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Technical University of Munich
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Refinement complexity in automotive requirements is driven more by architectural scope than by linguistic verbosity, revealing critical insights for improving product development efficiency.
SEDCoT achieves a 12% improvement in translation accuracy over existing methods while enhancing the readability of COBOL code translations into C.
Visualizing code dependencies can dramatically enhance issue-resolution performance, outperforming traditional text-based navigation methods.
Semantic Reference Frames reveal that optimizing the trajectory of language model computation can significantly enhance parameter efficiency and reduce complexity.
Current LLMs achieve negligible runtime and memory optimizations, while expert implementations deliver up to 15.5x speedup and 171.3x memory reduction.
Key contribution not extracted.
