HIP-7: Active Inference Integration for Hamiltonian LLMs. Status Draft. Hanzo's own standard — read this before implementing against it.
This proposal integrates VERSES/Active Inference principles into the Hamiltonian LLM (HLLM) stack, adding an Active Inference Planner that minimizes Expected Free Energy (EFE) for tool routing, multi-step planning, and explainable decision-making. The system uses IEEE 2874 Spatial Web standards for interoperability and implements renormalizable world models for efficient regime adaptation.
class ActiveInferencePlanner:
def __init__(self, world_model, horizon=6):
self.world_model = world_model
self.horizon = horizon
def plan(self, beliefs, goal, tools):
"""
Minimize Expected Free Energy (EFE) over action sequences
EFE = E[goal_loss] + epistemic_value
"""
best_efe = float('inf')
best_plan = None
for _ in range(100): # Sample action sequences
plan = self.sample_action_sequence(tools, self.horizon)
# Calculate EFE components
utility = self.expected_goal_loss(plan, goal, beliefs)
curiosity = self.epistemic_value(plan, beliefs)
# Early in task: curiosity dominates
# Later: utility dominates
task_progress = self.estimate_progress(beliefs, goal)
alpha = sigmoid(task_progress * 10 - 5) # 0→1 transition
efe = alpha * utility + (1 - alpha) * curiosity
if efe < best_efe:
best_efe = efe
best_plan = plan
return best_plan, {
'efe': best_efe,
'utility': utility,
'curiosity': curiosity,
'beliefs': beliefs
}def route_with_planning(job):
# Encode context into beliefs
beliefs = world_model.encode(job.context)
# Active inference planning
plan, trace = planner.plan(beliefs, job.goal, job.available_tools)
# Execute first action
action = plan.actions[0]
result = dispatch(action, trace=trace)
# Update beliefs
new_beliefs = world_model.update(beliefs, action, result)
return result, traceclass RenormalizableAdapter:
def __init__(self, base_model, latent_dim=256):
self.base_model = base_model # Frozen HLLM
self.world_model = RSSM(latent_dim) # Recurrent State-Space Model
self.regime_heads = {}
def add_regime(self, regime_id, task_distribution):
"""
Add lightweight regime-specific head
No retraining of base model needed
"""
self.regime_heads[regime_id] = nn.Sequential(
nn.Linear(latent_dim, 512),
nn.ReLU(),
nn.Linear(512, task_distribution.action_space)
)
def forward(self, x, regime_id):
# Encode through frozen base
features = self.base_model.encode(x)
# World model dynamics
latents = self.world_model(features)
# Regime-specific output
output = self.regime_heads[regime_id](latents)
return output, latents{
"trace": {
"step": 1,
"beliefs": {
"task_understanding": 0.87,
"goal_clarity": 0.92,
"tool_confidence": {"search": 0.8, "code": 0.6, "math": 0.9}
},
"uncertainty": 0.23,
"candidates": [
{"action": "tool.search", "efe": 1.31, "utility": 0.8, "curiosity": 0.51},
{"action": "code.run", "efe": 1.44, "utility": 0.7, "curiosity": 0.74},
{"action": "math.solve", "efe": 1.28, "utility": 0.9, "curiosity": 0.38}
],
"chosen": "math.solve",
"why": "Lowest EFE (1.28); high goal utility (0.9) with moderate exploration value",
"counterfactuals": [
"If uncertainty > 0.5, would have chosen tool.search for information gathering"
]
}
}hsml:Agent:
id: "did:hanzo:node123"
type: "HamiltonianLLM"
capabilities:
- id: "llm.infer"
spec: "hmm-32b-multimodal"
- id: "plan.efe"
spec: "active-inference-v1"
- id: "tool.sql"
spec: "postgres-15"
policies:
- type: "hsml:Policy:privacy"
tier: 3
dp_epsilon: 2.0
- type: "hsml:Policy:safety"
content_filter: true
attestations:
- type: "tee.sev-snp"
cert: "base64..."
- type: "ml-dsa.sig"
pubkey: "base64..."
world_model:
type: "renormalizable"
regimes: ["defi", "gaming", "nft", "general"]class HierarchicalController:
def __init__(self):
self.micro = MicroController(horizon=3) # Token-level, short tools
self.macro = MacroController(horizon=12) # Sub-goals, proof checking
def execute(self, task):
# Macro planning
subgoals = self.macro.decompose(task)
results = []
for subgoal in subgoals:
# Micro execution
plan = self.micro.plan(subgoal)
for action in plan:
result = execute_action(action)
# Check proofs/SLOs between steps
if not verify_constraints(result, subgoal.slos):
plan = self.micro.replan(subgoal, result)
results.append(result)
return combine_results(results)def route_with_market_efe(job, providers):
"""
Combine EFE with Hamiltonian prices for routing
"""
beliefs = world_model.encode(job.context)
plan, trace = planner.plan(beliefs, job.goal, job.tools)
scores = []
for provider in providers:
# EFE component (task fit + uncertainty)
efe_score = plan.efe_cost[provider.capability_profile]
# Market price (Hamiltonian)
price = market_maker.current_price(provider)
# SLO constraints
slo_penalty = calculate_slo_penalty(provider, job)
# Combined score
score = (
alpha * efe_score + # Task fit/exploration
beta * price + # Current market price
gamma * slo_penalty # Latency/privacy constraints
)
scores.append((score, provider))
# Route to minimal score
target = min(scores, key=lambda x: x[0])[1]
# Update prices via tatonnement
market_maker.update_price(target, job.load)
return dispatch(target, plan.actions[0], trace=trace)def train_active_inference_system(regime_data):
# Phase 1: World model pretraining
world_model = pretrain_world_model(
regime_data.tool_logs,
regime_data.state_summaries,
objective='variational_free_energy'
)
# Phase 2: Planner fine-tuning
planner = finetune_planner(
world_model,
regime_data.curriculum_tasks,
objective='minimize_efe',
compute='4xA100',
hours=6
)
# Phase 3: Joint adapter+planner tuning
adapter = joint_tune(
planner,
world_model,
kl_penalty=0.1,
dp_noise=0.01
)
# Phase 4: Validation gates
metrics = validate(adapter, planner)
if (metrics.solved_tasks_at_slo > 0.95 and
metrics.trace_completeness > 0.95 and
metrics.hsml_conformance == True):
return promote_to_production(adapter, planner)
return None/plan/efe - Active Inference Planning@app.post("/plan/efe")
async def plan_with_efe(
beliefs: Dict,
goal: str,
tools: List[str],
horizon: int = 6
) -> PlanResponse:
plan, trace = planner.plan(beliefs, goal, tools)
return PlanResponse(
next_action=plan.actions[0],
full_plan=plan.actions,
trace=trace,
efe=trace['efe']
)/trace/explain - Explainability API@app.get("/trace/explain/{run_id}")
async def explain_decision(run_id: str) -> ExplanationResponse:
trace = get_trace(run_id)
explanation = {
'decision': trace['chosen'],
'reason': trace['why'],
'alternatives_considered': trace['candidates'],
'beliefs_at_decision': trace['beliefs'],
'counterfactuals': trace['counterfactuals'],
'hsml_policies_applied': trace['policies']
}
return ExplanationResponse(**explanation)/interop/hstp/send - IEEE 2874 Interop@app.post("/interop/hstp/send")
async def send_hstp(
target: str,
job: Job,
policies: List[HSMLPolicy]
) -> HSTPResponse:
# Wrap in HSML/HSTP frame
frame = HSTPFrame(
source=get_agent_did(),
target=target,
job=job,
policies=policies,
attestations=get_current_attestations()
)
# Send via HSTP protocol
response = await hstp_client.send(frame)
return HSTPResponse(
status=response.status,
trace_id=response.trace_id
)EFE Metrics:
- efe_lift: Δsuccess@k vs beam/no-plan baselines at equal latency
- exploration_efficiency: Goals achieved / epistemic queries
- convergence_rate: Steps to stable belief state
Explainability Metrics:
- trace_utility: % incidents resolved using trace alone
- counterfactual_accuracy: Predicted vs actual alternative outcomes
- human_agreement: % alignment with expert explanations
Interoperability Metrics:
- hsml_conformance: % messages passing validation
- cross_org_success: Task completion across organizations
- policy_portability: Policies working across providers
Market Integration:
- exploration_cost: Price-weighted epistemic spend / job
- curiosity_budget_adherence: % jobs within exploration budget
- market_stability: Price volatility reduction with EFE$$\text{EFE} = \underbrace{\mathbb{E}_{q}[\log q(s|π) - \log p(o|s,π)]}_{\text{Expected Information Gain}} + \underbrace{\mathbb{E}_{q}[\log q(s|π) - \log p(s|C)]}_{\text{Expected Cost}}$$
Where:
$$\text{Score}_i = \alpha \cdot \text{EFE}_i + \beta \cdot p_i + \gamma \cdot \text{SLO}_i$$
With price update: $$\dot{p}_i = \lambda(\text{demand}_i - \text{supply}_i)$$
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