researchcodebench@1.0
Coding
ResearchCodeBench evaluates AI agents’ ability to implement algorithms from academic papers. Contains 212 code implementation tasks across 20 ML/AI research problems from top-tier venues (ICLR, NeurIPS, CVPR, COLM). Tests paper comprehension, algorithm understanding, and precise code implementation skills with 1,449 lines of reference code.
Run this task
CLI:
inspect eval inspect_harbor/researchcodebench_1_0 --model openai/gpt-5Python:
from inspect_ai import eval
from inspect_harbor import researchcodebench_1_0
eval(researchcodebench_1_0(), model="openai/gpt-5")Dataset information
| Harbor registry | researchcodebench@1.0 |
| Inspect task | researchcodebench_1_0 |
| Version | 1.0 |
| Samples | 212 |
| Paper | arxiv |
See Task Parameters for the parameter set shared across all Harbor tasks.