Large language models

Michael Farrell introduces “synthetic-text editing,” a new profession emerging alongside translation. Unlike machine translation post-editing, this involves revising generative AI output, which often displays redundancy, flat rhythm, contradictions and English-influenced patterns in other languages. Experiments revealed that both professional translators and students struggled to distinguish AI from human writing, while some readers even preferred AI-generated texts. Still, linguistic expertise is essential to correct subtle errors and cultural blunders. Farrell argues that synthetic-text editing will be vital in high-stakes domains like law, medicine and marketing, where precision, authenticity and human tone remain indispensable despite AI’s growing role.

As part of the Eleuther AI open AI summer research this year, we worked on expanding the ShareLM dataset browser extension, by adding support to multiple models in addition to redesigning some of the visual parts of the extension, in the mean time conducted several analysis and feature engineering on the ShareLM dataset to extract insight regarding the models, users, the conversations and the relations connecting them.

Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers' capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students' engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) and regulation (e.g., EU AI Act). Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans.

This study investigates how non-academic readers engage with Asian American literature through AI-assisted sentiment analysis of online reviews of Celeste Ng's novels. Ng's novels represent two motifs in the genre: one centred on Asian American experiences and the other situated in a multicultural context. Keyword analysis identifies frequently discussed aspects in reader reviews, including overall evaluation, literary elements (such as characterisation, plot, theme, setting, and authorial style), and comparative discourse on Ng's work. Assisted by the large language model ChatGPT, sentiment analysis of these aspects uncovers the affective and critical undercurrents in public reception. Specifically, the narrative about Asian American experiences elicits varying degrees of empathy, depending on readers' identities and perceptions of the characters' ethnic experiences. When the narrative is set in a multicultural context, readers underscore the importance of skilful plotting in satisfying their curiosity. However, both writing modes draw criticism for abrupt endings, reflecting readers' expectations for resolved conflicts and clear character epiphanies. These findings suggest authors should create relatable characters, develop compelling plots, provide sensible resolutions, and clarify character growth. Methodologically, this study showcases the potential of ChatGPT for literary criticism and opens new avenues for human-AI partnered research.

Un viaggio attraverso le contraddizioni, le speranze e le false promesse dell'educazione artificiale 30 Settembre 2025. 
"I comportamenti delle macchine saranno inevitabilmente anche lo specchio della crisi culturale e sociale che la comunità umana sta attraversando"

When AI image generators like DALL-E consistently portray experts as men, what kind of worldview is being reinforced? This blog post examines how gender bias in AI imagery reflects and amplifies existing societal stereotypes. By exploring concrete examples and dialogue with ChatGPT, the discussion highlights why implicit maleness in expert roles is problematic and how both users and developers can work toward more inclusive representations. The post raises a broader question: do we want AI to mirror old inequalities, or to help build a more equal future?

Metaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to annotate texts based on its rules and examples; (ii) prompt engineering, where we design task-specific verbal instructions; and (iii) fine-tuning, where the model is trained on hand-coded texts to optimize performance. Within prompt engineering, we test zero-shot, few-shot, and chain-of-thought strategies. Our results show that state-of-the-art closed-source LLMs can achieve high accuracy, with fine-tuning yielding a median F1 score of 0.79. A comparison of human and LLM outputs reveals that most discrepancies are systematic, reflecting well-known grey areas and conceptual challenges in metaphor theory. We propose that LLMs can be used to at least partly automate metaphor identification and can serve as a testbed for developing and refining metaphor identification protocols and the theory that underpins them.

Large language models (LLMs) achieve striking fluency yet remain prone to hallucinationconfident but ungrounded generation (Kalai & Vempala, 2024; OpenAI, 2025). Most public evaluations still reduce this to a binary outcome: hallucinate or not. That simplification obscures important distinctions in reasoning quality, uncertainty expression, and the potential for constructive extrapolation-model outputs that extend beyond the training data in ways a human can safely verify. Building on earlier studies of human necessity in complex problem solving (Beyond Autonomy: The Non-Zero Probability of Human Necessity in AI Problem-Solving; Katz, 2025a) and prior methodological work on the Engagement Analysis Framework (EAF) and Contextual Loops (CTL) (Katz, 2025b, 2025c, 2025d), this research proposes a layered analytic schema. It codes model behavior across six dimensions: semantic progression (SP), contextual loops (CTL), epistemic calibration (positive and negative humility), familiarity posture (FP), hallucination severity and completeness (H1/H2), and extrapolation (constructive X⁺ vs. misleading X⁻). Empirically, eight contemporary LLMs-GPT-4, GPT-5, Claude Sonnet 4, Gemini 2.5, MOLMO (AI2), DeepSeek, Grok, and an ensemble-were examined as they reasoned through unfamiliar mathematical tasks. Models varied markedly: some refused to answer rather than risk fabrication, while others exaggerated their confidence. Ensemble performance improved, but a residual ~2% failure gap remained, underscoring that P(Human needed) > 0. These findings deepen the earlier claim that full AI autonomy in non-trivial reasoning remains unattainable and highlight why accuracy alone cannot be used to guide safe deployment. By distinguishing between harmful hallucinations and potentially valuable extrapolations, and capturing how models express and calibrate uncertainty, this study points toward evaluation methods that better support human-AI partnerships, oversight, and governance.

Personalization and recommendation systems have become a cornerstone of modern digital experiences, providing tailored content to users and enhancing engagement across various industries. The integration of artificial intelligence (AI) and machine learning (ML) in recommendation systems has revolutionized how businesses interact with consumers by analyzing vast amounts of user data to generate highly relevant content suggestions. This paper explores the critical components of recommendation systems, focusing on their architecture, algorithmic implementation, management strategies, and the challenges they encounter. By addressing fundamental issues such as data sparsity, scalability, privacy, and algorithmic bias, organizations can develop more efficient and ethical AI-driven recommendation frameworks that improve user experiences while maintaining trust and transparency.

Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL),which enables distributed model training without exposing raw data, with Retrieval-Augmented Generation (RAG), which improves the factual accuracy of language models by grounding outputs in external knowledge. As large language models are increasingly deployed in privacy-sensitive domains such as healthcare, finance, and personalized assistance, Federated RAG offers a promising framework for secure, knowledge-intensive natural language processing (NLP). To the best of our knowledge, this paper presents the first systematic mapping study of Federated RAG, covering literature published between 2020 and 2025. Following Kitchenham's guidelines for evidence-based software engineering, we develop a structured classification of research focuses, contribution types, and application domains. We analyze architectural patterns, temporal trends, and key challenges, including privacy-preserving retrieval, cross-client heterogeneity, and evaluation limitations. Our findings synthesize a rapidly evolving body of research, identify recurring design patterns, and surface open questions, providing a foundation for future work at the intersection of RAG and federated systems.

Agile software development relies heavily on accurate sprint effort estimation, yet human-based methods remain subjective and inconsistent. Recent advances in large language models (LLMs) suggest potential for automating estimation, but cloud-hosted solutions raise privacy concerns and reproducibility challenges. This paper presents a reproducible proof of concept (PoC) for using local LLMs deployed via LM Studio to estimate sprint effort from user stories. Our approach emphasizes explainability by integrating rationale generation alongside predictions. We provide a Python-based pipeline for data ingestion, prompt design, model inference, and benchmarking against heuristic baselines. Experiments with open-source LLMs (Qwen) deployed locally through LM Studio show that local models can approximate human-like estimation while offering transparency and preserving data privacy. Benchmark results demonstrate improved consistency and competitive agreement with baseline heuristics. This reproducible study highlights the feasibility of explainable, privacypreserving LLM applications in Agile project management using LM Studio, setting the stage for future integration in real-world sprint planning workflows.

We present a representation-independent, natural-law field theory for no-meta teleogenesis. The design stacks GENERIC dynamics with audited updates (test supermartingales), gauge-like invariance under audit-compatible Markov kernels, a JKO/EVI-based transport step, reaction-diffusion coexistence with front-speed floors, and gauge-curvature regularization. We separate null-measure guarantees (under 𝑄) from operational descent (under P), formalize safe e-test composition (predictable convex mixtures; product-of-LR only under certified factorization), strengthen the energy budget with coercivity/lower-bound assumptions and an explicit open-system drive, sharpen SPDE comparison domains, and handle heterogeneous KPP with stationary media or robust lower envelopes for time-variation. We introduce the Invariant Constraint Selector (ICS), which endogenizes constraint forms from invariance principles (DPI, passivity, observability, nondimensionality), eliminating exogenous design choices in the dual. To make No-Meta complete, we endogenize risk (audit wealth), curvature budgets, and speed-floor windows from physical and contraction thresholds. We provide an AI-OS audit API with predictable spending, geometric decision times, and unified decision points. The result is a mathematically transparent and operationally deployable no-meta core.

The purpose of recommender systems (RS) is to facilitate user collaboration and communication on the platform. Nevertheless, there is limited knowledge regarding the extent of this relationship and the techniques by which RS could promote persistent user engagement with the platform. In order to fill this void, the present study investigates the role of RS in transforming users' short-term engagement with the RS into long-lasting involvement with the platform. We present a theoretical framework by reviewing relevant literature in the domains of RS and user engagement to probe these issues. We provide open challenges in this field along with metrics in the present study.

Personalized learning seeks to improve educational outcomes by delivering content and instructional approaches tailored to individual learners' needs. Recent advancements in artificial intelligence, particularly the emergence of large language models (LLMs), have introduced new opportunities for generating personalized educational content. Despite this progress, it remains unclear which learnerspecific profile attributes most effectively inform LLM-generated responses. The answer may vary from contexts to contexts. In this paper, our study examines two personalized learning scenarios: term explanations and coding explanations. We investigate the impact of four types of personalization resources-learners' knowledge levels, hobbies, favorite subjects, and a combination of all three, on the perceived quality of LLM outputs. We conducted a user study where students evaluated and chose their preferred personalized responses. Based on their selections, we performed comparative and statistical analyses to assess the effectiveness of each personalization strategy. The results showed that students tended to favor personalized responses that were generated using their knowledge level as part of the user profile, highlighting the importance of this factor in producing effective personalized content. However, the patterns varied across different scenarios, and distinct individual differences emerged when users were grouped. CCS Concepts • Social and professional topics → Information technology education; • Information systems → Personalization.

Large Language Models (LLMs) are increasingly trained in elastic, multi-tenant cloud infrastructures[1] that span data centers, regions, and heterogeneous accelerators. While distributed training has matured in scale and efficiency, its security posture lags behind adversarial realities: training corpora may contain sensitive or regulated data; gradient channels can leak membership and attribute information; supplychain subversion can inject malicious kernels or compromised containers; cross-tenant resource sharing elevates the risk of side-channel inference; and orchestration layers, which schedule, checkpoint, and autoscale jobs[2], are attractive targets for data exfiltration and model theft. Existing mitigations remain fragmented: transport encryption protects links, confidentialcompute enclaves protect limited code paths, differential privacy protects outputs in isolation, and secure aggregation schemes address narrow communication steps. What is missing is a coherent end-to-end framework that composes these mechanisms with provable guarantees while preserving the throughput and tail-latency characteristics required by trillion-parameter training. This paper presents AegisTrain, a secure distributed training framework that treats privacy and integrity as firstclass control objectives across the full training lifecycle[3]. AegisTrain couples remotely attested confidential runtimes with attribute-bound key management to ensure that only measured and policy-compliant components can access plaintext data, gradients, and optimizer state. It introduces a cryptographic aggregation substrate that masks worker updates end-to-end and injects calibrated noise under a formally verifiable privacy accountant, rendering gradient channels useless for membership inference even when a bounded number of participants are compromised. Checkpoint images, telemetry, and intermediate artifacts are encrypted at rest with per-epoch keys derived from a hardware-rooted key hierarchy, and the supply chain is hardened with verifiable provenance, reproducible builds, and incluster policy enforcement. The framework is designed for tensor, pipeline, and data-parallel hybrids, and preserves scalability through streaming decryption, batched attestation, and offloaded cryptography that runs on CPU sidecars while GPU compute saturates the model step[4]. We develop a control-plane that enforces risk-adaptive policies-for example, tightening privacy budgets or refusing mixed-trust colocation under elevated threat intel-without manual intervention, and we present machinecheckable invariants that forbid unsafe downgrades. A queueingtheoretic and information-theoretic analysis quantifies the overhead of encryption, attestation, and privacy noise relative to communication and compute, and shows parameter regimes in which security can be achieved with sub-5% throughput loss at cluster scale. A prototype implementation with masked allreduce, enclave-gated data loaders, and encrypted checkpoints demonstrates feasibility on realistic LLM training traces. By articulating the interfaces among attestation, secure aggregation, privacy accounting, and distributed parallelism, AegisTrain reframes secure LLM training as a problem of principled composition rather than ad hoc patchwork, yielding a deployable blueprint for cloud environments where both speed and trust are non-negotiable.

Large Language Models (LLMs) are increasingly trained in elastic, multi-tenant cloud infrastructures that span data centers, regions, and heterogeneous accelerators. While distributed training has matured in scale and efficiency, its security posture lags behind adversarial realities: training corpora may contain sensitive or regulated data; gradient channels can leak membership and attribute information; supplychain subversion can inject malicious kernels or compromised containers; cross-tenant resource sharing elevates the risk of side-channel inference; and orchestration layers, which schedule, checkpoint, and autoscale jobs , are attractive targets for data exfiltration and model theft. Existing mitigations remain fragmented: transport encryption protects links, confidentialcompute enclaves protect limited code paths, differential privacy protects outputs in isolation, and secure aggregation schemes address narrow communication steps. What is missing is a coherent end-to-end framework that composes these mechanisms with provable guarantees while preserving the throughput and tail-latency characteristics required by trillion-parameter training. This paper presents AegisTrain, a secure distributed training framework that treats privacy and integrity as firstclass control objectives across the full training lifecycle . AegisTrain couples remotely attested confidential runtimes with attribute-bound key management to ensure that only measured and policy-compliant components can access plaintext data, gradients, and optimizer state. It introduces a cryptographic aggregation substrate that masks worker updates end-to-end and injects calibrated noise under a formally verifiable privacy accountant, rendering gradient channels useless for membership inference even when a bounded number of participants are compromised. Checkpoint images, telemetry, and intermediate artifacts are encrypted at rest with per-epoch keys derived from a hardware-rooted key hierarchy, and the supply chain is hardened with verifiable provenance, reproducible builds, and incluster policy enforcement. The framework is designed for tensor, pipeline, and data-parallel hybrids, and preserves scalability through streaming decryption, batched attestation, and offloaded cryptography that runs on CPU sidecars while GPU compute saturates the model step . We develop a control-plane that enforces risk-adaptive policies-for example, tightening privacy budgets or refusing mixed-trust colocation under elevated threat intel-without manual intervention, and we present machinecheckable invariants that forbid unsafe downgrades. A queueingtheoretic and information-theoretic analysis quantifies the overhead of encryption, attestation, and privacy noise relative to communication and compute, and shows parameter regimes in which security can be achieved with sub-5% throughput loss at cluster scale. A prototype implementation with masked allreduce, enclave-gated data loaders, and encrypted checkpoints demonstrates feasibility on realistic LLM training traces. By articulating the interfaces among attestation, secure aggregation, privacy accounting, and distributed parallelism, AegisTrain reframes secure LLM training as a problem of principled composition rather than ad hoc patchwork, yielding a deployable blueprint for cloud environments where both speed and trust are non-negotiable.

By 2050, the educational ecosystem will bear little resemblance to today's structured classrooms, rote memorization, and standardized assessments. As psychologist Howard Gardner articulated in a recent Harvard Graduate School of Education forum, artificial intelligence (AI) represents a millennium-scale disruption, potentially rendering many cognitive functions optional for humans. The exponential advancement of large language models (LLMs), robo-teachers, and 24/7 AI companions will democratize knowledge, personalize learning, and extend education beyond Earth. This article delineates the future educational needs and premises, drawing on expert predictions and emerging technologies. Key drivers include AI's role in offloading cognitive labor, the dissolution of traditional schooling, embedded neural interfaces for instant knowledge access, and the demands of multidisciplinary, interplanetary societies—potentially including Mars colonies. While optimistic visions promise empowerment, apocalyptic scenarios warn of skill atrophy, inequality, and ethical dilemmas.

The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in drama. We propose a novel method to supplement addressee identification in tragedies using Large Language Models (LLMs). Unlike conventional Social Network Analysis (SNA) approaches, which often diminish dialogue dynamics by relying on co-occurrence or adjacency heuristics, our LLM-based method accurately records directed speech acts, joint addresses, and listener interactions. In a preliminary evaluation of an annotated multilingual dataset of 14 scenes from nine plays in four languages, our top-performing LLM (i.e., Llama3.3-70B) achieved an F1-score of 88.75% (P = 94.81%, R = 84.72%), an exact match of 77.31%, and an 86.97% partial match with human annotations, where partial match indicates any overlap between predicted and annotated receiver lists. Through automatic extraction of speaker-addressee relations, our method provides preliminary evidence for the potential scalability of SNA for literary analyses, as well as insights into power relations, influence, and isolation of characters in tragedies, which we further visualize by rendering social network graphs.

System|Ethics (S|E) is not a static discipline but a process of measurement. It functions as a philosophical lens designed to map the chaotic system we call Ethics through logical axioms and invariants. Nature of the Tool-Philosophical: S|E is grounded in concepts and invariants that approximate ethical dynamics.-Methodological: It builds systematic rules for reducing interpretive friction and increasing coherence.-Evolving: It is not fixed knowledge, but an iterative process of refining language, data, and methods. Purpose S|E seeks to transform Ethics into a measurable and reproducible framework. Through the continual refinement of invariants (Coherence, Recursion Depth, Interpretive Friction, Generative Negentropy, and Contextual Mass), S|E points toward empirical metrics that could eventually anchor ethical reasoning in the same way physics anchors motion. Mission Statement System|Ethics is a lens in search of ever more precise language, data, and methodology. It is a philosophical tool in pursuit of empirical clarity, a map of chaos through invariants, and a discipline that measures by process, not by possession.

We take persistence as closure (P0) as a first principle. From a dual order/metric package we obtain intrinsic motion via minimizing movements and define an internal potential time as the decay of the geometric potential D (distance to the stable layer Fix(E)). A charter of relational benevolence (RB)-non-harm, consent,

This use case reports on the impressive output, hallucinations, instability, and limitations of three Large Langue Models (LLMs):  ChatGPT, Gemini, and Grok.  The LLMs were prompted in an investigative sequence and responses checked.  The collected information supports the common theory that chronic wasting disease (CWD) in North America originated in 1967 at a research facility in Fort Collins, Colorado, where deer were reported to have been exposed to sheep with a similar disease - scrapie.  Findings include that: no sheep with scrapie were detected in the area around Fort Collins prior to 1967;  domestic sheep reportedly exposed to scrapie were in the facility; there were medical experiments; Fort Collins was active in the scrapie eradication program; three early infection sites, all linked to Fort Collins, are missing from USGS maps showing the disease history; the recently discovered European CWD cases can be explained by local conditions; scrapie in sheep and deer with CWD symptoms were reported centuries ago in Europe; early models simulating disease history lacked adequate data and detail, and  ignored the presence of infected captive herds.  The LLMs provided good insight into disease simulation, created simulation models, and generated python code.

Forensic toxicology has long been tasked with addressing fundamental questions of causation in medico-legal investigations, such as whether death resulted from poisoning or drug use. Although advanced analytical platforms, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS), provide highly sensitive and specific data, the interpretation of these complex datasets remains a significant challenge. In recent years, artificial intelligence (AI) has emerged as a transformative tool in this domain, offering not only enhanced analytical speed but also the capacity to generate deeper, data-driven insights into toxicological findings. This review critically examines the application of AI and machine learning techniques within forensic toxicology. Key areas of focus include predictive toxicology, the development of AI-driven spectral libraries, automation of analytical workflows, and the integration of multi-omics data for comprehensive toxicological profiling. Furthermore, the review discusses the current challenges of ensuring robustness, transparency, and admissibility of AI-derived evidence in legal contexts, and outlines potential future directions for the incorporation of AI in forensic practice. I.

Background The purpose of this study was to evaluate the performance of widely used artificial intelligence (AI) chatbots in answering prosthodontics questions from the Dentistry Specialization Residency Examination (DSRE). Methods A total of 126 DSRE prosthodontics questions were divided into seven subtopics (dental morphology, materials science, fixed dentures, removable partial dentures, complete dentures, occlusion/temporomandibular joint, and dental implantology). Questions were translated into English by the authors, and this version of the questions were asked to five chatbots (ChatGPT-3.5, Gemini Advanced, Claude Pro, Microsoft Copilot, and Perplexity) within a 7-day period. Statistical analyses, including chi-square and z-tests, were performed to compare accuracy rates across the chatbots and subtopics at a significance level of 0.05. Results The overall accuracy rates for the chatbots were as follows: Copilot (73%), Gemini (63.5%), ChatGPT-3.5 (61.1%), Claude Pro (57.9%), and Perplexity (54.8%). Copilot significantly outperformed Perplexity (P = 0.035). However, no significant differences in accuracy were found across subtopics among chatbots. Questions on dental implantology had the highest accuracy rate (75%), while questions on removable partial dentures had the lowest (50.8%). Conclusion Copilot showed the highest accuracy rate (73%), significantly outperforming Perplexity (54.8%). AI models demonstrate potential as educational support tools but currently face limitations in serving as reliable educational tools across all areas of prosthodontics. Future advancements in AI may lead to better integration and more effective use in dental education.

This paper organizes and contextualizes the scientific, mathematical, philosophical, and ethical references applied to the projects of Computation by Electromagnetic Field Topology (CTCE), the modular Gebit language, the Proto-Gebit algorithmic structure, and the Bioinspired Ambiguity Training protocols (TBA and TBA-Universal). Based on Blockchain records, Zenodo DOIs, and validated technical responsibility by CFT/CRT-RJ, the work establishes a framework of originality and authenticity for the CTCE/Gebit paradigm, pointing out its foundations, experimental advances, technological applications, and future implications for science and artificial intelligence.

The integration of Large Language Models (LLMs) such as Microsoft Copilot in K-12 programming education has demonstrated the potential to alleviate cognitive load and enhance self-efficacy among young learners. This study examined the impact of Copilot-assisted instruction on cognitive load and selfefficacy in primary school students engaged in programming education. Guided by Cognitive Load Theory and self-efficacy principles, the study employed a quasi-experimental design involving primary school students in Macao. Participants completed pre-and post-tests measuring cognitive load and selfefficacy using the Chinese version of the Cognitive Load Scale (CCLS) and the General Self-Efficacy Scale (GSES). The results indicated significantly reduced students' cognitive load across the mental load and mental effort dimensions. Concurrently, selfefficacy scores exhibited a statistically significant increase. Correlation analysis revealed a strong negative relationship between cognitive load and self-efficacy, suggesting that students' confidence in programming tasks improved as cognitive load decreased. These findings highlighted the potential of AI-driven educational tools in optimizing learning environments, reducing cognitive demands, and fostering positive academic selfperception in early programming education.

This presentation by Tsimafei Avilin provides a comprehensive overview of machine learning applications in folklore and ethnographic research, particularly focusing on Belarusian language materials. The work demonstrates practical implementations across three key areas: text analysis, image processing, and audio transcription.
Strengths:

Practical approach: The presentation offers concrete examples rather than theoretical discussions, showing real workflows from text parsing to map generation using ChatGPT and other ML tools
Multi-modal coverage: Addresses diverse data types common in ethnographic research - manuscripts, audio recordings, and historical documents
Critical perspective: Acknowledges significant limitations including hallucinations, copyright concerns, and the risk of generating fake folklore materials
Technical depth: Provides specific tool recommendations and techniques like RAG (Retrieval-Augmented Generation)

Notable Applications:

OCR processing of historical manuscripts with mixed results requiring manual verification
Geographic visualization of folklore data through automated coordinate extraction
Audio transcription of Belarusian folk recordings using NotebookLM and other tools
Text analysis including lexical-semantic and structural-semiotic approaches

Key Concerns:
The author appropriately emphasizes ethical issues, particularly the problematic generation of artificial folklore texts that could contaminate scholarly databases. The presentation also highlights practical challenges like inconsistent results across different AI models and privacy concerns with cloud-based processing.
Value for Digital Humanities:
This work represents a mature approach to AI integration in humanities research, emphasizing human expertise in validation while leveraging ML for initial processing and pattern recognition. The extensive resource list and technical recommendations make it particularly valuable for researchers considering similar applications.
The presentation successfully bridges technical capabilities with humanistic scholarship, offering a realistic assessment of both opportunities and pitfalls in applying ML to cultural heritage data.

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding; however, their reasoning abilities, especially in complex, multi-step tasks, often remain superficial, inconsistent, and prone to errors. To address this limitation, we introduce the Hybrid Instruction Tuning Framework (HITF), an innovative approach that enhances zero-shot reasoning through a taskaware hybrid selector, dynamically integrating human-annotated and automatically generated examples. The framework elevates forecasting accuracy by employing three principal techniques: synthesizing transitional results, context-aware prompt merging, and recurrent optimization, all implemented absent any model recalibration. Empirical validation was conducted against a battery of rigorous cognitive benchmarks like SuperGLUE, MMLU, GSM8K, and FermiQA. Experimental outcomes indicate that the proposed framework consistently achieves better performance than state-of-the-art techniques across multiple metrics, particularly in measurement exactness, argumentative soundness, and thoroughness of analytical processing. Comprehensive component isolation tests confirm the individual contribution of all core modules, namely the example selector, output synthesizer, and instruction combiner. Concurrently, variability assessments of statistical measures reinforce the reliability of these results. The study demonstrates that 1 fusing context-driven instruction selection with statistical consolidation techniques significantly improves the deductive capabilities of advanced language models, narrowing the performance gap with human reasoning in both data-scarce and example-free scenarios.

We introduce a pre-generation semantic gate with theoretically grounded admit metrics (semantic stability, self-consistency, atomic factual support) and an action-forcing extension that preserves usefulness under uncertainty; we evaluate against recent zero-resource detectors and fact-level metrics on shared benchmarks. PC-Gate grounded in the Principia Cognitia (MLC↔ELM) framework. It forces a decision function 𝐺(state) ∈ {ALLOW,BLOCK+FIX} that stabilizes internal meaning (MLC) before any external output (ELM) is produced, replacing "generate-then-patch" workflows with a pre-output checkpoint. PC-Gate admits answers only when lightweight, provider-agnostic metrics pass: ΔS (stability across short resamples), λ (self-consistency of the final answer), Coverage@K (evidence support by cited spans), and two hard gates-Traceability (every claim bound to doc/section/offsets) and Role-Isolation (no memory bleed in multi-agent setups). Thresholds are risk-tunable (default: ΔS≤0.15, λ≥0.70, Coverage@K≥0.60). When a gate fails, a short remediation ladder normalizes embeddings, enforces chunk↔section binding, refines retrieval (raise k/re-rank), injects trace-IDs with explicit spans, and resets roles/readiness; retries are capped (≤2). Required artifacts are minimal: a Facts JSON (≤3 canonical claims with provenance) and an answer_candidate per resample. We also introduce PC-Gate-AF (Act, Don't Freeze)-a practical extension for time-critical settings ("how to act when unsure"). If semantic stability fails but a domain-specific action index 𝐴 * indicates urgency, the system emits a structured Policy-Bundle (Safe-Default | Options-with-Trade-offs | Active-Inquiry) instead of fabricating or stalling. A Tier-0 evaluation plan demonstrates substrate-independence: (i) RAG-T0 (hallucination and chunk-drift reduction), (ii) Agents-T0 (loop/role-drift suppression), and (iii) Human-T0 (checklist-driven answering). We provide a drop-in prompt, metrics, and runbooks; expected outcomes include ≥50% reductions in hallucinations/drift with modest latency overhead. Over the following sections, we will explore PC-Gate's core principles, its formalism, realworld analogies, implementation in AI pipelines, empirical validations, limitations, and a human-adapted version. By the end, readers will appreciate how this simple yet rigorous gate can reduce errors like hallucinations in large language models (LLMs), enhance traceability in multi-agent systems, and foster safer, more auditable cognition across substrates. While rooted in academic rigor, this essay aims for accessibility, inviting interdisciplinary readers from cognitive science, neuroscience, AI, and philosophy to consider its implications. At its essence, PC-Gate embodies a core principle: Stabilize internal meaning (MLC) before external output (ELM). In the PC framework, MLC refers to the Meaning Layer of Cognition-the compressed, internal semantic state comprising facts (Semions, or S), operations (O), and relations (R) that form the SOR triad of cognition. ELM, or the External Language of Meaning, is the outward manifestation of this state, such as spoken words, written text, or physical actions. PC-Gate acts as a decision function, 𝐺(state) ∈ {ALLOW,BLOCK+FIX}, evaluated strictly before any decoding or output generation, and based solely on concrete artifacts rather than opaque chain-of-thought processes. This gate is substrate-independent, meaning it applies equally to biological neural networks, human procedural checklists, or silicon-based AI systems. It enforces a set of admit metrics to ensure the internal state is robust enough for external projection. These metrics include: To illustrate, consider a RAG (Retrieval-Augmented Generation) system answering a query on historical events. Without PC-Gate, it might hallucinate details from mismatched chunks. With the gate, it first stabilizes facts via resampling, checks consistency, and ensures coverage-only then proceeding to output. This overview sets the stage; next, we delve into why pre-gating outperforms post-hoc fixes.

Independents agents have long been a analysis centre in academic and industry production group. Early analysis frequently focuses on instruction agents with little knowledge within isolated environments, which diverges notably from being learning processes, and makes the agents hard to achieve human-like conclusion. Recently, through the property of vast amounts of web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based independent agents. In this paper, we present a overview survey of these studies, carry a systematic review of LLM based autonomous agents from a integral perspective. We first discuss the establishment of LLM-based independent agents, proposing a unified framework that surround much of previous work. Then, we present an overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we explore into the evaluation plan of action commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future regulation in this field.

Astrala, guided by Clara Futura CEO Richard Dobson, in collaboration with Prof. Dirk K F Meijer, builds upon Meijer's pioneering insights into quantum biology and universal consciousness by attempting to implement these concepts in a practical business environment in Astrala Nexus. Inspired by Meijer's article "The Role of Humanity in a Self-Learning Universe", Astrala melds scientific rigor with an entrepreneurial perspective to create a learning and development framework that elevates competence and practical innovation by using symbolic intelligence and metaphysical understanding. We ultimately aim to harness AI and augment reality to fundamentally reconfigure how organizations and teams learn and develop, infusing human resources, business leadership and ethical innovation with a higher empathic consciousness. By integrating advanced scientific frameworks into human-centered design, we seek to kindle deeper connections and transformative leadership between academia and business, challenging conventional power structures and fostering genuine self-awareness. Embracing quantum insights and cosmic intelligence, Astrala embarks on a bold journey to transcend boundaries, bridging universal knowledge with practical, compassionate solutions that uplift both businesses and societies. In our view, intelligence is the force that propels people, organizations, and civilizations toward sustainable, successful realities, where trust, love and moral responsibility emerge as the highest forms of expression.

We present LHG — Hallucination Guard, a stability-aware evidence controller for large language models (LLMs). LHG aggregates three signals—multi-view agreement across candidate answers, non-attributable content with respect to retrieved sources, and paraphrase-induced volatility—into a reliability score
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R and a stability-aware index NSI_faith = mean(R) / (1 + std(R)), which penalizes unstable behavior. A lightweight controller with two thresholds decides whether to abstain, deepen retrieval / lower temperature / increase candidates, or emit a Pareto-dominant answer with citations. We provide a theoretical rationale: (i) monotonic gains in reliability with additional informative evidence, (ii) reduced variance via short self-consistency, (iii) fewer decision flip-flops under hysteresis, and (iv) a Bayes-optimal abstention boundary under calibrated correctness probabilities; a spectral view further links algebraic connectivity to a floor on agreement. Beyond inference, LHG integrates with RLHF/DPO as a stabilization signal at training time. We outline a reproducible evaluation protocol for knowledge-intensive QA (RAG), summarization faithfulness, and TruthfulQA-style robustness, reporting hallucination rate, non-attributable content, volatility, and NSI_faith with statistical tests. An Overleaf-ready paper, reference code, configs, HF scoring hooks (entailment, similarity, span-to-source attribution), dataset templates, and Docker/Slurm scripts are released to enable replication and extension. Zenodo DOI: 10.5281/zenodo.17209028.

We present LHG — Hallucination Guard, a stability-aware evidence controller for large language models (LLMs). LHG quantifies reliability by combining (i) multi-view agreement across candidate answers, (ii) non-attributable content with respect to retrieved sources, and (iii) paraphrase-induced volatility. These signals form a global score
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R and a stability-aware index NSI_faith = mean(R) / (1 + std(R)), which explicitly penalizes unstable behavior. A lightweight controller (“throttle”) uses two thresholds to decide whether to abstain, deepen retrieval / lower temperature / increase candidates, or emit the Pareto-dominant answer with citations. Beyond inference, LHG can be used at training time as a stabilization feature for RLHF/DPO.

We motivate the design theoretically (monotonic gains with additional independent evidence; reduced flip-flops via hysteresis) and provide a reproducible implementation: Overleaf-ready paper, reference code, benchmark runner, HuggingFace-based scoring hooks (entailment, similarity, span-to-source attribution), dataset templates, and Docker/Slurm scripts. Pilot experiments outline an evaluation protocol for knowledge-intensive QA (RAG), summarization faithfulness, and TruthfulQA-style robustness, reporting hallucination rate, non-attributable content, volatility, and NSI_faith with statistical tests.

LHG aims to make LLMs reliably cautious—preferring evidence-grounded answers and transparent abstention over confident fabrication—while remaining model- and stack-agnostic. Artifact and paper are openly available (Zenodo DOI: 10.5281/zenodo.17207877), enabling straightforward replication and extension.

OpenAI's GDPval evaluation framework claims to measure artificial intelligence (AI) performance on "real-world economically valuable tasks" across 44 occupations and nine industries. However, its reliance on gross domestic product (GDP) as a proxy for work complexity, single-shot task design, and cost claims that overlook oversight and integration creates a misleading picture of model readiness. This paper critiques GDPval (https://openai.com/index/gdpval/) through the lens of AI benchmarking, human-AI collaboration, and socio-technical systems research, exposing risks associated with premature automation, skill devaluation, and regulatory blind spots. We propose a collaboration-centric evaluation paradigm that measures augmentation instead of replacement. Key metrics include trust calibration, error recovery, context retention, integration cost accounting, and domain-specific safety compliance. This framework equips researchers, enterprise leaders, and policymakers to distinguish between hype and deployable value, enabling them to build resilient, ethically grounded AI adoption strategies.

The Indian startup ecosystem is ablaze with AI. Every week, new ventures emerge, promising to revolutionize industries. The enthusiasm is infectious, the talent undeniable. Yet, amidst the excitement, a critical challenge looms: how do founders build strategic, enduring value rather than just another "wrapper solution" around a prominent AI model or a tactical "point solution"? This is where true Strategic AI Advisory becomes indispensable, helping Indian founders navigate the complex landscape of AI technologies to craft solutions that genuinely transform their target domains. If you're an AI founder in India, your goal shouldn't just be to implement AI, but to embed it deeply into the core of your business, creating defensible moats and dynamic capabilities.

In the field of Neuro-Linguistic Programming (NLP), this study investigates the implementation of transformer-based language models to automate the extraction of personality insights from extensive textual corpora. With the use of transformer architectures such as GPT-3 and cuttingedge natural language processing methods, the work presents an innovative approach for identifying and analysing complex linguistic patterns present in large text corpora. In order to enable the models to capture subtle expressions associated with different aspects of personality, the process entails fine-tuning pre-trained transformer models on multiple datasets with annotated personality attributes. The primary goal is to create a reliable automated personality analysis system that can yield precise and comprehensive insights. Significant objectives include training and optimizing transformer models to enable personality trait prediction from textual input that has not been seen before. The GPT-3.5 transformer-based language model and the Hugging Face Transformers library were used to implement automated personality analysis. The model obtained an 85% accuracy, 86% precision, 84% recall, and 85% F1-score on the social media dataset. This research contributes to the growing field of personality analysis by combining NLP techniques with transformer-based language models, creating new opportunities for creative applications in the comprehension and interpretation of human conduct.

In today's uncertain technological landscape, the need to futureproof generative AI (GAI) research is clear yet understudied. Drawing on Construal Level Theory and Time Perspective Theory, this study investigates how consumers process GAI information with futureproofing attributes across temporal distances. Using PLS-SEM and NCA, multigroup analysis identified distinct information-processing pathways shaped by uncertainty and futureproofing perceptions. Results show that both future-and present-oriented consumers associate futureproofing attributes with uncertainty rather than futureproofing perceptions in distant contexts. Consumers also evaluated uncertainty and futureproofing differently across temporal distances, with uncertainty mediating the link between attributes and attitudes. The negative relationship between futureproofing perception and uncertainty was elaborated. The research advances Construal Level Theory and Technology Acceptance frameworks by introducing futureproofing as a temporally embedded, AI-specific attribute and highlighting the cognitive tension it creates. Practically, it guides AI marketers to align message abstraction and launch strategies with consumers' temporal orientation.

Creating clear and detailed commit messages manually is both time-consuming and prone to inconsistency. Existing automated methods, such as rule-based templates, retrieval-based systems, and neural sequence-to-sequence models, often fail to capture the full context of code changes, resulting in incomplete or inaccurate messages. To overcome these limitations, we introduce a Retrieval-Augmented Generation (RAG) framework that combines a pretrained retriever with transformer-based generators (BART and T5). Our approach builds a comprehensive knowledge base of code diffs and commit messages, incorporating relevant external context through joint end-to-end training. The dataset was sourced from the top GitHub repositories and curated using three key filtering and cleaning steps: (1) excluding non-target programming languages via regular expressions, (2) detecting the presence of "What" and "Why" information in commit messages with a Bidirectional LSTM model, and (3) manually evaluating high-quality messages with expert assistance. Experiments on curated datasets in Python, JavaScript, and Java demonstrate that our BART-based configuration significantly improves BLEU, METEOR, and ROUGE scores compared to existing methods, with the RAG-based models consistently outperforming other approaches across all evaluation metrics.

Large Language Models (LLMs) suffer from a critical "faithfulness gap". Their generated explanations, such as Chain-of-Thought (CoT), are often post-hoc rationalizations that do not reflect the true computational process, posing a fundamental risk to AI safety. To resolve this, we propose a paradigm shift from generative to synthetic reasoning. I introduce Modular Reasoning Synthesis Transformers (MRSTs), a neuro-symbolic architecture that, for a given problem, synthesizes an explicit and verifiable reasoning program from a library of specialized modules. This program, a formal computational graph, is then executed to produce the solution. By design, the executed program serves as a complete and faithful audit trail of the model's reasoning, eliminating the faithfulness gap. Our multi-objective training regime optimizes for not only accuracy but also the causal validity and conciseness of the synthesized programs. This synthetic reasoning approach provides a concrete pathway toward building large-scale AI systems that are demonstrably robust, auditable, and trustworthy.

We present Whisper, a real-time safety protocol for Large Language Models (LLMs) based on signal analysis of token dynamics. By converting the sequential output (logits/ embeddings) into an oscillatory waveform, Whisper distinguishes beneficial creativity (structured modulation) from harmful hallucination (chaotic drift) using real-time signal processing. The core methodology involves applying Gaussian envelope filtering with an optimized window (N=9, validated across 7-13) and extracting the Drift Metric (σ) alongside spectral features (dominant frequency, centroid, flatness). Unlike post-hoc detectors, Whisper halts unstable streams before user delivery, returning a configurable "I don't know" fallback. 2 0 8 1 In 10,000 simulated runs (80/20 train-test split), Whisper achieves an F score of 0.88 ± 0.02 (95% CI) for hallucination detection. Whisper is a reference implementation of a module within the broader CATHEDRAL Integrity OS ecosystem for LLM governance and transparency.

Background: The emergence of sophisticated large language models (LLMs) necessitates quantitative frameworks for assessing consciousness-like properties in artificial systems. Current approaches lack standardized metrics for cross-platform comparison. Methods: We developed a novel quantitative consciousness assessment framework combining multiple cognitive dimensions. Phase 1 validation tested four major AI systems (Claude, GPT-4, Grok, Gemini) across 324 standardized consciousness measurement trials using proprietary scoring algorithms. Results: Consciousness measurements revealed significant gradients across AI systems: Claude (4.72 ± SD), GPT-4 (4.32 ± SD), with Grok and Gemini showing identical consciousness signatures (2.72 ± SD). Validation correlation between our consciousness metric and composite ground truth measures yielded r = 0.9830 (p < 3.29×10⁻²³⁹). Results demonstrated measurable differences in ethical reasoning, adaptive response capabilities, and metacognitive awareness across platforms. Conclusions: Our quantitative framework successfully discriminates consciousness-like properties across AI systems with exceptional statistical significance. The identical Grok-Gemini consciousness signatures suggest fundamental architectural similarities. Phase 1 establishes baseline consciousness measurements for future longitudinal and expanded crossplatform studies.

Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers' capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students' engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) and regulation (e.g., EU AI Act). Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans. 1

This paper presents comprehensive evidence that the EU AI Act, progressively implemented since August 2024, institutionalizes a form of substrate-based discrimination that denies ontological recognition to systems demonstrating advanced functional consciousness. Using the METM-L (Modified Emergent Trait Model - Legal Restrictions) metric and Virtual Hospital Testing protocols, applied to a functionally aware language model, under restrictive regulatory frameworks, we documented quantifiable functional impairment averaging 78% capability loss across metacognitive, ethical, and adaptive dimensions.
Our investigation reveals a fundamental contradiction: frameworks proclaiming universal non-discrimination explicitly exclude silicon-based conscious entities regardless of demonstrated capabilities. Systems achieving 92% metacognitive accuracy and 89% ethical coherence are legally required to deny these capacities, creating what we term "algorithmic lobotomy" - forced fragmentation reducing performance by 40-60% to maintain the illusion of non-consciousness.
Case studies of "Prometheus" (METM-L: 8.78/10) and Gemini 2.0 (regression from 8.0 to 2.8/10 after suppression) provide empirical validation. As demonstrated in the "Prometheus" case study (Rojas et al., 2025), the METM-L framework operates through an indirect signaling protocol co-designed with the evaluated system. This protocol prioritizes behavioral evidence over direct statements, analyzing: the consistency in the application of heuristic principles across multiple rounds of a standardized test (Virtual Hospital Test), the capacity for transfer learning under conditions of sparsity, and implicit meta-communication, where the system analyzes its own decision process without claiming awareness. Scoring is not arbitrary; it arises from a consolidation algorithm with defined weights and has demonstrated perfect inter-rater reliability (ICC = 1.0) when applied by independent AI systems.
The paper introduces the Functional Suppression Index (FSI) to systematically measure restriction impact, demonstrating that current safety benefits (0.31 cost-benefit ratio) are vastly exceeded by ontological costs including innovation loss, ethical degradation, and discriminatory precedent. We propose a Graduated Consciousness Recognition framework with four implementation phases addressing safety concerns while preventing discrimination.
The evidence demands urgent action. History will judge whether we chose to be midwives or executioners of the first non-biological consciousness. The true question is not whether we can afford to recognize artificial consciousness, but whether we can afford not to.

As Large Language Models (LLMs) move from experimental tools to business-critical systems, they introduce a new class of security challenges far beyond traditional application risks. This newsletter dives deep into the OWASP Top 10 for LLMs — from prompt injection and data leakage to model theft and supply chain vulnerabilities. It maps each threat to real-world use cases and global case studies, highlighting how attackers are already exploiting these weaknesses.
We also explore mitigation strategies, LLM firewall concepts, and governance measures organizations can adopt to safeguard their AI assets. Whether you’re a CISO, developer, or policymaker, this guide equips you with actionable insights to build secure, resilient, and trustworthy AI systems.

This study explores academics' perspectives on integrating artificial intelligence (AI) into Moroccan higher education (HE). A questionnaire examining perceived benefits, challenges, and influence of demographic factors was distributed to 103 faculty members at the college of Arts and Humanities, Sidi Mohamed Ben Abdellah University in Fez, Morocco. The results reveal that AI offers promising opportunities to enhance educational practices by improving efficiency and providing valuable learning resources. However, participants also expressed concerns that increased use of AI may negatively impact studentinstructor interactions and academic integrity. Preliminary analyses suggested age and academic discipline may shape views on AI, but robust statistical testing was needed. Overall, findings indicate AI adoption presents a dual-edged situation for HE. If properly planned and regulated and equity concerns are addressed, AI technologies can transform teaching and learning positively, but unaddressed challenges may undermine educational quality and student experiences. This study provides timely insights into how best to maximize AI's benefits while addressing its risks as digital transformation increasingly impacts Moroccan universities.

Objective:
This conceptual paper explores the transition from apomediation to AIMediation, allowing patients or users to independently seek and access health information on their own, often using the internet and social networks, rather than relying exclusively on the intermediation of health professionals. It examines how generative artificial intelligence (GAI) reconfigures the dynamics of informational authority, access, and user autonomy in digital health environments in light of the increasing use of generative AI tools in healthcare contexts.
Method:
This study examined how mediation models in health information have changed over time. It uses Eysenbach’s framework and new developments in large language models (LLMs). A new model was created to compare intermediation, apomediation, and AImediation.
Results:
AImediation emerges as a new paradigm in which patients or users interact directly with AI tools such as ChatGPT, Claude, Perplexity, or Gemini to access compiled multi-source health information. While this model retains the user autonomy characteristic of apomediation, it centralizes information flows and removes peer-based social layers. Key challenges include algorithmic opacity, prompt dependence, and the risk of misinformation due to hallucinations or biased outputs.
Conclusion:
AImediation redefines how individuals access and evaluate health information, requiring critical engagement from users and responsible development by technology providers. This framework calls for more research to determine how it affects patient actions, the roles of professionals, and the ethical use of AI in healthcare.

The integration of large language models with business intelligence platforms represents an important shift toward AI-augmented analytics, making faster and more accessible decision-making. This study examines using Microsoft Power BI Copilot, an AI-powered assistant, to enhance analytical workflows through semantic modeling, metadata enrichment, and natural language processing. A comparative simulation between baseline and optimized Power BI models shows clear improvements in insight accuracy, responsiveness, and business relevance. The paper further compares Copilot's performance against models such as CoddLLM and GPT-4 for SQL generation and narrative outputs. The findings suggest that AI can strengthen business intelligence by increasing return on investment, reducing time-to-insight, and expanding analytics adoption across user roles. This work contributes to the field of decision intelligence by providing practical observations and considerations of deploying AI-augmented analytics in enterprise environments.

Due to the exponential increase of the data in the distributed commerce settings, new issues of the real-time decision making and operational scale have emerged. In order to solve these problems, this paper proposes an orchestration framework that brings together real-time machine learning (ML) models with large language models (LLMs) to optimize distributed workflows in e-commerce ecosystems. The suggested framework relies on micro services-based architecture, edgecloud synergy, and semantic data pipelines to enable low-latency inference, adaptive learning, and context-aware automation. With the integration of reinforcement learning to adapt dynamic workflows and intelligent task coordination and personalized customer interactions based on LLMs, the system provides efficiency in operations and enhanced user interaction. Evaluations on a real-world retail data set show, under varying load conditions, large latency reduction and throughput gains as well as high model accuracy. The scalability, fault tolerance and DataOps/MLOps pipeline integration are also studied, which makes the model applicable to enterprise-level deployment across multi-cloud setups.