The rapid evolution of Large Language Models (LLMs) presents substantial challenges regarding trust and authenticity in multi-agent environments. One of the fundamental issues is how agents can confirm whether the output they receive originates from the claimed LLM, thus preventing deception by falsified results or inferior models. The proposed framework introduces a tractable asymmetric effort method that accomplishes verification at a lower computational expense than direct model regeneration.
By leveraging deterministic replicability, which requires a uniform computational environment across all agents, the framework effectively enables validators to conduct probabilistic audits on random segments of an LLM’s output. This results in a verification process that is over twelve times faster than regenerating the output entirely, offering flexibility through tunable parameters to modulate detection probabilities. Establishing such a verification method not only reinforces responsible AI development but also lays a foundation for investigating more complex and varied multi-agent systems in the future.
👉 Pročitaj original: arXiv AI Papers
