babylon.models.entities.attention_thread

Attention thread and Sparrow analysis entity models (Feature 039).

Defines: - AttentionThread: State intelligence resource tracking a specific target - SparrowAnalysis: Network vulnerability analysis results on G_observed

All models are frozen (immutable) Pydantic BaseModels.

See also

specs/039-state-apparatus-ai/data-model.md: Entity definitions. babylon.models.entities.state_apparatus_ai: FactionBalance, StateAction, etc.

Module Attributes

VALID_TARGET_TYPES

Valid target types for attention threads.

Classes

AttentionThread(**data)	State intelligence resource tracking a specific target (Feature 039).
SparrowAnalysis(**data)	Network vulnerability analysis results on G_observed (Feature 039).

babylon.models.entities.attention_thread.VALID_TARGET_TYPES: frozenset[str] = frozenset({'community', 'organization', 'territory'})

Valid target types for attention threads.

class babylon.models.entities.attention_thread.AttentionThread(**data)

Bases: BaseModel

State intelligence resource tracking a specific target (Feature 039).

Each thread maintains a growing G_observed subgraph (always incomplete, always distorted) of the target. Thread pool size derives from the sum of surveillance_capacity across all StateApparatus nodes. Sparrow analysis operates on G_observed per thread.

Parameters:

• thread_id (str)

• target_type (str)

• target_id (str)

• phase (ThreadPhase)

• intensity (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

• intel_completeness (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

• surveillance_methods (list[SurveillanceMethod])

• observed_node_ids (frozenset[str])

• observed_edge_ids (frozenset[tuple[str, str]])

• stickiness (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

• ticks_active (int)

• owning_apparatus_id (str)

thread_id

Unique identifier for this attention thread.

target_type

Type of target entity.

target_id

ID of target entity.

phase

Current intelligence phase.

intensity

Resource allocation intensity [0,1].

intel_completeness

Accumulated intelligence [0,1].

surveillance_methods

Active collection methods.

observed_node_ids

Node IDs discovered in G_observed.

observed_edge_ids

Edge ID pairs discovered in G_observed.

stickiness

Resistance to reallocation by meta-OODA [0,1].

ticks_active

Ticks since thread allocation.

owning_apparatus_id

StateApparatus that owns this thread.

Reference: FR-A01 through FR-A08, R-002, R-007.

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

thread_id: str

target_type: str

target_id: str

phase: ThreadPhase

intensity: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=quantize)]

intel_completeness: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=quantize)]

surveillance_methods: list[SurveillanceMethod]

observed_node_ids: frozenset[str]

observed_edge_ids: frozenset[tuple[str, str]]

stickiness: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=quantize)]

ticks_active: int

owning_apparatus_id: str

class babylon.models.entities.attention_thread.SparrowAnalysis(**data)

Bases: BaseModel

Network vulnerability analysis results on G_observed (Feature 039).

Implements Sparrow’s framework: centrality computation, equivalence class identification, singleton detection, and minimal cutset analysis. All results are contingent on G_observed quality – they may be wrong because the state’s view is always incomplete.

This is a COMPUTED artifact – derived from G_observed, not stored in the graph. Constitution II.2: derived state, not primitive.

Parameters:

• thread_id (str)

• tick (int)

• centrality_rankings (dict[str, dict[str, float]])

• equivalence_classes (list[frozenset[str]])

• identified_singletons (frozenset[str])

• known_cutsets (list[frozenset[str]])

• confidence (Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=~babylon.kernel.math.quantize)])

thread_id

Source thread ID.

tick

Tick of computation.

centrality_rankings

Per-node centrality scores.

equivalence_classes

Groups of structurally equivalent nodes.

identified_singletons

Nodes in singleton equivalence classes.

known_cutsets

Minimal node cutsets in G_observed.

confidence

Analysis confidence based on intel_completeness [0,1].

Reference: FR-A03, R-002.

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

thread_id: str

tick: int

centrality_rankings: dict[str, dict[str, float]]

equivalence_classes: list[frozenset[str]]

identified_singletons: frozenset[str]

known_cutsets: list[frozenset[str]]

confidence: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Value in range [0.0, 1.0] representing likelihood', metadata=[Ge(ge=0.0), Le(le=1.0)]), AfterValidator(func=quantize)]