mlsanityData to 7 Oct 2026

Ideal Attribution and Faithful Watermarks for Language Models

arXiv 2512.07038 ↗First posted 7 Dec 2025cs.CR cs.LG stat.MLcited by 1

On the map: LLM safety and security → Watermarking generated content and models

Abstract (arXiv)

We introduce ideal attribution mechanisms, a formal abstraction for reasoning about attribution decisions over strings. At the core of this abstraction lies the ledger, an append-only log of the prompt-response interaction history between a model and its user. Each mechanism produces deterministic decisions based on the ledger and an explicit selection criterion, making it well-suited to serve as a ground truth for attribution. We frame the design goal of watermarking schemes as faithful representation of ideal attribution mechanisms. This novel perspective brings conceptual clarity, replacing piecemeal probabilistic statements with a unified language for stating the guarantees of each scheme. It also enables precise reasoning about desiderata for future watermarking schemes, even when no current construction achieves them, since the ideal functionalities are specified first. In this way, the framework provides a roadmap that clarifies which guarantees are attainable in an idealized setting and worth pursuing in practice.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 16 of its 26 references that are in the map. First posted on arXiv 7 Dec 2025.

Builds on

16 of 26 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024An Undetectable Watermark for Generative Image Models9 Oct
  2. 2024Watermarking Language Models for Many Adaptive Users17 May
  3. 2024Pseudorandom Error-Correcting Codes14 Feb
  4. 2023A Survey of Text Watermarking in the Era of Large Language Models13 Dec
  5. 2023Publicly-Detectable Watermarking for Language Models27 Oct
  6. 2023Robust Distortion-free Watermarks for Language Models28 Jul
  7. 2023Provable Robust Watermarking for AI-Generated Text30 Jun
  8. 2023On the Reliability of Watermarks for Large Language Models7 Jun
  9. 2023Undetectable Watermarks for Language Models25 May
  10. 2023A Watermark for Large Language Models24 Jan

Other references · 6

  1. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  2. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  3. 2022Game-theoretic statistics and safe anytime-valid inferencecited by 145
  4. 2022Human heuristics for AI-generated language are flawedcited by 104
  5. 2025SynthID-Image: Image watermarking at internet scalecited by 26
  6. 2024Ideal Pseudorandom Codescited by 4

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Citing papers per year: 2026: 1.

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