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paperbench-expanded

PaperBench High-Difficulty and Expanded Tasks: paper reproduction beyond machine learning. 93 tasks in 12 research areas, run in domain-native software and graded by clean replay. 12 public tasks.

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Details

External ID
1410669838
Source
GITHUB
Company
—
Product
paperbench-expanded
Website domain
pipelinelab.ai
Launched
Oct. 8, 2026
Cohort
—
Upvotes
43
Upvotes percentile
0.66
Tags
ai-agents, benchmark, harbor, llm-evaluation, reproducibility, scientific-computing

Enrichment

Niche
modular skills for AI agents
Vertical
Education
Function
Observability & eval
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
paper reproduction benchmark for non-ml research domains
Manually corrected
False

Could you build this?

No Building an academic benchmark across 12 scientific disciplines requires deep domain research, manual paper curation, and specialized scientific software sandbox environments.

What it would actually take: This benchmark requires curating complex experimental setups from published papers across diverse scientific domains (e.g., biology, chemistry, physics), creating deterministic Dockerized execution environments containing domain-specific legacy software, and writing verified automated evaluation harnesses. The primary bottleneck is specialized post-doctoral scientific expertise across multiple academic disciplines to validate reproductions, not software engineering.

Competitors

Other products that read as similar to this one — 62 launches clear the similarity bar, closest 8 shown.

Attention rank: #19 of 63 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 319 days after the earliest competitor.

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