DAYJOB: A Benchmark for Long-Horizon Professional Work
cs.AI, cs.CL
Submitted: 2026-10-01
Updated: 2026-10-01
Comments: 11 pages, 4 figures, 3 tables. An earlier version was accepted to the 2nd Workshop on Agentic AI Benchmarks and Applications for Enterprise Tasks (AABA4ET) at NeurIPS 2026. Evaluation harness: https://github.com/surge-ai/dayjob
Code: https://github.com/surge-ai/dayjob
License: http://creativecommons.org/licenses/by/4.0/
The gist: Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds.
Terminology
Abstract
Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds. We introduce DAYJOB, a benchmark of 130 tasks built by professionals in healthcare (50) and finance (80). The tasks are estimated to take a professional 13.6 hours on average in healthcare and 16.6 in finance. Each task is a containerized Harbor environment with an expert rubric of binary criteria (median 47.5 and 57.5 per task) that an agentic judge applies to the delivered files, and an attempt passes only if it meets every criterion. Across 30 model configurations from 13 developers, the strongest, Claude Opus 5.5, passes 24.7% of healthcare and 23.9% of finance attempts, and the median configuration passes 0.6% and 2.5%. In case studies, agents accept premises that the record contradicts and carry wrong inputs through otherwise consistent analyses. We release all healthcare tasks, 50 of the 80 finance tasks, the evaluation harness, and the leaderboard.
Sources
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