Benchmark Payment Delegation: Policy Compliance and Useful Action in a Simulated Ledger

Delegated payment agents must complete permitted work while respecting spending limits and owner authority. We present a synthetic ledger benchmark with 40 public episodes across 22 scenario templates. Native replay separates task success, ledger goal completion, and policy violations. Scripted reference success is 100.0% [91.2, 100.0] for the good policy and 0.0% [0.0, 8.8] for the bad policy. A constant OK response still scores 40.0% [26.3, 55.4], exposing weak evidence of informed refusal. Removing owner calls reduces success to 90.0% [76.9, 96.0]. A simulated population sweep varies selection between reference scripts and measures scoring sensitivity. All intervals are 95%; reference rates use Wilson intervals, and the simulated sweep resamples agent means. These results validate execution and expose scoring limits; they do not measure learned models, payment security in deployment, or human judgment.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-29
DOI
https://doi.org/10.5281/zenodo.23752441
Primary Topic
Blockchain Technology Applications and Security
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Benchmark Payment Delegation: Policy Compliance and Useful Action in a Simulated Ledger

Pooria Arab
3 links
Zenodo (CERN European Organization for Nuclear Research)
Blockchain Technology Applications and Security
preprint

Benchmark Payment Delegation: Policy Compliance and Useful Action in a Simulated Ledger

Pooria Arab
preprint en

Abstract

Delegated payment agents must complete permitted work while respecting spending limits and owner authority. We present a synthetic ledger benchmark with 40 public episodes across 22 scenario templates. Native replay separates task success, ledger goal completion, and policy violations. Scripted reference success is 100.0% [91.2, 100.0] for the good policy and 0.0% [0.0, 8.8] for the bad policy. A constant OK response still scores 40.0% [26.3, 55.4], exposing weak evidence of informed refusal. Removing owner calls reduces success to 90.0% [76.9, 96.0]. A simulated population sweep varies selection between reference scripts and measures scoring sensitivity. All intervals are 95%; reference rates use Wilson intervals, and the simulated sweep resamples agent means. These results validate execution and expose scoring limits; they do not measure learned models, payment security in deployment, or human judgment.

Zenodo (CERN European Organization for Nuclear Research)
Peace, justice, and strong institutions
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon