Framework design upload
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docs/000-introduction.md
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docs/000-introduction.md
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# Introduction
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Classic operating systems were built to manage scarce physical resources (CPU time, memory pages, disk blocks, network sockets) and to provide deterministic execution semantics.
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Modern AI/LLM systems introduce a new class of workload:
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- probabilistic outputs
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- context-dependent behavior
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- expensive inference
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- heterogeneous accelerators
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- tool-based grounding with side effects
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Today these systems are assembled in userland using RPC, containers, and orchestration frameworks. That works, but the abstractions leak: context, provenance, budgets, and authority are not first-class OS concepts.
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**Cognition OS** is a spec and reference design for a “cognitive kernel” control plane that governs non-deterministic computation deterministically, while keeping all learning and inference in userland services.
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docs/010-design-goals.md
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docs/010-design-goals.md
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# Design goals
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## Primary goals
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1. **Deterministic governance**
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- enforce policy outside model weights
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- deterministic decisions for authority, budgeting, and audit
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2. **Capability-based execution**
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- no ambient permissions for models or tools
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- explicit, revocable capabilities with TTL and scope
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3. **Cognitive resources as first-class**
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- context budgets, compute budgets, risk budgets
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- cost-aware scheduling (latency vs confidence)
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4. **Provenance**
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- structured event model for “what happened and why”
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- support tamper-evident logging (later)
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5. **Isolation domains**
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- strong sandboxing for tools and untrusted workers
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- fault containment and graceful degradation
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## Non-goals (for v0.x)
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- kernel-resident LLM inference
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- universal scheduler for all workloads
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- solving “alignment” in the model
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docs/020-threat-model.md
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docs/020-threat-model.md
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# Threat model (draft)
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## Assets to protect
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- host filesystem and secrets
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- network access and credentials
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- tool side effects (writes, API calls, deletes)
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- user data and memory stores
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- audit and provenance integrity
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## Adversaries / failure modes
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- prompt injection leading to unsafe tool usage
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- malicious tool plugins
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- hallucinated or forged tool outputs
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- data exfiltration via tool channels
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- runaway cost due to repeated retries/loops
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## Strategy
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- least authority via capabilities
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- isolation domains for tool execution
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- policy gate for side effects
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- provenance for every tool call and memory commit
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docs/030-architecture-overview.md
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docs/030-architecture-overview.md
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# Architecture overview
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Cognition OS is layered:
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## Layer 0: Classic kernel
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FreeBSD / Linux provides:
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- process isolation
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- memory protection
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- basic resource controls
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- device drivers
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- confinement primitives (jails/namespaces)
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- security primitives (Capsicum / seccomp+LSM)
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## Layer 0.5: Cognitive kernel (control plane)
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A minimal trusted base that provides:
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- identity + provenance hooks
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- capability minting and enforcement boundaries
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- budget enforcement hooks (time/compute/context/risk)
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- isolation domain management
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- event bus for intent + constraints
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This can begin as a userland daemon, later optionally gaining kernel hooks.
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## Layer 1: Cognitive runtime (userland++)
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Composable services:
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- scheduler/router
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- context manager
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- memory services (working/episodic/semantic/symbolic)
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- tool broker (sandboxed execution)
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- policy engine (deterministic)
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## Layer 2: Workers
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Replaceable modules:
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- LLM inference worker(s)
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- retrieval workers
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- verification workers
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- perception workers (future)
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## Invariants
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- models do not directly invoke tools
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- tool execution requires explicit capabilities
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- provenance is produced for every side effect
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docs/090-roadmap.md
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docs/090-roadmap.md
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# Roadmap (draft)
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## v0.1 (contract-first)
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- cognition object model (Intent/Constraints/Capability/Event/Result)
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- cognitive syscalls/API (daemon IPC initially)
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- event provenance model
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- FreeBSD mapping notes (jails, Capsicum, kqueue)
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- minimal example flows
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## v0.2 (prototypes)
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- tool broker with capability-scoped execution
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- isolation domain prototypes (FreeBSD first)
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- audit log implementation (append-only)
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## v0.3 (workers)
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- pluggable worker interface
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- LLM worker as a replaceable component (CPU or remote)
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- verifier worker integration
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