ScaleSearch optimizes block floating point scales via fine-grained search to cut quantization error by 27% for NVFP4, improving PTQ by up to 15 points on MATH500 for Qwen3-8B and attention PPL by 0.77 on Llama 3.1 70B.
Proceedings of the IEEE conference on computer vision and pattern recognition , pages=
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2026 2verdicts
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Neural decoders for surface-code QEC achieve practical microsecond FPGA latency when trained on large datasets with appropriate inductive biases and INT4 quantization, rather than relying on architectural complexity.
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Search Your Block Floating Point Scales!
ScaleSearch optimizes block floating point scales via fine-grained search to cut quantization error by 27% for NVFP4, improving PTQ by up to 15 points on MATH500 for Qwen3-8B and attention PPL by 0.77 on Llama 3.1 70B.
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Rethink the Role of Neural Decoders in Quantum Error Correction
Neural decoders for surface-code QEC achieve practical microsecond FPGA latency when trained on large datasets with appropriate inductive biases and INT4 quantization, rather than relying on architectural complexity.