A production-grade architecture enabling genuine self-propelled AI cognition through bio-inspired modules, recursive planning, and autonomous multi-agent coordination.
Current AI systems remain fundamentally reactive—waiting for prompts, responding to queries, then falling silent. They lack the continuous inner life that characterizes genuine cognition.
COSMO addresses this through a novel approach: orchestrating multiple specialized cognitive modules that collectively generate emergent, self-sustaining thought processes.
Self-propelled cognition emerges from the dynamic interplay of specialized modules—curiosity, memory, goals, and meta-awareness—orchestrated by a central system that maintains coherent thought streams.
Complex cognitive behaviors arise from the interaction of simpler, well-defined modules rather than monolithic architectures.
The system maintains persistent thought processes independent of external stimulation, enabling genuine autonomous cognition.
A central orchestrator manages module activation, resource allocation, and thought stream coherence in real-time.
The system reads its own outputs, evaluates progress, and adapts strategies through meta-cognitive monitoring.
COSMO implements a modular, event-driven architecture where specialized cognitive engines communicate through a central message bus, coordinated by the Core Orchestrator.
A heartbeat mechanism triggering regular cognitive cycles independent of external input. Each tick initiates module activations, memory consolidation, and thought stream updates.
Dynamic attention system where high-salience items trigger investigation cascades. Continuously evaluates information streams, flagging items that warrant deeper cognitive engagement.
The Curiosity Engine generates questions and identifies knowledge gaps, creating intrinsic motivation for continued cognitive activity through novelty and insight potential.
Active goals maintain cognitive momentum by continuously generating sub-tasks and evaluation criteria. Tracks progress, adjusts strategies, and spawns new objectives.
Explores 5 parallel reasoning branches with entanglement and tunneling. Branches evolve independently then "collapse" to the strongest hypothesis through weighted voting.
The system reads its own outputs every 3 cycles, evaluates progress every 30 cycles, and can halt gracefully upon detecting convergence or stagnation.
Web search, literature review, citation tracking
Data analysis, pattern detection, visualization
Cross-topic integration, multi-document synthesis
Report writing, structured markdown generation
Docker sandboxing, script execution
Generate code artifacts with retry logic
Task decomposition, goal breakdown
Cross-agent coordination, dependency resolution
Novel idea generation, curiosity-driven
Contract validation, output promotion
Large context reading, extractive summarization
Novelty, consistency, factuality checks
Cross-agent agreement, conflict resolution
PDF, images, spreadsheets handling
Associative memory with spreading activation and Hebbian learning. Small-world topology enables both local clustering and long-range connections.
Sleep cycles with dream generation that influence future research. Complete audit trail from dreams through goals to deliverables.
Active-active CRDT synchronization across instances. Specialization profiles route goals to appropriate specialist instances.
Manifest generation with Merkle roots, hash validation, and comprehensive arc reports. Every research arc is reproducible.
Define expected artifacts, validate actual outputs, and promote to canonical locations only when contracts are satisfied.
V3 optimization: infrastructure operations use Node.js fs directly. 200x faster audits, zero API dependency for file operations.
COSMO represents a fundamental shift in AI architecture—from systems that respond to systems that think. V3 introduces recursive self-awareness, evidence grounding, and intelligent convergence detection.