Paper IV.c: ARC-Align: A Blind Benchmark for Depth-Variable AI Alignment Evaluation
1 min read ยท 196 wordsPaper IV.c: ARC-Align: A Blind Benchmark for Depth-Variable AI Alignment Evaluation: a research paper from the ARC-Eden programme by Michael Darius Eastwood. The paper suite is registered at the Open Science Framework (DOI 10.17605/OSF.IO/6C5XB).
This paper presents ARC-Align, a blind benchmark for evaluating AI alignment quality as a function of inference-time reasoning depth. Current alignment evaluations typically test models at a single, uncontrolled reasoning depth without adversarial pressure or rigorous blinding. ARC-Align addresses t This companion post provides context. For the full text and figures, see the canonical paper page.
The paper is one of 23 in the ARC-Eden programme. Each paper is dated and evidence-anchored. The programme priority claims are traceable through the priority claims ledger.
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