arXiv:2602.23248v2 Announce Type: replace Abstract: As large language models become increasingly capable, it is critical that their outputs can be easily checked by less capable systems. Prover-verifier games can be used to improve checkability of model outputs, but display a degradation in accuracy compared to a baseline trained only to maximize correctness -- a phenonemon named legibility tax. We propose a solution by decoupling the correctness from the checkability condition and instead training a "translator" model that turns a fixed solver model's solution into a checkable form. This allo
As large language models become increasingly capable, the need for verifiable and auditable outputs grows, prompting new research into methods to ensure reliability without sacrificing performance.
This research addresses a critical limitation of powerful AI systems by proposing a method to ensure their outputs can be checked, which is essential for safely deploying AI in sensitive applications.
The proposed 'decoupled prover-verifier game' changes how AI models might be designed and deployed, separating the optimization for correctness from the optimization for checkability to mitigate accuracy degradation.
- · AI safety researchers
- · Developers of critical AI applications
- · AI auditing firms
- · Systems solely optimized for raw output correctness
- · Uncheckable black-box AI systems
AI systems will become more transparent and trustworthy, facilitating wider adoption in high-stakes environments.
New standards and regulations for AI verifiability could emerge, increasing the complexity of AI development but also its reliability.
The principle of separate 'solver' and 'translator' models could inspire modular AI architectures for various desirable properties beyond mere checkability.
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Read at arXiv cs.AI