RuneForgeAI_Project_Hliðskjálf—Edge-Native_Sovereign_AGI_Architecture.md
Project Codename : Hliðskjálf (The Panoptic High Seat)
Hardware Target : Raspberry Pi 5 (16 GB LPDDR4X) + AI2+ Hailo-10 HAT (8 GB dedicated memory, 40 TOPS)
Cognitive Model : Meta Muse Framework (Muse Spark / Glimmer Localized Runtime)
Core Paradigm : Sovereign Active Inference, Multi-Agent Tool Synthesis & Edge Panopticism
Repository : https://github.com/hrabanazviking/RuneForgeAI-Project-Hlidhskjalf
- Executive Summary & The Edge-AGI Thesis
- Hardware Architecture & Memory Fabric Partitioning
- Mathematical Foundations of Autonomous Edge Intelligence
- System Topology: Project Hliðskjálf Subsystems
- Complete Reference Implementation
- End-to-End Operational Lifecycle
- Edge Compilation & Deployment Workflow
- Hardware Telemetry & Performance Profiles
Project Hliðskjálf establishes a sovereign, edge-native artificial general intelligence research substrate designed to operate on local consumer hardware.
In ancient Norse cosmology, Hliðskjálf is Odin's high seat, the vantage point from which the worlds can be observed. In this technical architecture, Hliðskjálf represents an autonomous panoptic cognitive observer capable of coordinating:
- perception
- multimodal state estimation
- persistent episodic memory
- causal world modeling
- policy selection
- task decomposition
- sub-agent execution
- dynamic tool generation
- continuous reflection
- security-constrained action
- self-correction
The central architectural thesis is:
General autonomous intelligence is not produced by model scale alone. It requires a closed cognitive loop connecting perception, memory, world modeling, planning, action, verification, learning, and safety.
flowchart TB
HIGHSEAT["PROJECT HLIÐSKJÁLF HIGH SEAT<br/>Global Panoptic World Model & AGI Runtime"]
HUGINN["HUGINN<br/>Perception Engine<br/><br/>Active-Inference Sensory Ingestion<br/>Multimodal Real-Time Tokenization"]
MUNINN["MUNINN<br/>Episodic Memory<br/><br/>Vectorized Episodic Buffer<br/>Continuous Graph Reflection"]
MUSE["META MUSE AGENTIC CORE<br/><br/>Task Decomposition<br/>Planning<br/>Sub-Agent Coordination"]
SENTINEL["SENTINEL SECURITY BOUNDARY<br/><br/>Zero-Trust Policy Enforcement<br/>Execution Validation"]
HAILO["HAILO-10 NPU EXECUTION<br/><br/>40 TOPS Neural Compute<br/>8 GB Dedicated Memory<br/>INT4 / INT8 Inference"]
PI["RASPBERRY PI 5 HOST<br/><br/>16 GB System Memory<br/>Linux Execution Cells<br/>Dynamic Tools & Orchestration"]
HIGHSEAT --> HUGINN
HIGHSEAT --> MUNINN
HUGINN --> MUSE
MUNINN --> MUSE
MUSE --> SENTINEL
SENTINEL --> HAILO
SENTINEL --> PI
HAILO --> HIGHSEAT
PI --> HIGHSEAT
classDef highseat fill:#21143d,stroke:#a78bfa,stroke-width:3px,color:#ffffff;
classDef cognition fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef security fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef hardware fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
class HIGHSEAT,MUSE highseat;
class HUGINN,MUNINN cognition;
class SENTINEL security;
class HAILO,PI hardware;
The architecture operates as a continuous cycle:
PERCEIVE
↓
ENCODE
↓
UPDATE BELIEFS
↓
RECALL MEMORY
↓
PREDICT
↓
SELECT GOAL
↓
PLAN
↓
VALIDATE
↓
ACT
↓
OBSERVE CONSEQUENCES
↓
REFLECT
↓
LEARN
↓
REPEAT
By decoupling neural matrix computation from host orchestration, Hliðskjálf attempts to preserve responsiveness under continuous operation.
The division is approximately:
Hailo-10:
Neural inference, embeddings, vision encoding, speech, fast model execution
Raspberry Pi 5:
Operating system, memory graph, orchestration, databases, sandboxes, security,
event processing, task scheduling, tool execution, telemetry, and persistence
The platform separates general-purpose ARM compute from accelerator-resident neural workloads.
flowchart LR
subgraph PI["RASPBERRY PI 5 - HOST INFRASTRUCTURE"]
CPU["Broadcom BCM2712<br/>4x ARM Cortex-A76 @ 2.4 GHz"]
RAM["16 GB SYSTEM MEMORY<br/>Host Runtime / Databases / Agent State"]
NVME["PCIe / NVMe STORAGE<br/>RootFS / Memory Vault / Sandboxes"]
SENT["KERNEL SENTINEL<br/>Policy / Egress / Process Monitoring"]
CPU <--> RAM
CPU <--> NVME
CPU <--> SENT
end
subgraph BUS["PCIe LINK"]
PCIE["PCIe x1<br/>DMA / Descriptor Queues"]
end
subgraph HAT["AI2+ HAILO-10 HAT"]
NPU["Hailo-10H NPU<br/>40 TOPS"]
NPURAM["8 GB DEDICATED MEMORY<br/>Model State / Accelerator Workloads"]
HEF["HAILO EXECUTABLE FORMAT<br/>Compiled Dataflow Graphs"]
NPU <--> NPURAM
NPU <--> HEF
end
CPU <-->|"PCIe DMA"| PCIE
PCIE <-->|"Tensor / Descriptor Traffic"| NPU
classDef host fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef link fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef npu fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
class CPU,RAM,NVME,SENT host;
class PCIE link;
class NPU,NPURAM,HEF npu;
A major limitation of local transformer inference is memory bandwidth.
For an autoregressive model, an approximate lower bound on token-generation latency can be expressed as:
where:
S_effective = effective model-state bytes that must be accessed per generated token
B_memory = sustainable memory bandwidth
The corresponding bandwidth-limited token rate is approximately:
This illustrates why separating neural inference memory from operating-system and cognitive-state memory is useful.
| Memory Domain | Capacity | Primary Workload Assignment | Interface |
|---|---|---|---|
| Hailo-10 Onboard Memory | 8 GB | Quantized neural models, accelerator tensors, supported KV/state buffers, vision/audio encoders | Accelerator-local memory fabric |
| Raspberry Pi 5 Host Memory | 16 GB | Linux, Muse coordination, Kista/Muninn memory, world graphs, SQLite/vector databases, sandbox processes, Himinbjörg | BCM2712 system memory |
| PCIe Interconnect | x1 | Tensor descriptors, encoded observations, inference inputs/outputs, command queues | PCIe |
┌──────────────────────────────────────────────────────────────┐
│ RASPBERRY PI 5 HOST MEMORY │
├──────────────────────────────────────────────────────────────┤
│ Linux Kernel / systemd │
│ Meta Muse Coordination │
│ Muninn Episodic Memory │
│ WYRD / World-State Graph │
│ Kista Persistent Storage Cache │
│ Sentinel Policy State │
│ Sandboxed Tool Processes │
│ Himinbjörg HUD │
└──────────────────────────────────────────────────────────────┘
│
│ PCIe
▼
┌──────────────────────────────────────────────────────────────┐
│ HAILO-10 ACCELERATOR MEMORY │
├──────────────────────────────────────────────────────────────┤
│ Compiled Neural Graphs │
│ Quantized Weights │
│ Intermediate Activations │
│ Vision / Audio Encoders │
│ Supported Local LLM State │
└──────────────────────────────────────────────────────────────┘
Hliðskjálf uses Active Inference as one possible mathematical framework for linking perception, belief updating, uncertainty reduction, planning, and action.
The system can be modeled as a Partially Observable Markov Decision Process:
where:
S = latent environmental states
A = actions
O = observations
T = state transition model
O = observation model
R = preferences / utility structure
Let the observation history be:
and hidden states:
The agent maintains:
as a generative model and:
as an approximate posterior over hidden state.
Variational free energy is:
It can also be written as:
Because KL divergence is non-negative:
Therefore minimizing:
reduces an upper bound on surprise.
Variational free energy may also be expressed as:
Conceptually:
Free Energy = Complexity - Accuracy
where:
Complexity = divergence between posterior beliefs and prior beliefs
Accuracy = how well the internal model explains observations
The Huginn sensory loop updates internal beliefs:
by descending the free-energy gradient:
where:
η = learning rate
Discrete approximation:
A candidate policy is:
over horizon:
The expected free energy of policy:
can be expressed as:
with:
where:
C = preferred outcomes / goal priors
A useful conceptual decomposition is:
The first term represents divergence from desired outcomes.
The second represents expected information gain.
Thus policy selection balances:
goal achievement
+
uncertainty reduction
Policies may be sampled using a Boltzmann distribution:
where:
γ = policy precision
Large:
produces more deterministic policy selection.
Small:
produces greater exploration.
flowchart TD
subgraph PERCEPTION["PANOPTIC INGESTION LAYER - HUGINN"]
CAM["Camera / Vision Feeds"]
AUDIO["Microphone / Audio Stream"]
SYSTEM["OS / Hardware Telemetry"]
TEXT["User / Network / Tool Events"]
VLM["Vision Encoder"]
ASR["Audio Transcription"]
TELEMETRY["Kernel / System Event Stream"]
TEXTENC["Text / Event Encoder"]
CAM --> VLM
AUDIO --> ASR
SYSTEM --> TELEMETRY
TEXT --> TEXTENC
end
subgraph CORE["THE HIGH SEAT - HLIÐSKJÁLF CORE"]
FUSION["Multimodal State Fusion"]
ACTIVE["Active Inference Engine<br/>Belief Update / Policy Evaluation"]
WORLD["Dynamic World-State Graph"]
VLM --> FUSION
ASR --> FUSION
TELEMETRY --> FUSION
TEXTENC --> FUSION
FUSION --> ACTIVE
ACTIVE <--> WORLD
end
subgraph MEMORY["MEMORY & REFLECTION - MUNINN"]
VECTOR["Local Vector Store"]
EPISODIC["Episodic Narrative Graph"]
SEMANTIC["Semantic Memory"]
PROCEDURAL["Procedural Skill Memory"]
WORLD <--> VECTOR
WORLD <--> EPISODIC
WORLD <--> SEMANTIC
WORLD <--> PROCEDURAL
end
subgraph PLAN["AGENTIC EXECUTION - META MUSE / GERI / FREKI"]
PLANNER["Meta Muse Task Planner"]
DECOMP["Sub-Agent Spawner<br/>Geri & Freki"]
TOOLGEN["Dynamic Tool Synthesizer<br/>Code Forge"]
ACTIVE --> PLANNER
PLANNER --> DECOMP
DECOMP --> TOOLGEN
end
subgraph SECURITY["EXECUTION & CONTAINMENT - SENTINEL"]
SENTINEL["Sentinel Zero-Trust Validator"]
DECISION{"Approved?"}
SANDBOX["Isolated Execution Workspace"]
OUTCOME["Environment Mutation / Tool Result"]
TOOLGEN --> SENTINEL
SENTINEL --> DECISION
DECISION -->|"Approved"| SANDBOX
DECISION -->|"Denied"| PLANNER
SANDBOX --> OUTCOME
OUTCOME --> SYSTEM
end
classDef perception fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef cognition fill:#21143d,stroke:#a78bfa,stroke-width:2px,color:#ffffff;
classDef memory fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef security fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
class CAM,AUDIO,SYSTEM,TEXT,VLM,ASR,TELEMETRY,TEXTENC perception;
class FUSION,ACTIVE,WORLD,PLANNER,DECOMP,TOOLGEN cognition;
class VECTOR,EPISODIC,SEMANTIC,PROCEDURAL memory;
class SENTINEL,DECISION,SANDBOX,OUTCOME security;
Huginn operates as the sensory pipeline.
It converts raw external stimuli into structured cognitive observations.
Inputs can include:
camera frames
audio
speech transcripts
system metrics
temperature
storage state
network events
serial I/O
user messages
tool results
files
service health
A multimodal observation vector may be represented as:
The components are fused into an internal latent state:
Accelerator-compatible projection and encoding operations may run on the Hailo-10 while host-side orchestration remains on the Raspberry Pi.
Muninn maintains contextual continuity across long operating horizons.
It combines:
vector retrieval
episodic records
semantic memory
causal graph links
procedural memory
Observation surprise is:
An episodic checkpoint may be triggered when:
where:
θ_surprise = configured episodic-storage threshold
Additional triggers may include:
goal completion
user correction
unexpected failure
important state transition
high prediction error
new capability discovery
Meta Muse acts as the high-level deliberative agent.
Responsibilities include:
goal interpretation
task decomposition
planning
tool selection
sub-agent delegation
code synthesis
verification
reflection
A high-level objective:
G
is decomposed into task graph:
where:
V_T = executable tasks
E_T = dependency relationships
The task graph should remain acyclic unless an explicit bounded iteration construct is used.
Where compatible with the available accelerator runtime, quantized local models can be used for:
classification
routing
summarization
structured extraction
short-horizon planning
tool selection
fast local reasoning
More demanding reasoning can be routed to a stronger Muse runtime when available.
Sub-agents execute bounded asynchronous subtasks.
Potential roles:
Geri:
research / environment analysis / retrieval
Freki:
verification / code execution / criticism
A more general worker pool can use:
planner
researcher
critic
coder
verifier
simulator
memory analyst
When no existing tool satisfies an objective, the system may generate a temporary utility.
Tool lifecycle:
NEED IDENTIFIED
↓
SPECIFICATION
↓
CODE GENERATION
↓
STATIC VALIDATION
↓
SANDBOX TEST
↓
RESULT VERIFICATION
↓
PROMOTE TO SKILL LIBRARY
OR
DISCARD
Autonomous execution should be treated as a privileged subsystem.
Sentinel operates as an out-of-band control boundary.
Its responsibilities include:
- execution policy enforcement
- filesystem boundary checks
- process restrictions
- network policy
- capability restrictions
- command validation
- audit logging
- approval requirements
- resource limits
- secret isolation
Tool execution should occur inside a restricted environment.
Possible mechanisms include:
systemd-nspawn
Linux namespaces
dedicated service users
read-only mounts
seccomp
cgroups
capability dropping
network namespaces
For example:
CAP_SYS_ADMIN removed
CAP_NET_ADMIN removed
private network namespace
read-only system paths
writable temporary workspace only
Network access should follow an explicit allow policy.
Conceptually:
Generated Tool
│
▼
Sentinel
│
├── destination allowed?
├── protocol allowed?
├── credentials required?
├── payload policy satisfied?
└── user approval required?
│
▼
Network
Models should not receive unrestricted access to credentials.
Preferred pattern:
Agent requests authenticated action
│
▼
Sentinel Broker
│
credential lookup
│
▼
external service
The secret remains outside the model context.
The following implementation is a research prototype and architectural reference, not a claim of a complete AGI implementation.
File:
hlidhskjalf_core.py
#!/usr/bin/env python3
"""
Project Hliðskjálf
Edge-Native Sovereign AGI Research Architecture
Coordinates:
- Hailo accelerator abstraction
- Active Inference world model
- Muninn episodic memory
- Meta Muse-style task decomposition
- Sentinel execution boundary
- Continuous perceive-plan-act-reflect loop
Author: RuneForgeAI / hrabanazviking
License: Apache-2.0
"""
import os
import sys
import time
import json
import hashlib
import tempfile
import subprocess
import logging
from dataclasses import dataclass
from typing import List, Dict, Any, Tuple, Optional
import numpy as np
# =====================================================================
# Structured Logging
# =====================================================================
logging.basicConfig(
level=logging.INFO,
format=(
"%(asctime)s "
"[%(levelname)s] "
"[HLIDHSKJALF-CORE] "
"%(message)s"
),
handlers=[
logging.StreamHandler(
sys.stdout
)
]
)
logger = logging.getLogger(
"Hlidhskjalf"
)
# =====================================================================
# 1. HARDWARE DETECTION & HAILO ACCELERATOR INTERFACE
# =====================================================================
class Hailo10DeviceManager:
"""
Prototype accelerator manager.
A production implementation should replace the simulated hardware
execution path with the installed HailoRT API appropriate for the
target Hailo-10 environment.
"""
def __init__(
self,
pcie_device_path: str = "/dev/hailo0"
):
self.pcie_path = (
pcie_device_path
)
self.device_available = (
self._detect_hardware()
)
self.npu_memory_total_mb = 8192
self.npu_memory_used_mb = 0
self.loaded_hef_models: Dict[
str,
str
] = {}
def _detect_hardware(
self
) -> bool:
if os.path.exists(
self.pcie_path
):
logger.info(
"Hailo accelerator detected at %s",
self.pcie_path
)
return True
logger.warning(
(
"Hailo device node %s not found. "
"Initializing software simulation mode."
),
self.pcie_path
)
return False
def load_hef(
self,
model_name: str,
hef_path: str,
memory_footprint_mb: int
) -> bool:
available = (
self.npu_memory_total_mb
-
self.npu_memory_used_mb
)
if (
memory_footprint_mb
> available
):
logger.error(
(
"Insufficient accelerator memory. "
"Requested: %d MB, Free: %d MB"
),
memory_footprint_mb,
available
)
return False
logger.info(
(
"Registering HEF model '%s' "
"(%d MB estimated footprint)..."
),
model_name,
memory_footprint_mb
)
self.loaded_hef_models[
model_name
] = hef_path
self.npu_memory_used_mb += (
memory_footprint_mb
)
logger.info(
(
"Model '%s' registered. "
"Estimated accelerator memory: "
"%d / %d MB"
),
model_name,
self.npu_memory_used_mb,
self.npu_memory_total_mb
)
return True
def forward(
self,
model_name: str,
input_embeddings: np.ndarray
) -> np.ndarray:
if (
model_name
not in self.loaded_hef_models
):
raise RuntimeError(
f"Model not registered: {model_name}"
)
if not self.device_available:
# Deterministic development fallback.
dimension = (
input_embeddings.shape[-1]
)
weights = np.sin(
np.linspace(
0,
np.pi,
dimension,
dtype=np.float32
)
)
return (
input_embeddings
* weights
* 0.95
)
# Production implementation:
#
# 1. Open HailoRT device.
# 2. Load / configure HEF.
# 3. Create input/output virtual streams.
# 4. Transfer tensor.
# 5. Run inference.
# 6. Return output tensor.
#
# Placeholder deterministic transform:
return np.tanh(
input_embeddings
)
# =====================================================================
# 2. ACTIVE INFERENCE & FREE-ENERGY ENGINE
# =====================================================================
@dataclass
class EnvironmentalObservation:
timestamp: float
sensory_vector: np.ndarray
telemetry: Dict[
str,
float
]
raw_text: str
class ActiveInferenceWorldModel:
"""
Simplified Active Inference-inspired world-state model.
This implementation demonstrates architectural concepts rather
than implementing a complete variational Bayesian FEP system.
"""
def __init__(
self,
state_dimension: int = 128
):
self.dim = (
state_dimension
)
self.mu_s = np.zeros(
self.dim,
dtype=np.float32
)
self.prior_preferences = np.zeros(
self.dim,
dtype=np.float32
)
self.learning_rate = 0.05
def set_goal_attractor(
self,
goal_vector: np.ndarray
):
norm = np.linalg.norm(
goal_vector
)
self.prior_preferences = (
goal_vector
/
(
norm
+ 1e-8
)
)
logger.info(
"New goal attractor registered."
)
def compute_variational_free_energy(
self,
observation_vector: np.ndarray
) -> float:
observation_norm = (
observation_vector
/
(
np.linalg.norm(
observation_vector
)
+ 1e-8
)
)
accuracy = float(
np.dot(
self.mu_s,
observation_norm
)
)
complexity = float(
0.5
*
np.sum(
np.square(
self.mu_s
)
)
)
variational_free_energy = (
complexity
-
accuracy
)
return (
variational_free_energy
)
def update_beliefs(
self,
observation_vector: np.ndarray
) -> float:
observation_norm = (
observation_vector
/
(
np.linalg.norm(
observation_vector
)
+ 1e-8
)
)
prediction_error = (
observation_norm
-
self.mu_s
)
self.mu_s += (
self.learning_rate
*
prediction_error
)
return (
self.compute_variational_free_energy(
observation_vector
)
)
def evaluate_expected_free_energy(
self,
candidate_policy_vector: np.ndarray
) -> float:
predicted_state = (
self.mu_s
+
candidate_policy_vector
)
predicted_state = (
predicted_state
/
(
np.linalg.norm(
predicted_state
)
+ 1e-8
)
)
epistemic_value = float(
np.linalg.norm(
predicted_state
-
self.mu_s
)
)
pragmatic_value = float(
np.dot(
predicted_state,
self.prior_preferences
)
)
expected_free_energy = -(
0.4
*
epistemic_value
+
0.6
*
pragmatic_value
)
return (
expected_free_energy
)
# =====================================================================
# 3. MUNINN
# EPISODIC VECTOR MEMORY & CONSOLIDATION
# =====================================================================
@dataclass
class MemoryEntry:
entry_id: str
timestamp: float
vector: np.ndarray
context: str
free_energy: float
class MuninnMemoryGraph:
"""
In-memory demonstration of episodic vector retrieval.
Production implementations should persist important episodes
to Kista / SQLite / vector storage rather than relying exclusively
on process RAM.
"""
def __init__(
self,
embedding_dimension: int = 128
):
self.dim = (
embedding_dimension
)
self.store: List[
MemoryEntry
] = []
def write_episode(
self,
vector: np.ndarray,
context: str,
free_energy: float
):
entry_id = hashlib.sha256(
(
f"{time.time()}_"
f"{context}"
).encode()
).hexdigest()[:12]
normalized_vector = (
vector
/
(
np.linalg.norm(
vector
)
+ 1e-8
)
)
entry = MemoryEntry(
entry_id=entry_id,
timestamp=time.time(),
vector=normalized_vector,
context=context,
free_energy=free_energy
)
self.store.append(
entry
)
logger.info(
(
"Muninn stored episode [%s] "
"| Free Energy: %.4f"
),
entry_id,
free_energy
)
def retrieve_relevant_context(
self,
current_vector: np.ndarray,
top_k: int = 3
) -> List[MemoryEntry]:
if not self.store:
return []
current_norm = (
current_vector
/
(
np.linalg.norm(
current_vector
)
+ 1e-8
)
)
scored = []
for item in self.store:
similarity = float(
np.dot(
current_norm,
item.vector
)
)
scored.append(
(
similarity,
item
)
)
scored.sort(
key=lambda item:
item[0],
reverse=True
)
return [
item
for _, item
in scored[:top_k]
]
# =====================================================================
# 4. SENTINEL GATEWAY
# POLICY & EXECUTION BOUNDARY
# =====================================================================
class SentinelSecurityBoundary:
"""
Demonstration policy gate.
IMPORTANT:
Lexical filtering plus subprocess execution is not a complete
security sandbox. Production deployments should combine explicit
tool policies with OS-level isolation, dedicated users, namespaces,
seccomp, cgroups, capability restrictions, filesystem policies,
and network controls.
"""
FORBIDDEN_PATTERNS = {
"rm -rf /",
"mkfs",
"dd if=",
":(){ :|:& };:",
"chmod -R 777 /",
"nc -e",
"/dev/tcp/"
}
def __init__(
self,
workspace_root: Optional[str] = None
):
if workspace_root is None:
self.workspace_root = (
tempfile.mkdtemp(
prefix=
"hlidhskjalf_sandbox_"
)
)
else:
self.workspace_root = (
workspace_root
)
os.makedirs(
self.workspace_root,
exist_ok=True
)
logger.info(
"Sentinel workspace initialized at %s",
self.workspace_root
)
def verify_action_safety(
self,
action_script: str
) -> Tuple[
bool,
str
]:
for pattern in (
self.FORBIDDEN_PATTERNS
):
if pattern in action_script:
message = (
"Security policy violation: "
f"forbidden pattern '{pattern}' "
"detected."
)
logger.error(
message
)
return (
False,
message
)
return (
True,
"Action passed prototype Sentinel policy."
)
def execute_in_sandbox(
self,
script_body: str,
timeout_seconds: int = 15
) -> Dict[str, Any]:
safe, reason = (
self.verify_action_safety(
script_body
)
)
if not safe:
return {
"exit_code":
-1,
"stdout":
"",
"stderr":
reason,
"policy_blocked":
True
}
script_path = os.path.join(
self.workspace_root,
(
"task_"
f"{int(time.time() * 1000)}"
".py"
)
)
with open(
script_path,
"w",
encoding="utf-8"
) as file:
file.write(
script_body
)
logger.info(
"Executing approved task inside restricted workspace."
)
command = [
sys.executable,
script_path
]
try:
process = subprocess.run(
command,
cwd=self.workspace_root,
capture_output=True,
text=True,
timeout=timeout_seconds
)
return {
"exit_code":
process.returncode,
"stdout":
process.stdout.strip(),
"stderr":
process.stderr.strip(),
"policy_blocked":
False
}
except subprocess.TimeoutExpired:
logger.error(
(
"Task exceeded timeout "
"of %d seconds."
),
timeout_seconds
)
return {
"exit_code":
-2,
"stdout":
"",
"stderr":
"Execution timed out.",
"policy_blocked":
False
}
finally:
if os.path.exists(
script_path
):
os.remove(
script_path
)
# =====================================================================
# 5. META MUSE AGENT ENGINE
# TASK DECOMPOSITION & TOOL SYNTHESIS
# =====================================================================
@dataclass
class AgentTask:
task_id: str
description: str
action_type: str
payload: str
expected_free_energy: float = 0.0
status: str = "PENDING"
class MetaMuseAgent:
"""
Prototype executive layer for task decomposition,
policy ranking, tool execution, and reflection.
"""
def __init__(
self,
hailo_device: Hailo10DeviceManager,
world_model: ActiveInferenceWorldModel,
memory: MuninnMemoryGraph,
sentinel: SentinelSecurityBoundary
):
self.hailo = (
hailo_device
)
self.world_model = (
world_model
)
self.memory = (
memory
)
self.sentinel = (
sentinel
)
def decompose_objective(
self,
high_level_objective: str
) -> List[AgentTask]:
logger.info(
(
"Meta Muse decomposing objective: "
"'%s'"
),
high_level_objective
)
tasks = [
AgentTask(
task_id=
"TASK_01_INGEST",
description=
(
"Sample system sensors "
"and resource status"
),
action_type=
"ENVIRONMENT_IO",
payload=
"""
import json
import os
data = {
"load_average": os.getloadavg()
}
print(
json.dumps(data)
)
"""
),
AgentTask(
task_id=
"TASK_02_TOOL_SYNTHESIS",
description=
(
"Execute a harmless "
"prototype maintenance task"
),
action_type=
"TOOL_USE",
payload=
"""
print(
"Prototype temporary maintenance task completed."
)
"""
),
AgentTask(
task_id=
"TASK_03_EVALUATE",
description=
(
"Evaluate convergence "
"toward the current goal attractor"
),
action_type=
"REFLECT",
payload=
"EVALUATE_FEP_CONVERGENCE"
)
]
deterministic_rng = (
np.random.default_rng(
seed=42
)
)
for task in tasks:
simulated_effect = (
deterministic_rng.normal(
0,
0.1,
size=self.world_model.dim
).astype(
np.float32
)
)
task.expected_free_energy = (
self.world_model
.evaluate_expected_free_energy(
simulated_effect
)
)
tasks.sort(
key=lambda task:
task.expected_free_energy
)
return tasks
def execute_plan(
self,
tasks: List[AgentTask]
) -> Dict[str, Any]:
results = {}
for task in tasks:
logger.info(
(
"Executing Task [%s]: %s "
"(EFE: %.4f)"
),
task.task_id,
task.description,
task.expected_free_energy
)
task.status = (
"EXECUTING"
)
if task.action_type in (
"ENVIRONMENT_IO",
"TOOL_USE"
):
execution_result = (
self.sentinel
.execute_in_sandbox(
task.payload
)
)
if (
execution_result[
"exit_code"
]
== 0
):
task.status = (
"COMPLETED"
)
results[
task.task_id
] = execution_result[
"stdout"
]
logger.info(
(
"Task [%s] finished: %s"
),
task.task_id,
execution_result[
"stdout"
]
)
else:
task.status = (
"FAILED"
)
results[
task.task_id
] = execution_result[
"stderr"
]
logger.error(
(
"Task [%s] failed: %s"
),
task.task_id,
execution_result[
"stderr"
]
)
elif (
task.action_type
== "REFLECT"
):
task.status = (
"COMPLETED"
)
free_energy = (
self.world_model
.compute_variational_free_energy(
self.world_model.mu_s
)
)
results[
task.task_id
] = (
"Current Variational "
"Free Energy: "
f"{free_energy:.4f}"
)
return results
# =====================================================================
# 6. PANOPTIC HIGH-SEAT ORCHESTRATION PIPELINE
# =====================================================================
class HlidhskjalfOrchestrator:
"""
Coordinates:
Huginn:
perception
Muninn:
memory
Meta Muse:
planning and executive control
Sentinel:
validation and execution policy
Hailo:
accelerator-facing inference abstraction
"""
def __init__(
self
):
logger.info(
(
"Booting Project Hliðskjálf "
"edge intelligence substrate..."
)
)
self.hailo = (
Hailo10DeviceManager()
)
self.world_model = (
ActiveInferenceWorldModel(
state_dimension=128
)
)
self.memory = (
MuninnMemoryGraph(
embedding_dimension=128
)
)
self.sentinel = (
SentinelSecurityBoundary()
)
self.hailo.load_hef(
"MuseGlimmer_Quant_INT8",
"/opt/models/muse_glimmer.hef",
memory_footprint_mb=4200
)
self.hailo.load_hef(
"Vision_Encoder_INT8",
"/opt/models/vision_encoder.hef",
memory_footprint_mb=1200
)
self.muse = MetaMuseAgent(
hailo_device=
self.hailo,
world_model=
self.world_model,
memory=
self.memory,
sentinel=
self.sentinel
)
@staticmethod
def text_to_vector(
text: str,
dimensions: int = 128
) -> np.ndarray:
output = bytearray()
counter = 0
while len(output) < dimensions:
digest = hashlib.sha256(
(
f"{counter}:"
f"{text}"
).encode(
"utf-8"
)
).digest()
output.extend(
digest
)
counter += 1
return (
np.frombuffer(
bytes(
output[:dimensions]
),
dtype=np.uint8
).astype(
np.float32
)
/ 255.0
)
def step_cognitive_cycle(
self,
user_objective: str,
sensory_input: str
):
logger.info(
(
"========== "
"COGNITIVE ITERATION "
"=========="
)
)
# ---------------------------------------------------------
# 1. HUGINN: Encode sensory input
# ---------------------------------------------------------
sensory_vector = (
self.text_to_vector(
sensory_input,
dimensions=
self.world_model.dim
)
)
# ---------------------------------------------------------
# 2. Accelerator-facing feature processing
# ---------------------------------------------------------
processed_features = (
self.hailo.forward(
"Vision_Encoder_INT8",
sensory_vector
)
)
# ---------------------------------------------------------
# 3. Belief update
# ---------------------------------------------------------
free_energy = (
self.world_model
.update_beliefs(
processed_features
)
)
logger.info(
(
"Belief state updated. "
"Variational Free Energy: %.4f"
),
free_energy
)
# ---------------------------------------------------------
# 4. MUNINN: Episodic memory
# ---------------------------------------------------------
self.memory.write_episode(
self.world_model.mu_s,
(
"Sensory observation: "
f"{sensory_input}"
),
free_energy
)
# ---------------------------------------------------------
# 5. Goal encoding
# ---------------------------------------------------------
goal_vector = (
self.text_to_vector(
user_objective,
dimensions=
self.world_model.dim
)
)
self.world_model.set_goal_attractor(
goal_vector
)
# ---------------------------------------------------------
# 6. Plan and execute
# ---------------------------------------------------------
tasks = (
self.muse
.decompose_objective(
user_objective
)
)
execution_results = (
self.muse
.execute_plan(
tasks
)
)
# ---------------------------------------------------------
# 7. Reflection / logging
# ---------------------------------------------------------
logger.info(
"Cycle complete."
)
logger.info(
json.dumps(
execution_results,
indent=2
)
)
return (
execution_results
)
def main():
orchestrator = (
HlidhskjalfOrchestrator()
)
test_objective = (
"Monitor Raspberry Pi thermals, "
"verify memory headroom, and inspect "
"local cognitive runtime health."
)
test_sensory_event = (
"System telemetry stream: "
"Temp=44.2C, Voltage=5.01V, Load=0.18"
)
orchestrator.step_cognitive_cycle(
user_objective=
test_objective,
sensory_input=
test_sensory_event
)
if __name__ == "__main__":
main()The cognitive loop crosses both host and accelerator boundaries.
sequenceDiagram
autonumber
participant Host as Raspberry Pi 5 Host
participant NPU as Hailo-10 NPU
participant FEP as Active Inference Engine
participant Muse as Meta Muse Agent
participant Guard as Sentinel
participant Sandbox as Isolated Workspace
participant Memory as Muninn / Kista
Host->>NPU: Submit encoded multimodal input
Note over NPU:
Accelerator executes
supported neural inference
NPU-->>Host: Return latent / inference output
Host->>FEP: Submit processed observation
FEP->>FEP: Update belief state
FEP->>Memory: Store important episode
Memory-->>FEP: Retrieve relevant prior context
FEP->>Muse: Current belief state + goal preferences
Muse->>Muse: Construct candidate task graph
Muse->>FEP: Submit candidate policy effects
FEP-->>Muse: Return policy scores
Muse->>Guard: Submit selected action / generated tool
Guard->>Guard: Validate policy and permissions
alt Action Approved
Guard->>Sandbox: Execute bounded task
Sandbox-->>Host: Return result / changed state
Host->>FEP: Feed resulting observation back
else Action Rejected
Guard-->>Muse: Return policy rejection
Muse->>Muse: Replan
end
1. OBSERVE
Huginn captures system or environmental input.
2. ENCODE
Supported neural encoders transform raw observations.
3. UPDATE
Active Inference updates the internal belief state.
4. REMEMBER
Muninn/Kista stores high-value episodes.
5. RECALL
Relevant prior memories are retrieved.
6. PLAN
Meta Muse creates candidate task graphs.
7. SCORE
Candidate policies are evaluated.
8. VALIDATE
Sentinel checks proposed actions.
9. EXECUTE
Approved tools run inside bounded execution environments.
10. VERIFY
The resulting world state is observed.
11. REFLECT
Prediction error and goal progress are evaluated.
12. LEARN
Useful outcomes become memory, knowledge, or reusable skills.
13. REPEAT
Accelerator deployment requires models compatible with the installed Hailo software stack.
The general pipeline is:
SOURCE MODEL
↓
SUPPORTED INTERMEDIATE REPRESENTATION
↓
PARSE
↓
CALIBRATE / QUANTIZE
↓
OPTIMIZE
↓
COMPILE
↓
HEF
↓
HAILO RUNTIME
A conceptual command flow may resemble:
# Parse a supported model representation.
hailo parser onnx model.onnx \
--har-path model.har
# Optimize using representative calibration data.
hailo optimize \
--har-path model.har \
--calib-set-path ./calibration_data.npy
# Compile the optimized model for the target accelerator.
hailo compiler \
--har-path model.har \
--output-hef-path /opt/models/model.hefExact parser, optimization, model-family, and target-device flags should follow the Hailo software version installed on the target system.
PCIe configuration is platform and HAT dependent.
Where PCIe Gen 3 operation is supported and stable for the installed hardware, Raspberry Pi configuration may include:
# /boot/firmware/config.txt
[all]
dtparam=pciex1
# Enable only when validated for the installed hardware.
dtparam=pciex1_gen=3Reboot after changing firmware configuration:
sudo rebootInspect the negotiated PCIe link:
lspci -vvvSearch the Hailo device entry for:
LnkCap
LnkSta
Speed
Width
Do not assume Gen 3 operation until the negotiated link state confirms it.
Create an isolated root filesystem:
sudo mkdir -p /var/lib/machines/hlidhskjalf-sandboxA Debian root filesystem can be prepared with:
sudo debootstrap \
--variant=minbase \
bookworm \
/var/lib/machines/hlidhskjalf-sandboxCreate a shared workspace:
sudo mkdir -p /tmp/hlidhskjalf_sharedLaunch a restricted container:
sudo systemd-nspawn \
-D /var/lib/machines/hlidhskjalf-sandbox \
--private-network \
--drop-capability=CAP_SYS_ADMIN \
--drop-capability=CAP_NET_ADMIN \
--bind=/tmp/hlidhskjalf_shared:/workspaceflowchart LR
MUSE["Meta Muse"]
TOOL["Generated Tool"]
POLICY["Sentinel Policy Engine"]
APPROVAL{"Allowed?"}
CONTAINER["systemd-nspawn<br/>Restricted Container"]
CGROUP["cgroup Resource Limits"]
SECCOMP["seccomp / Capabilities"]
RESULT["Verified Result"]
MUSE --> TOOL
TOOL --> POLICY
POLICY --> APPROVAL
APPROVAL -->|"No"| MUSE
APPROVAL -->|"Yes"| CONTAINER
CONTAINER --> CGROUP
CONTAINER --> SECCOMP
CGROUP --> RESULT
SECCOMP --> RESULT
RESULT --> MUSE
Performance should be treated as a measured property of the final hardware/software stack rather than assumed from architecture alone.
A benchmark record should include:
| Metric | Idle | Active Cognitive Loop | Unit |
|---|---|---|---|
| Hailo NPU Temperature | measured | measured | °C |
| Raspberry Pi CPU Temperature | measured | measured | °C |
| Total System Power | measured | measured | W |
| Accelerator Memory Usage | measured | measured | GB |
| Raspberry Pi Host RAM Usage | measured | measured | GB |
| PCIe Transfer Latency | measured | measured | ms / batch |
| Cognitive Loop Rate | measured | measured | cycles / s |
| Model Inference Latency | measured | measured | ms |
| Memory Retrieval Latency | measured | measured | ms |
| Tool Execution Latency | measured | measured | ms |
Recommended JSONL telemetry:
{
"timestamp": 1791360000000,
"cpu_temperature_c": 58.7,
"npu_temperature_c": 54.2,
"host_ram_used_gb": 6.8,
"npu_memory_used_gb": 5.4,
"pcie_latency_ms": 1.84,
"cognitive_cycle_hz": 12.5,
"power_watts": 17.4
}Historical measurements should be retained so regressions can be detected.
A useful efficiency metric is:
where:
E_cog = successful cognitive operations per joule
For task-oriented benchmarking:
Total closed-loop latency can be decomposed as:
A fast reflex path may bypass expensive deliberation:
Observation
↓
Local Classifier
↓
Known Safe Policy
↓
Action
while complex events use:
Observation
↓
World Model
↓
Memory
↓
Meta Muse
↓
Sub-Agent Deliberation
↓
Sentinel
↓
Action
flowchart TB
USER["USER / ENVIRONMENT"]
subgraph HIGHSEAT["PROJECT HLIÐSKJÁLF"]
HUGINN["HUGINN<br/>Perception"]
FUSION["Multimodal Fusion"]
ACTIVE["ACTIVE INFERENCE<br/>Belief State"]
WYRD["WORLD MODEL<br/>Causality / State"]
MUNINN["MUNINN / KISTA<br/>Persistent Memory"]
MUSE["META MUSE<br/>Executive Reasoning"]
GERI["GERI<br/>Worker / Research"]
FREKI["FREKI<br/>Worker / Verification"]
FORGE["TOOL FORGE"]
SENTINEL["SENTINEL<br/>Security Boundary"]
SANDBOX["ISOLATED EXECUTION"]
REFLECT["REFLECTION / LEARNING"]
HUD["HIMINBJÖRG<br/>Observability"]
end
subgraph HARDWARE["EDGE HARDWARE"]
PI["RASPBERRY PI 5<br/>16 GB Host Memory"]
HAILO["HAILO-10<br/>40 TOPS / 8 GB"]
STORAGE["LOCAL STORAGE"]
end
USER --> HUGINN
HUGINN --> FUSION
FUSION --> ACTIVE
ACTIVE <--> WYRD
WYRD <--> MUNINN
ACTIVE --> MUSE
MUSE --> GERI
MUSE --> FREKI
GERI --> MUSE
FREKI --> MUSE
MUSE --> FORGE
FORGE --> SENTINEL
SENTINEL --> SANDBOX
SANDBOX --> USER
USER --> HUGINN
SANDBOX --> REFLECT
REFLECT --> MUNINN
REFLECT --> WYRD
REFLECT --> MUSE
FUSION --> HAILO
MUSE --> HAILO
MUNINN --> STORAGE
WYRD --> PI
SENTINEL --> PI
SANDBOX --> PI
ACTIVE --> HUD
WYRD --> HUD
MUNINN --> HUD
MUSE --> HUD
SENTINEL --> HUD
classDef cognition fill:#21143d,stroke:#a78bfa,stroke-width:2px,color:#ffffff;
classDef memory fill:#132e2a,stroke:#34d399,stroke-width:2px,color:#ffffff;
classDef safety fill:#172554,stroke:#60a5fa,stroke-width:2px,color:#ffffff;
classDef hardware fill:#3b1d0b,stroke:#fb923c,stroke-width:2px,color:#ffffff;
classDef display fill:#301934,stroke:#e879f9,stroke-width:2px,color:#ffffff;
class HUGINN,FUSION,ACTIVE,MUSE,GERI,FREKI,FORGE,REFLECT cognition;
class WYRD,MUNINN memory;
class SENTINEL,SANDBOX safety;
class PI,HAILO,STORAGE hardware;
class HUD display;
Project Hliðskjálf can be summarized as:
HUGINN
Perceive
│
▼
HIGH SEAT
Understand
│
▼
MUNINN
Remember
│
▼
META MUSE
Reason
│
▼
GERI & FREKI
Explore / Verify
│
▼
SENTINEL
Constrain
│
▼
SANDBOX
Act
│
▼
WORLD
Changes
│
▼
HUGINN
Observe Again
The architecture therefore aims to transform a conventional:
prompt -> model -> answer
system into:
PERCEPTION
+
PERSISTENT MEMORY
+
CAUSAL WORLD MODEL
+
ACTIVE INFERENCE
+
META MUSE REASONING
+
PARALLEL SUB-AGENTS
+
DYNAMIC TOOLS
+
SECURE ACTION
+
OUTCOME VERIFICATION
+
REFLECTION
+
CONTINUAL LEARNING
The defining property of Hliðskjálf is not any single model.
It is the closed cognitive loop connecting models, memory, world state, tools, hardware, observation, and verified action into one persistent edge-native intelligence architecture.