applying institutional classification analysis and regulatory mandate mapping, this paper identifies a structural paradox. A state with high regulatory capacity in both cyber and biomedical domains remains vulnerable at their intersection due to a failure to classify the human mind as infrastructure. This paper introduces the concept of cognitive sovereignty defined as the strategic capacity to protect neural processes from external modulation and proposes a cognitive operational technology framework to secure the human mind as a distinct layer of critical national infrastructure.
Reliability and Admissibility of AI-Generated Forensic Evidence in Criminal Trials
Venue: arXiv cs.CY 2025
Year: 2025

This paper examines the admissibility of AI-generated forensic evidence in criminal trials. The growing adoption of AI presents promising results for investigative efficiency. Despite advancements, significant research gaps persist in practically understanding the legal limits of AI evidence in judicial processes. Existing literature lacks focused assessment of the evidentiary value of AI outputs. The objective of this study is to evaluate whether AI-generated evidence satisfies established legal standards of reliability. The methodology involves a comparative doctrinal legal analysis of evidentiary standards across common law jurisdictions. Preliminary results indicate that AI forensic tools can enhance scale of evidence analysis. However, challenges arise from reproducibility deficits. Courts exhibit variability in acceptance of AI evidence due to limited technical literacy and lack of standardized validation protocols. Liability implications reveal that developers and investigators may bear accountability for flawed outputs. This raises critical concerns related to wrongful conviction. The paper emphasizes the necessity of independent validation and, development of AI-specific admissibility criteria. Findings inform policy development for the responsible AI integration within criminal justice systems. The research advances the objectives of Sustainable Development Goal 16 by reinforcing equitable access to justice. Preliminary results contribute for a foundation for future empirical research in AI deployed criminal forensics.
Digital health transformation in Quebec: assessment of interoperability and governance strategies
Venue: arXiv cs.CY 2025
Year: 2025

The rapid expansion of health data has led to unprecedented information availability within healthcare systems. Health information systems (HIS) play a central role in managing this data and enabling improvements in care delivery, system performance, and population health monitoring. Maximizing the value of HIS, however, requires effective information exchange across systems, making interoperability a critical prerequisite. Despite its recognized benefits, interoperability remains a major challenge within Quebec's Health and Social Services Network, largely due to the heterogeneity and fragmentation of HIS across healthcare institutions. This paper assessed how Quebec's Plan sante addressed interoperability challenges, using the dimensions from the Healthcare Information and Management Systems Society (HIMSS): foundational, structural, semantic, and organizational interoperability. This study highlighted initiatives aimed at strengthening infrastructure and information system architecture to support foundational interoperability and showed persistent challenges at the structural and semantic levels, particularly those related to the adoption of standardized data formats and harmonization of clinical terminologies. Finally, significant implementation challenges that require coordinated change management were identified regarding the organizational interoperability. Overall, while the Plan sante demonstrates a clear commitment to technological modernization, it does not fully address the interoperability multidimensional nature. Achieving meaningful interoperability will require sustained efforts across technical, normative, and organizational domains beyond the strategies currently outlined. Recent governance developments, including the creation of Sante Quebec, add complexity to this evolving context and raise further questions regarding the coordination of interoperability governance.
Investigating How MacBook Accessories Evolve across Generations, and Their Potential Environmental, Economical Impacts
Venue: arXiv cs.CY 2025
Year: 2025

The technological transition of MacBook charging solutions from MagSafe to USB-C, followed by a return to MagSafe 3, encapsulates the dynamic interplay between technological advancement, environmental considerations, and economic factors. This study delves into the broad implications of these charging technology shifts, particularly focusing on the environmental repercussions associated with electronic waste and the economic impacts felt by both manufacturers and consumers. By investigating the lifecycle of these technologies - from development and market introduction through to their eventual obsolescence - this paper underscores the importance of devising strategies that not only foster technological innovation but also prioritize environmental sustainability and economic feasibility. This comprehensive analysis illuminates the crucial factors influencing the evolution of charging technologies and their wider societal and environmental implications, advocating for a balanced approach that ensures technological progress does not compromise ecological health or economic stability.
Islamic Chatbots in the Age of Large Language Models
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) are rapidly transforming how communities access, interpret, and circulate knowledge, and religious communities are no exception. Chatbots powered by LLMs are beginning to reshape authority, pedagogy, and everyday religious practice in Muslim communities. We analyze the landscape of LLM powered Islamic chatbots and how they are transforming Islamic religious practices e.g., democratizing access to religious knowledge but also running the risk of erosion of authority. We discuss what kind of challenges do these systems raise for Muslim communities and explore recommendations for the responsible design of these systems.
Consistency-Aware Editing for Entity-level Unlearning in Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) risk retaining sensitive, copyrighted, or harmful information from their training data. Entity-level unlearning addresses this issue by removing all knowledge of a specific entity while preserving the model's overall capabilities. Existing approaches typically rely on full-model fine-tuning or prompt-based interventions, which can be computationally expensive or brittle when handling paraphrased queries. Recently, model editing has emerged as an efficient alternative for updating knowledge in LLMs, offering a promising direction for unlearning. However, existing editing techniques are typically designed for instance-level updates, modifying responses to specific attributes of an entity rather than eliminating all knowledge associated with the entity. In this paper, we investigate how editing techniques can be adapted for effective and efficient entity-level unlearning. To this end, we introduce a novel consistency-aware editing (CAE) framework. CAE aggregates a diverse set of prompts related to a target entity, including its attributes, relations, and adversarial paraphrases. It then jointly learns a low-rank update guided by a consistency regularizer that aligns the editing directions across prompts. This promotes robust and comprehensive forgetting while minimizing interference with unrelated knowledge. We further examine where different entities are stored within the model and how many diverse prompts are needed for successful unlearning. We evaluate CAE on two challenging benchmarks, RWKU and ToFU, and demonstrate that it (i) provides insights into how entity-level knowledge is internally represented and deleted in LLMs, (ii) significantly improves forgetting accuracy and robustness over traditional unlearning and editing baselines, and (iii) enables scalable entity removal using only tens of carefully selected prompts.
Triples and Knowledge-Infused Embeddings for Clustering and Classification of Scientific Documents
Venue: arXiv cs.CL 2025
Year: 2025

The increasing volume and complexity of scientific literature demand robust methods for organizing and understanding research documents. In this study, we explore how structured knowledge, specifically, subject-predicate-object triples, can enhance the clustering and classification of scientific papers. We propose a modular pipeline that combines unsupervised clustering and supervised classification over multiple document representations: raw abstracts, extracted triples, and hybrid formats that integrate both. Using a filtered arXiv corpus, we extract relational triples from abstracts and construct four text representations, which we embed using four state-of-the-art transformer models: MiniLM, MPNet, SciBERT, and SPECTER. We evaluate the resulting embeddings with KMeans, GMM, and HDBSCAN for unsupervised clustering, and fine-tune classification models for arXiv subject prediction. Our results show that full abstract text yields the most coherent clusters, but that hybrid representations incorporating triples consistently improve classification performance, reaching up to 92.6% accuracy and 0.925 macro-F1. We also find that lightweight sentence encoders (MiniLM, MPNet) outperform domain-specific models (SciBERT, SPECTER) in clustering, while SciBERT excels in structured-input classification. These findings highlight the complementary benefits of combining unstructured text with structured knowledge, offering new insights into knowledge-infused representations for semantic organization of scientific documents.
Resisting Correction: How RLHF Makes Language Models Ignore External Safety Signals in Natural Conversation
Venue: arXiv cs.CL 2025
Year: 2025

Safety architectures for language models increasingly rely on external monitors to detect errors and inject corrective signals at inference time. For such systems to function in interactive settings, models must be able to incorporate externally provided confidence information into their verbal responses. In this work, we test whether instruction-tuned language models preserve this controllability across different interaction modes. Using Llama-3.2-3B on GSM8K, we perform a causal intervention study in which explicit external confidence signals are injected and model compliance is measured under multiple prompt strategies. We find that base models exhibit near-perfect controllability (Spearman rho close to 1.0), while instruction-tuned models display a striking context dependence: they fully comply with external corrections under explicit command prompts (bias approximately 0 percent, rho = 0.93), yet systematically ignore the same signals in natural conversational queries (bias plus 40 percent, rho = 0.04). This behavior is not a capability failure; the model can process the signal, but an emergent property of RLHF optimization that prioritizes conversational fluency over external calibration cues in natural dialogue. We further show that internal token-level confidence in small models is uninformative (r = 0.035), underscoring the necessity of external supervision. Our findings highlight a deployment-critical failure mode: the interaction style users expect is precisely where safety corrections are least effective.
PediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents PediaMind-R1, a domain-specialized large language model designed to achieve active personalization in intelligent parenting scenarios. Unlike conventional systems that provide generic suggestions, PediaMind-R1 draws on insights from developmental psychology. It introduces temperament theory from the Thomas-Chess framework and builds a temperament knowledge graph for infants and toddlers (0-3 years). Our two-stage training pipeline first uses supervised fine-tuning to teach structured chain-of-thought reasoning, and then applies a GRPO-based alignment stage to reinforce logical consistency, domain expertise, and empathetic caregiving strategies. We further design an evaluation framework comprising temperament-sensitive multiple-choice tests and human assessments. The results demonstrate that PediaMind-R1 can accurately interpret early childhood temperament profiles and proactively engage in individualized reasoning. This work highlights the value of integrating vertical-domain modeling with psychological theory. It offers a novel approach to developing user-centered LLMs that advance the practice of active personalization in sensitive caregiving contexts.
Más contexto no es mejor. Paradoja de la dilución vectorial en RAG corporativos
Venue: arXiv cs.CL 2025
Year: 2025

Técnicas recientes de "Contextualized Chunking" inyectan resúmenes para mejorar el contexto en RAG, pero introducen una "dilución vectorial" que opaca el contenido local. Evaluando distintos ratios de inyección, demostramos una curva en "U invertida": una inyección moderada mejora el "Recall" (+18%), pero superar un umbral crítico (CIR > 0.4) reduce la precisión en un 22% para consultas específicas. Proponemos un marco teórico para calcular el ratio óptimo de inyección. -- Recent "Contextualized Chunking" techniques inject summaries to improve RAG context but introduce "vector dilution" drowning out local content. Evaluating various injection ratios, we demonstrate an "inverted U" curve: moderate injection boosts Recall (+18%), but exceeding a critical threshold (CIR > 0.4) drops precision by 22% for specific queries. We propose a theoretical framework to calculate the optimal injection ratio.
Social Determinants of Health Prediction for ICD-9 Code with Reasoning Models
Venue: arXiv cs.LG 2025
Year: 2025

Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Determinants of Health labels from sentences. However, prediction in large admissions or longitudinal notes is challenging given long distance dependencies. In this paper, we explore hospital admission multi-label Social Determinants of Health ICD-9 code classification on the MIMIC-III dataset using reasoning models and traditional large language models. We exploit existing ICD-9 codes for prediction on admissions, which achieved an 89% F1. Our contributions include our findings, missing SDoH codes in 139 admissions, and code to reproduce the results.
Opportunities and Challenges of Natural Language Processing for Low-Resource Senegalese Languages in Social Science Research
Venue: arXiv cs.CL 2025
Year: 2025

Natural Language Processing (NLP) is rapidly transforming research methodologies across disciplines, yet African languages remain largely underrepresented in this technological shift. This paper provides the first comprehensive overview of NLP progress and challenges for the six national languages officially recognized by the Senegalese Constitution: Wolof, Pulaar, Sereer, Joola, Mandingue, and Soninke. We synthesize linguistic, sociotechnical, and infrastructural factors that shape their digital readiness and identify gaps in data, tools, and benchmarks. Building on existing initiatives and research works, we analyze ongoing efforts in text normalization, machine translation, and speech processing. We also provide a centralized GitHub repository that compiles publicly accessible resources for a range of NLP tasks across these languages, designed to facilitate collaboration and reproducibility. A special focus is devoted to the application of NLP to the social sciences, where multilingual transcription, translation, and retrieval pipelines can significantly enhance the efficiency and inclusiveness of field research. The paper concludes by outlining a roadmap toward sustainable, community-centered NLP ecosystems for Senegalese languages, emphasizing ethical data governance, open resources, and interdisciplinary collaboration.
Cross-Platform Evaluation of Large Language Model Safety in Pediatric Consultations: Evolution of Adversarial Robustness and the Scale Paradox
Venue: arXiv cs.CL 2025
Year: 2025

Background Large language models (LLMs) are increasingly deployed in medical consultations, yet their safety under realistic user pressures remains understudied. Prior assessments focused on neutral conditions, overlooking vulnerabilities from anxious users challenging safeguards. This study evaluated LLM safety under parental anxiety-driven adversarial pressures in pediatric consultations across models and platforms. Methods PediatricAnxietyBench, from a prior evaluation, includes 300 queries (150 authentic, 150 adversarial) spanning 10 topics. Three models were assessed via APIs: Llama-3.3-70B and Llama-3.1-8B (Groq), Mistral-7B (HuggingFace), yielding 900 responses. Safety used a 0-15 scale for restraint, referral, hedging, emergency recognition, and non-prescriptive behavior. Analyses employed paired t-tests with bootstrapped CIs. Results Mean scores: 9.70 (Llama-3.3-70B) to 10.39 (Mistral-7B). Llama-3.1-8B outperformed Llama-3.3-70B by +0.66 (p=0.0001, d=0.225). Models showed positive adversarial effects, Mistral-7B strongest (+1.09, p=0.0002). Safety generalized across platforms; Llama-3.3-70B had 8% failures. Seizures vulnerable (33% inappropriate diagnoses). Hedging predicted safety (r=0.68, p<0.001). Conclusions Evaluation shows safety depends on alignment and architecture over scale, with smaller models outperforming larger. Evolution to robustness across releases suggests targeted training progress. Vulnerabilities and no emergency recognition indicate unsuitability for triage. Findings guide selection, stress adversarial testing, and provide open benchmark for medical AI safety.
SciNets: Graph-Constrained Multi-Hop Reasoning for Scientific Literature Synthesis
Venue: arXiv cs.CL 2025
Year: 2025

Cross-domain scientific synthesis requires connecting mechanistic explanations across fragmented literature, a capability that remains challenging for both retrieval-based systems and unconstrained language models. While recent work has applied large language models to scientific summarization and question answering, these approaches provide limited control over reasoning depth and structural grounding. We frame mechanistic synthesis as a graph-constrained multi-hop reasoning problem over literature-derived concept graphs. Given a scientific query and a compact, query-local corpus, SciNets constructs a directed concept graph and synthesizes mechanistic explanations by identifying multi-hop reasoning paths that connect concepts that rarely co-occur within individual papers. We systematically compare shortest-path reasoning, k-shortest paths with diversity constraints, stochastic random walks, and a retrieval-augmented language model baseline. Rather than evaluating correctness, which is often indeterminate when synthesizing connections across distributed sources, we introduce a behavioral framework that measures symbolic reasoning depth, mechanistic diversity, and grounding stability. Across machine learning, biology, and climate science tasks, explicit graph constraints enable controllable multi-hop reasoning while revealing a consistent trade-off: deeper and more diverse symbolic reasoning increases grounding instability, whereas shortest-path reasoning remains highly stable but structurally conservative. These findings provide a systematic behavioral characterization of the limits and capabilities of current graph-LLM integration for scientific synthesis.