ai.hackcv
论文精选 85arXiv

DeliverBench: Benchmarking AI Agents on Delivering Realistic Multi-Stage Professional Projects· DeliverBench: 评估AI代理完成多阶段项目的能力

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 inheri

领域:cs.AI作者:Yumou Liu、Mingju Chen、Zhoufutu Wen
相关推荐
论文精选 65已解读

Strategist: Evolving Reasoning with Agents· 演进推理策略的AI元代理

A model is only half of an AI agent system. The other half is the inference strategy: the …

领域:cs.AI作者:Taiming Lu、Qi Xu、Zhigang Hua
📎 arXiv🕒 11-03 02:51🔗 arxiv.org
论文精选 65已解读

DiabAgent: Toward Agentic Physiological Control· DiabAgent:迈向代理生理控制

As pretraining scales, medical language models have advanced rapidly in clinical question …

领域:cs.AI作者:Chengxi Li、Shaoxuan Li、Deyao Hong
📎 arXiv🕒 11-03 02:41🔗 arxiv.org

本站内容由 LLM 精选聚合,原文版权归 arXiv 所有 · 摘录仅供参考