Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability

Verification is becoming critical as AI systems interact autonomously: As Large Language Models increasingly operate in multi-agent environments where they communicate with each other, there's a fundamental need to verify that outputs actually came from the claimed model rather than being falsified or substituted with cheaper alternatives—a problem this paper directly addresses.

Verification can be made dramatically more efficient than regeneration: The paper demonstrates that targeted verification of LLM outputs can be over 12 times faster than fully regenerating the output, making it practical to audit AI systems without prohibitive computational costs.

The approach requires standardized technical environments: The verification framework depends on "deterministic replicability," meaning all agents must operate on identical hardware and software. This highlights an important constraint for implementing trustworthy AI systems—standardization across infrastructure is necessary for verification to work reliably.

Random sampling enables probabilistic auditing: Rather than checking entire outputs, multiple validators can audit small random segments of an LLM's work, with tunable parameters that allow organizations to adjust how confident they want to be in detecting fraudulent or inferior outputs—balancing trust assurance with computational efficiency.

This work provides foundational infrastructure for responsible AI: By establishing a tractable mechanism to verify that LLMs are actually producing their claimed outputs, the paper contributes essential technical infrastructure for building accountable AI systems, particularly as these systems become more autonomous and interconnected.