MCMit proposes a constant-latency multi-control branch instruction, transformer and CNN discriminators, plus static MCM elimination and stochastic branching, evaluated on Qubic with QPU traces to cut latency by 70% and logical error rates by up to 9.4x.
Openai o3 and o4-mini system card
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.
citing papers explorer
-
MCMit: Mid-Circuit Measurement Error Mitigation
MCMit proposes a constant-latency multi-control branch instruction, transformer and CNN discriminators, plus static MCM elimination and stochastic branching, evaluated on Qubic with QPU traces to cut latency by 70% and logical error rates by up to 9.4x.
-
Characterizing AI Manipulation Risks in Brazilian YouTube Climate Discourse
Observational analysis of Brazilian YouTube climate content identifies psychological engagement traits and explores their use in generative AI campaigns, accompanied by a public dataset of 226K videos and 2.7M comments.