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The Alignment Stress Map: Runtime Per-Token Sensitivity Attribution via Weight Delta Projection in Transformer Language Models
We propose the Alignment Stress Map (ASM), a runtime monitoring architecture that derives per- token, per-layer sensitivity attribution from the weight delta between a base language model and its alignment-tuned counterpart. The core observation is simple: at any point during inference, the magnitude of the alignment correction — computed by projecting current activations through the precomputed weight delta — indicates how much corrective force the alignment training applies in that region of representational space. We formalize this as a layerwise amplitude field over the model's forward pass, show that the transformer's attention mechanism permits exact decomposition of this field into per-token contributions at each attention sublayer, and define the resulting per-token amplitude trajectory as a fine-grained, interpretable diagnostic signal. The architecture requires only one additional matrix multiplication per monitored layer, using quantities already computed during standard inference. We characterize the expected structure of the amplitude field, derive specific empirical predictions about the relationship between amplitude and adversarial vulnerability, and outline an experimental program to validate or falsify these predictions using open-weight model pairs.
The contribution is architectural rather than algorithmic: we assemble existing components — weight deltas from task arithmetic, attention decomposition from standard transformer inference, behavioral subspace identification from mechanistic interpretability — into a monitoring instrument that has not previously been implemented because the contributing research programs have not been connected.
