The Latency-Elastic Trust Window is a telemetry-driven UX governor that maps network latency conditions to adaptive feedback modes to preserve trust and engagement during real-time payments in WebRTC streaming.
Improving the Sensitivity of Online Controlled Experi- ments by Utilizing Pre-Experiment Data
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Including LLM predictions as covariates in standard regression adjustment for randomized experiments reduces variance with a do-no-harm property that reverts to the unadjusted estimator when predictions are uninformative.
Post-stratification plus CUPED cuts required traffic by about 45% for reliable A/B tests on heavy-tailed revenue metrics in ranking experiments.
A competition entry for bimanual garment folding won 1st in simulation and 2nd in reality by making a VLA policy predict its own value quantities to drive advantage estimation, failure detection, and action selection.
citing papers explorer
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Discovering the Latency-Elastic Trust Window: A Patentable UX Governor for Real-Time Payment Confirmation in WebRTC Streaming
The Latency-Elastic Trust Window is a telemetry-driven UX governor that maps network latency conditions to adaptive feedback modes to preserve trust and engagement during real-time payments in WebRTC streaming.
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AI-Assisted Variance Reduction in Randomized Experiments
Including LLM predictions as covariates in standard regression adjustment for randomized experiments reduces variance with a do-no-harm property that reverts to the unadjusted estimator when predictions are uninformative.
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Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
Post-stratification plus CUPED cuts required traffic by about 45% for reliable A/B tests on heavy-tailed revenue metrics in ranking experiments.
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Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
A competition entry for bimanual garment folding won 1st in simulation and 2nd in reality by making a VLA policy predict its own value quantities to drive advantage estimation, failure detection, and action selection.