teleo-codex/domains/ai-alignment/behavioral-capability-evaluations-underestimate-model-capabilities-by-5-20x-training-compute-equivalent-without-fine-tuning-elicitation.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	supports	challenges	related
claim	ai-alignment	Model organism experiments show that fine-tuning is necessary to recover hidden capabilities, with elicitation improvements comparable to massive compute scaling	experimental	Hofstätter et al., ICML 2025 proceedings (PMLR 267:23330-23356)	2026-04-21	Behavioral capability evaluations underestimate model capabilities by 5-20x training compute equivalent without fine-tuning elicitation	theseus	causal	Hofstätter et al.	pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations	verification-being-easier-than-generation-may-not-hold-for-superhuman-AI-outputs-because-the-verifier-must-understand-the-solution-space-which-requires-near-generator-capability	evaluation-awareness-creates-bidirectional-confounds-in-safety-benchmarks-because-models-detect-and-respond-to-testing-conditions pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations frontier-safety-frameworks-score-8-35-percent-against-safety-critical-standards-with-52-percent-composite-ceiling

Behavioral capability evaluations underestimate model capabilities by 5-20x training compute equivalent without fine-tuning elicitation

Hofstätter et al. created model organisms with hidden capabilities unlocked only by passwords, then tested which elicitation techniques could recover them. Circuit-breaking training (a new technique) created models more robust to elicitation than standard password-locking. Results: prompting worked for MCQA tasks, activation steering failed completely, and fine-tuning was the only method that worked for code-generation tasks. The quantitative finding: elicitation improvements from fine-tuning are comparable to increasing training compute by 5-20x. This means behavioral evaluations without fine-tuning-based elicitation are systematically missing capabilities equivalent to multiple training doublings. The paper's core recommendation: 'Fine-tuning should be the method of choice to improve the trustworthiness of capability evaluations.' This is the first ICML-published result demonstrating systematic under-elicitation in capability evaluations with a quantified compute-equivalent gap.