AI-Driven VC & Decision Science

Research on how AI, prediction systems, and decision frameworks reshape sourcing, screening, and judgment in venture investing.

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ASSET: 116f286d

DeliverBench: Benchmarking AI Agents on Delivering Realistic Multi-Stage Professional Projects

Yumou Liu Mingju Chen Zhoufutu Wen Qianyu Yang Zijie Wang Yeqian Liu Chenghao Yang Tiliang Duan Qiyu Cai Yanglihong Xiao Jiayun Dong Jianlan Ma Shenhao Miao Yuhan Cao Lu Xu Que Shen Tian Gao Ge Zhang Jianpeng Jiao Xuanhe Zhou Fan Wu

Published: 2026-11-02
Signal: Related
DOI: Unavailable
Paper ID: 116f286d

Abstract

LLM agents are judged by what they deliver, and in professional work what gets delivered is a project, not a task. A project pays off only if the whole package holds, and one weak part can limit the value of the rest. Yet existing benchmarks mostly test agents on one task at a time, leaving a gap between the capability we measure and the outcome that makes agents useful. To address this gap, we introduce DeliverBench, which tests whether agents can carry a project from brief to final delivery. Its 11 projects, spanning financial due diligence, legal contract drafting, and drug safety reporting, are reconstructed with 13 domain experts from real materials and chain 150 subtasks through 2-15 rounds of new materials and client feedback. Because no reference outputs are injected, agents inherit their own errors from one task to the next. Expert rubrics then score every checkpoint as well as the final package. Across seven model-harness systems, even the best system scores only 65.0 out of 100 on final delivery and passes 50.0% of subtasks at a 90-point threshold. The failures are often failures of carrying work forward: in one audit project, all seven systems addressed the review findings, but only one preserved traceability. Together, these results suggest that task-level capability does not simply compose into delivery, which also depends on keeping the work coherent as it evolves.

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