small language model

Rank Access and Closure Depth in the Relation-Layer Cohomology of Token Arenas

Rank Access and Closure Depth in the Relation-Layer Cohomology of Token Arenas A refuted conjecture, a refuted mechanism, and two species of invariant harmonic residue Driven by Dean Kulik October 2026 Abstract A Kdisj-type relation layer over a token arena assigns cohomology to a graph by declaring which closed loops...

Rank Access and Closure Depth in the Relation-Layer Cohomology of Token Arenas

Rank Access and Closure Depth in the Relation-Layer Cohomology of Token Arenas A refuted conjecture, a refuted mechanism, and two species of invariant harmonic residue Driven by Dean Kulik October 2026 Abstract A Kdisj-type relation layer over a token arena assigns cohomology to a graph by declaring which closed loops...

PREreview of "Who judges the judges? Governance from metrics: a runtime framework for continuous LLM compliance monitoring"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23129468. Summary This paper attacks what it calls the compliance fiction: the industry practice of treating regulatory conformity as a binary verdict declared at deployment time, w...

When External Numerical Guards Detect Injected Faults in Neural-Network Inference

External guards can withhold an accelerator's output and trigger recovery, but their usefulness depends on which faults their numerical tests detect. We examine software fault-injection campaigns in convolutional networks, a vision transformer, and a decoder language model, distinguishing reported results from independ...

When External Numerical Guards Detect Injected Faults in Neural-Network Inference

External guards can withhold an accelerator's output and trigger recovery, but their usefulness depends on which faults their numerical tests detect. We examine software fault-injection campaigns in convolutional networks, a vision transformer, and a decoder language model, distinguishing reported results from independ...

LLM-Driven Verilog Generation and Verification for RISC-V Processor Design

Large language models (LLMs) are increasingly used in Electronic Design Automation (EDA) to write hardware description code. This paper reviews how LLMs generate and verify Verilog for a RISC-V processor datapath, a core topic in Computer Architecture and Organization. The review is built around the AI-driven logic syn...

Quality, safety, and readability of consumer-facing generative AI responses to end-of-life questions relevant to surrogate decision-makers for older adults: a benchmark evaluation

As the population ages and the prevalence of multiple coexisting conditions increases, end-of-life decision-making for older adults is becoming increasingly complex in clinical practice. limited access to relevant medical information and differences in medical knowledge between clinicians and surrogate decision-makers....

Generating Explainable Domain Models via Attention and Confidence

LLM-Driven Verilog Generation and Verification for RISC-V Processor Design

Large language models (LLMs) are increasingly used in Electronic Design Automation (EDA) to write hardware description code. This paper reviews how LLMs generate and verify Verilog for a RISC-V processor datapath, a core topic in Computer Architecture and Organization. The review is built around the AI-driven logic syn...

Responsible use of large language models in digital health: an equity-first governance framework

Large language models (LLMs) are being rapidly deployed for digital health, yet their equity impacts remain poorly characterized. We present an equity-first audit framework anchored in the first nations mental wellness continuum framework (FNMWCF) and apply it to three open-source LLMs (LLaMA-3.2:latest, Mistral-7B, an...

MIND-T1D: study protocol for a multidimensional neurocognitive and psychological assessment of Indian children and young adults with type 1 diabetes.

The MIND-T1D protocol is described, a culturally sensitive, clinically feasible, family-centred assessment framework for Indian children and young adults with T1D that addresses language-related barriers and educational variability by incorporating neuropsychological assessments and integrating child, parent and biolog...

Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models

Hallucination detection is particularly important for medical language models, but repeated-sampling approaches are expensive and existing uncertainty-head resources do not directly transfer to a new backbone and language. We adapt the LLM Uncertainty Head (LUH) framework to Aya-Expanse-8B-based Persian medical models,...