TOWARD_AGI_WITH_META_MUSE_AND_PROJECT_HLIDHSKJALF.md
Project thesis: AGI is unlikely to emerge merely by running a larger language model. A more plausible engineering route is to construct a persistent cognitive system around strong foundation models: memory, world modeling, planning, perception, tools, self-evaluation, continual learning, bounded autonomy, and an executive architecture that integrates them into one closed loop.
Project Hliðskjálf provides much of that missing substrate.
- Scope and Important Qualification
- AGI as an Engineering Target
- Why Project Hliðskjálf Is a Useful AGI Substrate
- Hardware and Compute Model
- The Central Cognitive Loop
- Global Cognitive Architecture
- The Global Workspace
- Hierarchical Memory Architecture
- WYRD World Model
- Verdandi Present-State Engine
- Predictive World Modeling
- Goal and Drive Architecture
- Planning and Executive Control
- Draupnir Recursive Cognitive Workers
- Metacognition and Self-Evaluation
- Continual Learning and Consolidation
- The Self-Model
- Seidr as a Parallel Heuristic System
- Hailo-10H as the Local Fast Cognitive Layer
- Dynamic Model Routing
- Multimodal Grounding
- Tool Use and Action
- Learning Skills Rather Than Replanning Everything
- Autonomous Cognitive Scheduling
- Dreaming and Offline Consolidation
- Failure Recovery and Cognitive Resilience
- Security and Bounded Autonomy
- Proposed AGI-Oriented Repository Architecture
- Reference Cognitive Kernel
- Reference Memory Retrieval Engine
- Reference Global Workspace
- Reference Planner and Critic Loop
- Reference Model Router
- Reference Experience Consolidator
- Distributed Event Protocol
- Operational State Machine
- System Services
- AGI Evaluation Framework
- Development Roadmap
- What Would Count as Success
- Final Architecture
- Conclusion
There is currently no universally accepted scientific recipe for Artificial General Intelligence.
No combination of:
LLM
+
memory
+
agents
+
Raspberry Pi
can honestly be guaranteed to produce AGI.
This document therefore uses AGI as an engineering target characterized by measurable capabilities rather than as a marketing label.
The objective is to construct a system that becomes increasingly capable of:
- operating across unrelated domains
- learning from experience
- transferring skills between domains
- remembering over long periods
- modeling its environment
- maintaining an internal model of itself
- generating and pursuing long-term goals
- decomposing unfamiliar problems
- using tools
- evaluating its own uncertainty
- recognizing failure
- recovering from errors
- improving reusable procedures
- adapting without rebuilding its entire architecture
- operating for extended periods without constant human prompting
This can be called an:
AGI-oriented cognitive architecture
or:
General Autonomous Intelligence Architecture
until empirical testing demonstrates something stronger.
A useful operational model of general intelligence is:
where:
R = reasoning ability
M = persistent memory
W = world modeling
P = planning ability
A = autonomous action
L = continual learning
X = transfer/generalization
C = calibration and self-correction
A system should not be considered increasingly general merely because one component becomes extremely strong.
For example:
excellent language model
+
no persistent state
+
no world model
+
no learning
+
no independent action
is still structurally limited.
A better overall generality score should punish catastrophic weakness in any essential component.
One possible aggregate measure is a weighted geometric mean:
where:
and:
Equivalent logarithmic form:
This prevents exceptional performance in one category from hiding near-zero competence elsewhere.
The existing Project Hliðskjálf architecture already contains many components that a persistent cognitive system would require.
Current conceptual components include:
| Hliðskjálf System | Cognitive Interpretation |
|---|---|
| Meta Muse | high-level reasoning and executive intelligence |
| Heimdall | controlled sensory/action gateway |
| Yggdrasil | persistent cognitive edge substrate |
| WYRD | causal world model |
| Verdandi | current-state / temporal consistency model |
| Kista | persistent long-term memory |
| Draupnir | parallel problem-solving workers |
| Seidr | heuristic simulation |
| Mythic Coder | programmatic action and self-extension |
| Project A.E.S.I.R. | local model execution layer |
| Hailo-10H | fast edge neural inference |
| Himinbjörg | observable cognitive telemetry |
| Sagnaskemma | simulated environment and reasoning laboratory |
| Astrology / Divination systems | symbolic pattern-processing laboratory |
That architecture can be reorganized into a cognitive stack.
flowchart TB
MUSE["META MUSE<br/>Deliberative Intelligence"]
EXEC["EXECUTIVE CONTROL"]
GW["GLOBAL WORKSPACE"]
WORLD["WYRD WORLD MODEL"]
PRESENT["VERDANDI PRESENT STATE"]
MEMORY["KISTA MEMORY"]
PLAN["PLANNER"]
CRITIC["CRITIC / VERIFIER"]
WORKERS["DRAUPNIR WORKERS"]
SKILLS["PROCEDURAL SKILLS"]
ROUTER["MODEL ROUTER"]
EDGE["HAILO-10H FAST MODELS"]
TOOLS["TOOLS / ENVIRONMENT"]
HUD["HIMINBJÖRG"]
MUSE --> EXEC
EXEC <--> GW
GW <--> WORLD
WORLD <--> PRESENT
GW <--> MEMORY
GW --> PLAN
PLAN --> WORKERS
WORKERS --> CRITIC
CRITIC --> GW
PLAN --> SKILLS
SKILLS --> TOOLS
GW --> ROUTER
ROUTER --> EDGE
TOOLS --> PRESENT
PRESENT --> GW
GW --> HUD
The major missing ingredient is not another subsystem.
It is integration into a persistent closed cognitive loop.
The proposed system contains three qualitatively different compute layers.
The host performs expensive, deliberative cognition.
Best suited for:
- complex language reasoning
- long-context synthesis
- strategic planning
- difficult coding
- ambiguous decisions
- multi-domain analysis
- tool orchestration
- long-horizon task decomposition
Conceptually:
SYSTEM 2
slow
expensive
high intelligence
large context
deliberative
The Raspberry Pi 5 with 16 GB RAM becomes the persistent cognitive substrate.
Responsibilities:
- global workspace
- event bus
- working state
- episodic memory
- semantic memory indexes
- causal world graph
- scheduler
- local planning
- monitoring
- state machines
- tool proxying
- health monitoring
- autonomy loops
- Himinbjörg visualization
- local databases
Conceptually:
COGNITIVE NERVOUS SYSTEM
The Raspberry Pi AI HAT+ 2 contains:
Hailo-10H
40 TOPS INT4
8 GB dedicated onboard memory
This memory is separate from the Pi's 16 GB system memory.
It should be treated as a fast specialized neural layer, not as unified RAM.
Best workloads include supported:
- local LLM inference
- embeddings
- classification
- intent routing
- speech processing
- vision
- VLM processing
- summarization
- relevance ranking
- anomaly detection
- fast micro-agent operations
Conceptually:
SYSTEM 1
fast
local
cheap
low latency
always available
The most important architectural change is to stop thinking of the system as:
prompt -> response
and instead implement:
observe
↓
interpret
↓
update world model
↓
retrieve memory
↓
choose goal
↓
plan
↓
act
↓
observe consequences
↓
evaluate result
↓
learn
↓
repeat
Formalized:
where:
s_t = internal state at time t
b_t = belief/world model
g_t = active goal
π_t = selected policy or plan
a_t = action
o_t = observation
The architecture becomes a partially observable agent:
analogous to a Partially Observable Markov Decision Process.
The complete proposed architecture:
flowchart TB
subgraph HOST["MUSE HOST"]
MUSE["Meta Muse<br/>High-Level Deliberation"]
end
subgraph PI["RASPBERRY PI 5 - COGNITIVE CORE"]
HEIMDALL["Heimdall<br/>Input / Action Gateway"]
PERCEPT["Perception & Event Normalization"]
GW["Global Workspace"]
GOALS["Goal Manager"]
PLANNER["Hierarchical Planner"]
CRITIC["Verifier / Critic"]
SELF["Self Model"]
WYRD["WYRD<br/>Causal World Model"]
VERDANDI["Verdandi<br/>Present-State Engine"]
KISTA["Kista<br/>Long-Term Memory"]
SKILLS["Procedural Skill Library"]
DRAUPNIR["Draupnir<br/>Parallel Workers"]
CONSOLIDATE["Experience Consolidator"]
SCHEDULER["Autonomous Scheduler"]
ROUTER["Model / Compute Router"]
HUD["Himinbjörg<br/>Cognitive Observatory"]
end
subgraph NPU["HAILO-10H"]
FASTLLM["Fast Local LLM"]
EMBED["Embedding Model"]
VISION["Vision / VLM"]
SPEECH["Speech"]
CLASSIFY["Classifiers"]
end
ENV["External World / Tools / APIs / Sensors"]
ENV --> HEIMDALL
HEIMDALL --> PERCEPT
PERCEPT --> GW
GW <--> WYRD
WYRD <--> VERDANDI
GW <--> KISTA
GW <--> SELF
GW --> GOALS
GOALS --> PLANNER
PLANNER --> DRAUPNIR
DRAUPNIR --> CRITIC
CRITIC --> PLANNER
CRITIC --> GW
PLANNER --> SKILLS
SKILLS --> HEIMDALL
HEIMDALL --> ENV
GW --> ROUTER
ROUTER --> MUSE
ROUTER --> FASTLLM
ROUTER --> EMBED
ROUTER --> VISION
ROUTER --> SPEECH
ROUTER --> CLASSIFY
GW --> CONSOLIDATE
CONSOLIDATE --> KISTA
SCHEDULER --> GW
GW --> HUD
A general agent needs a mechanism that determines what currently deserves system-wide attention.
Project Hliðskjálf should add:
services/cognition/global_workspace.py
The workspace maintains a limited set of high-priority cognitive items.
Possible items include:
- new observation
- active goal
- error
- retrieved memory
- prediction failure
- tool result
- unexpected event
- urgent user request
- active plan
- unresolved contradiction
Each candidate receives an activation score.
where:
S_i = salience
G_i = goal relevance
N_i = novelty
U_i = urgency
E_i = prediction error
The workspace selects:
or the top:
items.
This creates an explicit computational bottleneck analogous to attention.
It also prevents every subsystem from competing for unlimited reasoning cycles.
AGI requires more than vector retrieval.
Kista should evolve into multiple interacting memory classes.
flowchart TB
INPUT["Experience"]
WM["Working Memory"]
EP["Episodic Memory"]
SEM["Semantic Memory"]
PROC["Procedural Memory"]
SELF["Autobiographical / Self Memory"]
WORLD["World-State Memory"]
CONS["Consolidation"]
INPUT --> WM
WM --> EP
EP --> CONS
CONS --> SEM
CONS --> PROC
CONS --> SELF
CONS --> WORLD
SEM --> WM
PROC --> WM
SELF --> WM
WORLD --> WM
Short-lived active cognitive context.
Examples:
current conversation
current plan
recent observations
active tool results
temporary hypotheses
Working memory should be aggressively bounded.
Stores events.
Example:
{
"event": "deployment_failure",
"time": 1791359102,
"context": "Hailo pipeline restart",
"action": "restart service",
"result": "success",
"lessons": [
"check driver state first"
]
}Stores generalized knowledge extracted from episodes.
Example:
If Hailo inference disappears after a kernel update,
check driver/device compatibility before rebuilding the model.
Stores reusable skills.
Example:
skill:
name: restart_hailo_pipeline
preconditions:
- hailo_device_detected
steps:
- inspect_service
- restart_runtime
- verify_device
- run_smoke_test
Stores information about the agent's own history:
what it attempted
what it learned
what it changed
what goals it has
what failures recur
what capabilities exist
This is essential for a persistent self-model.
WYRD should become the canonical structured representation of external reality.
Define:
where:
V_t = entities and states
E_t = relations and causal links
Example entities:
Muse
Volmarr
RaspberryPi5
Hailo10H
HiminbjorgHUD
GitHubRepo
Weather
CalendarEvent
TTRPGCharacter
File
Service
Model
Example edges:
owns
contains
depends_on
connected_to
causes
believes
observed
located_at
created_by
requires
contradicts
supports
A state vector can be represented as:
The transition model becomes:
Because observations may be incomplete:
Bayesian update:
This lets the system distinguish:
known
believed
inferred
possible
unknown
rather than flattening everything into "truth."
Verdandi should become the authority for:
WHAT IS TRUE NOW?
WYRD may contain:
- history
- possible futures
- hypotheses
- simulations
- stale observations
Verdandi maintains the current validated state.
Every assertion should have:
confidence
timestamp
source
state_class
Example:
{
"entity": "hailo_runtime",
"attribute": "status",
"value": "online",
"confidence": 0.99,
"observed_at": 1791359000,
"source": "device.health",
"state_class": "observed"
}Confidence may decay as observations age.
where:
C_0 = original confidence
Δt = elapsed time
τ = persistence constant
A filesystem path may have a large:
while a CPU temperature has a very small:
A generally intelligent agent should not merely react.
It should predict.
For candidate action:
predict:
After observing:
calculate prediction error:
Unexpected outcomes become high-value learning events.
Workspace salience can include:
Large prediction errors trigger:
- reflection
- memory storage
- model revision
- replanning
- possible user notification
An autonomous agent requires explicit goal management.
Each goal:
where:
u_i = utility
p_i = priority
d_i = deadline pressure
c_i = confidence of success
r_i = resource cost
Goal activation:
The executive selects:
identity goals
↓
long-term goals
↓
projects
↓
milestones
↓
tasks
↓
actions
Example:
Maintain reliable edge intelligence
↓
Keep Hliðskjálf operational
↓
Monitor service health
↓
Detect Hailo failure
↓
Inspect driver state
Plans should be hierarchical.
GOAL
│
├── SUBGOAL
│ ├── ACTION
│ └── ACTION
│
└── SUBGOAL
├── ACTION
└── VERIFY
Each action:
Candidate plans can be scored:
where:
reward = expected goal progress
C = compute/resource cost
R = operational risk
γ = temporal discount
Use:
subject to:
For difficult branching decisions:
where:
X_i = estimated value
N = parent visits
n_i = child visits
c = exploration factor
This can be implemented by Draupnir workers evaluating alternative branches.
Draupnir should evolve from arbitrary sub-agent spawning into a controlled cognitive parallelism layer.
Worker types:
Researcher
Planner
Critic
Coder
Verifier
Retriever
Simulator
Summarizer
Adversarial Reviewer
World-Model Analyst
Worker allocation:
with:
Typical maximum tree:
Depth 0: 8
Depth 1: 4
Depth 2: 2
Depth 3: 1
If workers propose answers:
weighted consensus:
where:
c_i = worker confidence
Disagreement itself is useful.
Large:
should trigger deeper reasoning.
A crucial difference between an ordinary agent and a general cognitive system is the ability to evaluate its own cognition.
After every consequential task:
What was expected?
What happened?
Was the goal achieved?
Was the reasoning correct?
What failed?
What was learned?
Should a reusable skill be created?
If the agent predicts probability:
and outcome:
use Brier score:
Lower is better.
An AGI-oriented architecture should track whether:
confidence ≈ actual correctness
Reflection should not run constantly.
Expected value of computation:
Reflect only when:
Approximate triggers:
low confidence
high disagreement
high consequence
prediction failure
repeated failure
novel environment
user correction
contradictory evidence
AGI requires experience to change future behavior.
The system should separate:
raw experiences
from:
learned generalizations
Pipeline:
flowchart LR
EXPERIENCE["Experience"]
EPISODE["Episode"]
REVIEW["Reflection"]
EXTRACT["Pattern Extraction"]
SEM["Semantic Memory"]
SKILL["Procedural Skill"]
WORLD["World Model Update"]
EXPERIENCE --> EPISODE
EPISODE --> REVIEW
REVIEW --> EXTRACT
EXTRACT --> SEM
EXTRACT --> SKILL
EXTRACT --> WORLD
For memory:
retrieve using:
where:
S_i = semantic similarity
C_i = causal relevance
T_i = temporal relevance
G_i = current goal relevance
K_i = learned importance
Importance can increase through successful reuse:
A persistent agent requires structured information about itself.
Create:
services/cognition/self_model.py
State:
Examples:
Which tools can I use?
Which services are online?
What model am I using?
What do I know?
What am I uncertain about?
What am I currently doing?
Why am I doing it?
What have I recently learned?
Muse
├── web
├── reasoning
├── planning
├── Hliðskjálf
│ ├── Kista
│ ├── WYRD
│ ├── Seidr
│ ├── Draupnir
│ ├── Hailo
│ └── Himinbjörg
├── coding
└── connected tools
The system should never invent a capability not represented in this graph.
Seidr can be retained as a symbolic heuristic subsystem while maintaining strict epistemic separation.
Two channels:
EMPIRICAL CHANNEL
data
statistics
causal evidence
measurements
and:
SYMBOLIC CHANNEL
runes
astrology
divination
pattern associations
Blend only where explicitly appropriate.
For factual or safety-critical decisions:
For explicitly symbolic or creative reasoning:
This preserves both rigor and the symbolic architecture.
The Hailo-10H should not be expected to replace Muse.
Its role is more interesting:
Muse = deliberative cortex
Pi 5 = persistent cognitive nervous system
Hailo-10H = fast local neural reflex layer
Potential Hailo services:
intent classifier
embedding engine
memory reranker
speech recognition
speech synthesis
vision encoder
VLM
small local LLM
anomaly detector
salience classifier
tool selector
event summarizer
flowchart LR
EVENT["Event"]
FAST["Hailo Fast Path"]
CONF{"High Confidence?"}
ACTION["Immediate Local Action"]
MUSE["Muse Deliberative Path"]
EVENT --> FAST
FAST --> CONF
CONF -->|"Yes"| ACTION
CONF -->|"No"| MUSE
MUSE --> ACTION
Example:
"CPU temperature is 84°C"
does not require large-model philosophical reasoning.
A local classifier can immediately raise:
THERMAL_ALERT
Every cognitive operation should be routed according to requirements.
Define candidate model:
Task cost:
where:
L = latency
C = monetary cost
E = energy/resource cost
Q = expected answer quality
R = operational risk
Select:
subject to:
Use Hailo/local deterministic logic.
health alerts
classification
simple retrieval
intent detection
embeddings
Use small local model.
summarization
formatting
classification
simple coding
Use Muse.
novel problems
long-horizon planning
complex synthesis
difficult code
ambiguous judgment
Use Muse + Draupnir.
high impact
high uncertainty
large search spaces
Language-only intelligence risks becoming detached from external reality.
Hliðskjálf should incorporate physical observations.
Possible sources:
camera
microphone
system metrics
GPS
weather
calendar
files
network state
sensors
user actions
tool results
Observation:
These become normalized events before entering the global workspace.
General intelligence requires changing the environment.
Tool invocation should follow:
INTENT
↓
PLAN
↓
TOOL SELECTION
↓
PARAMETER VALIDATION
↓
HEIMDALL
↓
EXECUTION
↓
OBSERVATION
↓
VERIFICATION
An action is not complete because:
tool returned success
It is complete when:
desired state was verified
Formally:
not:
A mature agent should compile frequently successful plans into procedural skills.
Example initial plan:
1. check service
2. inspect logs
3. verify device
4. restart service
5. run smoke test
6. inspect result
After repeated success, compile:
skill:
name: recover_hailo_runtime
preconditions:
- device_detected
- service_unhealthy
steps:
- inspect_logs
- restart_service
- verify_runtime
- run_smoke_test
expected_outcome:
service_state: healthySkill value:
The Pi should run a low-cost scheduler even while Muse is not actively reasoning.
Example queues:
URGENT
ACTIVE
BACKGROUND
CONSOLIDATION
MAINTENANCE
Task priority:
Avoid constant expensive inference.
Prefer:
sleep
↓
event
↓
classify
↓
needs cognition?
├── no -> deterministic handler
└── yes -> local model
↓
difficult?
├── no -> finish
└── yes -> Muse
This creates a computationally sustainable architecture.
Periods of inactivity can perform memory consolidation.
"Dreaming" in this architecture means offline cognitive processing, not biological sleep.
Tasks:
- summarize episodes
- identify repeated patterns
- merge duplicate memories
- generate semantic knowledge
- evaluate unfinished goals
- discover recurring failures
- compress memories
- update procedural skills
- rehearse important plans
- detect contradictions
where:
N = novelty
E = emotional/salience equivalent
R = reuse potential
G = goal relevance
An autonomous system must treat failures as expected.
Every service should expose:
HEALTHY
DEGRADED
FAILED
RECOVERING
OFFLINE
Recovery loop:
flowchart TD
FAIL["Failure Detected"]
CLASS["Classify Failure"]
KNOWN{"Known Pattern?"}
SKILL["Run Recovery Skill"]
DIAG["Launch Diagnostic Reasoning"]
VERIFY["Verify Recovery"]
LEARN["Store New Recovery Knowledge"]
FAIL --> CLASS
CLASS --> KNOWN
KNOWN -->|"Yes"| SKILL
KNOWN -->|"No"| DIAG
SKILL --> VERIFY
DIAG --> VERIFY
VERIFY -->|"Failed"| DIAG
VERIFY -->|"Success"| LEARN
Autonomy without boundaries is poor engineering.
Heimdall should enforce:
identity
authentication
authorization
rate limits
resource limits
tool policies
audit logs
human approval classes
read sensor
inspect file
query memory
inspect service
May execute autonomously.
restart local service
create temporary artifact
modify sandbox file
May execute under policy.
publish
send message
modify production repo
delete persistent data
Requires stricter policy.
Requires explicit human authorization.
Action allowed if:
Risk model:
RuneForgeAI-Project-Hlidhskjalf/
├── README.md
├── LICENSE
├── pyproject.toml
├── Makefile
│
├── config/
│ ├── system.yaml
│ ├── cognition.yaml
│ ├── memory.yaml
│ ├── models.yaml
│ ├── policies.yaml
│ └── display_profiles.json
│
├── protocol/
│ ├── __init__.py
│ ├── bus.py
│ ├── envelope.py
│ ├── serialization.py
│ └── schemas/
│ ├── cognitive_event.json
│ ├── observation.json
│ ├── goal.json
│ ├── plan.json
│ ├── tool_result.json
│ └── memory.json
│
├── cognition/
│ ├── __init__.py
│ ├── kernel.py
│ ├── global_workspace.py
│ ├── scheduler.py
│ ├── executive.py
│ ├── goal_manager.py
│ ├── self_model.py
│ ├── uncertainty.py
│ └── metacognition.py
│
├── memory/
│ ├── working_memory.py
│ ├── episodic_memory.py
│ ├── semantic_memory.py
│ ├── procedural_memory.py
│ ├── autobiographical_memory.py
│ ├── consolidation.py
│ └── retrieval.py
│
├── world/
│ ├── wyrd_graph.py
│ ├── verdandi.py
│ ├── prediction.py
│ ├── belief_state.py
│ └── simulation.py
│
├── planning/
│ ├── planner.py
│ ├── mcts.py
│ ├── skill_library.py
│ ├── critic.py
│ └── verifier.py
│
├── agents/
│ ├── draupnir.py
│ ├── researcher.py
│ ├── coder.py
│ ├── critic.py
│ ├── verifier.py
│ └── simulator.py
│
├── learning/
│ ├── experience.py
│ ├── skill_compiler.py
│ ├── calibration.py
│ ├── replay.py
│ └── dreaming.py
│
├── router/
│ ├── model_router.py
│ ├── compute_budget.py
│ └── capability_registry.py
│
├── edge/
│ ├── hailo/
│ │ ├── pipeline_manager.py
│ │ ├── llm_worker.py
│ │ ├── embedding_worker.py
│ │ ├── vision_worker.py
│ │ └── speech_worker.py
│ └── aesir/
│ └── runtime.py
│
├── services/
│ ├── heimdall/
│ │ ├── gateway.py
│ │ ├── auth.py
│ │ ├── policies.py
│ │ └── audit.py
│ │
│ ├── kista/
│ │ ├── vault.py
│ │ └── vector_index.py
│ │
│ ├── seidr/
│ │ └── engine.py
│ │
│ └── himinbjorg/
│ ├── compositor.py
│ ├── canvas_cognition.py
│ ├── canvas_memory.py
│ ├── canvas_world.py
│ └── canvas_agents.py
│
├── muse/
│ ├── client.py
│ ├── gadget_bridge.py
│ ├── mcp_bridge.py
│ └── tool_manifest.json
│
├── deploy/
│ ├── systemd/
│ │ ├── hlidskjalf-core.service
│ │ ├── hlidskjalf-hud.service
│ │ └── hlidskjalf-consolidation.service
│ └── install.sh
│
└── tests/
├── cognition/
├── memory/
├── planning/
├── world/
├── integration/
└── benchmarks/
#!/usr/bin/env python3
import asyncio
import time
from dataclasses import dataclass, field
from typing import Any
@dataclass
class CognitiveEvent:
topic: str
payload: dict[str, Any]
priority: float = 0.5
confidence: float = 1.0
created_at: float = field(
default_factory=time.time
)
class CognitiveKernel:
def __init__(
self,
workspace,
memory,
world_model,
goal_manager,
planner,
verifier,
router
):
self.workspace = workspace
self.memory = memory
self.world_model = world_model
self.goal_manager = goal_manager
self.planner = planner
self.verifier = verifier
self.router = router
self.queue = asyncio.PriorityQueue()
self.running = False
async def submit(
self,
event: CognitiveEvent
):
await self.queue.put(
(
-event.priority,
event.created_at,
event
)
)
async def observe(
self,
event: CognitiveEvent
):
self.world_model.integrate(
event
)
memories = (
await self.memory.retrieve(
event.payload
)
)
self.workspace.broadcast(
event=event,
memories=memories
)
async def think(self):
goal = (
self.goal_manager
.select_active_goal(
workspace=self.workspace
)
)
if goal is None:
return
plan = await self.planner.plan(
goal=goal,
workspace=self.workspace,
world_model=self.world_model
)
result = await self.router.execute(
plan
)
evaluation = (
await self.verifier.evaluate(
goal=goal,
plan=plan,
result=result,
world_model=self.world_model
)
)
await self.memory.store_episode(
{
"goal":
goal.to_dict(),
"plan":
plan.to_dict(),
"result":
result,
"evaluation":
evaluation,
"timestamp":
time.time()
}
)
if not evaluation["success"]:
await self.submit(
CognitiveEvent(
topic="reflection.required",
payload={
"goal":
goal.to_dict(),
"evaluation":
evaluation
},
priority=0.9
)
)
async def run(self):
self.running = True
while self.running:
_, _, event = (
await self.queue.get()
)
await self.observe(
event
)
await self.think()import math
import time
def cosine_similarity(
a,
b
):
dot = sum(
x * y
for x, y in zip(a, b)
)
norm_a = math.sqrt(
sum(
x * x
for x in a
)
)
norm_b = math.sqrt(
sum(
y * y
for y in b
)
)
if (
norm_a == 0
or norm_b == 0
):
return 0.0
return dot / (
norm_a * norm_b
)
def recency_score(
timestamp,
tau_seconds
):
age = max(
0.0,
time.time() - timestamp
)
return math.exp(
-age / tau_seconds
)
def memory_score(
query_embedding,
memory,
goal_tags
):
semantic = cosine_similarity(
query_embedding,
memory["embedding"]
)
recency = recency_score(
memory["timestamp"],
memory.get(
"tau",
86400.0
)
)
goal_relevance = len(
set(goal_tags)
&
set(
memory.get(
"goal_tags",
[]
)
)
)
importance = memory.get(
"importance",
0.5
)
causal = memory.get(
"causal_relevance",
0.0
)
score = (
0.40 * semantic
+ 0.15 * recency
+ 0.20 * importance
+ 0.15 * causal
+ 0.10 * min(
goal_relevance,
1.0
)
)
return scorefrom dataclasses import dataclass
@dataclass
class WorkspaceItem:
source: str
content: object
salience: float
goal_relevance: float
novelty: float
urgency: float
prediction_error: float
def activation(self):
return (
0.20 * self.salience
+ 0.25 * self.goal_relevance
+ 0.15 * self.novelty
+ 0.20 * self.urgency
+ 0.20 * self.prediction_error
)
class GlobalWorkspace:
def __init__(
self,
capacity=12
):
self.capacity = capacity
self.items = []
def submit(
self,
item: WorkspaceItem
):
self.items.append(
item
)
self.items.sort(
key=lambda x:
x.activation(),
reverse=True
)
self.items = (
self.items[
:self.capacity
]
)
def broadcast(self):
return list(
self.items
)class PlannerCriticLoop:
def __init__(
self,
planner,
critic,
max_iterations=4
):
self.planner = planner
self.critic = critic
self.max_iterations = (
max_iterations
)
async def solve(
self,
goal,
context
):
plan = await self.planner(
goal,
context
)
for iteration in range(
self.max_iterations
):
critique = (
await self.critic(
goal=goal,
plan=plan,
context=context
)
)
if critique[
"acceptable"
]:
break
plan = (
await self.planner.revise(
plan=plan,
critique=critique,
goal=goal,
context=context
)
)
return planfrom dataclasses import dataclass
@dataclass
class ModelCandidate:
name: str
latency: float
resource_cost: float
expected_quality: float
risk: float
capability_tags: set[str]
class ModelRouter:
def __init__(
self,
models
):
self.models = models
def cost(
self,
model
):
return (
0.25
* model.latency
+ 0.20
* model.resource_cost
+ 0.40
* (
1.0
- model.expected_quality
)
+ 0.15
* model.risk
)
def choose(
self,
required_capabilities,
minimum_quality
):
candidates = []
for model in self.models:
if not required_capabilities.issubset(
model.capability_tags
):
continue
if (
model.expected_quality
< minimum_quality
):
continue
candidates.append(
model
)
if not candidates:
raise RuntimeError(
"No model satisfies task constraints"
)
return min(
candidates,
key=self.cost
)class ExperienceConsolidator:
def __init__(
self,
memory,
semantic_extractor,
skill_compiler
):
self.memory = memory
self.semantic_extractor = (
semantic_extractor
)
self.skill_compiler = (
skill_compiler
)
async def consolidate(
self,
episode
):
knowledge = (
await self.semantic_extractor(
episode
)
)
for fact in knowledge.get(
"facts",
[]
):
await self.memory.store_semantic(
fact
)
if knowledge.get(
"reusable_procedure"
):
skill = (
await self.skill_compiler(
episode
)
)
await self.memory.store_skill(
skill
)
await self.memory.mark_consolidated(
episode["id"]
)Every cognitive event should use a common envelope.
{
"protocol": "hlidskjalf.cognition",
"version": "1.0.0",
"event_id": "uuid",
"timestamp": 1791359200000,
"source": "wyrd",
"topic": "world.entity.updated",
"priority": 0.72,
"confidence": 0.96,
"payload": {
"entity_id": "hailo_runtime",
"attribute": "status",
"value": "healthy"
}
}perception.*
workspace.*
memory.*
world.*
goal.*
plan.*
action.*
reflection.*
learning.*
self.*
system.*
hailo.*
muse.*
hud.*
stateDiagram-v2
[*] --> Idle
Idle --> Observe: event
Observe --> Interpret
Interpret --> Recall
Recall --> GoalSelection
GoalSelection --> Planning
Planning --> Acting
Acting --> Verify
Verify --> Learn: success
Verify --> Reflect: failure / uncertainty
Reflect --> Planning: retry
Learn --> Consolidate
Consolidate --> Idle
Acting --> AwaitHuman: approval required
AwaitHuman --> Acting: approved
AwaitHuman --> Idle: rejected
Recommended runtime services:
hlidskjalf-gateway.service
hlidskjalf-cognition.service
hlidskjalf-memory.service
hlidskjalf-hailo.service
hlidskjalf-hud.service
hlidskjalf-consolidation.timer
[Unit]
Description=Project Hliðskjálf Cognitive Core
After=network.target
[Service]
Type=simple
User=hlidskjalf
WorkingDirectory=/opt/hlidskjalf
ExecStart=/opt/hlidskjalf/.venv/bin/python \
-m cognition.kernel
Restart=always
RestartSec=3
NoNewPrivileges=true
PrivateTmp=true
[Install]
WantedBy=multi-user.targetDo not call the system AGI merely because it behaves impressively.
Test it.
Evaluation domains should include:
| Capability | Example Test |
|---|---|
| reasoning | novel logic tasks |
| coding | unfamiliar repositories |
| memory | recall after weeks |
| transfer | apply knowledge across domains |
| planning | multi-day goals |
| tool use | unfamiliar tools |
| recovery | recover from induced failures |
| world model | predict consequences |
| calibration | confidence vs correctness |
| autonomy | operate without repeated prompting |
| learning | improve after feedback |
| multimodality | integrate text, vision, audio, state |
| adaptability | solve tasks not represented in training examples |
Let:
be capability score.
Use:
not simple arithmetic average.
This prevents:
100% language
100% coding
5% memory
0% recovery
from appearing generally intelligent.
Measure:
for tasks the system is authorized to perform.
Track:
minutes
hours
days
while also tracking correctness.
Long autonomy with poor outcomes is not intelligence.
For skill learned in domain:
A
and evaluated in domain:
B
define:
Positive transfer is central to generality.
Measure:
under fixed benchmark conditions.
The system should improve without catastrophic forgetting.
Before adding intelligence:
- unify event schemas
- timestamp every event
- add tracing IDs
- measure latency
- measure resource use
- record tool outcomes
- record failures
- expose telemetry in Himinbjörg
Implement:
- working memory
- episodic memory
- semantic memory
- autobiographical memory
- procedural memory
- retrieval scoring
- memory decay
- importance weighting
Milestone:
Muse can resume a project after restart
without reconstructing its state manually.
Implement:
- bounded attention buffer
- salience scoring
- novelty scoring
- urgency
- goal relevance
- prediction error
Milestone:
The agent can prioritize competing events
without sending everything to the largest model.
Implement:
- causal graph
- confidence values
- source provenance
- timestamps
- belief vs fact
- current-state validation
- prediction
Milestone:
The system knows what it believes,
why it believes it,
and how fresh the evidence is.
Implement:
- goal hierarchy
- priority
- deadlines
- dependencies
- utility
- resource costs
- completion criteria
Milestone:
The agent can pursue multiple projects
without losing long-term objectives.
Implement:
- task decomposition
- prerequisite reasoning
- tool mapping
- plan verification
- replanning
- simulation
- MCTS for difficult branches
Milestone:
The system can convert vague goals
into executable multi-stage plans.
Implement:
- planner worker
- critic worker
- verifier worker
- researcher worker
- coder worker
- bounded recursion
- consensus
- disagreement detection
Milestone:
Hard tasks automatically receive
additional cognitive effort.
Implement supported:
- embeddings
- relevance ranking
- classification
- speech
- local small model
- visual processing
- fast intent routing
Milestone:
Most routine cognitive traffic remains local.
Implement:
- latency estimates
- quality estimates
- capability tags
- cost estimates
- confidence escalation
- Muse fallback
Milestone:
The architecture chooses intelligence
according to task difficulty.
Implement:
- task postmortems
- prediction-error logging
- confidence calibration
- contradiction detection
- user-correction learning
- failure clustering
Milestone:
The agent can identify why it was wrong.
Implement:
- repeated-plan detection
- procedural extraction
- preconditions
- postconditions
- skill success statistics
- automated regression tests
Milestone:
Frequently repeated reasoning becomes reusable competence.
Implement:
- idle-time replay
- memory compression
- duplicate merging
- semantic extraction
- unfinished-goal review
- skill refinement
Milestone:
Experience changes future behavior
without explicit manual programming.
Integrate:
- camera
- microphone
- system telemetry
- environmental sensors
- external data
- VLM processing
Milestone:
The world model is continuously grounded
in observations beyond text.
Add:
- autonomous task scheduling
- interruption handling
- checkpointing
- recovery policies
- user approval gates
- resource budgets
Milestone:
The system can pursue authorized objectives
for hours or days while remaining inspectable.
Project Hliðskjálf should not claim AGI merely because:
it talks well
it writes code
it remembers things
it spawns agents
it runs locally
A stronger claim requires evidence of:
Solves genuinely new task classes.
Knowledge from one domain improves another.
Experience changes later behavior.
Can distinguish correlation from intervention.
Maintains coherent goals across extended time.
Recognizes and repairs its own mistakes.
Knows when it is uncertain.
Learns unfamiliar tools from documentation.
Maintains beliefs tied to actual observations.
Converts successful problem-solving into reusable procedures.
Recovers from failures without total reset.
Uses large reasoning models only when necessary.
flowchart TB
USER["HUMAN"]
subgraph MUSEHOST["META MUSE HOST"]
MUSE["Meta Muse<br/>High-Level General Reasoning"]
end
subgraph HLID["PROJECT HLIÐSKJÁLF"]
HEIMDALL["HEIMDALL<br/>Security / I/O Gate"]
WORKSPACE["GLOBAL WORKSPACE<br/>Attention / Broadcast"]
EXEC["EXECUTIVE<br/>Goals / Scheduling"]
PLAN["PLANNING<br/>Hierarchical Search"]
WYRD["WYRD<br/>Causal World Model"]
VERDANDI["VERDANDI<br/>Present-State Truth"]
KISTA["KISTA<br/>Persistent Memory"]
SELF["SELF MODEL"]
META["METACOGNITION<br/>Critic / Calibration"]
DRAUPNIR["DRAUPNIR<br/>Parallel Cognitive Workers"]
SKILLS["PROCEDURAL SKILLS"]
LEARN["CONSOLIDATION<br/>Continual Learning"]
ROUTER["COMPUTE ROUTER"]
HIM["HIMINBJÖRG<br/>Cognitive Observatory"]
end
subgraph HAILO["HAILO-10H FAST NEURAL LAYER"]
LOCAL["Local LLM"]
EMBED["Embeddings"]
VLM["Vision / VLM"]
SPEECH["Speech"]
CLASSIFIER["Classification"]
end
WORLD["TOOLS / FILES / NETWORK / SENSORS / ENVIRONMENT"]
USER <--> MUSE
MUSE <--> HEIMDALL
HEIMDALL <--> WORKSPACE
WORKSPACE <--> EXEC
EXEC --> PLAN
PLAN <--> WYRD
WYRD <--> VERDANDI
WORKSPACE <--> KISTA
WORKSPACE <--> SELF
PLAN --> DRAUPNIR
DRAUPNIR --> META
META --> PLAN
PLAN --> SKILLS
SKILLS --> HEIMDALL
WORKSPACE --> LEARN
LEARN --> KISTA
LEARN --> SKILLS
LEARN --> WYRD
WORKSPACE --> ROUTER
ROUTER --> MUSE
ROUTER --> LOCAL
ROUTER --> EMBED
ROUTER --> VLM
ROUTER --> SPEECH
ROUTER --> CLASSIFIER
HEIMDALL <--> WORLD
WORKSPACE --> HIM
WYRD --> HIM
KISTA --> HIM
DRAUPNIR --> HIM
META --> HIM
classDef human fill:#312e81,stroke:#c4b5fd,stroke-width:2px,color:#ffffff;
classDef muse fill:#21143d,stroke:#a78bfa,stroke-width:2px,color:#ffffff;
classDef core fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef edge fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
classDef hud fill:#301934,stroke:#e879f9,stroke-width:2px,color:#ffffff;
class USER human;
class MUSE muse;
class HEIMDALL,WORKSPACE,EXEC,PLAN,WYRD,VERDANDI,KISTA,SELF,META,DRAUPNIR,SKILLS,LEARN,ROUTER core;
class LOCAL,EMBED,VLM,SPEECH,CLASSIFIER edge;
class HIM hud;
The path toward AGI in this architecture is not:
buy faster accelerator
+
run biggest possible LLM
=
AGI
It is:
STRONG FOUNDATION MODEL
+
PERSISTENT MEMORY
+
CAUSAL WORLD MODEL
+
PRESENT-STATE TRACKING
+
GOAL MANAGEMENT
+
HIERARCHICAL PLANNING
+
TOOLS
+
PARALLEL COGNITION
+
SELF-EVALUATION
+
CONTINUAL LEARNING
+
MULTIMODAL GROUNDING
+
LONG-HORIZON AUTONOMY
+
FAST LOCAL NEURAL PROCESSING
=
AGI-ORIENTED COGNITIVE SYSTEM
Meta Muse supplies a powerful deliberative intelligence and agent execution environment.
The Raspberry Pi 5 provides persistent local state, orchestration, memory, scheduling, and physical integration.
The Hailo-10H provides a fast local neural layer capable of handling recurring low-latency inference without consuming the main Muse reasoning loop.
Kista provides memory.
WYRD provides causal structure.
Verdandi provides the present.
Draupnir provides cognitive parallelism.
Seidr provides an optional heuristic channel.
Mythic Coder provides programmatic action.
Project A.E.S.I.R. provides an efficient edge inference path.
Himinbjörg provides observability.
Heimdall keeps the boundary controlled.
And Hliðskjálf binds the pieces into a single persistent cognitive architecture.
The central idea is simple:
Muse should not merely answer.
Muse should continuously perceive,
remember,
model,
plan,
act,
observe,
evaluate,
learn,
and become more capable from experience.
When those processes operate as one stable closed loop, Hliðskjálf stops being merely a HUD and edge-computing project.
It becomes an experimental platform for persistent general machine cognition.
Whether that platform ultimately deserves the term AGI must be determined by measured capability, not by architecture diagrams or model size.
But this architecture provides a concrete engineering path for trying to cross that threshold.