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.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
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Ask4VG learns a risk estimator from counterfactual visual probes to rerank question rewrites, reducing held-out hallucination risk from 0.658 to 0.623 and raising accuracy from 0.337 to 0.356 on VQA-RAD.
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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.
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Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA
Ask4VG learns a risk estimator from counterfactual visual probes to rerank question rewrites, reducing held-out hallucination risk from 0.658 to 0.623 and raising accuracy from 0.337 to 0.356 on VQA-RAD.