Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
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Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
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Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
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An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.