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The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers introduced Zero-CoT Truncation to detect evasive data contamination in large language models designed to boost benchmark performance.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 28 October 2026
- Collected by OneBench
- 29 Oct 2026, 02:02 UK
Stored source excerpt
arXiv:2605.21856v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the…
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