Opportunity summary
Score4.0PainA framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
Evidence0 refs | 0 sources | 17% coverage
Blockerno shell-level blocker reported
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ARXIV:2603.21636 · LLM EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21636LLM EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEYiliang Song · Hongjun An · Jiangan Chen · Xuanchen Yan · Huan Song · Jiawei Shao · +1 at arXiv
A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
Opportunity summary
Pain A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a…
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results suggest that similar benchmark scores may carry substantially different levels of confidence. Code availability is flagged in the production record; the public…
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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Score4.0Analysis summary
A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
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10.48550/arXiv.2603.21636A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
Abstract
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile assumption: that benchmark scores directly reflect genuine generalization. In practice, however, such scores may conflate exam-oriented competence with principled capability, especially when contamination and semantic leakage are difficult to exclude from modern training pipelines. We therefore propose an audit framework for analyzing contamination sensitivity and score confidence in LLM benchmarks. Using a router-worker setup, we compare a clean-control condition with noisy conditions in which benchmark problems are systematically deleted, rewritten, and perturbed before being passed downstream. For a genuinely clean benchmark, noisy conditions should not consistently outperform the clean-control baseline. Yet across multiple models, we find widespread but heterogeneous above-baseline gains under noisy conditions, indicating that benchmark-related cues may be reassembled and can reactivate contamination-related memory. These results suggest that similar benchmark scores may carry substantially different levels of confidence. Rather than rejecting benchmarks altogether, we argue that benchmark-based evaluation should be supplemented with explicit audits of contamination sensitivity and score confidence.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Dimensions overall score 4.0
PROBLEM
A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile assumption: that benchmark sc...
METHOD
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile assumption: that benchmark scores direct...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results suggest that similar benchmark scores may carry substantially different levels of confidence. Code availability is flagged in the production record; the public repository link still needs pr...
WHY NOW
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile assumption: that benchmark scores directly reflect genuine generalization.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Public benchmarks increasingly govern how large language models (LLMs) are ranked, selected, and deployed. We frame this benchmark-centered regime as Silicon Bureaucracy and AI Test-Oriented Education, and argue that it rests on a fragile assumption: that benchmark scores directly reflect genuine generalization.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. These results suggest that similar benchmark scores may carry substantially different levels of confidence. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Evaluation moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework to audit LLM benchmarks for contamination sensitivity, providing confidence scores for model evaluations.
Segment
LLM Evaluation
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4.0/10 public viability
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missing
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Write integration checklist from prototype path and target workflow.
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