I’ve released a research monograph proposing the Two-Body Hypothesis : capability production and behavioral routing may be functionally separable enough for targeted alignment. Across several small transformer settings, safety and sycophancy attribution repeatedly peaked near the end of the network. I then tested sparse intervention, late-layer safety fine-tuning, layer-frozen GRPO, and adapter merging. The main result is not that models possess one universal “alignment layer.” It is that spatially targeted alignment appears testable and potentially useful. Important limitations: the audits are small, some comparisons use unmatched learning rates, and the 96–97% depth observation is not established as universal. I’d especially value criticism of the causal interpretation and suggestions for decisive cross-architecture experiments. Paper: https://doi.org/10.5281/zenodo.20691149 submitted by /u/Technocratix902
Originally posted by u/Technocratix902 on r/ArtificialInteligence