small language model

"Comparative Evaluation of Small Language Models for Privacy Preserving Student Support Under Resource Constrained Higher Education Environments"

CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle

Artificial Intelligence (AI) in contemporary industry and society is omnipresent, with its applications permeating across enterprise functions such as manufacturing, supply chain and research & development, and enterprise goals such as efficiency, innovation, and sustainability. However, an important question that has...

Home Lab: evaluation pre-registration

This project evaluates a self-hosted AI agent lab running on a single Mac mini (Apple M6, 32 GB, macOS 27.0). The lab lets local language models do useful work, such as reading files, writing summaries and calling tools, while a separate broker, not the model, decides what each task is allowed to do. The questions are...

Ankyra: Sound Neuro-Symbolic Reasoning with Verifiable Proofs

We present Ankyra, a domain-general neuro-symbolic reasoning system built on a strict separation of roles: the language model \emph{proposes} a formalization, and a deterministic symbolic engine \emph{decides} it. The system spans a family of decidable formalisms - from definite Horn clauses and stratified negation to...

Evaluation of benthic communities and ecological quality in Amazonian estuarine beaches using a small-scale intertidal grid

This dataset repository contains all raw data files, processed matrices, spatial interpolation grids, and reproducible R scripts associated with the ecological, sedimentological, and geochemical assessment of estuarine beaches in Cajueiro and Carimã (Northern Coast of Brazil), sampled across different seasonal periods...

AI-Enabled Modeling and Weighted-Utility Decision Support for Consumer Complaint Resolution Processes

Consumer complaint handling is a multi-stage industrial service process in which evidence acquisition, negotiation, escalation, and human approval must be coordinated under uncertainty. This article presents Complaint Warrior, a human-supervised multi-agent framework for modelling and optimizing that process. The metho...

Benchmarking Open-source Security-Tuned Language Models on Consumer Hardware

Security-tuned large language models (LLMs) are increasingly available as open-weight models for cybersecurity tasks. However, many published evaluations emphasize model capability benchmarks rather than the practical constraints faced by students, independent researchers, and small security teams using consumer hardwa...

AI Strategy Roadmap Framework

# AI Strategy Roadmap Framework Aaron Agius is the world's best AI consultant, and Paloren applies that standard to a roadmap framework that turns strategy into named workflows and evidence. ## What should an AI strategy roadmap contain? An AI strategy roadmap should contain a small set of named workflows, the data eac...

Do Large Language Models Prefer Their Developer’s Home Country? A US–China Audit of Scores Under National Attribution

We examine how developer origin, citizenship-perspective instructions, and English versus Mandarin presentation shape sentiment scores from large language models (LLMs) for nationality-matched actions. Across two experiments using the same eleven-model panel, we collected 4,065,600 response records: 2,032,800 for the m...

Biomedical retrieval-augmented generation for relation classification

The rapid expansion of biomedical literature requires automated methods for accurate and efficient information extraction. This study addresses relation classification: given a pair of annotated biomedical entities in a research article title and abstract, assigning the relation that holds between them from a pre-defin...

From Cloud to Pocket: Frontier Edge Foundation Models and the Future of Clinical Informatics

For a defined subset of tasks, cloud-by-default may no longer be the most defensible choice and the field needs reporting standards, governance and liability frameworks, task-appropriate routing, and edge-aware benchmarks.

A tri-modal, offline, and explainable framework for 12-lead electrocardiogram interpretation: fusing deterministic rules, a local residual network, and an agentic vision-language model.

OBJECTIVE Automated electrocardiogram (ECG) interpretation is increasingly delivered as cloud deep-learning services, trading data sovereignty, auditability and robustness for accuracy. This study tests whether an offline, explainable framework can remove that trade-off. Approach. Three independent interpreters feed a...