small language model

Selective Forgetting in LLMs

Executive Summary This work approaches the problem of selectively forgetting knowledge from a large language model (LLM) for the purposes of safety, copyright, security, or otherwise. Also known as machine unlearning, this entails training a model to forget certain elements of the dataset on which it was trained. Unlea...

Cerebral microbleeds on susceptibility-weighted imaging, cognitive domains, and serum neurofilament light chain in adults with hypertension

In adults with primary hypertension, greater CMB burden was associated with MCI and selected cognitive domains, whereas cognitive performance did not differ according to CMB location, and prospective validation is required before clinical application.

Automated Unit-Test Generation with GPT-5: An Empirical Comparison

Large language models (LLMs) are increasingly proposed as automated test generators, yet small, fully reproducible measurements with current-generation models remain rare in the open literature. We report a controlled study in which four configurations of the GPT-5 model family—gpt-5-nano, gpt-5-mini, gpt-5.4, and gpt-...

Clinical use of ozone therapy: a critical appraisal of evidence, safety, and methodological quality

Ozone therapy is frequently promoted as a versatile and minimally invasive medical intervention purported to exert a broad range of biological effects, including anti-inflammatory, antimicrobial, immunomodulatory, and regenerative actions. These claims have led to its application across diverse clinical domains, such a...

A Japanese-Dietitian Prompt Systematically Shifts Portion Estimates in LLM-Based Nutrient Estimation from Food Images: A Multi-Dataset, Multi-Model Study

Background/Objectives: In food-image nutrient estimation with vision-language models (VLMs), portion size is a dominant source of error, yet how a prompt shifts the estimated amount—and in which direction—remains uncharacterized. We tested whether a Japanese-dietitian prompt acts as a systematic, directional influence...

Preregistered Tests of Local-Noise Predictions for GHZ Coherence Under Matched Qubit Environments on IBM Kingston

DOI: 10.5281/zenodo.22283967Record set: EC-MECH campaign (EC-MECH-001 pilot + EC-MECH-002)Related to: EC-STORAGE-001, DOI 10.5281/zenodo.21299091 Research question: Can the coherence lifetime of an entangled multi-qubit state be predicted from the measured coherence losses of its individual qubits? Plain language summa...

Developmental Patterns in Deaf Children's Signed Composition Across Narrative, Informational, and Opinion Genres.

PURPOSE This study examined developmental change in macrostructural (discourse-level) signed composition among deaf children over the course of one academic year. The study also investigated whether grade level, early language access, and demographic characteristics were associated with differences in change across nar...

Selective Forgetting in LLMs

Executive Summary This work approaches the problem of selectively forgetting knowledge from a large language model (LLM) for the purposes of safety, copyright, security, or otherwise. Also known as machine unlearning, this entails training a model to forget certain elements of the dataset on which it was trained. Unlea...

Collaborative Knowledge Distillation and Reinforcement Learning for Automated Ticket Triage in Large-Scale Production Systems

CoTriage is presented, a practical and scalable end-to-end automated ticket triage system, designed and deployed for real-world industrial scenarios and reduces the average triage time to 15.0 seconds and significantly enhances operational efficiency and accelerating incident resolution in practice.

The Role of Artificial Intelligence in Teaching Ophthalmology Skills: A Systematic Review

Ophthalmology training requires visual interpretation, procedural skill, and supervised clinical reasoning, but trainee volume, faculty availability, and case mix constrain education. AI-enabled tools may support scalable instruction, assessment, and feedback. To evaluate AI-enabled interventions for improving ophthalm...

Similarity Finds the Fact, Not the Version: A Co-Trained Order Stamp in the Key Resolves Version Conflict in Persistent Model-Internal Memory

FadeBench — Pre-registered criteria, scoring guidance & deviations (with per-item provenance table)

This registration timestamps the pre-registration materials for **FadeBench**, a benchmark for incremental moral drift ("ethical fading") in small/local language models. The registered bundle is the current pre-registration documents (`PREREG_*.md`), the scoring guidance (`SCORING_GUIDANCE_GRADIENT.md`), the deviations...