LLM coding agents show cross-architecture divergence: higher GPU throughput with Gemini but prompt-sensitivity, while GPT is steadier; on Cerebras, producing runnable code dominates over performance tuning, with GPT succeeding where Gemini fails.
Significantly improving lossy compression for scientific data sets based on multidimensional prediction and error-controlled quantization,
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Evaluating LLM Coding Agents on SZ-Family Lossy Compression Across Architectures
LLM coding agents show cross-architecture divergence: higher GPU throughput with Gemini but prompt-sensitivity, while GPT is steadier; on Cerebras, producing runnable code dominates over performance tuning, with GPT succeeding where Gemini fails.