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Core Concept

Extracted from: ref-docs/OctoLLM-Concept_Idea.md

Architectures to Borrow from the Octopus

1. Local-Autonomy "Arms," Central-Integration "Brain"

  • Spin up task-specific peripheral controllers (code tools, web searchers, planners, UI drivers, data labelers) with narrow policies and short-term memory.
  • A central integrator (LLM) sets intent, allocates subtasks, imposes constraints, and fuses results—only intervening when goals or safety are at stake.
  • Mechanism: hierarchical control + explicit contracts (inputs/outputs/invariants). Think: Mixture-of-Experts + Orchestrator rather than a single giant monolith.

2. Reflex Layer Before Cognition

  • Pre-LLM reflex filters handle fast, predictable decisions (schema validation, PII/safety checks, rate limiting, cache hits) using small models/finite-state machines.
  • The LLM only engages for "novelty." This reduces latency, cost, and attack surface.

3. Decentralized Memory

  • Each arm has a local episodic store (vector DB or KV cache) bounded by its domain ontology; the brain has a global semantic map.
  • Routing: classifier/gating picks which memories to consult.
  • Prevents cross-domain contamination and keeps retrieval precise.

4. Embodied Tool-Use

  • Treat tools as sensors/actuators. The arm owns its tools (APIs, shells, browsers), maintains affordances/capabilities metadata, and reports action traces upward.
  • The brain reasons over traces, not raw environments—like a commander reading squad reports.

5. Elastic Specialization via MoE + Skill Distillation

  • Train small specialists per domain (planning, SQL, regex, code fixes, UI automation); distill their strengths back into a generalist for robustness while keeping specialists online for hard cases.
  • Gate by uncertainty/entropy or cost budget.

6. Swarm Deliberation with Quorum

  • For critical decisions, run N lightweight "arm" proposals (diverse prompts/seeds/models), aggregate with verifiable voters (majority, Borda, or learned ranker).
  • The brain resolves conflicts using explicit rules (risk thresholds, SLAs).

7. Active Inference for Exploration

  • Arms maintain simple world models and choose actions that reduce expected uncertainty (information gain) subject to task goals.
  • Great for web research agents and code-repair loops.

Concrete System Design (Drop-In Blueprint)

Orchestrator (Brain)

One robust LLM with a Task Contract Schema:

  • goal, constraints, budget (tokens/time/$), security policy, deliverables, acceptance tests.

Arms (Specialists)

  • Planner: Decomposes tasks → subgoals + acceptance criteria.
  • Retriever: Structured + vector search with domain ontologies.
  • Tool-Executor: Browser/API/shell; enforces allowlists; captures provenance.
  • Coder: Patch proposals + self-tests.
  • Judge: Spec compliance, hallucination detection, unit/property checks.
  • Safety/PII Guardian: Static rules + tiny classifier; runs before and after LLM calls.

Memories

  • Local: Per-arm episodic stores (short retention, domain schema).
  • Global: Project knowledge graph (entities, tasks, decisions, citations).

Control

  • Reflex gate → Arm(s) → Orchestrator escalate-on-novelty.
  • Uncertainty triggers: escalate, fork more arms, or ask for user input (with minimally sufficient questions).

Provenance

Every artifact tagged with tool, prompt hash, data source, time, and tests passed.

Quick-Start Experiments You Can Run This Week

  1. Reflex gate + cache: Put a rules/regex/PII filter + embedding cache in front of your LLM; measure latency/cost drop on your common tickets.
  2. Two-arm prototype: Planner → Tool-Executor (browser or repo) with a Judge. Orchestrator only resolves conflicts.
  3. Specialist MoE: Add a code-fix small model (e.g., 1–3B) gated by a classifier; fall back to the big model on low confidence.
  4. Decentralized memory: Split your RAG into per-domain stores; add a router; watch precision improve and leakage drop.
  5. Quorum for critical ops: Require 3 proposals for risky actions; aggregate; compare error rates.

See Also