Human Context Protocol
| Field | Value |
|---|---|
| Status | Map, do not adopt |
| Version pinned | Position paper SSRN 5403981 (project page dated 18 December 2025); no protocol version or file format published |
| Source | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5403981 |
| What is taken | Nothing yet; a mapping from user-held preferences into the declared lane is planned |
| Why | Lets people who keep preferences in an HCP server bring them into a record as declared entries |
| What is extended or replaced | Replaces language-model-mediated disclosure with deterministic, person-approved renderings |
| Specification home | A11 import mapping (planned) |
Version pinned
"Robust AI Personalization Controls: The Human Context Protocol", SSRN 5403981, from the Loyal Agents project of the Stanford Digital Economy Lab with the Consumer Reports Innovation Lab; the project page is dated 18 December 2025. It is a research proposal with a prototype (github.com/tobinsouth/hcp-demo, no release). There is no versioned specification to pin, so this record pins the paper.
What is taken
Nothing in version 1. An importer is planned that maps a person's HCP preferences into proposals for declared entries, each confirmed by the person (A4.2), with the mapping table published here.
Why
HCP describes a user-owned preference layer exposed to agents through three server tools (schema definition, preference search and preference update). People who already keep preferences there should be able to bring them into a record without retyping them.
What is extended
Open Personal Context discloses through deterministic compiled renderings at a granted level (A5), rather than letting a model decide what to reveal from natural-language preferences.
What is replaced or not used
HCP's language-model-mediated disclosure is not used.
Deviations
HCP publishes no file format: the paper stores preferences "primarily in natural language", and the project site mentions JSON, Markdown, vector databases or personal data pods. The planned importer therefore needs a concrete export to map before it can be specified. The work is joint between the Stanford Digital Economy Lab and the Consumer Reports Innovation Lab.
Conformance checks
None in version 1. PORT-01 will cover the importer once it is specified.
References to other standards and projects describe intended technical alignment only and do not imply endorsement or partnership.