Physics-Informed
Neural Architecture
Unlocking the next generation of predictive intelligence through the Principle of Least Semantic Action.
Unlocking the next generation of predictive intelligence through the Principle of Least Semantic Action.
Standard AI models are trapped in a heuristic loop. They master correlation but fail at causation.
Models optimized solely for prediction error learn brittle shortcuts that collapse in novel environments.
Opague decision making processes that offer no audit trail or explainability for critical failures.
Without physical grounding, large models confidently generate plausible but factually incorrect outputs.
Standardizes entropy into discrete semantic mass.
Tap for detailsVerifiable Interoception validating hardware reality.
Tap for detailsPredictive World Model simulating causal consequences.
Tap for detailsGraph Memory state holding temporal context.
Tap for detailsLogic Engine inferring causal relationships.
Tap for detailsBeyond the components, the system exhibits the properties of a living organism.
The agent consumes entropy (data) as a fuel source. It expends computational energy to reduce this entropy into ordered knowledge.
A "System 2" watchdog that actively rejects hallucinations. It uses counterfactual simulation to test new data against established physical laws before integration (Veto Power).
A temporal graph optimized for signal propagation. Unlike static databases, the Relational Engine allows "pain" (error signals) to travel instantly across the entire memory architecture.
Real-time code execution path from sensation to action.
We turn chaos into a standard signal. Raw data—text, images, logs—is ingested and standardized into a unified format. This turns separate streams of entropy into a single, cohesive Semantic Mass ready for processing.
class DataIngestionModule(nn.Module):
def process_text(self, text):
# 1. Real Tokenization (GPT-2 BPE)
encoded = self.tokenizer(text, return_tensors='pt')
# 2. Embed into Latent Space (768D)
# assigning "mass" to the information
vectors = self.embedding(encoded['input_ids'])
return {
"timestamp": time.time(),
"vectors": vectors,
"source": "user_input"
}The architecture doesn't just store data; it relates it. The Relational Engine binds new inputs to historical context, constructing a dynamic Temporal Graph of cause and effect.
class RelationalEngine(nn.Module):
def forward(self, input_embedding):
# 1. Upsert Thought to Relational Engine (Persistent Memory)
self.memory_client.upsert_points("working_memory", input_embedding)
# 2. Retrieve Causal Context (Neighbors)
# "What implies this? What does this imply?"
results = self.memory_client.search_points(input_embedding)
# 3. Refine via Graph Attention (GAT)
refined_memory = self.gat(results, edge_index)
return refined_memory # Context-Aware ThoughtTo understand the world, the agent must simplify it. The Claustral Core (GATE) compresses the complex graph into a single, probabilistic belief state ($z$), filtering out noise to find the signal.
class OntonicGATE(nn.Module):
def forward(self, x, edge_index):
# 1. Aggregate Graph Info
x = F.elu(self.gat1(x, edge_index))
# 2. Project to Gaussian Parameters (μ, σ)
mu = self.fc_mu(x) # Mean (Belief)
logvar = self.fc_logvar(x) # Uncertainty
# 3. Sample Latent Belief State (z)
z = self.reparameterize(mu, logvar)
return z, mu, logvarThis is where thinking happens. The iGLNN Physics Engine minimizes "Semantic Action", evolving the belief state along the most parsimonious trajectory—mimicking how nature finds the path of least resistance.
class iGLNN(nn.Module):
def compute_euler_lagrange_residual(self, q, q_dot):
# 1. Compute Lagrangian: L = T - V
L = self.compute_lagrangian(q, q_dot)
# 2. Euler-Lagrange Equation
# d/dt(∂L/∂q̇) - ∂L/∂q = F_nc
# This forces the agent to follow the path of least action.
residual = dt_dL_dq_dot - dL_dq - F_nc
return residualTrust requires verification. If internal "Kinetic Energy" (Surprise) spikes, System 2 engages to simulate counterfactuals and veto dangerous thoughts before they are acted upon.
# 1. Calculate Semantic Kinetic Energy (Surprise)
T_scalar = self.physics.compute_kinetic_energy(mu_t, prev_mu)
# 2. Engage System 2 if Surprise > Threshold
if self.cee.should_engage_system2(T_scalar):
# 3. CPTM: Simulate Counterfactuals (What if?)
outcome = self.ree.simulate_counterfactual(input, action)
# 4. Check Prosocial Utility
u_prosocial = self.ree.estimate_prosocial_utility(outcome)
if u_prosocial < -0.5:
return np.nan # VETO ACTIONWe model intelligence not as statistical pattern matching, but as a physical system minimizing Cognitive Action.
The "velocity" of thought. It represents Surprise—the KL divergence between the agent's prior belief and the new posterior state.
The "position" relative to truth. It represents Confusion—the reconstruction loss or distance between prediction and reality.
Just as light takes the fastest path, the agent naturally seeks the most parsimonious causal explanation by minimizing the Action integral.
A critical experiment to determine if Artificial General Intelligence can be grounded in physical truth.
If we constrain the latent dynamics of a neural network to satisfy the Principle of Least Action ($\delta S = 0$), then the network will be forced to learn interpretable, generalizable physical laws instead of statistical correlations.
The physics-constrained model fails to outperform the Transformer on out-of-distribution tasks.
The model successfully recovers invariant physical laws and generalizes perfectly to novel environments.
We are building the testbench. We need rigorous validation.
We build consumer applications that solve real problems to generate the high-entropy, causal data streams needed to train the Ontonic model.
A narrative engine for Tabletop RPGs. Tracks complex, multi-agent social dynamics and improvisational storytelling to teach the model causal creativity.
Free enterprise-grade security for consumers, funded by a privacy-preserving ad model. Generates high-fidelity threat intelligence and anomaly detection data.
Automating legal reasoning and contract analysis. Validating the model's ability to navigate complex logic, rigid rule systems, and ethical constraints.
"Physics is the only verifiable truth. Everything else is just a pattern."
Joseph is a Critical Infrastructure Technologist with over a decade of field experience securing the nation’s most sensitive assets. From maintaining navigation systems on nuclear aircraft carriers in the South China Sea to designing integrated security grids for financial institutions and state infrastructure, Joseph has lived on the bleeding edge where hardware meets the real world.
At Infinite Context Labs, Joseph leads the research into Physics-Informed AI Architectures. He is the author of the Ontonic Thesis, a groundbreaking framework for "Integrity by Design" that anchors artificial intelligence in verifiable physical laws rather than statistical correlations.
Grounding AI decision-making in hardware-attested computational states (DPU/TPM integration).
Optimizing bandwidth for edge intelligence in resource-constrained environments.
Solving the "Copy Problem" in AI safety using Physical Unclonable Functions (PUF).
Infinite Context Labs doesn't just run simulations. We validate our architectures in the mud, on the pavement, and in the air.
Current AI scales data. We scale truth. We are looking for engineers who are tired of the 'Heuristic Trap' and want to build systems that understand physics.
You dream in Verilog and have deep experience with AMD SEV, ARM TrustZone, and PUF integration.
You know how to write custom drivers for the NVIDIA BlueField-2 and aren't afraid of manual memory management.
You understand that the Principle of Least Action is the ultimate loss function.
Join the few who are building the reliable future of Artificial General Intelligence.
