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FrontierCS 2 benchmarks LLM agents on 48 open-ended CS tasks

Original title: FrontierCS 2: A Benchmark for Computer Science R&D

Hugging Face daily papersby Qiuyang Mang, Wenhao Chai, Runyuan He ()1 min read1 · 1 read
AI summary · x-ai/grok-4.7Summaries by Vani AI

Claude Opus 5.5 leads 12 agents with a 58% pass rate, and 16 tasks have no passing run.

FrontierCS 2 puts agents in a research repository and lets them submit patches, unlike benchmarks that grade one program from a problem statement. A private judge returns a development-suite score and grades the final submission against a hidden human reference. The 48 tasks span 13 domains. Beating the reference on the combined score is not a pass if some metrics or workloads still fail.

Key facts
  • The evaluator, held-out final suite and reference stay private.
  • A private set of more than 100 tasks is not yet released.
  • Most runs that beat the reference still miss some metric or workload.
  • The median task is about as hard as the strongest agent can handle.
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From the original article

1 min read at huggingface.co

Can LLM agents make contributions to frontier computer science R&D? We introduce FrontierCS 2, a benchmark of 48 open-ended tasks. We take a paper and the code its authors released and delete the paper's contribution.

Hugging Face daily papers

By Qiuyang Mang, Wenhao Chai, Runyuan He · huggingface.co

Read the full article at Hugging Face daily papers

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.