Attribution Protocol

A machine-readable schema that embeds creator, license, and provenance data directly into or alongside a digital asset. This moves attribution from ambiguous natural language to a deterministic, parseable format.

• Uses standards like Schema.org CreativeWork or IPTC fields
• Includes fields for author, dateCreated, license, and copyrightNotice
• Enables automated rendering of credit lines in generative AI outputs

A compact, unique digital signature generated by a cryptographic hash function (e.g., SHA-256) from the asset's binary content. This fingerprint serves as the asset's immutable identifier within the protocol.

• Enables deduplication and exact-match lookup across distributed registries
• Any modification to the asset produces a completely different hash
• Forms the root of a verifiable provenance chain

Standardized message formats that communicate the origin and transformation history of an asset. Each assertion is a signed statement linking an entity (creator, editor) to a specific action on a content fingerprint.

• Modeled on W3C PROV data model concepts: Entity, Activity, Agent
• Assertions are cryptographically signed by the asserting party
• Enables construction of a complete, auditable provenance graph

Persistent HTTP endpoints that resolve a content fingerprint or identifier to its current attribution metadata. Similar to a Digital Object Identifier (DOI) resolver, these provide a stable lookup mechanism.

• Returns structured metadata (JSON-LD) about the asset
• Can redirect to the canonical source location
• Supports content negotiation for different serialization formats

A standardized way to encode the specific rights, restrictions, and obligations associated with a digital asset. This transforms legal text into actionable, machine-readable rules.

• Uses SPDX identifiers or Creative Commons URIs
• Encodes permissions: reproduction, derivation, commercialUse
• Enables automated license compliance checking in AI training pipelines

The protocol component that validates the authenticity and integrity of attribution claims. It cryptographically verifies that a content attestation was signed by a trusted party and that the asset hasn't been tampered with.

• Verifies digital signatures against known public keys
• Validates hash chains to confirm content integrity
• Can integrate with decentralized identifiers (DIDs) for trustless verification

The process of linking a generated claim directly to a specific, verifiable segment within an authoritative source document. Unlike vague references, grounding requires span-level annotation—mapping an output text range to an input text range. This is achieved through:

• Attention weight analysis in transformer models
• Retrieval index lookups that return document IDs and character offsets
• NLI-based entailment checks that verify the source actually supports the claim

Grounding is the technical backbone of attribution fidelity in RAG systems.

The classification of an author's purpose for including a reference, critical for evaluating citation quality. Common intent categories:

• Supporting — the cited work provides evidence for a claim
• Contrasting — the citation highlights disagreement or alternative findings
• Background — provides context without direct evidentiary weight
• Methodology — references a technique or tool used

Automated intent classification uses SciBERT and other domain-tuned models trained on annotated citation contexts like the ACL Anthology and PubMed Central datasets.