Keep the memory schema free of launch and adapter shapes

schemas.py was 473 lines and the memory lifecycle work needs to add
fields to it. The seven adapter and launch-profile models were the
right thing to move: they describe what trained the model and how a
machine starts it, they change when the evidence rules change, and the
memory model should not move with them. They joined the runtime shapes
file that already held the adapter load report.

schemas.py 473 -> 314 lines, runtime_schemas.py 30 -> 192. Every moved
class body is byte-identical (compared with ast.get_source_segment
against HEAD: 37 classes then, 30 + 9 now, none missing, none
differing).

Gates: 333 passed, ruff clean, filesize clean, mypy 24 errors in 9
files on both trees with identical messages, pyright 9 errors on the
package on both trees.

backend/src/ely_eye/adapters.py

@@ -13,8 +13,14 @@ from pydantic import ValidationError
 from . import cartridge_store
 from .config import Settings
 from .db import Database
-from .runtime_schemas import AdapterChoice, AdapterLoadReport, AdapterOrigin, AdapterState
+from .runtime_schemas import (
-from .schemas import AdapterManifest, TrainingAdapterKind
+    AdapterChoice,
+    AdapterLoadReport,
+    AdapterManifest,
+    AdapterOrigin,
+    AdapterState,
+)
+from .schemas import TrainingAdapterKind
 
 logger = logging.getLogger("ely_eye.adapters")
 

backend/src/ely_eye/api.py

@@ -30,10 +30,8 @@ from .profiles import runtime_profile_report
 from .proofs import HashHopEvaluator, PRDProofSuite
 from .retrieval import RetrievalService
 from .runtime import QwenRuntime, RuntimeUnavailableError
-from .runtime_schemas import AdapterLoadReport
+from .runtime_schemas import AdapterLoadReport, AdapterManifest, AdapterStatus, RuntimeProfileReport
 from .schemas import (
-    AdapterManifest,
-    AdapterStatus,
     AnswerResponse,
     AtomPage,
     EvidenceAtom,
@@ -44,7 +42,6 @@ from .schemas import (
     PathIngestRequest,
     RememberRequest,
     RuntimeProfile,
-    RuntimeProfileReport,
     SearchRequest,
     SearchResult,
     SupersedeRequest,

backend/src/ely_eye/api_cartridges.py

@@ -12,7 +12,8 @@ from .cartridge import CartridgeService
 from .cartridge_assets import CartridgeAssetService
 from .config import Settings
 from .db import Database
-from .schemas import AppendRequest, CartridgeAssetReport, CartridgeManifest, MountState
+from .runtime_schemas import CartridgeAssetReport
+from .schemas import AppendRequest, CartridgeManifest, MountState
 
 
 def register(app: FastAPI, settings_dep: Callable[[], Settings]) -> None:

backend/src/ely_eye/cartridge.py

@@ -15,8 +15,8 @@ from . import cartridge_store
 from .config import Settings, get_settings
 from .db import Database
 from .kv_fingerprint import compute_fingerprint, fingerprint_diff, unverifiable_fields
+from .runtime_schemas import AdapterManifest
 from .schemas import (
-    AdapterManifest,
     CartridgeManifest,
     EvidenceAtom,
     MountState,

backend/src/ely_eye/cartridge_assets.py

@@ -17,7 +17,7 @@ from PIL import Image
 from .cartridge import JSON_PARQUET_COLUMNS, CartridgeService
 from .config import Settings, get_settings
 from .db import Database
-from .schemas import CartridgeAssetReport, CartridgeAssetStatus
+from .runtime_schemas import CartridgeAssetReport, CartridgeAssetStatus
 from .storage import ObjectStore, sha256_file
 
 TEXT_VECTOR_DIMS = 384

backend/src/ely_eye/profiles.py

@@ -7,7 +7,7 @@ import subprocess
 from pathlib import Path
 
 from .config import Settings
-from .schemas import RuntimeLaunchProfile, RuntimeProfileReport, RuntimeProfileValidation
+from .runtime_schemas import RuntimeLaunchProfile, RuntimeProfileReport, RuntimeProfileValidation
 
 EXTREME_YARN_OVERRIDE = {
     "text_config": {

backend/src/ely_eye/proofs/checks_training.py

@@ -14,10 +14,8 @@ from ..db import Database
 from ..evaluation_schemas import (
     ProofCheck,
 )
-from ..schemas import (
+from ..runtime_schemas import AdapterManifest
-    AdapterManifest,
+from ..schemas import TrainingAdapterKind
-    TrainingAdapterKind,
-)
 from ..training_common import sha256_file, sha256_tree
 from ..training_metrics import safetensor_weight_stats
 

backend/src/ely_eye/proofs/training_trace.py

@@ -8,10 +8,8 @@ import math
 from pathlib import Path
 from typing import Any
 
-from ..schemas import (
+from ..runtime_schemas import AdapterManifest
-    AdapterManifest,
+from ..schemas import TrainingAdapterKind
-    TrainingAdapterKind,
-)
 from ..training_common import (
     AUTOCAST_DTYPE,
     GRADIENT_CHECKPOINTING_MODE,

backend/src/ely_eye/runtime_schemas.py

@@ -1,10 +1,13 @@
-"""Runtime observability shapes: what the model holds in memory right now."""
+"""Runtime and adapter shapes: what the model holds in memory, what trained it, and how a machine
+launches it. A proof report moves when the evidence rules move; the memory model should not.
+"""
 
+from datetime import datetime
 from typing import Literal
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
-from .schemas import TrainingAdapterKind
+from .schemas import TrainingAdapterKind, utc_now
 
 AdapterState = Literal["loaded", "available", "deferred", "missing"]
 AdapterOrigin = Literal["library", "cartridge"]
@@ -28,3 +31,162 @@ class AdapterLoadReport(BaseModel):
     model_id: str
     mounted: list[str]
     adapters: list[AdapterChoice]
+
+class AdapterManifest(BaseModel):
+    adapter_id: str
+    kind: TrainingAdapterKind
+    created_at: datetime
+    proof_version: int = 1
+    training_method: str = "BF16 LoRA"
+    precision: str = "bf16"
+    framework: str = "unsloth_patched_transformers_peft"
+    base_model: str
+    dataset_path: str
+    dataset_sha256: str
+    sample_count: int
+    max_steps: int
+    max_length: int
+    rank: int
+    alpha: int
+    target_modules: list[str]
+    trainable_params: int
+    total_params: int
+    optimizer: str
+    optimizer_family: str | None = None
+    learning_rate: float
+    gradient_accumulation_steps: int
+    gradient_checkpointing: bool = False
+    gradient_checkpointing_mode: str | None = None
+    autocast_dtype: str | None = None
+    unsloth_version: str | None = None
+    triton_version: str | None = None
+    xformers_version: str | None = None
+    unsloth_model_type: str | None = None
+    bf16: bool
+    final_loss: float | None
+    loss_history: list[float] = Field(default_factory=list)
+    initial_trainable_sha256: str | None = None
+    final_trainable_sha256: str | None = None
+    initial_trainable_l2: float | None = None
+    final_trainable_l2: float | None = None
+    optimizer_update_count: int = 0
+    max_optimizer_update_l2: float | None = None
+    loss_delta: float | None = None
+    training_proof_path: str | None = None
+    training_proof_sha256: str | None = None
+    training_trace_path: str | None = None
+    training_trace_sha256: str | None = None
+    training_summary_path: str | None = None
+    training_summary_sha256: str | None = None
+    training_started_at: datetime | None = None
+    training_finished_at: datetime | None = None
+    training_wall_seconds: float | None = None
+    training_device: str | None = None
+    torch_version: str | None = None
+    cuda_version: str | None = None
+    adapter_weight_sha256: str | None = None
+    adapter_tensor_count: int = 0
+    adapter_lora_tensor_count: int = 0
+    adapter_total_elements: int = 0
+    adapter_nonzero_elements: int = 0
+    adapter_max_abs: float | None = None
+    adapter_weights_finite: bool = False
+    artifacts: dict[str, str]
+    sha256: str
+    cartridge_id: str | None = None
+
+class AdapterStatus(BaseModel):
+    name: str
+    kind: TrainingAdapterKind
+    proof_version: int = 1
+    training_method: str = "BF16 LoRA"
+    precision: str = "bf16"
+    framework: str = "unsloth_patched_transformers_peft"
+    base_model: str
+    path: str
+    adapter_id: str
+    sample_count: int
+    max_steps: int
+    trainable_params: int
+    final_loss: float | None
+    sha256: str
+    weight_bytes: int
+    safetensor_keys: int
+    cartridge_id: str | None = None
+    cartridge_bound: bool
+    training_trace_path: str | None = None
+    training_trace_sha256: str | None = None
+    training_trace_steps: int = 0
+    training_proof_path: str | None = None
+    training_proof_sha256: str | None = None
+    optimizer: str | None = None
+    optimizer_family: str | None = None
+    gradient_checkpointing: bool = False
+    gradient_checkpointing_mode: str | None = None
+    autocast_dtype: str | None = None
+    unsloth_version: str | None = None
+    triton_version: str | None = None
+    xformers_version: str | None = None
+    unsloth_model_type: str | None = None
+    optimizer_update_count: int = 0
+    max_optimizer_update_l2: float | None = None
+    loss_delta: float | None = None
+    training_summary_path: str | None = None
+    training_wall_seconds: float | None = None
+    training_device: str | None = None
+    bf16: bool = False
+    torch_version: str | None = None
+    cuda_version: str | None = None
+    adapter_weight_sha256: str | None = None
+    adapter_tensor_count: int = 0
+    adapter_lora_tensor_count: int = 0
+    adapter_total_elements: int = 0
+    adapter_nonzero_elements: int = 0
+    adapter_max_abs: float | None = None
+    adapter_weights_finite: bool = False
+
+class CartridgeAssetStatus(BaseModel):
+    name: str
+    path: str
+    sha256: str
+    size_bytes: int
+    item_count: int
+    dtype: str
+    status: Literal["ready", "missing"]
+
+class CartridgeAssetReport(BaseModel):
+    cartridge_id: str
+    status: Literal["ready", "incomplete"]
+    created_at: datetime = Field(default_factory=utc_now)
+    assets: list[CartridgeAssetStatus]
+
+class RuntimeLaunchProfile(BaseModel):
+    name: str
+    runtime: str
+    role: str
+    context_tokens: int
+    script: str
+    command: list[str]
+    environment: dict[str, str] = Field(default_factory=dict)
+    required_features: list[str] = Field(default_factory=list)
+
+class RuntimeProfileValidation(BaseModel):
+    name: str
+    runtime: str
+    status: Literal["ready", "missing"]
+    script_exists: bool
+    package: str | None = None
+    package_version: str | None = None
+    executable: str | None = None
+    gpu_available: bool = False
+    probe_command: list[str] = Field(default_factory=list)
+    probe_status: Literal["passed", "failed", "skipped"] = "skipped"
+    probe_exit_code: int | None = None
+    probe_output: str | None = None
+    detail: str
+
+class RuntimeProfileReport(BaseModel):
+    status: Literal["ready", "incomplete"]
+    created_at: datetime = Field(default_factory=utc_now)
+    profiles: list[RuntimeLaunchProfile]
+    validations: list[RuntimeProfileValidation] = Field(default_factory=list)

backend/src/ely_eye/schemas.py

@@ -266,165 +266,6 @@ class CartridgeManifest(BaseModel):
     kv_fingerprint: KvFingerprint | None = None
     dna: str | None = None
 
-class AdapterManifest(BaseModel):
-    adapter_id: str
-    kind: TrainingAdapterKind
-    created_at: datetime
-    proof_version: int = 1
-    training_method: str = "BF16 LoRA"
-    precision: str = "bf16"
-    framework: str = "unsloth_patched_transformers_peft"
-    base_model: str
-    dataset_path: str
-    dataset_sha256: str
-    sample_count: int
-    max_steps: int
-    max_length: int
-    rank: int
-    alpha: int
-    target_modules: list[str]
-    trainable_params: int
-    total_params: int
-    optimizer: str
-    optimizer_family: str | None = None
-    learning_rate: float
-    gradient_accumulation_steps: int
-    gradient_checkpointing: bool = False
-    gradient_checkpointing_mode: str | None = None
-    autocast_dtype: str | None = None
-    unsloth_version: str | None = None
-    triton_version: str | None = None
-    xformers_version: str | None = None
-    unsloth_model_type: str | None = None
-    bf16: bool
-    final_loss: float | None
-    loss_history: list[float] = Field(default_factory=list)
-    initial_trainable_sha256: str | None = None
-    final_trainable_sha256: str | None = None
-    initial_trainable_l2: float | None = None
-    final_trainable_l2: float | None = None
-    optimizer_update_count: int = 0
-    max_optimizer_update_l2: float | None = None
-    loss_delta: float | None = None
-    training_proof_path: str | None = None
-    training_proof_sha256: str | None = None
-    training_trace_path: str | None = None
-    training_trace_sha256: str | None = None
-    training_summary_path: str | None = None
-    training_summary_sha256: str | None = None
-    training_started_at: datetime | None = None
-    training_finished_at: datetime | None = None
-    training_wall_seconds: float | None = None
-    training_device: str | None = None
-    torch_version: str | None = None
-    cuda_version: str | None = None
-    adapter_weight_sha256: str | None = None
-    adapter_tensor_count: int = 0
-    adapter_lora_tensor_count: int = 0
-    adapter_total_elements: int = 0
-    adapter_nonzero_elements: int = 0
-    adapter_max_abs: float | None = None
-    adapter_weights_finite: bool = False
-    artifacts: dict[str, str]
-    sha256: str
-    cartridge_id: str | None = None
-
-class AdapterStatus(BaseModel):
-    name: str
-    kind: TrainingAdapterKind
-    proof_version: int = 1
-    training_method: str = "BF16 LoRA"
-    precision: str = "bf16"
-    framework: str = "unsloth_patched_transformers_peft"
-    base_model: str
-    path: str
-    adapter_id: str
-    sample_count: int
-    max_steps: int
-    trainable_params: int
-    final_loss: float | None
-    sha256: str
-    weight_bytes: int
-    safetensor_keys: int
-    cartridge_id: str | None = None
-    cartridge_bound: bool
-    training_trace_path: str | None = None
-    training_trace_sha256: str | None = None
-    training_trace_steps: int = 0
-    training_proof_path: str | None = None
-    training_proof_sha256: str | None = None
-    optimizer: str | None = None
-    optimizer_family: str | None = None
-    gradient_checkpointing: bool = False
-    gradient_checkpointing_mode: str | None = None
-    autocast_dtype: str | None = None
-    unsloth_version: str | None = None
-    triton_version: str | None = None
-    xformers_version: str | None = None
-    unsloth_model_type: str | None = None
-    optimizer_update_count: int = 0
-    max_optimizer_update_l2: float | None = None
-    loss_delta: float | None = None
-    training_summary_path: str | None = None
-    training_wall_seconds: float | None = None
-    training_device: str | None = None
-    bf16: bool = False
-    torch_version: str | None = None
-    cuda_version: str | None = None
-    adapter_weight_sha256: str | None = None
-    adapter_tensor_count: int = 0
-    adapter_lora_tensor_count: int = 0
-    adapter_total_elements: int = 0
-    adapter_nonzero_elements: int = 0
-    adapter_max_abs: float | None = None
-    adapter_weights_finite: bool = False
-
-class CartridgeAssetStatus(BaseModel):
-    name: str
-    path: str
-    sha256: str
-    size_bytes: int
-    item_count: int
-    dtype: str
-    status: Literal["ready", "missing"]
-
-class CartridgeAssetReport(BaseModel):
-    cartridge_id: str
-    status: Literal["ready", "incomplete"]
-    created_at: datetime = Field(default_factory=utc_now)
-    assets: list[CartridgeAssetStatus]
-
-class RuntimeLaunchProfile(BaseModel):
-    name: str
-    runtime: str
-    role: str
-    context_tokens: int
-    script: str
-    command: list[str]
-    environment: dict[str, str] = Field(default_factory=dict)
-    required_features: list[str] = Field(default_factory=list)
-
-class RuntimeProfileValidation(BaseModel):
-    name: str
-    runtime: str
-    status: Literal["ready", "missing"]
-    script_exists: bool
-    package: str | None = None
-    package_version: str | None = None
-    executable: str | None = None
-    gpu_available: bool = False
-    probe_command: list[str] = Field(default_factory=list)
-    probe_status: Literal["passed", "failed", "skipped"] = "skipped"
-    probe_exit_code: int | None = None
-    probe_output: str | None = None
-    detail: str
-
-class RuntimeProfileReport(BaseModel):
-    status: Literal["ready", "incomplete"]
-    created_at: datetime = Field(default_factory=utc_now)
-    profiles: list[RuntimeLaunchProfile]
-    validations: list[RuntimeProfileValidation] = Field(default_factory=list)
-
 class ForgetReport(BaseModel):
     """What a forget actually removed, including the derived copies that have to go with it."""
 

backend/src/ely_eye/training.py

@@ -6,7 +6,8 @@ from pathlib import Path
 
 from .config import Settings, get_settings
 from .db import Database
-from .schemas import AdapterManifest, TrainingAdapterKind
+from .runtime_schemas import AdapterManifest
+from .schemas import TrainingAdapterKind
 from .training_common import TrainingSample
 from .training_datasets import (
     build_prd_dataset,

backend/src/ely_eye/training_lora.py

@@ -10,7 +10,8 @@ from typing import Any
 
 from .config import Settings
 from .db import Database
-from .schemas import AdapterManifest, TrainingAdapterKind
+from .runtime_schemas import AdapterManifest
+from .schemas import TrainingAdapterKind
 from .training_common import (
         AUTOCAST_DTYPE,
         GRADIENT_CHECKPOINTING_MODE,

backend/src/ely_eye/training_retrieval.py

@@ -9,7 +9,8 @@ from typing import Any
 
 from .config import Settings
 from .db import Database
-from .schemas import AdapterManifest, TrainingAdapterKind
+from .runtime_schemas import AdapterManifest
+from .schemas import TrainingAdapterKind
 from .training_common import (
         AUTOCAST_DTYPE,
         GRADIENT_CHECKPOINTING_MODE,