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Sagan

Paper

Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark

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AI summary

The authors introduce a new multibit watermarking scheme for LLMs that encodes every bit of the payload at every token position using binomial encoding, paired with a stateful encoder that dynamically shifts encoding pressure toward underencoded bits during generation. Evaluated against 8 baselines on payloads up to 64 bits, the scheme achieves superior message accuracy and robustness, with the gap widening in large-payload and low-distortion settings. The paper also critiques prior evaluation metrics and proposes per-bit confidence scoring as a practically relevant alternative.

Main takeaways:

  • Encodes every bit of the payload (e.g., user ID, timestamp) at every token, rather than spreading bits across the sequence
  • Uses a stateful encoder that tracks which bits are underrepresented and redirects encoding effort on the fly
  • Outperforms 8 baselines in message accuracy and robustness, especially for large payloads (up to 64 bits) and low-distortion regimes
  • Challenges existing evaluation metrics as lacking practical insight; introduces per-bit confidence scoring
  • Already deployed commercially, so practical multibit watermarks are a real-world concern