당신의 안전 벤치마크는 고장 났다: 100M 토큰이 에이전트의 숨겨진 능력을 잠금 해제
영국 AISI가 사이버 과제에서 추가 컴퓨팅을 제공하자 AI 에이전트 성공률이 25%나 급등하는 것을 발견하여, 표준 고정 예산 평가가 최첨단 모델의 위험성과 능력을 체계적으로 과소평가하고 있음을 보여줍니다.
the uk's ai safety institute tested frontier models across seven benchmarks and found that performance scales significantly with test-time compute, contradicting standard fixed-budget evaluation methods. in cybersecurity and software engineering tasks, success rates increased by up to 25% when token budgets rose from one million to ten million, with some tasks requiring over 100 million tokens to solve. researchers discovered a power law linking human task duration to agent token consumption, implying that fixed budgets cap potential on complex problems before the agent actually fails. newer models gain more from extra compute than older ones, extending their reach to harder tasks and increasing reliability on existing ones, which signals that current safety testing may provide a false sense of security about model limits.