SWE-bench reveals that even top language models like Claude 2 resolve only 1.96% of 2,294 real-world GitHub issues, highlighting a gap in practical coding capabilities.
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An LLM-as-judge scanner detects reasoning–answer inconsistency in AI safety evaluation transcripts at rates of 0–26%, varying systematically across model and task type.
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
SWE-bench reveals that even top language models like Claude 2 resolve only 1.96% of 2,294 real-world GitHub issues, highlighting a gap in practical coding capabilities.
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Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations
An LLM-as-judge scanner detects reasoning–answer inconsistency in AI safety evaluation transcripts at rates of 0–26%, varying systematically across model and task type.