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arXiv AI By Ismail Erbas, Xavier Intes, Vikas Pandey

When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference

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The paper introduces the concept of recurrent‑state write‑back, the rule that determines how a quantized state is stored and retrieved in recurrent neural networks. Using a compact GRU encoder–decoder for fluorescence lifetime imaging, the authors show that replacing continuous state propagation with deterministic 4‑bit storage dramatically increases estimation errors for lifetime parameters. They demonstrate that error feedback, residual memory, and direction memory can recover accuracy without retraining, and that the phenomenon also appears in LSTM networks, highlighting the importance of the state‑storage interface in low‑precision recurrent inference.

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