AI Research
More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses
Uncovering the difference between brute-force sampling and genuine LLM task specialization.
Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated execution of the same program, making specialization difficult to identify. We introduce a controlled evaluation that separates ...
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ArXiv CS.AI
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ArXiv CS.AI
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Distinguishing between increased sample volume and genuine algorithmic specialization is critical for accurately benchmarking LLM reasoning capabilities. This research provides a framework to prevent performance inflation in automated evaluation pipelines by isolating the impact of repeated execution.
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