small language model

How Far Does a Given Level of Descriptive Granularity Remain Valid in Models of Biological Behavior?A Comparative Research Program for Reproduction, Prediction, Intervention, and Coarse-Graining

This research note proposes a comparative program for examining how far different levels of descriptive granularity remain sufficient in models of biological behavior. Rather than assuming that one explanatory level is universally correct, the paper separates model scope, granularity, form, available information, and l...

Evaluating Large Language Models with a Unified Prompt-Based Pipeline for Turkish News Summarization and Tag Extraction

As access to information grows ever faster, the acceleration of news flow demands the automatic and accurate analysis of texts. This study aims to measure the multitasking performance of summarizing and tag extraction tasks in Turkish news texts using a prompt-based unified pipeline with a small dataset, employing diff...

OSIRIS v4.2.1: a governed local console for a living language model

OSIRIS is a local-first console built around a small language model (osiris.nclm) that learns from every conversation and from the author's documents, and answers in its own voice only after passing a held-out test (30 held-out exchanges at ≤ 2.0 bits/byte and below a unigram baseline). Until then a local Ollama model...

Generative AI-Mediated Feedback and L2 Grammatical-Lexical Development: A Scoping Review of Longitudinal Research Gaps

The rapid adoption of generative artificial intelligence (AI) tools such as ChatGPT in second language (L2) writing instruction has produced a fast-growing body of research on AI-generated written corrective feedback. Two recent scoping reviews have mapped this literature broadly, focusing on feedback quality, learner...

Editorial: Artificial intelligence and anti-cancer drugs: advancing discovery, personalization, and precision oncology

It is fitting that the Topic opens with a broad review of the field itself. Nagrami et al. map AI's footprint across the entire anti-cancer drug pipeline -target identification, virtual screening, molecular docking, multi-omic integration, and the still-unresolved question of clinical validation. Their review is a usef...

Tron-1B: Fast, Calibrated Typed Decisions with a Set-Attention Option Head

Applications built around large language models make many small decisions: which queue a ticket belongs to, whether a message is a prompt-injection attempt, how urgent a request is, whether an agent's step succeeded. Routing each of these through a generative LLM is slow, costly and hard to calibrate. We present Tron-1...

A Driven, Finite‑Memory Dissipative System: The Empirical Boundary of Non‑Equilibrium Dynamics and Criticality in a Quantized Hybrid Language Model

Where the Answer Is Born: Causal Interventions on the Information Trajectory of a Quantized Hybrid Language Model

Few-Shot Versus Zero-Shot Detection of Novel Objects Using Pretrained Vision-Language Models: A Comparative Study on Kikoriki Characters

Pukara

A privacy-first, self-hosted gateway for local large language models (Ollama). Pukara runs a Dockerized Ollama server behind an application-layer encrypted proxy, and ships a desktop client that pseudonymises sensitive entities on-device (BERT + regex) before anything leaves the machine. The name Pukara (Quechua for "f...

Spot and Explain Public Trust: Large Multimodal Model-Based Forensic Reasoning for Synthetic Image Detection

Clinically Structured Retrieval: Can a Well-Designed RAG Layer Add Value Over a Base Language Model in Clinical Oncology?

Study 1 (currently under review) found that retrieval-augmented generation (RAG) and fine-tuning both *degraded* clinical answer quality relative to an un-augmented base model in oncology. That study bounded its own retrieval conclusion explicitly: its index used 500-character chunks with 100-character overlap, small r...