Which surrogate should train a verifier that can escalate?
research/reject-surrogates
Polyhedral surrogates for accept/reject/escalate verifiers: minimiser embeds the Bayes action, linear regret transfer with constant exactly 2d, logistic transfer ratio 2d(1-d)/δ, and a negative SGD result.
What it found
Reject-option surrogates for accept/reject/escalate decisions: the generalised-hinge conditional risk embeds the Bayes action at its kinks, regret transfers linearly with constant exactly 2d (grid-verified), logistic transfer degrades as 2d(1−d)/δ, and a negative result shows logistic-plus-thresholds still learns faster in scalar SGD. Stylised.
A figure from its own output
Paper, code and results
More in Proper scoring & elicitation
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1−H^{1−k}, colluders' blind spot at ε=2Γ/(1+Γ). - Is a skill score normalised by the realised base rate safe to pay on?Brier skill scores normalised by the realised base rate are improper: exact optimal report,
≈ −(1−2p)/nbias, cross-task leakage through the pooled baseline, and a leave-one-out fix.
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Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.