large language models

Time‑Accumulated Vacuum Cosmology v3.6 Unified Proper‑Time Tracking Model for Evolving Dark Energy ρ₍vac₎(x) = ρ₀ [1 + k Φ(x)/c²]

Time‑Accumulated Vacuum Cosmology v3.6 FEU: Proper‑Time Modulated Vacuum with Early‑Time Suppression and Density‑Dependent Screening FEU v3.6 is the latest corrected and stabilized version of the Time‑Accumulated Vacuum Cosmology framework. The model extends ΛCDM by introducing a small, smooth modulation of vacuum ener...

Data and Supplementary Materials for "Quantifying Materiality Misalignment Risk in ESG Reporting Using Large Language Models"

Canonical Noncommutative Defects Beyond Parikh Projection: Magnus Towers and an Unbounded Static-Dynamic Boundary Gap

We develop an order-sensitive defect formalism for substitution prefixes using the classical Magnus expansion of words. A substitution morphism induces a filtered endomorphism of the completed noncommutative tensor algebra, and an anchored substitution cut produces a multiplicative residual whose finite truncations for...

Consciousness Is a Phase Transition: A Two-Channel Thermodynamic Criterion and an Exclusion Principle

We propose that consciousness is a phase transition of a non-equilibrium dissipative structure, characterised by two independently controlled channels that must both be driven past threshold. The central result is an exclusion criterion: a macroscopic dissipative structure cannot be conscious unless it satisfies three...

A Large Language Model Agent Framework for Token-Efficient Tabular Data Error Detection

Reproducibility materials for the PeerJ manuscript “A Large Language Model Agent Framework for Token-Efficient Tabular Data Error Detection”. This release contains the LAED source code, five aligned dirty/clean benchmark CSV pairs, the dependency specification, and documentation required to reproduce the supplied exper...

A Multi-Agent Retrieval-Augmented Large Language Model Framework for Automated Soil Nail Design

Supplementary materials for

DOPS: Dynamic OPerator Sorting for Heterogeneous NPU-PIM LLM Inference

DOPS is a simulation and analysis framework for heterogeneous NPU-PIM large language model inference. It builds stage-aware execution graphs, evaluates dynamic operator placement policies, explores persistent weight layouts, and exports schedule summaries plus operator and communication traces.

Governed Software Construction for Large Language Models: Capability-Bounded Mutation, Dual-Gate Verification, and Evidence-Aware State Transitions

Large Language Models are evolving from code-completion tools into autonomous software agents capable of inspecting repositories, modifying multiple files, invoking tools, executing tests, and iteratively repairing software systems. This increased capability also creates a governance problem: contemporary coding agents...

PTTB-600: A Benchmark and Reproducibility Dataset for Long-Tail Plant Taxonomic Knowledge

PTTB-600 is a reproducibility dataset for evaluating large language models on long-tail plant taxonomic knowledge. It contains the complete Chinese-language question bank, answer key, answer-free evaluation set, model responses, item-level score matrix, model-level and dimension-level results, English figures, analysis...

Design and Evaluation of a RAG-Based Educational Assistant Grounded in Course Materials: A Case Study in Vocational Training

This study presents the design, implementation, and exploratory classroom evaluation of a course-constrained educational assistant combining Retrieval-Augmented Generation (RAG) with large language models (LLMs) in Vocational Education and Training (VET). The platform retrieves teacher-provided materials, generates cou...

boster-hub/llm-surgical-free-text-annotation: Version 2.0.0 – Reproducible Python pipeline

Overview Version 2.0.0 provides the revised reproducible Python pipeline for binary symptom annotation in German emergency department reports using locally deployed open-weight large language models. This release supersedes the original R-based workflow published in version 1.0.0. The earlier code and prompts remain av...

DPO vs. RLHF: An Empirical Comparison of Alignment Techniques for Large Language Models

This work presents a reproducible empirical comparison of Direct Preference Optimization (DPO) and Reinforcement Learning from Human Feedback (RLHF) for Large Language Model alignment. Experiments are conducted on GPT-2 (124M parameters) using the Anthropic HH-RLHF dataset. The study evaluates alignment quality, reward...