Mechanisms Overview

The 19 mechanisms grouped into 5 architectural pillars

The Core Problem

LLM-based simulations face a fundamental tension: full-fidelity simulation is prohibitively expensive (O(entities × timepoints × tokens)) and causes context collapse, but naive compression destroys causal structure. You can’t reason about “what did Jefferson know when he wrote this letter” if you’ve summarized away the exposure events that gave him that knowledge.

Traditional approaches assume uniform fidelity—every entity at every moment rendered at the same resolution. This is wasteful (most detail is never queried) and inflexible (no way to dynamically allocate detail where it matters).

The Architectural Insight

Timepoint-Pro treats fidelity as a query-driven 2D surface over (entity, timepoint) space. Resolution is heterogeneous and mutable: a minor attendee exists as a 200-token tensor embedding until someone asks about them, at which point the system elevates their resolution while preserving causal consistency with everything already established.

This enables 95% cost reduction without temporal incoherence—but only because the system maintains explicit causal structure (exposure events, temporal chains, validation constraints) that compression-based approaches discard.

The Five Pillars

The 19 mechanisms group into five architectural pillars:

Pillar	Problem	Mechanisms
Fidelity Management	Allocate detail where queries land	M1, M2, M5, M6
Temporal Reasoning	Multiple notions of time and causality	M7, M8, M12, M14, M17
Knowledge Provenance	Track who knows what, from whom, when	M3, M4, M19
Entity Simulation	Generate and synthesize entity behavior	M9, M10, M11, M13, M15, M16
Infrastructure	Model selection, cost optimization	M18

Complete Mechanism Table

Fidelity Management (M1, M2, M5, M6)

ID	Mechanism	Purpose	Token Impact
M1	Heterogeneous Fidelity Graphs	Each (entity, timepoint) maintains independent resolution	95% reduction
M2	Progressive Training	Entity quality accumulates with queries	Lazy quality investment
M5	Query-Driven Lazy Resolution	Resolution decisions at query time, not simulation time	Pay only for what’s queried
M6	TTM Tensor Compression	Structured tensor representation at TENSOR_ONLY resolution	97% compression ratio

Temporal Reasoning (M7, M8, M12, M14, M17)

ID	Mechanism	Purpose	Key Feature
M7	Causal Temporal Chains	Explicit causal links between timepoints	Prevents anachronisms
M8	Vertical Timepoint Expansion	Add detail within a moment (not just forward)	Intra-moment depth
M12	Counterfactual Branching	Create alternate timelines from intervention points	”What if” analysis
M14	Circadian Activity Patterns	Time-of-day behavior probability	Natural scheduling
M17	Modal Temporal Causality	Five temporal modes with different causal semantics	FORWARD, PORTAL, BRANCHING, DIRECTORIAL, CYCLICAL

Knowledge Provenance (M3, M4, M19)

ID	Mechanism	Purpose	Key Insight
M3	Exposure Event Tracking	Log every knowledge acquisition	Knowledge has origins
M4	Constraint Enforcement	Five validators using conservation laws	Structural consistency
M19	Knowledge Extraction Agent	LLM-based semantic extraction from dialog	No magic knowledge

Entity Simulation (M9, M10, M11, M13, M15, M16)

ID	Mechanism	Purpose	Key Feature
M9	On-Demand Entity Generation	Generate entities when queries reference them	Lazy entity creation
M10	Scene-Level Entity Sets	Environment, Atmosphere, Crowd entities	Context shapes behavior
M11	Dialog Synthesis	Per-character turn generation with LangGraph	Voice differentiation
M13	Multi-Entity Synthesis	Relationship evolution tracking	Belief alignment over time
M15	Entity Prospection	Entities model their own futures	Planning and anxiety
M16	Animistic Entity Extension	Objects and institutions have agency	7 animism levels

Infrastructure (M18)

ID	Mechanism	Purpose	Key Feature
M18	Intelligent Model Selection	Capability-based model routing	16 action types × 15 capability dimensions

Performance Characteristics

Token Cost Reduction

Approach	Tokens	Cost
Naive (uniform high fidelity)	50M	~$500/query
Heterogeneous fidelity	2.5M	~$25/query
With TTM compression	250k	~$2.50/query

Compression Ratios

• Context tensor: 1000 dims → 8 dims (99.2%)
• Biology tensor: 50 dims → 4 dims (92%)
• Behavior tensor: 100 dims → 8 dims (92%)
• Overall at TENSOR_ONLY: 50k → 200 tokens (99.6%)

Integration Flow

Next Steps

Explore each pillar in detail:

Fidelity Management

M1, M2, M5, M6 - How fidelity follows attention

Temporal Reasoning

M7, M8, M12, M14, M17 - Five temporal modes

Knowledge Provenance

M3, M4, M19 - Who knows what, from whom, when

Entity Simulation

M9-M11, M13, M15, M16 - Entity behavior synthesis

Infrastructure

M18 - Model selection and routing

Implementation Status

All 19 mechanisms implemented and verified across 21 templates (February 2026). The castaway_colony_branching template exercises all 19 mechanisms in a single scenario (10 entities, 3 counterfactual branches).

All 5 temporal modes fully implemented:

• FORWARD: Standard causality
• PORTAL: Backward inference from endpoints
• BRANCHING: Counterfactual timelines
• DIRECTORIAL: Narrative-driven with dramatic tension
• CYCLICAL: Prophecy and time loops