large language models

BigMixSolDB: Extraction of a solubility database in solvent mixtures with an uncertainty-quantified large language model-based pipeline

Supplementary material for the publication "BigMixSolDB: Extraction of a solubility database in solvent mixtures with an uncertainty-quantified large language model-based pipeline". This Zenodo record contains the data and supporting materials for BigMixSolDB, a workflow for extracting experimental solubility data from...

ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning

Reasoning-Driven Design of Single-Atom Catalysts via a Multiagent Large Language Model Framework

Abstract Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally require human expertise. This progress has extended into materials discovery, where LLMs introduce a new paradigm by leveraging rea...

Cost-Efficient Large Language Model-Assisted Title and Abstract Screening for Systematic Reviews: Method Evaluation and Validation

Reproducibility Package for "A Governance Architecture for AI-Assisted Validation of Construction Cost Knowledge: Combining Domain Rules with LLM Semantic Analysis"

This deposit contains the reproducibility materials for the manuscript "A Governance Architecture for AI-Assisted Validation of Construction Cost Knowledge: Combining Domain Rules with LLM Semantic Analysis." The manuscript presents a deployed tender management system that validates Bill of Quantities (BOQ) pricing for...

Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions

Background. Large language models (LLMs) are increasingly proposed as clinical assistants in critical care, yet their behaviour under prolonged clinical context, conflicting data and authoritative pressure remains insufficiently evaluated. This is particularly relevant for offline or resource-constrained environments,...

DroneServer v2.0.2: an LLM-agnostic, MAVLink-based drone command-and-control MCP server with an untrusted-commander safety layer

Archived copy of the DroneServer code release v2.0.2 (git tag v2.0.2, commit 3281e6c3755814318d436556690c28e669f8c2e9), the version reported in the article An LLM-Agnostic, MAVLink-Based Drone Command and Control Interface and Agentic Harness Using the Model Context Protocol (Ramos Silva and Burke; preprint arXiv:2601....

A community-driven recommender system for speed safety camera policy using large language models and knowledge graphs

Speeding is a persistent contributor to roadway fatalities, accounting for over 30% of traffic deaths in the U.S., prompting the widespread use of Speed Safety Cameras (SSCs) as a proven countermeasure. However, despite their effectiveness, public skepticism, which is driven by concerns over fairness, transparency, and...

On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study

Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To further improve data quality, numerous training data optimization techniques have been proposed; however, their overall effectiveness has not bee...

Retrieval-Augmented Decoding for Improving Truthfulness in Open-Ended Generation

Ensuring truthfulness in large language models (LLMs) remains a critical challenge for reliable text generation. While supervised fine-tuning and reinforcement learning with human feedback have shown promise, they require a substantial amount of annotated data and computational resources, limiting scalability. In contr...

The Epistemic Chain Reaction: Human-AI Multiplication from Questions to Readout-Distinguishable Hypotheses — A Controlled Amplification Architecture for Scientific Discovery

Series. Paper IV of the Bounded Knower sequence. Paper I: From Problem to Hypothesis (DOI 10.5281/zenodo.22307148). Paper II: Knowledge Topology and the First Passage to Usable Hypotheses (DOI 10.5281/zenodo.22307561). Paper III: Before Evidence Can Decide (DOI 10.5281/zenodo.22307564). Status. GLOSA K0 conceptual and...

Automated literature screening using large language models in headache research: a comparative performance evaluation of seven models using zero-shot and iterative prompt refinement

Large language models (LLMs) show promise for automating literature screening, although their performance varies across clinical domains and further evaluation is required for broader implementation. This study aimed to assess the accuracy, reliability and feasibility of literature screening in headache research...