{
    "schema": "lxkeys.world.entry@2.2.1",
    "language": "en",
    "entry": {
        "id": "ENT-00001803",
        "status": "published",
        "name": "Denoising Diffusion Probabilistic Model",
        "aliases": [],
        "entity_type": "AI Architecture",
        "classification": "AI Architecture",
        "creator": "Ho, Jain and Abbeel",
        "organization": "Research community",
        "origin_context": "Public AI and technical record",
        "first_public_appearance": "2020",
        "current_status": "Active",
        "official_website": "https://arxiv.org/abs/2006.11239",
        "image": "",
        "short_description": "Denoising Diffusion Probabilistic Model is a AI architecture or method associated with Research community, classified in LXKeys.world as AI Architecture.",
        "public_description": "Denoising Diffusion Probabilistic Model is a AI architecture or method associated with Research community. This Entry records its public identity, classification as AI Architecture, source provenance, release or appearance history, documented capabilities and graph relationships. Primary-source material is preferred for factual maintenance; secondary references are retained only as supporting context.",
        "technical_description": "Structured LXKeys.world registry record for Denoising Diffusion Probabilistic Model. Entity type: AI Architecture; classification: AI Architecture; creator/organization context: Ho, Jain and Abbeel. Canonical source anchor: https://arxiv.org/abs/2006.11239. The record tracks source authority, first public appearance, timeline, capabilities, limitations, registry status and graph relationships. Automatic refresh is limited to sources explicitly classified as OFFICIAL and enabled for updates; documentary and research references remain non-authoritative unless reviewed.",
        "capabilities": [
            "Technical method",
            "Model design",
            "Research reference"
        ],
        "limitations": [
            "This Entry describes an architectural concept rather than one fixed implementation.",
            "Behavior and performance depend on the concrete model, training procedure, data and hyperparameters in which the architecture is used.",
            "Historical attribution and terminology may span multiple publications and later variants."
        ],
        "timeline": [
            {
                "date": "2020",
                "title": "Initial research publication",
                "description": "Denoising Diffusion Probabilistic Model entered the documented public record in 2020. This event is retained at the precision supported by the Entry’s reviewed source history.",
                "source_url": "https://arxiv.org/abs/2006.11239",
                "verification_status": "source_backed_curated_baseline"
            }
        ],
        "sources": [
            {
                "label": "Research or encyclopaedic reference",
                "url": "https://arxiv.org/abs/2006.11239",
                "source_type": "Research Paper",
                "verification_status": "verified",
                "authority": "TRUSTED_PRIMARY",
                "role": "research_paper",
                "update_enabled": false,
                "authority_basis": "curated-corpus-refresh-2026-09-08"
            }
        ],
        "relationships": [
            {
                "target": "Research community",
                "type": "Associated organization",
                "description": "Denoising Diffusion Probabilistic Model is associated with Research community through its documented source context.",
                "evidence_level": "documentary"
            }
        ],
        "tags": [
            "AI Architecture",
            "Method",
            "Research System"
        ],
        "registry_status": "Connected",
        "created_at": "2026-06-16T23:59:28+00:00",
        "updated_at": "2026-09-08T04:55:00+00:00",
        "dypclt_created": "D-0 Y-2 P-3 C-3 L-21 T-3",
        "dypclt_updated": "D-0 Y-2 P-3 C-3 L-21 T-3",
        "temporal_index": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-16T23:59:28+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-21 T-3",
            "updated_utc": "2026-06-16T23:59:28+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-21 T-3"
        },
        "documentation_status": "Documented",
        "machine_readable_purpose": "Machine-readable registry record for Denoising Diffusion Probabilistic Model: entity type, classification, organization, source, public appearance, capability profile and graph relationships.",
        "schema_ready": true,
        "first_public_appearance_precision": "year",
        "first_public_appearance_verification": "preserved_from_source_record",
        "image_status": "missing_official_image_candidate",
        "reviewed_at": "2026-09-08T04:55:00+00:00",
        "quality_status": "curated_official_first_baseline",
        "source_policy": "official_first_secondary_context_only",
        "quality_review": {
            "reviewed_at": "2026-09-08T04:55:00+00:00",
            "official_sources": 0,
            "trusted_primary_sources": 1,
            "secondary_sources": 0,
            "date_status": "preserved_from_source_record",
            "image_status": "missing",
            "manual_followup_required": true,
            "review_scope": "official-first structural curation; records flagged for follow-up are not claimed as individually exhaustive fact-checks"
        },
        "dypclt_reviewed": "D-0 Y-2 P-4 C-4 L-33 T-6",
        "world_id": "ENT-00001803",
        "kind": "Methods and Research",
        "type": "AI Architecture",
        "subtype": "",
        "facts": [],
        "canonical": [],
        "image_meta": [],
        "source_state": [],
        "i18n": {
            "en": [],
            "fr": []
        }
    },
    "computed": {
        "category": "Methods and Research",
        "type": "AI Architecture",
        "subtype": "",
        "documentation_index": {
            "total": 74,
            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 11,
            "level": "Level IV Connected"
        },
        "is_lxkeys_entity": false,
        "graph_endpoint": "graph.php?center=ENT-00001803&depth=1"
    }
}