# Calibration

Hadronic jet energy calibration at the Large Hadron Collider has relied on fitting parametric functional forms to selected observables, correcting pile-up, detector response and data–simulation differences in sequence. A prototype pipeline instead trains a neural network for each stage directly against the quantity that stage removes, imposes no functional form, and extends by adding inputs. On simulated dijet and large-radius jet samples it matches or exceeds the parametric baseline at nearly every stage, with residual pile-up dependence averaging about a tenth of the baseline's.

Hadronic jet energy calibration at the Large Hadron Collider has relied on fitting parametric functional forms to selected observables, correcting pile-up, detector response and data–simulation differences in sequence. A prototype pipeline instead trains a neural network for each stage directly against the quantity that stage removes, imposes no functional form, and extends by adding inputs. On simulated dijet and large-radius jet samples it matches or exceeds the parametric baseline at nearly every stage, with residual pile-up dependence averaging about a tenth of the baseline's.

Observational data often leave causal directions unresolved or unstable, and large language models can propose directions from variable names and descriptions. This study splits the use of semantic information into query allocation, proposal admission and graph retention, and tests each on the Asia, Sachs, Child, Insurance and Alarm benchmark networks. Semantic orientation beats coin-flip orientation, but targeting unstable edges shows no established advantage over matched-random allocation; the tested admission certificate can only abstain given the available calibration tasks, and learned references contain the true DAG in only three of 80 planned tasks.

Observational data often leave causal directions unresolved or unstable, and large language models can propose directions from variable names and descriptions. This study splits the use of semantic information into query allocation, proposal admission and graph retention, and tests each on the Asia, Sachs, Child, Insurance and Alarm benchmark networks. Semantic orientation beats coin-flip orientation, but targeting unstable edges shows no established advantage over matched-random allocation; the tested admission certificate can only abstain given the available calibration tasks, and learned references contain the true DAG in only three of 80 planned tasks.