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A novel interactive agent outperforms traditional LLMs by transforming vague candidate requests into precise sourcing outcomes, achieving unmatched relevance and coverage.
PRAXIS reveals that systematically extracting and surfacing tacit knowledge can dramatically enhance LLM performance in domain-specific code generation, outperforming traditional methods.
LLMs can significantly improve code generation by interleaving reasoning steps *within* the code itself, adaptively focusing on high-complexity areas.
