Framework design upload

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deamonkai
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# Introduction
Classic operating systems were built to manage scarce physical resources (CPU time, memory pages, disk blocks, network sockets) and to provide deterministic execution semantics.
Modern AI/LLM systems introduce a new class of workload:
- probabilistic outputs
- context-dependent behavior
- expensive inference
- heterogeneous accelerators
- tool-based grounding with side effects
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.
**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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# Design goals
## Primary goals
1. **Deterministic governance**
- enforce policy outside model weights
- deterministic decisions for authority, budgeting, and audit
2. **Capability-based execution**
- no ambient permissions for models or tools
- explicit, revocable capabilities with TTL and scope
3. **Cognitive resources as first-class**
- context budgets, compute budgets, risk budgets
- cost-aware scheduling (latency vs confidence)
4. **Provenance**
- structured event model for “what happened and why”
- support tamper-evident logging (later)
5. **Isolation domains**
- strong sandboxing for tools and untrusted workers
- fault containment and graceful degradation
## Non-goals (for v0.x)
- kernel-resident LLM inference
- universal scheduler for all workloads
- solving “alignment” in the model

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# Threat model (draft)
## Assets to protect
- host filesystem and secrets
- network access and credentials
- tool side effects (writes, API calls, deletes)
- user data and memory stores
- audit and provenance integrity
## Adversaries / failure modes
- prompt injection leading to unsafe tool usage
- malicious tool plugins
- hallucinated or forged tool outputs
- data exfiltration via tool channels
- runaway cost due to repeated retries/loops
## Strategy
- least authority via capabilities
- isolation domains for tool execution
- policy gate for side effects
- provenance for every tool call and memory commit

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# Architecture overview
Cognition OS is layered:
## Layer 0: Classic kernel
FreeBSD / Linux provides:
- process isolation
- memory protection
- basic resource controls
- device drivers
- confinement primitives (jails/namespaces)
- security primitives (Capsicum / seccomp+LSM)
## Layer 0.5: Cognitive kernel (control plane)
A minimal trusted base that provides:
- identity + provenance hooks
- capability minting and enforcement boundaries
- budget enforcement hooks (time/compute/context/risk)
- isolation domain management
- event bus for intent + constraints
This can begin as a userland daemon, later optionally gaining kernel hooks.
## Layer 1: Cognitive runtime (userland++)
Composable services:
- scheduler/router
- context manager
- memory services (working/episodic/semantic/symbolic)
- tool broker (sandboxed execution)
- policy engine (deterministic)
## Layer 2: Workers
Replaceable modules:
- LLM inference worker(s)
- retrieval workers
- verification workers
- perception workers (future)
## Invariants
- models do not directly invoke tools
- tool execution requires explicit capabilities
- provenance is produced for every side effect

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# Roadmap (draft)
## v0.1 (contract-first)
- cognition object model (Intent/Constraints/Capability/Event/Result)
- cognitive syscalls/API (daemon IPC initially)
- event provenance model
- FreeBSD mapping notes (jails, Capsicum, kqueue)
- minimal example flows
## v0.2 (prototypes)
- tool broker with capability-scoped execution
- isolation domain prototypes (FreeBSD first)
- audit log implementation (append-only)
## v0.3 (workers)
- pluggable worker interface
- LLM worker as a replaceable component (CPU or remote)
- verifier worker integration