Continuous Fresh Benchmark Generation for AI Models
C7/10April 11, 2026
WhatA service that programmatically generates novel, uncontaminated coding and reasoning benchmarks from recent real-world data, ensuring no overlap with training corpora.
SignalPractitioners are deeply skeptical of existing benchmarks like SWE-bench because the test data is publicly available on HuggingFace and almost certainly exists in frontier model training sets, making results meaningless — even OpenAI has acknowledged the contamination problem and abandoned the benchmark.
Why NowTraining data contamination has reached a tipping point where major labs are publicly disavowing their own benchmarks, and the community consensus is shifting from accepting published scores to demanding proof of uncontaminated evaluation.
MarketAI labs paying for clean evaluation, enterprise teams doing model selection, and AI investors doing due diligence. Tens of millions near-term, growing with the AI evaluation market. Competitors like Scale AI do human eval but not automated fresh benchmark generation.
MoatProprietary pipeline for generating and validating novel test cases at scale, plus temporal advantage — benchmarks are only valuable when provably unseen by models, creating a natural freshness moat.
How We Broke Top AI Agent Benchmarks: And What Comes NextView discussion ↗ · Article ↗ · 429 pts · April 11, 2026
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