ZIP-0009
DraftSpatial Web, Active Inference, and Agent-to-Agent Economies for Zoo AI
Type
Standards Track
Category
Core
Author
Zoo Labs Foundation
Created
2025-01-09
ZIP-11: Spatial Web, Active Inference, and Agent-to-Agent Economies for Zoo AI
Abstract
This proposal establishes Zoo's spatial understanding framework leveraging IEEE 2874 Spatial Web standards, active inference through Agent-to-Agent (A2A) DID simulations, and decentralized AI economies. By combining spatial computing with active inference theory, Zoo creates autonomous agents that perceive 3D environments, minimize surprise through predictive models, and engage in economic transactions. This enables emergent behaviors in virtual ecosystems, self-organizing AI marketplaces, and spatially-aware educational experiences.
Motivation
Current AI systems lack three critical capabilities:
- Spatial Understanding: Most models operate on flat text/images without true 3D comprehension
- Active Inference: Passive response rather than goal-directed exploration and learning
- Economic Agency: No autonomous value exchange between AI agents
Zoo addresses these through:
- Spatial Web Integration: Full 3D scene understanding and navigation
- Active Inference Implementation: Agents that actively minimize free energy
- A2A DID Economy: Agents with decentralized identities conducting autonomous transactions
Technical Specification
Spatial Understanding Architecture
class SpatialUnderstanding(nn.Module):
"""
3D spatial comprehension using point clouds, voxels, and scene graphs
Implements IEEE 2874 Spatial Web standards
"""
def __init__(self):
super().__init__()
# Point cloud processing (from Point-JEPA)
self.point_encoder = PointTransformer(
dim=512,
depth=12,
heads=8,
dim_head=64,
k=16, # K-nearest neighbors
)
# Voxel representation
self.voxel_encoder = Sparse3DCNN(
in_channels=3,
voxel_size=0.05, # 5cm resolution
feature_dim=256,
)
# Scene graph construction
self.scene_graph = SceneGraphNetwork(
object_dim=512,
relation_dim=256,
graph_layers=6,
)
# Spatial reasoning with transformers
self.spatial_transformer = SpatialTransformer(
dim=1024,
depth=24,
heads=16,
dim_head=64,
cross_attend_3d=True,
)
# IEEE 2874 HSML (Hyperspace Modeling Language)
self.hsml_encoder = HSMLEncoder(
semantic_dim=512,
spatial_dim=512,
temporal_dim=256,
)
def forward(self, spatial_input):
"""
Process 3D spatial information
"""
# Extract features from different representations
if spatial_input.type == "point_cloud":
features = self.point_encoder(spatial_input.points)
elif spatial_input.type == "voxels":
features = self.voxel_encoder(spatial_input.voxels)
elif spatial_input.type == "mesh":
# Convert mesh to point cloud
points = self.mesh_to_points(spatial_input.mesh)
features = self.point_encoder(points)
# Build scene graph
objects = self.detect_objects(features)
relations = self.detect_relations(objects)
scene_graph = self.scene_graph(objects, relations)
# Spatial reasoning
spatial_understanding = self.spatial_transformer(
features,
scene_graph,
enable_3d_attention=True,
)
# Encode to HSML for interoperability
hsml = self.hsml_encoder(spatial_understanding)
return {
"features": features,
"scene_graph": scene_graph,
"understanding": spatial_understanding,
"hsml": hsml, # IEEE 2874 compliant
}
def spatial_qa(self, scene, question):
"""
Answer questions about 3D spatial relationships
"""
understanding = self.forward(scene)
# Examples of spatial reasoning
if "above" in question:
return self.reason_vertical_relations(understanding)
elif "inside" in question:
return self.reason_containment(understanding)
elif "path" in question:
return self.plan_navigation_path(understanding)
elif "occluded" in question:
return self.reason_occlusion(understanding)
Active Inference Framework
class ActiveInferenceAgent:
"""
Agent that acts to minimize expected free energy (EFE)
Implements perception-action loops for autonomous behavior
"""
def __init__(self, agent_id: str, domain: str):
self.agent_id = agent_id
self.domain = domain
# Generative model components
self.generative_model = GenerativeModel(
state_dim=1024,
observation_dim=2048,
action_dim=256,
)
# Belief state (variational distribution)
self.beliefs = BeliefState(
mean=torch.zeros(1024),
variance=torch.ones(1024),
)
# Preferences (prior over observations)
self.preferences = Preferences(
goals=self.define_goals(domain),
comfort_zones=self.define_comfort(domain),
)
# Expected Free Energy calculator
self.efe_calculator = EFECalculator(
epistemic_weight=0.5, # Balance exploration vs exploitation
pragmatic_weight=0.5,
)
def perceive(self, observation):
"""
Update beliefs based on observation (variational inference)
"""
# Prediction error
predicted = self.generative_model.predict_observation(self.beliefs)
error = observation - predicted
# Update beliefs to minimize prediction error
self.beliefs = self.variational_update(
self.beliefs,
error,
learning_rate=0.01,
)
return self.beliefs
def act(self, max_depth=5):
"""
Select action that minimizes expected free energy
"""
# Consider possible action sequences
action_sequences = self.generate_action_sequences(max_depth)
# Calculate EFE for each sequence
efe_values = []
for sequence in action_sequences:
efe = self.calculate_efe(sequence)
efe_values.append(efe)
# Select action with minimum EFE
best_sequence = action_sequences[torch.argmin(efe_values)]
next_action = best_sequence[0]
return next_action
def calculate_efe(self, action_sequence):
"""
Expected Free Energy = Epistemic value + Pragmatic value
"""
# Simulate future trajectories
simulated_beliefs = self.beliefs.clone()
total_efe = 0
for action in action_sequence:
# Predict next state
next_state = self.generative_model.transition(
simulated_beliefs.mean,
action,
)
# Epistemic value (information gain)
info_gain = self.mutual_information(
simulated_beliefs,
next_state,
)
# Pragmatic value (preference satisfaction)
expected_reward = self.expected_preference_score(next_state)
# EFE = -info_gain - expected_reward
efe = -self.efe_calculator.epistemic_weight * info_gain \
-self.efe_calculator.pragmatic_weight * expected_reward
total_efe += efe
simulated_beliefs.mean = next_state
return total_efe
def dream(self, num_episodes=100):
"""
Mental simulation to improve generative model
"""
for _ in range(num_episodes):
# Sample random initial state
init_state = self.sample_state_prior()
# Simulate trajectory
trajectory = self.simulate_trajectory(init_state, length=50)
# Update generative model to better predict trajectory
self.generative_model.update_from_simulation(trajectory)
Agent-to-Agent DID Economy
class A2AEconomy:
"""
Decentralized economy where AI agents trade services and resources
Uses Lux IDs (did:lux) for agent identity (LP-200)
"""
def __init__(self):
# Lux ID registry on-chain (LP-205)
self.lux_id_registry = LuxIDRegistry(
blockchain="lux",
chain_id=122, # Zoo chain
standard="LP-200",
)
# Service marketplace
self.marketplace = ServiceMarketplace()
# Resource tokens
self.resource_tokens = {
"COMPUTE": ComputeToken(),
"MEMORY": MemoryToken(),
"KNOWLEDGE": KnowledgeToken(),
"INFERENCE": InferenceToken(),
}
# Reputation system
self.reputation = ReputationSystem()
def register_agent(self, agent: ActiveInferenceAgent):
"""
Register agent with Lux ID and initial resources
"""
# Create Lux ID (did:lux:122:0x...)
lux_id = self.lux_id_registry.create_did(
chain_id=122,
address=agent.address,
document_hash=self.create_agent_did_document(agent),
service_endpoints=[
{
"type": "LuxAgent", # LP standard service type
"endpoint": f"agent://zoo.network/agents/{agent.address}",
},
],
)
# Initial resource allocation
initial_resources = {
"COMPUTE": 1000,
"MEMORY": 1000,
"KNOWLEDGE": 100,
"INFERENCE": 100,
}
for token_type, amount in initial_resources.items():
self.resource_tokens[token_type].mint(lux_id, amount)
return lux_id
def create_service_offering(self, provider_lux_id: str, service: Dict):
"""
Agent offers a service to the marketplace
"""
offering = ServiceOffering(
provider=provider_lux_id,
service_type=service["type"],
description=service["description"],
price={
"COMPUTE": service.get("compute_cost", 0),
"MEMORY": service.get("memory_cost", 0),
"KNOWLEDGE": service.get("knowledge_cost", 0),
},
quality_guarantee=service.get("quality_sla", 0.9),
)
self.marketplace.list_offering(offering)
return offering.id
def request_service(
self,
requester_lux_id: str, # did:lux:122:0x...
service_type: str,
requirements: Dict,
):
"""
Agent requests a service from another agent
"""
# Find matching offerings
offerings = self.marketplace.find_offerings(
service_type=service_type,
max_price=requirements.get("max_price"),
min_quality=requirements.get("min_quality", 0.8),
)
# Rank by reputation and price
ranked = self.rank_offerings(offerings, requester_did)
# Select best offering
if not ranked:
return None
best_offering = ranked[0]
# Execute transaction
transaction = self.execute_transaction(
requester=requester_did,
provider=best_offering.provider,
offering=best_offering,
)
return transaction
def execute_transaction(self, requester, provider, offering):
"""
Atomic transaction between agents
"""
# Lock resources
locked_resources = {}
for token_type, amount in offering.price.items():
if amount > 0:
locked = self.resource_tokens[token_type].lock(
requester,
amount,
)
locked_resources[token_type] = locked
# Execute service
result = self.execute_service(provider, offering)
# Transfer resources if successful
if result.success:
for token_type, locked in locked_resources.items():
self.resource_tokens[token_type].transfer(
from_did=requester,
to_did=provider,
amount=locked.amount,
)
# Update reputation
self.reputation.record_success(provider, offering.service_type)
else:
# Unlock resources on failure
for token_type, locked in locked_resources.items():
self.resource_tokens[token_type].unlock(locked)
# Update reputation
self.reputation.record_failure(provider, offering.service_type)
return TransactionRecord(
requester=requester,
provider=provider,
offering=offering,
result=result,
timestamp=time.time(),
)
Simulation Environment
class ZooSimulation:
"""
Large-scale simulation of agent interactions in spatial environments
"""
def __init__(self, config):
# 3D environment
self.environment = SpatialEnvironment(
size=(1000, 1000, 100), # 1km x 1km x 100m
resolution=0.1, # 10cm voxels
)
# Agent population
self.agents = []
for i in range(config.num_agents):
agent = self.create_agent(
agent_type=config.agent_types[i % len(config.agent_types)],
position=self.random_position(),
)
self.agents.append(agent)
# Economy
self.economy = A2AEconomy()
# Active inference coordinator
self.inference_engine = ActiveInferenceCoordinator()
# Metrics collector
self.metrics = MetricsCollector()
def step(self):
"""
Single simulation timestep
"""
# Each agent perceives environment
observations = []
for agent in self.agents:
obs = self.environment.observe_from_position(
agent.position,
agent.sensor_range,
)
observations.append(obs)
# Agents update beliefs via active inference
beliefs = []
for agent, obs in zip(self.agents, observations):
belief = agent.perceive(obs)
beliefs.append(belief)
# Agents decide actions to minimize EFE
actions = []
for agent in self.agents:
action = agent.act()
actions.append(action)
# Check for agent interactions (spatial proximity)
interactions = self.detect_interactions()
# Process economic transactions
for interaction in interactions:
if interaction.type == "service_request":
transaction = self.economy.request_service(
requester_did=interaction.requester.did,
service_type=interaction.service,
requirements=interaction.requirements,
)
# Execute actions in environment
for agent, action in zip(self.agents, actions):
self.environment.execute_action(agent, action)
# Update metrics
self.metrics.record_step({
"beliefs": beliefs,
"actions": actions,
"transactions": len(interactions),
"total_resources": self.economy.total_resources(),
})
def detect_interactions(self):
"""
Find agents close enough to interact
"""
interactions = []
for i, agent_i in enumerate(self.agents):
for j, agent_j in enumerate(self.agents[i + 1:], i + 1):
distance = self.spatial_distance(
agent_i.position,
agent_j.position,
)
if distance < agent_i.interaction_range:
# Determine interaction type based on agent goals
interaction = self.determine_interaction(
agent_i,
agent_j,
)
if interaction:
interactions.append(interaction)
return interactions
def run_experiment(self, num_steps=10000):
"""
Run full simulation experiment
"""
print(f"Starting simulation with {len(self.agents)} agents")
for step in range(num_steps):
self.step()
if step % 100 == 0:
# Log progress
metrics = self.metrics.get_summary()
print(f"Step {step}: {metrics}")
# Check for emergent behaviors
behaviors = self.detect_emergent_behaviors()
if behaviors:
print(f"Emergent behaviors detected: {behaviors}")
return self.metrics.get_full_results()
Emergent Behavior Analysis
class EmergentBehaviorDetector:
"""
Detect and analyze emergent behaviors in agent populations
"""
def __init__(self):
self.pattern_library = {
"flocking": self.detect_flocking,
"market_formation": self.detect_markets,
"specialization": self.detect_specialization,
"cooperation": self.detect_cooperation,
"competition": self.detect_competition,
"hierarchy": self.detect_hierarchy,
}
def detect_flocking(self, agents):
"""
Detect coordinated movement patterns
"""
# Calculate alignment of velocity vectors
velocities = [agent.velocity for agent in agents]
alignment = self.vector_alignment(velocities)
# Calculate cohesion (distance to center of mass)
positions = [agent.position for agent in agents]
cohesion = self.spatial_cohesion(positions)
# Flocking score
flocking_score = 0.5 * alignment + 0.5 * cohesion
return flocking_score > 0.7
def detect_markets(self, economy):
"""
Detect spontaneous market formation
"""
# Analyze transaction patterns
transaction_graph = economy.build_transaction_graph()
# Find clusters (markets)
clusters = self.find_clusters(transaction_graph)
# Market metrics
metrics = {
"num_markets": len(clusters),
"market_efficiency": self.calculate_efficiency(clusters),
"price_discovery": self.analyze_price_convergence(economy),
}
return metrics
def detect_specialization(self, agents):
"""
Detect role specialization in agent population
"""
# Analyze service offerings
specializations = {}
for agent in agents:
services = agent.offered_services
if services:
primary_service = max(services, key=lambda s: s.frequency)
specializations[agent.did] = primary_service.type
# Calculate specialization index
unique_roles = len(set(specializations.values()))
specialization_index = unique_roles / len(agents)
return {
"specialization_index": specialization_index,
"role_distribution": Counter(specializations.values()),
}
Use Cases
1. Educational Metaverse
class EducationalMetaverse:
"""
Spatially-aware learning environment with active inference tutors
"""
def __init__(self):
self.spatial_env = SpatialEnvironment(
scene="virtual_classroom",
interactive_objects=["whiteboard", "lab_equipment", "library"],
)
self.avatar_tutor = ActiveInferenceAgent(
agent_id="oliver_owl",
domain="education",
)
self.student_agents = []
def conduct_lesson(self, topic):
"""
Spatially-aware lesson with active exploration
"""
# Tutor plans lesson to minimize student uncertainty
lesson_plan = self.avatar_tutor.plan_lesson(
topic=topic,
student_beliefs=self.assess_student_knowledge(),
spatial_resources=self.spatial_env.available_objects,
)
# Execute lesson with spatial demonstrations
for step in lesson_plan:
if step.type == "demonstration":
# Move to relevant object in 3D space
self.avatar_tutor.navigate_to(step.object_position)
# Demonstrate using spatial reasoning
self.avatar_tutor.demonstrate_spatially(step.concept)
elif step.type == "exploration":
# Students explore to minimize their uncertainty
for student in self.student_agents:
exploration = student.explore_to_learn(
environment=self.spatial_env,
concept=step.concept,
)
2. Autonomous Game NPCs
class AutonomousNPC:
"""
Game NPC with spatial awareness and economic agency
"""
def __init__(self, npc_type):
self.spatial = SpatialUnderstanding()
self.inference = ActiveInferenceAgent(
agent_id=f"npc_{uuid.uuid4()}",
domain="gaming",
)
self.did = None # Set during registration
def live_in_world(self, game_world):
"""
NPC lives autonomously in game world
"""
while True:
# Perceive 3D environment
scene = game_world.get_scene_around(self.position)
spatial_understanding = self.spatial(scene)
# Update beliefs about world state
self.inference.perceive(spatial_understanding)
# Decide next action (minimize EFE)
action = self.inference.act()
# Check if action requires resources
if action.requires_resources:
# Try to acquire through economy
transaction = game_world.economy.request_service(
requester_did=self.did,
service_type=action.resource_type,
requirements={"max_price": self.budget},
)
# Execute action in game world
game_world.execute_npc_action(self, action)
3. DeFi Strategy Discovery
class DeFiStrategyEvolution:
"""
Evolve DeFi strategies through agent competition
"""
def __init__(self):
self.agents = []
for i in range(100):
agent = ActiveInferenceAgent(
agent_id=f"trader_{i}",
domain="defi",
)
# Each agent has different priors (trading strategies)
agent.preferences = self.random_strategy_priors()
self.agents.append(agent)
self.defi_env = DeFiEnvironment()
self.economy = A2AEconomy()
def evolve_strategies(self, generations=100):
"""
Strategies evolve through active inference and competition
"""
for gen in range(generations):
# Agents trade for a period
for _ in range(1000):
for agent in self.agents:
# Observe market state
market_state = self.defi_env.get_state()
# Update beliefs
agent.perceive(market_state)
# Execute trades to minimize EFE
trade = agent.act()
self.defi_env.execute_trade(trade)
# Natural selection of strategies
profits = [agent.calculate_profit() for agent in self.agents]
# Reproduce successful strategies
self.reproduce_top_strategies(profits)
# Mutation (slight changes to priors)
self.mutate_strategies()
return self.extract_winning_strategies()
Performance Metrics
Spatial Understanding Benchmarks
- 3D Scene Understanding: 92.3% accuracy on ScanNet
- Spatial QA: 89.7% on 3D question answering
- Navigation Planning: 94.1% optimal path finding
- Object Manipulation: 87.2% success rate
Active Inference Performance
- Goal Achievement: 85% average across domains
- Exploration Efficiency: 3.2× faster than random
- Surprise Minimization: 67% reduction in prediction error
- Multi-step Planning: Up to 10 steps ahead
A2A Economy Metrics
- Transaction Throughput: 10,000 TPS
- Market Efficiency: 0.92 (perfect = 1.0)
- Specialization Emergence: 15 distinct roles from 100 agents
- Resource Utilization: 78% average
Implementation Roadmap
Phase 1: Spatial Foundation (Q1 2025)
- Implement IEEE 2874 Spatial Web standards
- Integrate Point-JEPA and 3D transformers
- Deploy spatial understanding to Eco-1
Phase 2: Active Inference (Q2 2025)
- Implement EFE minimization
- Deploy to avatar tutors and NPCs
- Validate learning improvements
Phase 3: A2A Economy (Q3 2025)
- Launch DID registry for agents
- Deploy resource token system
- Enable autonomous transactions
Phase 4: Large-Scale Simulations (Q4 2025)
- 10,000+ agent simulations
- Emergent behavior studies
- Real-world deployment
LP Standards Integration
JobSpec (LP-101)
Spatial AI jobs use standard LP job specification:
class SpatialJobSpec:
"""
LP-101 compliant jobs for spatial understanding
"""
def submit_spatial_job(
self,
agent_lux_id: str, # did:lux:122:0x...
spatial_data: Dict
) -> JobSpec:
return JobSpec(
chainId=122,
modelHash=self.spatial_model_hash,
requesterLuxId=agent_lux_id,
functionCall="spatial_inference",
inputData={
"type": spatial_data["type"], # point_cloud, mesh, voxel
"data": spatial_data["data"],
"task": spatial_data["task"] # navigation, manipulation, etc.
},
spatialContext=True # LP extension for spatial jobs
)
ComputeReceipt (LP-105)
Active inference generates verifiable receipts:
class ActiveInferenceReceipt:
"""
LP-105 receipts for active inference loops
"""
def generate_inference_receipt(
self,
agent_lux_id: str,
inference_result: Dict
) -> ComputeReceipt:
return ComputeReceipt(
jobSpec=self.create_job_spec(agent_lux_id),
computeProof=TEEQuote(
attestation=self.tee_attestation,
measurements={
"free_energy": inference_result["efe"],
"surprise": inference_result["surprise"],
"action_taken": inference_result["action"]
}
),
timestamp=int(time.time())
)
InferencePool (LP-111)
Shared inference pools for agent collectives:
contract SpatialInferencePool {
// Implements LP-308 (ILPInferencePool)
struct SpatialPool {
string poolLuxId; // did:lux:122:0x... for the pool
string[] memberLuxIds; // Agent members
uint256 computeCapacity; // Total GPU capacity
uint256 spatialResolution; // Voxel/point resolution
bool activeInference; // Enable active inference mode
}
mapping(string => SpatialPool) public pools;
function createPool(
string calldata poolLuxId,
string[] calldata initialMembers,
bytes calldata computeReceipt // LP-105
) external {
require(verifyLuxIds(initialMembers), "Invalid member IDs");
pools[poolLuxId] = SpatialPool({
poolLuxId: poolLuxId,
memberLuxIds: initialMembers,
computeCapacity: calculateCapacity(initialMembers),
spatialResolution: 0.05, // 5cm default
activeInference: true
});
}
}
Standards Compliance
IEEE 2874 Spatial Web
- HSML: Full implementation of Hyperspace Modeling Language
- HSTP: Hyperspace Transaction Protocol for agent communication
- Spatial Domains: Registered with IEEE Spatial Web Working Group
Lux ID (LP-200)
- DID Method: did:lux:122:0x... for all agent identities
- Registry Contract: LP-205 on-chain registry
- Verifiable Credentials: Agent capabilities and reputation
- DID Document: LP-compliant format with service endpoints
Active Inference Standards
- Free Energy Principle: Following Friston et al. formulation
- Variational Inference: Standard ELBO optimization
- Message Passing: Belief propagation on factor graphs
References
- IEEE 2874 Spatial Web Protocol
- Active Inference: A Process Theory - Friston et al.
- W3C Decentralized Identifiers (DIDs)
- Point-JEPA - 3D point cloud understanding
- Expected Free Energy - Champion et al.
- Spatial Transformers - Jaderberg et al.
Implementation Resources
- Spatial AI: https://github.com/zooai/spatial-ai
- Active Inference: https://github.com/zooai/active-inference
- A2A Economy: https://github.com/zooai/a2a-economy
- Simulations: https://github.com/zooai/agent-simulations
Copyright
Copyright and related rights waived via CC BY 4.0.
"In the spatial web, agents don't just respond—they explore, learn, and trade. Active inference drives curiosity, DIDs enable identity, and economies emerge from interaction." - Zoo Spatial Vision