Title: Evaluating Weak-to-Strong Legibility in Reasoning Models

Abstract: Reasoning language models (RLMs) and their intermediate chains of thought are becoming increasingly pivotal in multi-agent frameworks, including applications like inter-model monitoring and the distillation of knowledge into smaller architectures. When agents operating at varying capability levels must collaborate, stronger models are required to generate traces that are comprehensible to weaker counterparts. We define this objective as "weak-to-strong legibility." The reliability of large-scale models is partially contingent upon this legibility characteristic. Specifically, for safety oversight, employing weaker monitors could become a standard practice for establishing reliability scaffolds, provided it remains cost-effective. Achieving legibility necessitates that the structure of these decision-making traces be accessible to weaker monitoring systems. Current metrics focused on efficiency fall short by emphasizing conciseness rather than capturing the essential dimension of "thoroughness."

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC