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{
  "$schema": "./frontier-adoption.schema.json",
  "schema_version": "szl.frontier-adoption.v1",
  "as_of": "2026-08-31",
  "status": "GOVERNED_PLAN_NOT_LOCAL_QUALIFICATION",
  "evidence_policy": {
    "upstream_facts": "PRIMARY_REPORTED_NOT_LOCAL_MEASUREMENT",
    "local_measurements_present": false,
    "download_performed_by_this_contract": false,
    "promotion_authority": false,
    "unknown_repository_policy": "DENY",
    "zero_download_policy": "ZERO_DOWNLOADS_ALONE_NEVER_AUTHORIZES_DELETE_OR_ARCHIVE"
  },
  "brain_model_truth": {
    "raw_nodes_observed": 9475,
    "raw_nodes_available_to_retrieval_and_evaluation": 9475,
    "raw_nodes_admitted_to_gradients": 0,
    "current_weight_model": "SZLHOLDINGS/SZL-Khipu-1.5B",
    "current_weight_model_relation": "TRAINED_ON_SYNTHETIC_HANDLE_NAVIGATION_NOT_RAW_BRAIN_NODE_CONTENT",
    "current_release_limit": "OWNER_EVALUATED_2_OF_6_ABSTENTION_CORRECT_AND_SIGNED_RECEIPT_ARTIFACT_BINDING_CONFLICT_BLOCK_AUTONOMOUS_HIGH_STAKES_OR_PROMOTION",
    "next_release_predicate": [
      "Admit every proposed gradient row through the signed Brain training-admission contract; raw node count alone is never training authority.",
      "Keep canonical Brain content outside weights and retrieve it through a provenance-bound hybrid index; train only the navigation, abstention, citation, and tool-use behavior that passes rights and contamination gates.",
      "Demonstrate statistically meaningful gains on a frozen independent BrainQrels split with per-case receipts, confidence intervals, zero invented node identifiers, and materially improved abstention.",
      "Bind the exact base, adapter, data split, trainer, runtime, evaluation, license review, and human release decision in a signed candidate receipt before any v2 promotion."
    ]
  },
  "flagship_strategy": {
    "primary_model": "SZLHOLDINGS/SZL-Khipu-1.5B",
    "distribution_model": "SZLHOLDINGS/SZL-Khipu-1.5B-GGUF",
    "specialist_candidate": "SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent",
    "unification_mode": "ONE_PRODUCT_FAMILY_ROUTER_COLLECTION_AND_SHARED_EVALS",
    "tensor_merge_authorized": false,
    "strategy": [
      "Market one coherent SZL-Khipu product family: BrainNavigator is the experimental flagship, GGUF is its local distribution, and the artifact labeled ReceiptAgent remains a candidate whose target role and behavior are not established.",
      "Package the commercial surface as SZL Khipu Research OS: Khipu proposes bounded plans; SZL-Lake and the Brain retrieve immutable evidence; the formula registry supplies typed obligations; Lean and mathlib verify supported proof terms; and Ayllu plus SZL Router enforce bounded tool use and abstention. If ReceiptAgent behavior is later established, its target role is receipt drafting only; the governed runtime must validate and bind drafts and an external signer must emit DSSE receipts.",
      "Keep both Khipu and ReceiptAgent experimental and unpromoted until the canonical Forge manifest's signed-receipt artifact-binding conflict is reconciled against the published files and independently read back.",
      "Route every computation through the existing SZL-Yupaq typed compute plane (model_release/szl-compute-plane.json and szl_yupaq_compute.py); engines compute outside model weights and remain UNAVAILABLE when their real runtime, license, isolation, or receipt evidence is absent.",
      "Unify the model cards, collection, Space demo, router contract, evaluation dashboard, citations, and feedback loop; do not merge distinct adapters or weights without an ablation proving the merged artifact is better.",
      "Move kernels, formula libraries, invariants, Ouroboros, provenance controls, and signing tools into a named supporting-kernel collection so they strengthen the flagship without pretending to be language-model weights.",
      "Use the next release to fix the measured abstention weakness and add independently reproducible Brain navigation receipts, not to multiply repository names."
    ]
  },
  "github_estate_strategy": {
    "source_reported_repository_count": 54,
    "attachment_entries_observed": 30,
    "inventory_complete": false,
    "public_github_repositories_observed": 50,
    "public_archived_observed": 9,
    "public_readback_as_of": "2026-07-16",
    "public_readback_source": "https://github.com/orgs/szl-holdings/repositories",
    "reconciliation_state": "SOURCE_REPORT_54_PUBLIC_READBACK_50_ATTACHMENT_TRUNCATED_REVIEW_REQUIRED",
    "reported_not_publicly_readback": [
      "szl-org-health",
      "szl-substrate",
      "pitch-collateral"
    ],
    "publicly_observed_outside_attachment": [
      "immune",
      "anatomy",
      "governed-receipt-spec",
      "szl-guardrail-receipt",
      "lambda-gate-holo",
      "energy-attest-holo",
      "szl-kernels-live",
      "szl-provctl-live",
      "szl-holdings.github.io",
      "khipu-sda-core"
    ],
    "canonical_flow": "a11oy -> hatun-mcp -> szl-router -> szl-forge profiles -> szl-yupaq compute -> szl-lake and szl-receipt -> anatomy -> approved verticals",
    "layers": [
      {
        "layer": "company-governance-and-brand",
        "canonical_repositories": [
          ".github",
          "szl-brand",
          "szl-doctrine",
          "docs-site"
        ],
        "responsibility": "Organization policy, community health, brand assets, documentation, and measured estate health.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "control-plane",
        "canonical_repositories": [
          "a11oy",
          "platform"
        ],
        "responsibility": "Governed user and API control plane plus product integration; shared-substrate ownership remains a reconciliation item.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "tool-and-inference-routing",
        "canonical_repositories": [
          "hatun-mcp",
          "szl-router",
          "szl-mesh",
          "khipu-consensus"
        ],
        "responsibility": "Typed tool exposure, canonical model routing, distributed coordination, and witnessed agreement.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "qualified-weight-program",
        "canonical_repositories": [
          "szl-forge"
        ],
        "responsibility": "Training and qualification recipes for Khipu and ReceiptAgent profiles; no other repository becomes weights by association.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "evidence-and-observability",
        "canonical_repositories": [
          "szl-lake",
          "szl-receipt",
          "vsp-otel",
          "szl-telemetry",
          "szl-energy-attest"
        ],
        "responsibility": "Append-only evidence, DSSE receipts, telemetry, tracing, resource evidence, and public counters.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "proof-and-formula-tiers",
        "canonical_repositories": [
          "lutar-lean",
          "lean-kernel",
          "szl-formula-ledger",
          "szl-lambda-gate"
        ],
        "responsibility": "Separate theorem-source, executable-kernel, formula-status, and advisory-aggregation tiers; extracted formulas are not automatically proven.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "bounded-computation",
        "canonical_repositories": [
          "szl-quant",
          "yarqa"
        ],
        "responsibility": "Typed quantitative and scientific computation behind Yupaq with real external-engine receipts and explicit domain limits.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK_PLUS_LOCAL_ARTIFACT_READBACK",
        "in_repository_artifacts": [
          "szl_yupaq_compute.py",
          "model_release/szl-compute-plane.json"
        ]
      },
      {
        "layer": "governed-loops-and-build",
        "canonical_repositories": [
          "ouroboros",
          "szl-build-env",
          "warhacker-demo"
        ],
        "responsibility": "Bounded recursion, reproducible local build fabric, and defensive dry-run verification.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "publications-and-commercial-evidence",
        "canonical_repositories": [
          "szl-papers"
        ],
        "responsibility": "Preprints, thesis lineage, release evidence, and claims constrained to independently verifiable artifacts.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      },
      {
        "layer": "presentation-and-anatomy",
        "canonical_repositories": [
          "anatomy"
        ],
        "responsibility": "Generated evidence visualization and estate anatomy; it consumes canonical manifests and does not create proof or readiness.",
        "assertion_basis": "PUBLIC_GITHUB_READBACK_OUTSIDE_TRUNCATED_ATTACHMENT"
      },
      {
        "layer": "vertical-applications",
        "canonical_repositories": [
          "killinchu"
        ],
        "responsibility": "Human-governed defensive application surfaces consuming the common control, routing, evidence, and compute layers.",
        "assertion_basis": "ATTACHMENT_AND_PUBLIC_GITHUB_READBACK"
      }
    ],
    "code_to_weight_policy": "CODE_REPOSITORIES_ARE_SERVICES_LIBRARIES_OR_EVIDENCE_NOT_MODEL_WEIGHTS",
    "unclassified_repository_policy": "DISCOVER_CLASSIFY_FAIL_CLOSED_NO_ARCHIVE",
    "archive_policy": "ARCHIVE_ONLY_AFTER_REPLACEMENT_REDIRECT_HEALTH_AND_PROVENANCE_VERIFICATION",
    "known_alignment_gaps": [
      "The supplied 54-repository attachment is truncated after pitch-collateral, so the machine inventory is explicitly incomplete and unseen repositories remain unclassified.",
      "The public Brain surfaces do not yet share one signed manifest or retrieval plane: one reports 9,465 raw graph nodes while another exposes a 536-chunk fallback.",
      "a11oy.net and a-11-oy.com require explicit domain, backend, repository-identity, and flagship-link parity before the estate can be called unified.",
      "The Machine Innovate hardening files described in the working thread are absent from the checked repository and need a recoverable branch, commit, or pull request.",
      "SZL Forge and SZL Mesh documentation contain stale or conflicting readiness language and must be generated from qualified manifests.",
      "Hatun MCP and routing logic have overlapping copies; Hatun MCP and SZL Router should be canonical versioned dependencies rather than source duplication."
    ]
  },
  "candidates": [
    {
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          "rights_cleared_audio_manifest_sha256",
          "resource_preflight_receipt_sha256",
          "heldout_split_sha256"
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        "Measure WER or CER, cpWER or DER, timestamp error, hallucinated-segment count, schema parse rate, real-time factor, cold load, peak VRAM and RAM, thermals, and restart identity on rights-cleared audio.",
        "Emit a signed transcript receipt that binds the source audio hash, runtime revision, model revision, decoding parameters, and segment output before canonical Brain admission."
      ],
      "hard_blockers": [
        "No pinned custom-code review or offline execution receipt exists locally.",
        "No rights-cleared audio evaluation set or local quality/resource receipt exists.",
        "No production authorization follows from the upstream card or paper."
      ],
      "non_claims": [
        "The upstream paper and model card are not local quality measurements.",
        "Registering this candidate does not download, load, train, serve, or promote it."
      ],
      "sources": [
        "https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize",
        "https://github.com/OpenMOSS/MOSS-Transcribe-Diarize",
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    },
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      "id": "bonsai-27b-q1-local",
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        ]
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      "runtime": {
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      "local_state": "PLANNED_NOT_DOWNLOADED_NOT_MEASURED",
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          "license_review_sha256",
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        "EVALUATE_SANDBOXED": [
          "runtime_revision",
          "runtime_binary_sha256",
          "build_flags_sha256",
          "network_isolation_receipt_sha256",
          "resource_preflight_receipt_sha256",
          "heldout_split_sha256",
          "restart_receipt_sha256"
        ]
      },
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        "Run the first independent 1-bit local inference qualification at a fixed 4096-token context without drafter or multimodal projector artifacts.",
        "Compare quality per byte, tokens per second, first-token latency, peak VRAM and RAM, thermal envelope, OOM behavior, restart identity, citation integrity, and refusal behavior against Khipu Q4_K_M and conventional Q4 controls.",
        "Adopt only measured low-bit kernel and KV-cache techniques that reproduce locally; never rebrand Prism weights or vendor benchmark tables as SZL results."
      ],
      "hard_blockers": [
        "The required runtime fork and exact GGUF have not been locally built, loaded, or measured.",
        "No independent quality, safety, resource, or restart receipt exists.",
        "The GGUF is an inference artifact and is categorically not a training input."
      ],
      "non_claims": [
        "The reported fit and performance figures are upstream claims, not measurements on the RTX 5050 laptop.",
        "Registering the artifact does not qualify it for the router or production serving."
      ],
      "sources": [
        "https://huggingface.co/prism-ml/Bonsai-27B-gguf",
        "https://github.com/PrismML-Eng/llama.cpp",
        "https://github.com/PrismML-Eng/Bonsai-demo"
      ]
    },
    {
      "id": "ternary-bonsai-27b-eval",
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      "decision": "EVALUATION_ONLY_INFERENCE_ARTIFACT",
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      "upstream": {
        "repository_id": "prism-ml/Ternary-Bonsai-27B-gguf",
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        "model_card_url": "https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf/blob/20e435f518bd5b882795954aba81e80a91894321/README.md",
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        "license_kind": "OPEN_SOURCE_REPORTED",
        "license_evidence_url": "https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf/blob/20e435f518bd5b882795954aba81e80a91894321/LICENSE.txt",
        "artifact_inventory": [
          {
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        ]
      },
      "runtime": {
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        "revision": "62061f91088281e65071cc38c5f69ee95c39f14e",
        "custom_code_policy": "PIN_RUNTIME_FORK_AND_RUN_OFFLINE"
      },
      "local_state": "PLANNED_NOT_DOWNLOADED_NOT_MEASURED",
      "allowed_operations": [
        "READ_METADATA",
        "DOWNLOAD_PINNED",
        "EVALUATE_SANDBOXED"
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        "SERVE_PRODUCTION",
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        "MERGE_WEIGHTS",
        "INGEST_UPSTREAM_TRAINING_TRACES",
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      ],
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        "DOWNLOAD_PINNED": [
          "exact_revision",
          "artifact_sha256",
          "license_review_sha256",
          "disk_preflight_receipt_sha256"
        ],
        "EVALUATE_SANDBOXED": [
          "runtime_revision",
          "runtime_binary_sha256",
          "group_format_compatibility_receipt_sha256",
          "network_isolation_receipt_sha256",
          "resource_preflight_receipt_sha256",
          "restart_receipt_sha256",
          "heldout_split_sha256"
        ]
      },
      "adoption_plan": [
        "Use only as a ternary compression comparison arm against 1-bit Bonsai and conventional Q4; record full-GPU OOM as FAIL and label CPU or hybrid offload as a separate execution class.",
        "Measure at 4096 tokens with exact group-format compatibility and immutable runtime identity before any inference-quality comparison."
      ],
      "hard_blockers": [
        "The selected GGUF alone is 7.17 GB and upstream reports an approximately 8.4 GB 4K peak; an 8 GB laptop fit is not established.",
        "Group-128 Q2_0 requires the pinned Prism runtime fork; mainline compatibility cannot be assumed.",
        "Training from a GGUF inference artifact is prohibited."
      ],
      "non_claims": [
        "Upstream memory and benchmark tables are not local evidence.",
        "A reduced-layer CPU or GPU-offload diagnostic is not a full-GPU qualification."
      ],
      "sources": [
        "https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf",
        "https://github.com/PrismML-Eng/Bonsai-demo"
      ]
    },
    {
      "id": "thinking-machines-inkling-methods",
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      "decision": "REMOTE_ONLY",
      "artifact_class": "REMOTE_FRONTIER_MODEL",
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        "license_evidence_url": "https://huggingface.co/thinkingmachines/Inkling/blob/86b4d430ab871652a707666b89203a866888c5e5/README.md",
        "artifact_inventory": []
      },
      "runtime": {
        "repository_url": "https://huggingface.co/thinkingmachines/Inkling",
        "revision": "MODEL_CARD_ONLY",
        "custom_code_policy": "NO_LOCAL_RUNTIME_ADOPT_METHODS_ONLY"
      },
      "local_state": "REMOTE_NOT_LOCAL_CANDIDATE",
      "allowed_operations": [
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        "ADOPT_METHOD"
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        "MERGE_WEIGHTS",
        "INGEST_UPSTREAM_TRAINING_TRACES",
        "IDENTITY_EDIT_REAL_PERSON"
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      "required_evidence_by_operation": {
        "ADOPT_METHOD": [
          "method_source_revision",
          "independent_ablation_plan_sha256",
          "rights_review_sha256",
          "local_evaluation_protocol_sha256"
        ]
      },
      "adoption_plan": [
        "Implement controllable reasoning budgets in Ayllu and the router, then measure answer quality, thinking tokens, latency, energy, and abstention as a Pareto surface.",
        "Use tool-schema randomization and multimodal evaluation ideas in first-party curricula; never ingest unavailable training traces or copy branding."
      ],
      "hard_blockers": [
        "The model is far beyond the local hardware envelope.",
        "No local measurement or provider authorization exists."
      ],
      "non_claims": [
        "A method reference is not an SZL model qualification.",
        "The registry does not download or route private data to the upstream model."
      ],
      "sources": [
        "https://huggingface.co/thinkingmachines/Inkling",
        "https://thinkingmachines.ai/news/introducing-inkling/",
        "https://github.com/thinking-machines-lab/tinker-cookbook"
      ]
    },
    {
      "id": "glm52-methods",
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      "artifact_class": "REMOTE_FRONTIER_MODEL",
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        "license_expression": "MIT-weights AND Apache-2.0-code",
        "license_kind": "MIXED_CODE_AND_WEIGHT_LICENSES",
        "license_evidence_url": "https://huggingface.co/zai-org/GLM-5.2/blob/b4734de4facf877f85769a911abafc5283eab3d9/README.md",
        "artifact_inventory": []
      },
      "runtime": {
        "repository_url": "https://github.com/zai-org/GLM-5",
        "revision": "MODEL_CARD_ONLY",
        "custom_code_policy": "NO_LOCAL_RUNTIME_ADOPT_METHODS_ONLY"
      },
      "local_state": "REMOTE_NOT_LOCAL_CANDIDATE",
      "allowed_operations": [
        "READ_METADATA",
        "ADOPT_METHOD"
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      "prohibited_operations": [
        "TRAIN",
        "SERVE_PRODUCTION",
        "PROMOTE",
        "MERGE_WEIGHTS",
        "INGEST_UPSTREAM_TRAINING_TRACES",
        "IDENTITY_EDIT_REAL_PERSON"
      ],
      "required_evidence_by_operation": {
        "ADOPT_METHOD": [
          "method_source_revision",
          "independent_sparse_index_ablation_sha256",
          "local_acceptance_metric_protocol_sha256"
        ]
      },
      "adoption_plan": [
        "Prototype shared sparse-attention indexing and index-cache reuse on compact SZL models, with retrieval fidelity and memory receipts.",
        "Measure speculative-decoding acceptance, long-horizon completion, and reasoning-budget tradeoffs independently of upstream benchmark claims."
      ],
      "hard_blockers": [
        "The model is not a local hardware candidate.",
        "No remote provider qualification, price, privacy, or latency receipt exists."
      ],
      "non_claims": [
        "Architecture study does not make GLM weights part of SZL.",
        "No upstream benchmark is relabeled as local evidence."
      ],
      "sources": [
        "https://huggingface.co/zai-org/GLM-5.2",
        "https://github.com/zai-org/GLM-5",
        "https://arxiv.org/abs/2602.15763",
        "https://arxiv.org/abs/2603.12201"
      ]
    },
    {
      "id": "tencent-hy3-methods",
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      "decision": "REMOTE_ONLY",
      "artifact_class": "REMOTE_FRONTIER_MODEL",
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        "artifact_inventory": []
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      "local_state": "REMOTE_NOT_LOCAL_CANDIDATE",
      "allowed_operations": [
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      "prohibited_operations": [
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        "SERVE_PRODUCTION",
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      "required_evidence_by_operation": {
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          "product_feedback_rights_manifest_sha256",
          "tool_recovery_eval_protocol_sha256",
          "reasoning_effort_ablation_sha256"
        ]
      },
      "adoption_plan": [
        "Add explicit low, medium, and high reasoning-effort levels to governed routing with fixed spend, latency, energy, and quality ceilings.",
        "Adopt tool-call recovery tests, scaffold-variance tests, and product-feedback admission controls in the Forge evaluation program."
      ],
      "hard_blockers": [
        "The model requires infrastructure far beyond the laptop.",
        "No local or remote execution receipt exists."
      ],
      "non_claims": [
        "Product-feedback methodology does not authorize reuse of Tencent data.",
        "No Hy3 weights are merged into an SZL artifact."
      ],
      "sources": [
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        "https://github.com/Tencent-Hunyuan/Hy3"
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    },
    {
      "id": "thinkingcap-evaluation-methods",
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        "license_kind": "OPEN_SOURCE_REPORTED",
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        "artifact_inventory": []
      },
      "runtime": {
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        "revision": "MODEL_CARD_ONLY",
        "custom_code_policy": "NO_LOCAL_RUNTIME_ADOPT_METHODS_ONLY"
      },
      "local_state": "METHOD_REFERENCE_NOT_WEIGHT_CANDIDATE",
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        "ADOPT_METHOD": [
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          "paired_base_candidate_protocol_sha256",
          "safety_retention_protocol_sha256"
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      },
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        "Add five-seed confidence intervals and paired base-versus-candidate comparisons to SZL-Nemo and Khipu qualification.",
        "Measure answer quality per thinking token, loop rate, truncation rate, safety retention, latency, and energy rather than reporting one cherry-picked score."
      ],
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        "The 27B weights do not fit the laptop comfortably.",
        "The curated training data is not sufficiently documented for reuse."
      ],
      "non_claims": [
        "Evaluation methodology is adopted only after an SZL ablation.",
        "No ThinkingCap weight or training row becomes an SZL asset."
      ],
      "sources": [
        "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B"
      ]
    },
    {
      "id": "qwen35-2b-governed-bakeoff",
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      "artifact_class": "FULL_CHECKPOINT",
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        "license_kind": "OPEN_SOURCE_REPORTED",
        "license_evidence_url": "https://huggingface.co/Qwen/Qwen3.5-2B/blob/15852e8c16360a2fea060d615a32b45270f8a8fc/LICENSE",
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          "artifact_manifest_sha256",
          "network_isolation_receipt_sha256",
          "resource_preflight_receipt_sha256",
          "heldout_split_sha256",
          "loop_termination_protocol_sha256"
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        "Start at 8192 and 16384 tokens; the upstream 262144-token capability is not a local operating claim and must not become a default resource envelope.",
        "If the candidate qualifies, create a new explicitly Qwen3.5-derived adapter program. Treat existing Qwen2.5 adapters as incompatible unless a separate tensor-shape and tokenizer-identity receipt proves otherwise."
      ],
      "hard_blockers": [
        "No weight or tokenizer has been downloaded or locally measured under this contract.",
        "The pinned Transformers revision has not completed the offline code, dependency, and binary review required by this registry.",
        "Training remains hard-denied until a separate signed contract binds rights-cleared rows, frozen evaluations, resource limits, base and tokenizer identity, trainer code, and release authority."
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      ],
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