Cognitive-OS Research

AGI Architecture proposals comparison

An interactive analysis comparing proposed architectures generated by 8 frontier AI systems across 11 key dimensions to synthesize a combined architecture (CORTEX).

Dimension OpenAI GPT-4o Anthropic Claude 3.5 Sonnet Google Gemini 1.5 Pro xAI Grok 2 DeepSeek V3 Alibaba Qwen 2.5 Meta Llama 3.1 Mistral Large 2
memory architecture Multi-tier: STM in Redis cache, Episodic in ChromaDB vector space, Semantic in Neo4j graph DB. Typed & Immutable: Active context in sliding window, Episodic store as structured ledger, Semantic web in pgvector. In-Context Buffer: Large 2M token context window containing full history; Semantic vector cache in ChromaDB for overflow. Real-time Grounded: Hot memory in Redis cache, Cold memory in Qdrant; Real-time search query cache expansion. MLA Optimized: MLA latent KV caching to reduce GPU footprint; Episodic/Semantic unified in Milvus with hierarchical clustering. DB Structured: Factual/Semantic in PostgreSQL with pgvector, Session states in dyn-caching local KV store. Local Stack: Local Qdrant vector DB, episodic in local JSON text files, context in vLLM PagedAttention cache. GDPR Compliant: Semantic index in pgvector, episodic logs and user files tracked in transactional audit ledger.
reasoning/planning loop System 1 (LLM API router) & System 2 (MCTS + Tree-of-Thought search graph). Self-correction via validation scheme check. System 1 (heuristic action plans) & System 2 (formal verification checker). Backtracks when constraints are violated. In-Context planning: Search-guided MCTS and Tree-of-Thought simulated directly inside the 2M context window. Dual planning loop (reactive planner & search planner) triggered by semantic density metrics. MoE-guided chain-of-thought (CoT) with dedicated routing for self-correction. Continuous online policy evaluation. Recursive Goal Decomposition (RGD): breaks high-level instruction into DAG steps. Corrects code bugs iteratively. ReAct (Reasoning and Acting) execution chain. Fine-tunes behavior using local LoRA pipeline on execution logs. Native Function Calling execution loops with verification gates. Introspection model checks output coherence.
learning or self-improvement mechanism Off-line analysis of episodic success records; updates system schemas and prompt templates accordingly. Meta-cognitive reflection loops: modifies constitutional rules based on behavioral audit reports. Continuous in-context learning (ICL) by storing successful traces in the active context window. Active learning via web search results and user corrections; updates local fact database. Reinforcement Learning (RL) feedback loops using runtime reward models to update expert models. Feedback-driven prompt modification and tool registry updates based on runtime errors. Overnight local parameter updates (LoRA fine-tuning) on failure traces collected during runs. Iterative schema evolution; refines tool specifications based on tool execution rates and cost metrics.
tool use and action execution Structured JSON schema validation. Execution in K8s + gVisor sandbox. Post-execution sanitization. Strict typed functional interfaces. Confined in ephemeral Firecracker MicroVMs. Event-ledger logging. Direct in-context parsing of api docs. Ephemeral Docker sandbox. Verification piped to context. Rust runner engine. Confined in Podman containers with egress firewalls and CPU limits. Low-latency expert dispatchers. Confined in Linux namespaces/cgroups with execution-expert review. DAG-based tool pipeline (pipes tool outputs to next inputs). Docker container confinement. Local shell execution scripts. Confined in LXD containers with system call restrictions. Native function calling interface. Confined in epoll-based micro-sandboxes.
world model or representation layer State Graph representing environment variables. Actions are simulated in graph before execution. Causal Bayesian Network for probability and causality checks; simulations estimate side effects. Dynamic document-based model updated inside long context. Simulations run in-context. Real-time state graph representing variables, user patterns, and live search facts. Latent-space representations decoded to schemas only during tool call actions. Factual ontology schema mapping database structure, files, and API endpoints. Local path/system state graph mapping system config files, variables, and folders. Relational database schema representing API models, permissions, and directory states.
safety/governance layer Input/Output moderation APIs, runtime capability bounding, read-only system mounts. Constitutional AI rules, compile-time and runtime invariant checks, append-only cryptographic log. Context invariants (permanent context pins), out-of-band evaluation models. Heuristic blacklist filter and rule-bound action bounds checked by external daemon. Safety expert routing within MoE structure, continuous verification of data access patterns. Role-Based Access Control (RBAC) scopes for tools, automated code security scanner. Llama Guard model filters on inputs and outputs, strict local system execution blacklists. GDPR data compliance layer with automated PII masking on outbound payloads.
evaluation and benchmark strategy Success metrics, token efficiency, memory search degradation metrics over time. Safety regression tests, logic constraint checks, audit ledger validation runs. In-context needle recall tests, coherence check metrics across long sequences. Task latency metrics, API cost benchmarks, search verification rate metrics. FLOP efficiency benchmarks, response latency, reward model score telemetry. SQL query correctness benchmarks, schema validation error rate metrics. Dynamic regression tests using local task scenarios. GDPR auditing logs, latency, cost-performance efficiency benchmarks.
persistence/runtime architecture Protobuf serialization to persistent disk. Asynchronous celery worker pool execution. Rust backend with BSON serialization. Async thread execution via Tokio runtime. Context log token state saves. Python asyncio execution loop. Rust orchestrator. Thread pool worker runtime with binary state blobs. C++ backend with PyTorch. Tensor checkpoint saves. FastAPI with celery. PostgreSQL stores runtime state structures. SQLite database storage. Docker/vLLM local serving runtime. Rust runtime. PostgreSQL datastore for persistent execution metadata.
multi-agent or orchestration design Manager-Worker topology. Communication via RabbitMQ structured JSON messages. Federated delegation model. Akka-like typed actor messages. Shared Context Whiteboard model. All agents interact within the same 2M token context. Decentralized P2P message bus. Pub/Sub routing via Redis. Hierarchical routing with coordinator experts and worker experts. Group-based role topologies (e.g. Developer, Tester, Deployer). Llama Stack broker pattern coordinating multiple local stack instances. Broker pattern matching lightweight native function-calling threads.
engineering feasibility High feasibility; relies on standard enterprise Redis/K8s/Chroma components. Medium feasibility; microVM cold starts and formal verification add complexity and latency. High feasibility; extremely simple stack but relies on costly long-context inference APIs. High feasibility; utilizes highly responsive Rust framework and simple Docker structures. Low-to-Medium feasibility; requires heavy optimization of MoE model routing and MLA configs. High feasibility; uses standard relational database schemas and celery workflows. Medium feasibility; requires local GPUs with sufficient VRAM to handle vLLM. High feasibility; lightweight, standard relational structure and function calls.
originality or non-obvious insight Decoupling execution from planning via deterministic K8s tool sandboxes. A security audit ledger that is cryptographically signed to prevent agent rewriting its history. Replacing database RAG search loops with continuous in-context document-based updates. Dynamic ground checks using live search data feeds directly in the planning loop. Integrating reinforcing reward feedback loop directly into local expert runtime. Piping tool call dependencies directly as a DAG, skipping sequential intermediate planners. Self-improving local model parameters using local LoRA fine-tuning on yesterday's execution data. GDPR-compliant regulatory masking layer embedded in agent tool dispatchers.
Cognitive-OS Combined Synthesis (CORTEX)

CORTEX Architecture

By extracting the most viable design patterns from all eight models, CORTEX bypasses theoretical debates to outline a concrete, production-ready implementation plan for Cognitive-OS.

1. Cryptographically Signed Invariant Ledgers

To secure state-transition auditability and prevent an agent from retroactively rewriting its own execution log or tampering with historical records, all state transformations are committed as block ledger structures containing cryptographic SHA-256 parent hash verification.

2. PII Regulatory Masking Proxy

Before outgoing payloads are dispatched to external tool adapters or public LLM inference APIs, a local, rule-bound proxy intercept validates and redacts PII data (phone numbers, emails, system secrets) with synthetic tokens to guarantee absolute data privacy.

3. DAG-Structured Tool Pipelines

To eliminate model planning delays and API overhead during execution, tool-chains are mapped as Directed Acyclic Graphs (DAGs), feeding output parameters directly into downstream dependent steps without sequential agent intervention loops.

4. Local Offline LoRA Tuning Loops

Instead of relying on broad, online API changes, the system schedules nightly offline training loops that generate LoRA weights using collected execution error traces, dynamically improving local tool call accuracy without exposing data.