arXiv:2610.32017v1 Announce Type: new Abstract: LLM-generated code is increasingly reused across downstream applications and often undergoes identifier renaming or structural refactoring, making robustness to functionality-preserving edits essential for generative multi-bit code watermarking. Existing methods commonly derive message routes from surface-token prefixes, so such edits can desynchronize routing and degrade watermark recovery. We propose SynMark, which replaces surface-dependent routing with learned semantic coordinates and further calibrates residual recovery errors after transformation. For watermark embedding, Scope learns semantic coordinates from aligned prefixes of functionally equivalent variants for keyed routing and applies a syntax-aware adaptive bias during generation. For watermark extraction, Traced recomputes semantic routes from transformed code and uses reliability-weighted aggregation to suppress unreliable token contributions. Under 100% identifier renaming, SynMark retains 82.27% and 83.77% BitAcc on CodeLlama and DeepSeek-Coder, respectively. Across three code LMs, it also improves clean BitAcc and Pass@1 by up to 4.53 and 11.18 percentage points over the strongest watermarked baselines.