ABSTRACT Recent grammar error correction (GEC) systems have scaled rapidly in model size and architectural depth, creating a growing mismatch between algorithmic improvements and the latency and energy constraints of edge devices. The method reformulates English GEC as a task‐constrained latent editing problem, where grammatical corrections are represented as low‐rank perturbations in a compact linear subspace. A Tiny‐LM‐style weight re‐parameterization aligns the latent editing vectors with a minimal set of re‐parameterized weights, ensuring that English grammatical reasoning is concentrated in a hardware‐friendly linear manifold. To improve correction fidelity under tight computational budgets, a two‐stage progressive refinement strategy is employed: a fixed‐window lookahead performs coarse structural edits, followed by a sparse consistency filter that selectively verifies candidate token corrections under INT8/INT4 quantization. The entire pipeline is static‐shape and operator‐regular, relying solely on linear, NPU‐native operations for predictable latency and bounded memory footprint. Experiments on public datasets show that the proposed model outperforms large Transformer baselines in F0.5 score on typical edge NPUs while reducing latency by 3–7×, demonstrating that accurate, low‐latency, on‐device English GEC is achievable using generic NPU operators without heavyweight language models.
Shaoli Xiong (2026) studied this question.