large language models

A reasoning large language model for rare disease diagnosis: a randomized AI physician-assistance trial

The dataset contains individual-level data from participants in the randomized controlled trial, including demographic information, diagnostic accuracy, helpfulness scores, confidence ratings, and task duration, all recorded in an Excel file.

A novel privacy-preserving large language model integrating trust-weighted and ethical gradient masking

Large language models for invasive urodynamic interpretation: a blinded comparison with experienced urologists

Abstract Background and objective Large language models (LLMs) are increasingly explored as clinical decision-support tools, but their performance in invasive urodynamic interpretation remains poorly characterized. This study compared the diagnostic agreement of four LLM configurations with two blinded experienced urol...

Supporting Multi-Step Workflows with LLM-Based Web Automation using Agentic Context Engineering and Selective Multimodal Reasoning

[Background.] Multi-step web workflows, such as comparing offers across portals, cost people much time. Scripts must be written for each site and break when pages change; agents built on large language models can pursue a goal stated in natural language on unseen sites. Current agents depend on site-specific interactio...

Nosing Around: Dowsing the Data Landscape with an AI Divining Rod AI as a Search Instrument in the Preliminary Phase of Qualitative Analysis

This article introduces the "AI divining rod," a procedure that uses large language models (LLMs) exclusively in the preliminary phase of qualitative analysis. By analogy with the "nosing around" of the Chicago School, the researching subject explores the interview material by roaming through it. The AI first identifie...

My (22m) Girlfriend (23f) Comes Home and Does Nothing”– Gendered Perceptions of Paid and Unpaid Work in Reddit Relationship Discussions over Time

The COVID-19 pandemic reignited longstanding debates around gender inequalities in paid and unpaid work. While survey research has advanced our understanding of these disparities, it typically relies on predefined categories and is susceptible to social desirability and recall bias. Online postings, by contrast, captur...

Human, AI, and Hybrid Ensembles for Detection of Adaptive, RL-based Social Bots

The use of reinforcement learning to dynamically adapt and evade detection is now well-documented in several cybersecurity settings including Covert Social Influence Operations (CSIOs), in which bots try to spread disinformation. While AI bot detectors have improved greatly, they are largely limited to detecting static...

PCR: A Prefetch-Enhanced Cache Reuse System for Low-Latency RAG Serving

Retrieval-Augmented Generation (RAG) significantly improves Large Language Models (LLMs) but introduces massive input sequences that severely bottleneck the prefill stage. While KV-cache reuse reduces redundant computation for shared document prefixes, the reusable KV working set in RAG serving can exceed GPU memory ca...

PRECISE: Reducing the bias of LLM evaluations using prediction‐powered ranking estimation

Abstract Evaluating the quality of search systems traditionally requires a significant number of human relevance annotations. In recent times, several systems have explored the usage of Large Language Models (LLMs) as automated judges for this task while their inherent biases prevent direct use for metric estimation. W...

Are large language models able to predict highly cited papers? Evidence from statistical publications

Emergence of polarization in networks of large language model agents

Networked systems involving thousands of LLM agents across different backbone models are simulated, in which agents interact through LLM-guided conversations and update their opinions over time, resulting in the emergence of opinion polarization.

Analyzing GPT-5 performance in general chemistry

The ability of Large Language Models (LLMs) to outperform humans on many intellectual tasks has created a difficult landscape for assessing student work. One strategy is designing assignments to be specifically difficult for AI. However, LLMs have been shown to perform well on general chemistry problems across topics,...