The accountability gap in LLM inference: Proving which model weights actually ran
Some_parts_Bi · reddit · 2026-08-16
The author highlights an "accountability gap" in current AI inference stacks: while inputs and outputs are logged, there is no independent proof of which specific model version or weights were executed, or if the process was tampered with. This trust chain is critical for applications where decisions are disputed.
The post mentions OpenGradient as a project addressing this by attaching proofs (via TEE or zkML) to each inference binding. Although verified paths introduce latency and cost, they offer a mechanism for trust in sensitive scenarios. The author seeks community input on the necessity of such verifiable inference.
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