# Sovereign Context Protocol: An Open Attribution Layer for Human-Generated Content in the Age of Large Language Models

arXiv: https://arxiv.org/abs/2603.27094 · HTML: https://mlsanity.com/paper/2603.27094/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [TRAK: Attributing Model Behavior at Scale](https://mlsanity.com/paper/2303.14186/) (2023)
- [DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models](https://mlsanity.com/paper/2310.00902/) (2023)
- [The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI](https://mlsanity.com/paper/2310.16787/) (2023)
- [Scalable Influence and Fact Tracing for Large Language Model Pretraining](https://mlsanity.com/paper/2410.17413/) (2024)
- [Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?](https://mlsanity.com/paper/2404.12691/) (2024)
- [Source Attribution for Large Language Model-Generated Data](https://mlsanity.com/paper/2310.00646/) (2023)
- [Who Owns the Output? Bridging Law and Technology in LLMs Attribution](https://mlsanity.com/paper/2504.01032/) (2025)
- [Overview of the TREC 2025 Retrieval Augmented Generation (RAG) Track](https://mlsanity.com/paper/2603.09891/) (2026)
- [Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets](https://mlsanity.com/paper/2512.21775/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) consume vast quantities of human-generated content for both training and real-time inference, yet the creators of that content remain largely invisible in the value chain. Existing approaches to data attribution operate either at the model-internals level, tracing influence through gradient signals, or at the legal-policy level through transparency mandates and copyright litigation. Neither provides a runtime mechanism for content creators to know when, by whom, and how their work is being consumed. We introduce the Sovereign Context Protocol (SCP), an open-source protocol specification and reference architecture that functions as an attribution-aware data access layer between LLMs and human-generated content. Inspired by Anthropic's Model Context Protocol (MCP), which standardizes how LLMs connect to tools, SCP standardizes how LLMs connect to creator-owned data, with every access event logged, licensed, and attributable. SCP defines six core methods (creator profiles, semantic search, content retrieval, trust/value scoring, authenticity verification, and access auditing) exposed over both REST and MCP-compatible interfaces. We formalize the protocol's message envelope, present a threat model with five adversary classes, propose a log-proportional revenue attribution model, and report preliminary latency benchmarks from a reference implementation built on FastAPI, ChromaDB, and NetworkX. We situate SCP within the emerging regulatory landscape, including the EU AI Act's Article 53 training data transparency requirements and ongoing U.S. copyright litigation, and argue that the attribution gap requires a protocol-level intervention that makes attribution a default property of data access.