12 AMI Cognitive Stack

12 — AMI cognitive stack

Updated: 2026-09-17

Nokido implements the Autonomous Machine Intelligence (AMI) loop proposed by Yann LeCun in A Path Towards Autonomous Machine Intelligence (2022). This stack is what makes Nokido a planning agent, not just a prompt-response wrapper.

🧠 The AMI loop

                Perception
                    ↓
            ┌───────────────┐
            │  World Model  │
            └───────┬───────┘
                    │
              ┌─────┴─────┐
              ↓           ↓
         ┌─────────┐ ┌─────────┐
         │  Cost   │ │  Actor  │
         └────┬────┘ └────┬────┘
              │           │
              └─────┬─────┘
                    ↓
              ┌─────────┐
              │  MPC    │  ← planner over horizons
              │ Planner │
              └────┬────┘
                   ↓
                Action
                   ↓
         (feed back to Perception)

Plus DeepMind-style policy and value networks for sampling actions and estimating returns.

📦 Module mapping

LeCun component	Nokido module	Notes
Perception	app/forge_byte_router.py + sentinels	13 sentinels filter the raw input stream.
World Model	app/forge_world_model.py	Predicts next state given current + action.
Cost	app/forge_cost_net.py	Scores outcomes (preference model).
Actor	app/forge_policy_net.py	Proposes candidate actions.
MPC Planner	app/forge_mpc.py	Model-Predictive Control over N-step horizons.
Policy / Value	app/forge_policy_net.py, app/forge_value_net.py	DeepMind-style nets.
MCTS	app/forge_mcts_engine.py	Monte-Carlo Tree Search over the world model.
Self-supervised pre-train	app/forge_world_model.py (JEPA branch), app/forge_sigreg.py	JEPA (Joint-Embedding Predictive Architecture, LeCun 2023).

All modules built continual-learning ready (no batch retraining required) — see app/forge_continual_backprop.py.

🎼 The strategist — orchestrating the loop

tools/forge_ami_strategist.py ties everything together :

1. Drafter — a cloud LLM proposes a CodePatchProposal (DSPy Signature, JSON-strict). Drafter providers : cerebras > groq (instruction-following hit rate).
2. Judge — forge_scorecard.evaluate_symbolic() runs the 6 deterministic axes. Zero LLM in the verdict.
3. GOAP router — based on the score, picks one of three actions :
   • close : score ≥ threshold → ship the patch.
   • refine : score middling → spawn an EDITOR refine task.
   • ban_and_retry : score below floor → ban this draft, try another drafter.
4. Continual update — every accepted patch updates the world model trace (forge_offline_trainer.py).

This is the neuro-symbolic governance principle in practice : LLMs generate, symbolic logic judges.

🦋 Active Inference — the surprise minimizer

app/forge_active_inference_agent.py wraps pymdp (Heins, 2024) for the cyber-defense agent.

Instead of maximizing a reward signal (RL paradigm), the agent minimizes expected free energy :

F = E_q[log q(s) - log p(o, s)]
   ≈ KL(q(s)‖p(s)) - E_q[log p(o|s)]
   = complexity - accuracy

In plain terms : the agent has a generative model of what should happen. When reality diverges (observed o doesn't match predicted o), the KL divergence spikes — a "surprise" signal. An attack, by definition, is something the system did not predict.

Implementation :

from forge_active_inference_agent import ActiveInferenceAgent

agent = ActiveInferenceAgent(num_states=10, num_observations=5)
agent.observe([2])     # observation index
surprise = agent.compute_surprise()
if surprise > THRESHOLD:
    fire_alert()

6/6 tests pass (per memory forge_active_inference_agent).

💧 Liquid Neural Networks — the edge pulse

app/forge_lnn_monitor.py wraps ncps (Hasani, MIT, 2022) — Closed-form Continuous-time networks (CfC).

Used for CPU/RAM/NPU telemetry in continuous time. Why :

• ~100× lighter than an LLM equivalent (the system stats are time-series, not language).
• Continuous-time : no discrete steps, naturally handles irregular sampling.
• Causal : closed-form solution, no ODE solver needed at inference.
• Runs on NPU : the Radeon 780M's iGPU handles a CfC at sub-ms latency.

Train step / loss curves : 4/4 tests pass (memory forge_lnn_monitor).

🧬 JEPA — Joint Embedding Predictive Architecture

The JEPA branch of app/forge_world_model.py implements LeCun's JEPA (2023), with SIGReg in app/forge_sigreg.py :

• Two encoders (target + predictor) on different views of the data.
• Predict the embedding of the target view from the predictor view, NOT the raw pixels/text.
• Anti-collapse via SIGReg (LeCun 2026, paper "Signal Regularization for Self-Supervised Learning").

Nokido uses JEPA in forge_world_model : it predicts the next system-state embedding from the current state + action (latent-space model-predictive planning, gradient_refine_action), not raw text.

🧬 Embedding & state dimensionality (unification in progress)

The stack couples two vector spaces that are not yet aligned :

• Memory / RAG = 1024d BGE-M3 (forge_embed_router → NokidoLlamaEmbed :8099, GGUF GPU). ~531k chunks.
• Cognition (forge_world_model JEPA + forge_value_net / policy_net / cost_net) = 384d (all-MiniLM, or nomic fallback) via forge_state_encoder.

So the world-model can't directly consume RAG-indexed lessons (different dim). Unification to 1024d is a non-breaking rail in progress :

• forge_state_encoder.encode_state(text, dim=1024) routes via embed_router (BGE-M3, same space as RAG). Default dim = STATE_DIM (env LAFORGE_STATE_DIM, default 384 → non-breaking ; flip to 1024 after retraining).
• Cognition dims centralized on a single source — forge_state_encoder.STATE_DIM (= LAFORGE_STATE_DIM) — across forge_world_model + value/policy/cost nets (COGNITION_DIM imports it, no double env read). cost_module + active_inference are dim-agnostic (verified, 0 edit). forge_goap_intuition's value-net embedder is checkpoint-tied : 384 → MiniLM legacy, 1024 → embed_router BGE-M3 (matches the retrained value_net's space, no silent mismatch). The flip is one env var, proven end-to-end by tools/forge_cognition_dim_selftest.py (default-384 non-breaking + 1024 propagates everywhere, zero silent 384 fallback).
• get_current_state_text_rich() enriches state (tasks + mood + hormones) for the 1024d path (vs the thin tasks:… of the legacy 384d traces).
• tools/forge_embed_bridge.py — learned ridge projection 384↔1024 (cos ~0.8), bridges text-less latents (predicted states) between the two spaces.
• tools/forge_trace_collector_rich.py — accumulates fresh rich traces (traces_rich, text-first, embeds deferred to --reencode via :8099) for the eventual retrain (collect → train → A/B → cutover). Now a supervised service (NokidoTraceCollectorRich, LaForge-Master) — survives reboot, heartbeat-monitored, single-instance guard. Not a scheduled task : centralized in the supervisor.

Until cutover, cognition stays 384d (untouched), memory is 1024d ; the bridge + the dim=1024 opt-in let them interoperate.

🎯 GOAP — Goal-Oriented Action Planning

app/forge_goap.py — symbolic planner. Used when :

• The strategist needs a multi-step plan (e.g. generate_module, fix_failing_test, ingest_doc).
• The user calls plan MCP tool with a goal string.

Algorithm : forward-chaining BFS over the action graph. Actions : run_shell, run_python, ask_llm, ingest_url, search_rag, run_tests. Each action has preconditions + effects.

Goals are defined in app/forge_goap.py::GOALS — extend as needed.

🔁 The autonomous evolution loop

tools/forge_auto_evolution_loop.py — runs 24/7, daemon-managed :

• Every 10 min : scan heartbeats for degraded services → propose restart.
• Every 30 min : scan lessons_learned.md for 3+ recurring errors → propose a new lesson or refactor.
• Every 1h : recompute forge_scorecard on modified modules → flag drift.

The output : entries in task_queue (forge_task_queue.py), which any EDITOR-capable agent (Claude, Codex…) can claim.

🌗 Skill curation

app/forge_skill_curator.py — picks execution_traces from past sessions, distills them into reusable curated_skills (RAG domain). 11/11 tests pass. Dry-run kept 18/19 traces (per memory).

This is continual learning at the system level : Nokido's behavior yesterday becomes a skill it can recall today.

⚖️ Token efficiency

The AMI stack is the reason Nokido consumes 5–15× fewer tokens than LLM-as-orchestrator setups (see MANIFESTO §3.5).

• LLM-as-orchestrator : re-emits the full chain-of-thought to the LLM each tool call. Context grows linearly with steps.
• AMI orchestrator : world model + cost model + planner consolidate the state symbolically. The LLM sees only the relevant slice for the current step.

🧪 Tests

Module	Test file	Coverage
forge_scorecard	tests/test_forge_scorecard.py	51 tests
forge_active_inference_agent	tests/test_forge_active_inference.py	6/6
forge_lnn_monitor	tests/test_forge_lnn_monitor.py	4/4
forge_goap	tests/test_goap_execution.py, tests/nr/test_appui_forge_goap_nr.py	partial

📚 Reading list

If you want to dig deeper :

• LeCun (2022). A Path Towards Autonomous Machine Intelligence. Meta AI.
• Friston (2010). The Free-Energy Principle. Nature Rev. Neuroscience.
• Hasani et al. (2022). Closed-form Continuous-time Neural Networks. ICML.
• Heins et al. (2024). pymdp : a Python library for active inference.
• Marcus (2020). The Next Decade in AI : Four Steps Toward Robust AI.
• Hassabis et al. (DeepMind) — AlphaGo, MuZero papers.
• Sutton (2024). The Era of Experience. ICML keynote.

🛣️ Roadmap

• Phase A (current) : modules live but daemons partially active. 16/16 AMI modules + 7/7 weights .npz. GOAP E2E PASS.
• Phase B : reactivate offline_trainer / consolidator / self_patcher daemons (currently dormant — memory roadmap_ami_lecun).
• Phase C : port the SNN cervelet to neuromorphic hardware (Loihi, Akida) — see 13 — Hardware roadmap.
• Phase D : full autonomous continual learning grid — analog substrate. See MANIFESTO §6.5.