arXiv:2604.18170v2 Announce Type: replace Abstract: LLMs edit text and code by autoregressively regenerating the full output, even when most tokens appear verbatim in the input. We study Copy-as-Decode, a decoding-layer mechanism that recasts edit generation as structured decoding over a two-primitive grammar: references an input line range, ... emits new content. A token-level FSM guarantees syntactic validity, and a serving-layer primitive updates the KV cache for each copy span via a single parallel-prefill forward rather than $N$ autoregressive steps -- sharing the parallel-forward kernel
The increasing demand for more efficient and faster LLM operations, especially in editing tasks, is driving innovation in foundational architectural components.
This development significantly enhances the practical utility and computational efficiency of LLMs for editing tasks, reducing inference costs and latency for large models.
LLM text and code editing, previously inefficient due to full regeneration, can now be performed with substantially reduced computational overhead by 'copying' existing tokens in parallel.
- · LLM developers
- · AI SaaS providers
- · Cloud computing platforms
- · AI-driven content creation
- · Inefficient LLM editing paradigms
- · High-latency content generation workflows
Reduced operational costs for services heavily relying on LLM-based text and code editing.
Faster iteration cycles for developers and content creators using LLMs, leading to more sophisticated applications.
Enhanced user experience and broader adoption of LLM-powered editing tools across various industries due to performance improvements.
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Read at arXiv cs.CL