Comprehensive technical specifications for the structural analysis of AI safety research methodologies, alignment verification protocols, and risk mitigation frameworks. This document details the load-bearing components of current safety architectures and identifies critical failure points requiring immediate engineering attention.
Circuit-level analysis of transformer architectures to identify and catalog computational subgraphs responsible for specific model behaviors. Current resolution limited to attention head granularity.
Systematic verification of RLHF reward signals against ground-truth human preference distributions. Includes overoptimization stress testing and Goodhart failure mode analysis across deployment conditions.
Debate, recursive reward modeling, and iterated amplification frameworks for maintaining human oversight as model capabilities exceed direct human evaluation capacity.
Red-team evaluation infrastructure for systematic discovery of jailbreaks, prompt injection vectors, and emergent misuse pathways. Includes automated attack surface enumeration and regression testing suites.
Development of reproducible, cross-laboratory benchmark specifications for quantifying alignment properties. Covers honesty, helpfulness, harmlessness, and corrigibility dimensions with defined tolerance bands.
Policy frameworks and technical checkpoints for staged model release. Includes capability evaluation thresholds, safety case documentation requirements, and incident response coordination protocols.