Structural–Relational Cognitive Robustness: A Failure-Oriented Evaluation Framework for Large Language Models

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Authors: HIDEYUKI CHINO
Cited by: 0

Abstract

This repository accompanies the manuscript submitted to the Journal of Machine Learning Research (JMLR): “Structural–Relational Cognitive Robustness: A Failure-Oriented Evaluation Framework for Large Language Models.” The project introduces SCRC (Structural–Relational Cognitive Robustness), a failure-oriented evaluation framework designed to measure relational stability of large language models under epistemic stress, rather than task accuracy. The repository contains: Complete evaluation code used in the experiments Full experimental logs for all tested models Aggregated metric tables reported in the manuscript Task definitions and prompt structures Instructions to fully reproduce all reported results No external datasets are required. All experiments are deterministic and fully specified. This archive is intended to ensure transparency, reproducibility, and independent verification of the reported findings. GitHub repository: https://github.com/hideyuki49/llm-cognitive-stability

Fields of Study & Subjects

Adversarial Robustness in Machine Learning Topic Modeling Natural Language Processing Techniques

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 February 2026
Language
EN
Document type
Preprint