The article shows that when an LLM evaluates if text meets a given criterion, the answer already sits in its hidden state before any token is generated. By capturing the hidden representation at a designated seed token and training a small MLP head (with optional LoRA sharpening and isotonic calibration), you get a fast, calibrated classifier that accepts arbitrary English criteria without per-criterion retraining.

• LLMs encode the yes/no answer to a criterion in their hidden states before generating any text, so you can extract a calibrated probability from the hidden state at a seed token (~70% layer depth) instead of generating output.
• A small probe (MLP/linear head) trained on hidden states from a few thousand labeled triples, plus isotonic calibration, generalizes to arbitrary unseen criteria without retraining.
• Training a LoRA to produce one-sentence verdicts (but only reading the resulting hidden state, never generating) sharpens the signal by reshaping the residual stream geometry at the seed position.
• Prefilling content once and treating criteria as short continuations enables cache-efficient scoring against many criteria, though it can fail on cases requiring deep criterion-content interaction, which still need full cross-encoder prompts.