aiqbench analysis
This research introduces "weak-to-strong legibility," a critical metric for ensuring that high-capability reasoning models produce decision traces accessible to lower-tier agents in multi-agent systems. By shifting focus from mere conciseness to thoroughness, the study addresses a key gap in current efficiency metrics that is essential for building reliable safety oversight and distillation pipelines. This work provides a necessary foundation for scaling heterogeneous model ecosystems where trustworthiness depends on interoperable reasoning traces.
Measuring Weak-to-Strong Legibility of Reasoning Models
arXiv:2603.20508v2 Announce Type: replace-cross Abstract: Reasoning language models (RLMs) and the intermediate chains of thought they emit play an increasingly central role in multi-agent setups such as inter-model monitoring or distillation into smaller models. When agents at different capability tiers must cooperate, strong models need to produce traces digestible by weaker ones. We refer to this goal as "weak-to-strong legibility". Trustworthiness of large models depends in part on this legibility property. For safety oversight in particular, adoption of weak monitors may become a standard for reliability scaffolds on a healthy budget. Legibility requires that the shape of these decision-making traces takes some form accessible to weaker monitors. Existing efficiency-based metrics for legibility fail to capture "thoroughness", instead focusing on conciseness.
Abstract:Reasoning language models (RLMs) and the intermediate chains of thought they emit play an increasingly central role in multi-agent setups such as inter-model monitoring or distillation into smaller models. When agents at different capability tiers must cooperate, strong models need to produce traces digestible by weaker ones. We refer to this goal as "weak-to-strong legibility". Trustworthiness of large models depends in part on this legibility property. For safety oversight in particular, adoption of weak monitors may become a standard for reliability scaffolds on a healthy budget. Legibility requires that the shape of these decision-making traces takes some form accessible to weaker monitors. Existing efficiency-based metrics for legibility fail to capture "thoroughness", instead focusing on conciseness.
Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2603.20508 [cs.MA]
(or arXiv:2603.20508v2 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2603.20508 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 214 words