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OpenAI flags SWE-bench Pro coding benchmark flaws; new research reframes NN training

By PulseAugur Editorial · [2 sources] ·

OpenAI has identified significant reliability issues within the SWE-bench Pro coding benchmark, a tool it had previously endorsed as a replacement for the contaminated SWE-bench benchmark. This new analysis suggests that SWE-bench Pro itself may not be a dependable measure of AI coding performance. Separately, a new arXiv preprint proposes reframing neural network training as an optimal control problem, aiming to insert layers where error is highest, though evidence for this approach is currently limited. AI

IMPACT Critiques of AI evaluation benchmarks and novel training methodologies highlight ongoing challenges in reliably assessing and improving AI capabilities.

RANK_REASON The cluster contains a research paper and a critique of a benchmark, fitting the research bucket.

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

OpenAI flags SWE-bench Pro coding benchmark flaws; new research reframes NN training

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14 / 100
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The cluster contains a research paper and a critique of a benchmark, fitting the research bucket.
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2 independent sources
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paper, other
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COVERAGE [2]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    OpenAI finds flaws in SWE-bench Pro coding benchmark OpenAI's new analysis exposes reliability issues in SWE-bench Pro — the very benchmark it recommended to re

    OpenAI finds flaws in SWE-bench Pro coding benchmark OpenAI's new analysis exposes reliability issues in SWE-bench Pro — the very benchmark it recommended to replace the contaminated SWE-bench Verified — leaving A https://www. notatechguy.com/openai-finds-f laws-in-swe-bench-pro-…

    LINKS notatechguy.com/openai-finds-flaws-in-swe…
  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Optimal control grows neural network layers where error peaks A new arXiv preprint reframes neural network training as optimal control, inserting layers where e

    Optimal control grows neural network layers where error peaks A new arXiv preprint reframes neural network training as optimal control, inserting layers where error is highest — but evidence is limited to scientific datase https://www. notatechguy.com/optimal-contro l-grows-neura…

    LINKS notatechguy.com/optimal-control-grows-neu…

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