large language models

Misaligned by design: evaluating and deploying generative AI for the real-world conditions of primary care: a Nordic and European perspective

The decisive problem is not raw performance but misalignment between how these systems are evaluated and deployed and real primary-care conditions, and some proposed uses may not survive evaluation under real-world conditions.

The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions

This work investigates how large language models reason about responsibility and its consequences, tracing their judgments across successive levels, from the behavior, to the resulting illness, to the denial of care.

Machine learning for high-entropy catalysts: methods and applications

This review systematically summarizes the latest methodological advances and applications of ML methods for HEAs in catalysis, discusses the challenges, and offers insights into future research directions to support the rational design and efficient development of catalysts.

From narrative to action: A hierarchical cognitive large language model agent framework for interpretable and adaptive human mobility behavior generation

The Coordination Compression Trap: A Dynamic Model of AI-Enabled Productivity, Verification Debt, and Organisational Resilience in Global Knowledge Firms

Aims: This study develops a dynamic explanation for the gap between task-level artificial intelligence productivity and durable enterprise value. It introduces verification debt, the accumulated stock of unverified assumptions, weak review, concealed dependencies, deferred validation, and capability loss. Study Design:...

Sufficient for Whose Answer? Three Predicates Under One Word in Selective Retrieval-Augmented Generation, and Why the Only One Tied to Correctness Cannot Be Computed Where the Gate Runs

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A retrieval-augmented system that answers only when its retrieved context suffices needs a predicate that says when it does. Three incompatible predicates are in circulation under the word "sufficient", and results obtained under different ones are routinely compar...

Emergent Reasoning in Large Language Models: A Systematic Evaluation Across Task Complexity

It is argued that emergent reasoning in LLMs is a product of three factors: model size, prompting approach, and evaluation metric, and proposed implications for designing benchmarks and assessing capabilities are proposed.

Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Cross-Generational Framework Applied to CS2013 and CS2023

This archive contains the data, alignment maps, rater materials, and source code needed to audit and reproduce every figure and table in the manuscript. The study measures how completely an accredited BSc Computer Science program covers the ACM/IEEE/AAAI computing body of knowledge through three nested lenses: topical...

Mechanistic control of large language models as simulated participants via linear representation

Spatiotemporal dynamics of lexical and morphosyntactic planning during language production revealed by magnetoencephalography

Fluent sentence production relies on the precise and timely coordination of lexical retrieval, morphosyntactic planning, and motor execution. While a left-dominant fronto-parieto-temporal network has been identified for these subcomponents of sentence production, less is known about the time course of engagement of the...

An Improved Prediction Method for Telemetry Time Series Based on a Large Language Model

Is there a way to improve language models accuracy in solving physics problems in Russian and English languages?

Large language models are increasingly used to solve educational physics problems, but their accuracy remains inconsistent. This study investigates whether prompt formulation can improve the performance of language models on school-level physics problems in Russian and English. In total 21 language models were tested o...