large language models

NHOS Oracle Matrix Search Engine™ A Local Hybrid Retrieval and Contextual Discovery Architecture for Personal Health Knowledge

NHOS Oracle Matrix Search Engine™: A Local Hybrid Retrieval and Contextual Discovery Architecture for Personal Health Knowledge Technical Research White Paper — Version 1.2 · September 2026Corresponding Software Version: NHOS Oracle Matrix Search Engine v8.0.0Research Stage: Implemented Research System & Validation Fra...

Capability-Oriented Software Engineering (COSE): An Incremental Methodology for Building Reusable Capability Libraries

Software engineering projects frequently suffer from architectural re-invention, encapsulating functional knowledge within individual project silos. This paper presents Capability-Oriented Software Engineering (COSE), a formal incremental methodology designed to systematically convert software engineering effort into a...

The Data of Six Large Language Models on Four Core Medical School Courses

Large language models (LLMs) have demonstrated remarkable potential in medical education, yet their performance on discipline-specific medical school course examinations remains incompletely characterized. This study evaluated six contemporary LLMs on final examinations for four core medical school courses: Surgery, Mu...

Cost-Amortised Reasoning via Self-Distilling Neuro-Symbolic Cascades: From Knowledge-Graph QA to Regulatory-Compliance Checking

Large language models make knowledge-graph question answering (KGQA) fluent, but every query pays the full price of an LLM call. We present TACET (Latin: it is silent), a self-distilling neuro-symbolic cascade that amortises that cost by progressively migrating a streamed workload off the LLM and onto cheap, checkable...

Multilingual Sentiment-Aware Text Summarization: A Reinforcement Learning Approach for Consistency Maintenance

Reproducibility Artifacts for an Integrated Blockchain and Large Language Model Decision Support System for Organ Donation and Transplantation Workflows

This release adds reproducibility support for the revised evaluation: separates strict output completion, classification performance conditional on valid output, and failure-penalized end-to-end performance; adds a 20-scenario post hoc robustness suite covering safeguard boundary conditions, invalid model outputs, and...

spec-resistance corpus: 30-model, 627,491-trial dataset for "Large language models that near-perfectly evaluate products systematically recommend a worse familiar brand"

Primary computational corpus for the manuscript "Large language models that near-perfectly evaluate products systematically recommend a worse familiar brand" (Affonso, Spears School of Business, Oklahoma State University). Contains 627,491 controlled product-recommendation trials across 30 frontier language models from...

The Core Ring: Composition, Not Components, Is What Large Language Models Lack

Large language models have improved chiefly by scaling one monolithic computation. We argue the binding constraint is no longer missing components—language, reasoning, and knowledge are all present—but a missing composition in which they form a closed loop. We propose the Core Ring, an architecture with no single core....

UMOSU/UMOKWAI — Foundational Phenomena and Domains of Complexity: What an AI Needs to Answer Reliably

AbstractUMOSU/UMOKWAI — Foundational Phenomena and Domains of Complexity: What an AI Needs to Answer Reliably**This deposit collects the first systematic, phenomenon-by-phenomenon analysis conducted within the UMOSU/UMOKWAI ontological and epistemic-operational model. The model classifies reality through six foundation...

SSQT: A Hardware-Friendly Fusion Compression Framework of Structured Sparsification and Sensitivity-Driven Quantization for Large-Scale Language Models

Large language models (LLMs) are often memory-bandwidth bound during autoregressive decoding, so reducing weight storage does not automatically produce parallel speedup when the compressed representation is irregular. We present SSQT, a post-training framework that jointly applies hardware-aligned structured sparsifica...

The Core Ring: Composition, Not Components, Is What Large Language Models Lack

Large language models have improved chiefly by scaling one monolithic computation. We argue the binding constraint is no longer missing components—language, reasoning, and knowledge are all present—but a missing composition in which they form a closed loop. We propose the Core Ring, an architecture with no single core....

Cross-Layer Performance Analysis of Single-GPU Large Language Model Inference

The performance of single-GPU LLM inference is characterized by strong cross-layer interactions spanning model architecture, runtime scheduling, operator execution, and GPU microarchitecture. Unfortunately, a unified understanding of single-GPU LLM inference bottlenecks is still lacking due to two limitations: the lack...