Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents
Agents driven by large language models are increasingly deployed on long-horizon tasks, where they use tools and modify real workspaces. How well they perform depends as much on the harness around the model as on the model itself. Existing benchmarks measure downstream task success under a fixed harness, leaving harness engineering itself unexplored. We propose Doc2Harness, a two-stage benchmark that evaluates long-horizon harness engineering directly. In Stage 1, a build model operating inside a fixed builder harness receives an interface-only contract skeleton and a development document that consolidates behavior across twelve frontier harnesses, and then constructs a complete candidate...
| Authors | Yuyao Ge, Shenghua Liu, Peizhong Gao et al. |
|---|---|
| Primary category | cs.SE |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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