3cfaa1e1d3
Phase 1 delivers the complete durability and storage layer:
- WAL with crash recovery: Append-only journal with BLAKE3 checksums,
fsync guarantees, and proper seek-to-EOF on reopen
- Storage engine: sled-backed KVStore with scan_prefix for range queries
- Content-addressed storage: H:{hash}, V:{hash}, E:{hash} key patterns
- Ingestor: Background worker tailing WAL, writing to KV with 8-byte
aligned record headers for rkyv zero-copy deserialization
- Comprehensive tests: 31 tests covering crash recovery, round-trips,
and multi-cycle durability
New crates: stemedb-wal, stemedb-storage, stemedb-ingest
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
97 lines
3.6 KiB
Markdown
97 lines
3.6 KiB
Markdown
# The Simulation: "The Infinite Game"
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> **Codename:** The Arena
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> **Goal:** Validate StemeDB's behavior under emergent, adversarial, and evolutionary pressure.
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## 1. The Vision
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We are not building a database for humans to query manually. We are building the **Cortex** for AI agents. Therefore, the only way to truly validate StemeDB is to simulate a society of agents living, arguing, and reasoning within it.
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The Simulation is an **Agent-Based Modeling (ABM)** environment where StemeDB is the physics engine of truth.
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## 2. The Players (Personas)
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We instantiate a swarm of agents with conflicting goals and personalities:
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| Persona | Goal | Behavior Pattern |
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| :--- | :--- | :--- |
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| **The Scientist** | Converge on Truth | Publishes assertions with high confidence, cites sources, verifies others. |
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| **The Troll** | Sow Chaos | Publishes low-confidence contradictions, forks reality frequently. |
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| **The Believer** | Amplify Consensus | Blindly trusts high-reputation agents, creates echo chambers. |
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| **The Skeptic** | Find Variance | Queries for high-conflict nodes, reduces confidence of unverified claims. |
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| **The Historian** | Preserve Context | Audits "Dormant" assertions, resurrects old truths if new evidence appears. |
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## 3. The Gameplay Loop
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The simulation runs in "Ticks" (Logic Frames).
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### Tick 1: The Assertion
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* **Scientist** reads a "Paper" (simulated ground truth).
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* Asserts: `Subject="Protein_X", Predicate="binds_to", Object="Receptor_Y"`.
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* Sign: `Key_Scientist`.
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### Tick 2: The Fork
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* **Troll** reads the assertion.
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* Forks reality: `Branch="Counter_Narrative"`.
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* Asserts: `Subject="Protein_X", Predicate="binds_to", Object="Nothing"`.
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* Sign: `Key_Troll`.
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### Tick 3: The Lens Resolution
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* **Believer** queries `Protein_X`.
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* Applies **Lens::Consensus**.
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* Result: `Receptor_Y` (Weight 1.0 vs 0.0).
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* **Believer** signs the original assertion (Weight increases).
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### Tick 4: The Reputation Update
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* **Gardener** (System Process) runs TrustRank.
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* Sees **Scientist** verified by **Believer**.
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* Increases **Scientist** Reputation.
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* Decreases **Troll** Reputation (low consensus).
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### Tick 5: The Decay
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* Time passes.
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* **Dormancy Protocol** calculates "Confidence Half-Life".
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* Unverified assertions fade. High-reputation assertions persist.
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## 4. Technical Architecture
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### 4.1. The Arena (Runner)
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A Rust binary (`stemedb-sim`) that orchestrates the swarm.
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* **Runtime:** `tokio` (Async).
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* **Communication:** Agents talk *only* via StemeDB (Writes/Reads).
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* **Metrics:** Prometheus/Grafana dashboard tracking:
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* `global_truth_convergence` (Entropy of the graph).
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* `agent_reputation_distribution`.
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* `fork_depth_max`.
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### 4.2. The Scenario Config
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We define scenarios in YAML:
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```yaml
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scenario: "The Rumor Mill"
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agents:
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scientists: 5
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trolls: 2
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believers: 20
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duration: 1000 ticks
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ground_truth:
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- "Sky is Blue"
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- "Water is Wet"
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```
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## 5. Success Criteria
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We know StemeDB works when:
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1. **Truth Survives:** High-reputation assertions outlive spam.
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2. **Lenses Work:** A `Consensus` lens correctly filters out the Troll's noise.
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3. **Performance:** The system handles 1000 concurrent agents forking reality without locking up (SMT efficiency).
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4. **Emergence:** We see "Trust Clusters" form naturally without hardcoded rules.
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## 6. Implementation Plan
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1. **Basic Agent Logic**: Implement `Agent` struct with `Signer` and `Strategy`.
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2. **Scenario Runner**: Build the loop that ticks agents.
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3. **Metric Export**: Expose internal graph stats.
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4. **Chaos Injection**: Randomly kill nodes/agents and verify recovery.
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**The Simulation is the Integration Test.**
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