CORTEX-PERSIST: Cognitive Governance and LogOP Consensus for Autonomous Agent Swarms

1. Introduction: The Cognitive Continuity Paradigm

Excessive reliance on passive data persistence exacerbates «knowledge entropy». Vector databases operating with raw RAG fail epistemically when retrieving semantically similar but logically invalidated fragments.

Cortex-Persist is not a database; it is a Cognitive Hypervisor. It orchestrates operational truth in real time, governing what is retained and what is pruned. To operate in sub-milliseconds, it discards Python on its critical path in favor of a strongly typed, memory-safe, asynchronous ecosystem (Rust-first).

2. Formal Ontology: Belief Objects (BO)

The atomic unit of state is the Belief Object:

• Identity: UUIDv7 (guarantees native temporal lexicographical ordering).
• Epistemic State: Conditional Bayesian Probability (P(H|E)) and bounded variance (uncertainty).
• Decay Rate: Logarithmic decay simulating biological forgetting.
• Provenance (PROV-AGENT): Cryptographic immutable audit trail (who, how, and when the belief was derived).
• Logical Dependencies: Vectors entails (⊢) and discards (≪) to enforce graph consistency. If a premise collapses, all downstream dependencies expire mechanically in O(1).

3. The Core API

Hypervisor governance is exposed via 5 high-density atomic operations:

1. ingest_episode(event_obj): Binary segregation of episodic noise from the immediate attention span of the agent.
2. revise_belief(belief_id, evidence_ref): Triggers the Assumption-Based Truth Maintenance System (ATMS). Executes transitive closure of dependencies.
3. resolve_context(query_params): Evaluates the tensor context injection equation in microseconds and emits an optimized Context Packet (KV-Cache-Refs).
4. attest_lineage(artifact_id): Traces causal lineage for cryptographic verification in regulated environments, answering: «Why did the model believe this?».
5. fork_memory(agent_id, context_delta): Semantically branches state into isolated zero-copy cognitive sandboxes for Monte Carlo simulation.

4. Swarm Sync: Bayesian Consensus and Multi-Agent Resolution

In a decentralized autonomous swarm, epistemic divergence is guaranteed. Cortex-Persist resolves this at the infrastructure level:

5. The Memory Scheduler Equation

Context packet assembly avoids blind KNN by evaluating strict tensor scores:

If a memory incurs logical contradictions that threaten attention coherence (Risk_contam → ∞), the Score collapses to zero instantly, triggering the containment shield.

6. Hardcore Technical Stack

The architecture bypasses the Python GIL with a vertical Rust-first tier:

• L0/L1 Hot Storage (Zero-Copy SHM): Eliminates IPC/JSON serialization overhead. Uses the OS Blackboard pattern powered by iceoryx2, enabling sub-millisecond lock-free feeds into inference engines.
• L3/L4 Swarm Sync: Deploys Zenoh (decentralized pub/sub). Eliminates JVM or heavy broker overhead, delivering async CRDT deltas across Edge nodes with >50 Gbps throughput on local 100GbE fabrics.
• L2/L5 Episodic Data Log: Transactional persistence backed by RocksDB (or PostgreSQL/SurrealDB LVS engines) to ingest raw append-only streams.
• Integrity Layer (Merkle Trees - mssmt): Mathematically immutable state verification (O(log N)), anchoring regulatory proof chains.

7. Memory Consolidation (The Swarm's "Sleep" Cycle)

Cortex-Persist partitions the topology into 3 subgraphs:

• G_e: Continuous episodic event subgraph.
• G_s: Entity semantics and invariant relation subgraph.
• G_c: Community knowledge and procedural memory subgraph.

During model idle cycles, background daemons execute heuristics that consolidate complex inferences and algorithmically prune episodic noise, halting unbounded context expansion.

8. Conclusion

Attempting to build asynchronous swarm intelligence on cosine similarity alone guarantees a collision with the Consistency Wall. Cortex-Persist combines SHM (iceoryx2), native L3 Pub/Sub (Zenoh), and LogOP merges to orchestrate epistemic resolution in microseconds.