Is The Watermarking Of LLM-Generated Code Robust?

By Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic. 2024

Subfields: Safety, Robustness & Interpretability, Code Generation & Program Synthesis, Privacy & Security

Citations in the corpus

• CRANE: Reasoning with constrained LLM generation (2025, unknown): “However, as highlighted in recent studies (Ugare et al., 2024b; Lundberg et al., 2023; Poesia et al., 2022), pre-trained LLM outputs do not always comply with downstream tools’ input requirements.”
• CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis (2025, unknown): “…et al., 2022; Liu et al., 2023c; Li et al., 2023; Jain et al., 2024a; Banerjee et al., 2025; Ni et al., 2024b; Liu et al., 2023d; Du et al., 2023; Suresh et al., 2025b; Zhuo et al., 2024; Yin et al., 2022; Lai et al., 2023; Patil et al., 2025; Ugare et al., 2024; 2025; Wei et al., 2025d; Wu et…”