large language models

DS@GT ARC at Touché: Large Language Models for Retrieval-Augmented Debate

David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

LLM Compression with Jointly Optimizing Architectural and Quantization Choices

The Promise and Perils of Smart (City) Bots as Educational Tools

Written from an educator’s perspective, the paper aims to examine the increasing use of chatbot technology capable of answering user queries in various fields such as commerce, public service delivery, urban areas, higher education etc. by simulating human conversation through text messages or voice commands. We analys...

The Cell, Not the Call: A Preregisterable Audit Protocol for Repeated-Call Behavioral Experiments With Large Language Models

Abstract. When a hosted large language model answers the same prompt many times, the resulting completions can estimate that model's response distribution for that one prompt. They cannot, on their own, stand in for a sample of scenarios, participants, or models, and treating them as if they could undermines the claims...

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.