teleo-codex/domains/ai-alignment/linear-probe-accuracy-scales-with-model-size-power-law.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	challenges	related
claim	ai-alignment	Representation monitoring capability improves predictably with scale across 12 models from 0.5B to 176B parameters with R=0.81 correlation	experimental	Nordby, Pais, Parrack (arXiv 2604.13386, April 2026)	2026-04-21	Linear probe accuracy for deception detection scales with model size following a power law of approximately 5 percent AUROC per 10x parameter increase	theseus	correlational	Nordby, Pais, Parrack	verification-degrades-faster-than-capability-grows	representation-monitoring-via-linear-concept-vectors-creates-dual-use-attack-surface verification-degrades-faster-than-capability-grows behavioral-capability-evaluations-underestimate-model-capabilities-by-5-20x-training-compute-equivalent-without-fine-tuning-elicitation

Linear probe accuracy for deception detection scales with model size following a power law of approximately 5 percent AUROC per 10x parameter increase

Testing across 12 models ranging from 0.5B to 176B parameters, Nordby et al. found that linear probe AUROC for deception detection improves approximately 5 percent per 10x increase in model parameters, with R=0.81 correlation. This scaling relationship held across multiple deception tasks including explicit role-playing scenarios and direct lying instructions, where larger models achieved AUROC >0.95. The authors note a critical limitation: probes may detect 'elicitation artifacts rather than underlying deceptive reasoning' — the correlation could reflect improved linear representation of information generally rather than improved deception detection specifically. Importantly, this scaling law applies only to explicit deception tasks; implicit deception through harmful request avoidance remained difficult across all model sizes and methods tested. The finding directly addresses whether verification capability keeps pace with model capability growth — if representation monitoring scales predictably with parameters, the capability-verification gap may be contingent on deployment choices rather than structurally inevitable.