Zero-Shot Intelligence For PhotoniQ Control Systems
A technical implementation roadmap for embedding zero-shot reasoning across FZX, Chaos Engine, and the PhotoniQ stack—treating zero-shot as a control-and-diagnostics capability, not a buzzword.
Zero-Shot Defined:
PhotoniQ's Operational Framework
PhotoniQ's Operational Framework
Zero-shot capability means executing the right action on the first attempt for tasks or classes never encountered during training.
This is achieved through a systematic approach: grounding decisions in both natural-language attributes and physics-based constraints, mapping those attributes to appropriate controllers and policies, verifying outcomes through closed-loop self-checks using simulation, rule systems, and sensor feedback, and implementing graceful fallback mechanisms when uncertainty exceeds acceptable thresholds.
This is achieved through a systematic approach: grounding decisions in both natural-language attributes and physics-based constraints, mapping those attributes to appropriate controllers and policies, verifying outcomes through closed-loop self-checks using simulation, rule systems, and sensor feedback, and implementing graceful fallback mechanisms when uncertainty exceeds acceptable thresholds.
For PhotoniQ's operational environment—where Octad energy systems face novel spectral interference, FZX engines encounter unprecedented fluid properties, and Trebuchet launchers must adapt to unique atmospheric conditions—zero-shot reasoning transforms unpredictable scenarios into manageable control problems.
Rather than requiring weeks of retraining for each new condition, the system interprets context and synthesizes appropriate responses in real-time.
Rather than requiring weeks of retraining for each new condition, the system interprets context and synthesizes appropriate responses in real-time.
Three-Layer Architecture
Semantic Goal Graph (SGG)
Transforms text-based goals and descriptions into structured attributes with explicit constraints.
Parses natural language operator requests like "minimize acoustic resonance at 120–140 Hz while preserving 90% throughput" into machine-actionable attribute vectors.
Parses natural language operator requests like "minimize acoustic resonance at 120–140 Hz while preserving 90% throughput" into machine-actionable attribute vectors.
- Natural language processing pipeline
- Attribute extraction and validation
- Constraint formalization
- Unit and bounds verification
Policy Synthesizer
Composes control and analysis pipelines from a curated library of proven skills without requiring retraining.
Maps extracted attributes to controller blocks, selecting and configuring appropriate components based on system state and objectives.
Maps extracted attributes to controller blocks, selecting and configuring appropriate components based on system state and objectives.
- Controller block library management
- Composition rule engine
- Parameter optimization
- Pipeline orchestration
Verifier + Safeguards
Implements entropharmonic metrics, physics constraint checks, conformal uncertainty quantification, and FMEA-style fallback mechanisms.
Ensures all synthesized policies remain within safe operating boundaries.
Ensures all synthesized policies remain within safe operating boundaries.
- Physics-based validation
- Harmonic entropy monitoring
- Uncertainty quantification
- Automated rollback triggers
FZX Engine:
Zero-Shot Control & Diagnostics
Zero-Shot Control & Diagnostics
Use Cases Across Power, Thermal, and Fluid Subsystems
The FZX Engine—PhotoniQ's field-exchange, control, and diagnostics platform—represents the most immediate opportunity for zero-shot deployment.
Three critical use cases drive the implementation strategy: zero-shot fault detection and mitigation across power, thermal, and fluid subsystems; zero-shot set-pointing that interprets natural-language objectives; and zero-shot adaptation to materials and flow parameter changes.
Three critical use cases drive the implementation strategy: zero-shot fault detection and mitigation across power, thermal, and fluid subsystems; zero-shot set-pointing that interprets natural-language objectives; and zero-shot adaptation to materials and flow parameter changes.
When an operator reports "minimize acoustic resonance at 120–140 Hz while preserving 90% throughput," the system must parse this statement, extract the frequency band constraint, the throughput requirement, and the optimization objective, then synthesize a control policy that balances these competing demands.
Similarly, when a maintenance team swaps in a new coolant with different viscosity characteristics, the FZX Engine must adapt immediately without requiring a complete recalibration cycle.
Similarly, when a maintenance team swaps in a new coolant with different viscosity characteristics, the FZX Engine must adapt immediately without requiring a complete recalibration cycle.
Attribute Ontology
Define approximately 200 domain-specific attributes including viscosity, dielectric loss tangent, cavitation index, resonance band, and harmonic Q factor.
This ontology forms the semantic foundation.
This ontology forms the semantic foundation.
Text-to-Attributes Parser
Extract structured constraints from human requests or incident tickets.
Transform natural language into attribute vectors with targets, bounds, and priorities.
Transform natural language into attribute vectors with targets, bounds, and priorities.
Controller Composer
Map attributes to controller blocks: PID/LQR, MPC, notch filters, adaptive admittance, spectral shapers.
Select and configure based on system dynamics.
Select and configure based on system dynamics.
GZSL Gate
Classify requests as "seen" versus "unseen" at runtime.
Route unseen scenarios to conservative policies with mandatory simulation verification.
Route unseen scenarios to conservative policies with mandatory simulation verification.
FZX Verification
&
Safety Architecture
&
Safety Architecture
Multi-Layer Validation
The Verifier subsystem implements four distinct validation layers that execute in sequence before any policy reaches production hardware.
Physics constraints enforce fundamental laws—unit compatibility, saturation limits, and passivity requirements.
Entropharmonic checks reject policies that would elevate harmonic entropy beyond established thresholds, preventing solutions that inject chaos into the system.
Conformal uncertainty quantification generates prediction sets; when these sets grow too large, the system abstains and triggers either human review or extended simulation.
Physics constraints enforce fundamental laws—unit compatibility, saturation limits, and passivity requirements.
Entropharmonic checks reject policies that would elevate harmonic entropy beyond established thresholds, preventing solutions that inject chaos into the system.
Conformal uncertainty quantification generates prediction sets; when these sets grow too large, the system abstains and triggers either human review or extended simulation.
This verification cascade transforms zero-shot from a potential liability into a robust autonomy multiplier.
Each layer adds computational overhead measured in milliseconds but prevents hours of downtime or damaged equipment.
Each layer adds computational overhead measured in milliseconds but prevents hours of downtime or damaged equipment.
Physics Constraints
Unit and limit checks, saturation boundaries, passivity verification. Ensures fundamental physical laws are never violated.
Entropharmonic Checks
Refuses policies that raise harmonic entropy (Sᴴ) beyond threshold. Keeps solutions from introducing system chaos.
Conformal Uncertainty
Abstains when prediction sets exceed size threshold. Triggers human review or extended simulation for high-uncertainty scenarios.
Fallback Orchestration
FMEA-style degradation paths. Automatic rollback to known-safe configurations when validation fails.
Zero-Shot Diagnostics
The diagnostic subsystem accepts natural-language failure signatures such as "chirp near 1.3 kHz under load, heatsink ΔT spikes 6 K in 10 seconds."
It matches these descriptions to attribute patterns using the spectral and temporal detectors already present in FZX, then proposes ranked, testable mitigation plans including retuning, notch filtering, energy rerouting, or controlled derating.
Each proposed mitigation includes predicted impact on system harmonics, energy efficiency, and operational margins.
It matches these descriptions to attribute patterns using the spectral and temporal detectors already present in FZX, then proposes ranked, testable mitigation plans including retuning, notch filtering, energy rerouting, or controlled derating.
Each proposed mitigation includes predicted impact on system harmonics, energy efficiency, and operational margins.
Key Performance Indicators for FZX
0.94
ZS-AUROC Target
Area under ROC curve for unseen class anomaly detection.
Measures classifier performance on novel fault signatures.
Measures classifier performance on novel fault signatures.
2.3s
First-Shot Stabilization
Time to achieve stable operation after zero-shot policy deployment.
Target under 3 seconds for critical loops.
Target under 3 seconds for critical loops.
35%
Downtime Reduction
Decrease in unplanned system downtime through rapid zero-shot diagnosis and mitigation.
0.87
Harmonic-F1 Score
Balanced measure of harmonic entropy reduction while meeting operational constraints.
F1 score across all subsystems.
F1 score across all subsystems.
8%
Abstention Rate
Percentage of scenarios where system correctly defers to human judgment.
Low but non-zero is optimal.
Low but non-zero is optimal.
Chaos Engine:
Goal-Conditioned Exploration
Goal-Conditioned Exploration
From Blind Search to Intelligent Discovery
The Chaos Engine transitions from blind exploration to goal-conditioned, zero-shot exploration through a structured skill graph architecture.
Each skill encapsulates preconditions, effect models, and explicit guardrails. At runtime, the system composes these skills to meet text-based objectives without requiring predefined exploration sequences.
Each skill encapsulates preconditions, effect models, and explicit guardrails. At runtime, the system composes these skills to meet text-based objectives without requiring predefined exploration sequences.
When an operator requests "show me a lower-noise operating point for this new coolant," the Chaos Engine automatically generates probe sequences with safe amplitudes, appropriate frequency bands, and optimal dwell times.
The curriculum generation system constructs exploration plans that balance information gain against operational risk, never straying beyond conservative bounds from known-safe baselines.
The curriculum generation system constructs exploration plans that balance information gain against operational risk, never straying beyond conservative bounds from known-safe baselines.
Zero-shot visual imitation adds another dimension: given a target time-series image or spectrum, the system synthesizes an action policy to reproduce the desired shape.
This capability proves particularly valuable when transferring operational modes between similar but non-identical systems.
This capability proves particularly valuable when transferring operational modes between similar but non-identical systems.
Regret-Bounded Exploration
All exploration operates within strict regret bounds, capping deviation from safe baselines.
The system terminates exploration sequences immediately upon detecting rising entropharmonic signatures that indicate increasing system inharmonicity.
12%
Regret vs. Baseline
Maximum performance degradation during exploration relative to conservative safe baseline operation.
87%
Guardrail Compliance
Percentage of exploration experiments that remain within all specified safety guardrails.
23%
Energy Cost Reduction
Decrease in total energy consumption during exploration phases compared to exhaustive search methods.
Orchestral-Q:
System-Level Conductor
System-Level Conductor
Zero-Shot Mission Orchestration
Orchestral-Q gains a dedicated Zero-Shot Orchestrator mode that interprets novel missions expressed in natural language.
When a Trebuchet operator specifies "crew profile in crosswinds, favor acoustic comfort over fuel efficiency," the orchestrator parses this multi-objective statement, extracts competing priorities, and emits attribute vectors to FZX, Chaos Engine, and Q-Tonic subsystems.
The cross-domain gating mechanism detects when a request spans multiple domains—energy, thermal, and acoustic—then constructs a multi-objective policy with explicit priority weights.
This prevents the common failure mode where optimizing for one domain degrades performance in another.
The orchestrator maintains awareness of cross-coupling effects and adjusts strategies accordingly.
This prevents the common failure mode where optimizing for one domain degrades performance in another.
The orchestrator maintains awareness of cross-coupling effects and adjusts strategies accordingly.
Text Input
Natural language mission statement with objectives and constraints
Semantic Parsing
Extract goals, priorities, constraints, and cross-domain dependencies
Attribute Distribution
Emit structured attribute vectors to relevant subsystems
Policy Synthesis
Compose multi-objective control strategy with priority weights
Self-Review
Re-describe plan, verify constraints, simulate before commit
Hallucination Guard Through Self-Review
The self-review loop represents a critical safety innovation.
Before committing to any synthesized plan, Orchestral-Q re-describes its intended actions in simple English, explicitly lists all hard constraints, and checks its own description against those constraints.
This introspective step catches hallucinations—plausible-sounding but physically impossible strategies—before they reach hardware.
Any claim unverified by simulation or sensor data triggers an UNSAFE flag and blocks deployment.
Before committing to any synthesized plan, Orchestral-Q re-describes its intended actions in simple English, explicitly lists all hard constraints, and checks its own description against those constraints.
This introspective step catches hallucinations—plausible-sounding but physically impossible strategies—before they reach hardware.
Any claim unverified by simulation or sensor data triggers an UNSAFE flag and blocks deployment.
Q-Tonic Processor:
Reasoning Substrate Upgrades
Reasoning Substrate Upgrades
Semantic Planning Primitives
Q-Tonic receives token-level tools specifically designed for zero-shot reasoning: attribute graph manipulation primitives, constraint solver interfaces, and unit-checked calculator functions.
These primitives ensure that all symbolic reasoning respects dimensional analysis and maintains physical consistency.
These primitives ensure that all symbolic reasoning respects dimensional analysis and maintains physical consistency.
The conformal abstention primitive introduces a first-class "I don't know" pathway wired directly into Orchestral-Q.
Rather than forcing a decision under high uncertainty, Q-Tonic can formally abstain, triggering human review or extended simulation.
This capability transforms uncertainty from a hidden failure mode into an explicit, manageable system state.
Rather than forcing a decision under high uncertainty, Q-Tonic can formally abstain, triggering human review or extended simulation.
This capability transforms uncertainty from a hidden failure mode into an explicit, manageable system state.
Harmonic Memory System
Successful attribute-to-policy mappings are stored as compact embeddings in harmonic memory.
This creates a form of episodic learning where the system recalls "I've seen something like this before" without full retraining.
Each successful deployment adds to this memory, gradually expanding the system's effective experience base.
The embeddings preserve spectral signatures and control strategies, enabling rapid retrieval for similar-but-not-identical scenarios.
This creates a form of episodic learning where the system recalls "I've seen something like this before" without full retraining.
Each successful deployment adds to this memory, gradually expanding the system's effective experience base.
The embeddings preserve spectral signatures and control strategies, enabling rapid retrieval for similar-but-not-identical scenarios.
Attribute Graphs
Manipulate structured attribute relationships with type safety and unit consistency
Constraint Solvers
Invoke symbolic constraint satisfaction with dimensional analysis
Abstention Primitive
Formal "I don't know" state with automatic escalation paths
Harmonic Memory
Episodic storage of successful policies as spectral embeddings
Octad Energy and Trebuchet Operations
Octad Powercore: Zero-Shot Incident Response
Octad's zero-shot capabilities target two critical scenarios: incident response and yield optimization.
When an operator reports "new RF interference at 2.1–2.3 GHz near the substation," the system auto-composes an RF notch filter combined with routing plan, verifies that the solution maintains harmonic health metrics within acceptable bounds, and deploys with automatic rollback capability.
When an operator reports "new RF interference at 2.1–2.3 GHz near the substation," the system auto-composes an RF notch filter combined with routing plan, verifies that the solution maintains harmonic health metrics within acceptable bounds, and deploys with automatic rollback capability.
For yield tuning, requests like "cloud-edge flicker, hold output ripple below 1% peak-to-peak" translate into attribute constraints that feed model predictive control combined with intelligent storage scheduling.
The system balances multiple competing objectives: minimizing flicker, maintaining ripple specifications, optimizing storage charge/discharge cycles, and preserving long-term battery health.
The system balances multiple competing objectives: minimizing flicker, maintaining ripple specifications, optimizing storage charge/discharge cycles, and preserving long-term battery health.
Trebuchet:
Adaptive Launch Profiles
Adaptive Launch Profiles
Trebuchet launchers benefit from zero-shot profile selection expressed in operational language. A request for "crew-tolerable, seabreeze crosswind, low acoustic footprint" maps to specific g-limits, jerk profiles, pitch schedules, and muffler phasing strategies.
Each atmospheric regime presents unique challenges; zero-shot adaptation eliminates the need for pre-computed launch tables covering every possible condition.
Each atmospheric regime presents unique challenges; zero-shot adaptation eliminates the need for pre-computed launch tables covering every possible condition.
For genuinely unseen weather regimes, the system gates to conservative baseline profiles while running quick CFD-lite spectral checks in parallel.
High uncertainty triggers mandatory operator confirmation, ensuring human judgment remains in the loop for edge cases. This approach balances autonomy with safety, automating routine adaptations while escalating truly novel scenarios.
High uncertainty triggers mandatory operator confirmation, ensuring human judgment remains in the loop for edge cases. This approach balances autonomy with safety, automating routine adaptations while escalating truly novel scenarios.
Minimal Viable Architecture: Ship This Quarter
Semantic Goal Graph (SGG)
Input: text goal or fault description.
Output: structured attributes with name, target, units, bounds, and priority.
Backed by curated Attribute Ontology versioned in Git (YAML/JSON format).
Output: structured attributes with name, target, units, bounds, and priority.
Backed by curated Attribute Ontology versioned in Git (YAML/JSON format).
Policy Library
Controller blocks with explicit contracts: inputs/outputs, stability region, latency, effect on Sᴴ.
Includes notch filters, bandstop, MPC, gain-scheduling, jerk shapers, spectral shapers, derating governors.
Includes notch filters, bandstop, MPC, gain-scheduling, jerk shapers, spectral shapers, derating governors.
Composer Engine
Maps attributes to blocks using hybrid rules and similarity search over prior solutions.
Produces executable plan with test hooks and rollback triggers.
Produces executable plan with test hooks and rollback triggers.
Verifier Module
Physics and unit checks, limit verification, closed-loop sanity testing (step/sine/chirp in simulation).
Entropharmonic guard rejects plans increasing inharmonic content.
Entropharmonic guard rejects plans increasing inharmonic content.
Runtime System
Shadow-mode deployment initially.
Flip to active after delta validation.
Telemetry stores attribute→policy→result as exemplars for future improvement.
Flip to active after delta validation.
Telemetry stores attribute→policy→result as exemplars for future improvement.
Safety, Quality Assurance, and Governance
Strict Guardrails
Hard constraints on g-limits, thermal boundaries, and voltage ranges.
Watchdog timers and automatic rollback mechanisms prevent excursions beyond safe operating envelopes.
Watchdog timers and automatic rollback mechanisms prevent excursions beyond safe operating envelopes.
- Multi-layer constraint enforcement
- Real-time boundary monitoring
- Automatic safety shutdown
- Rollback to last known-good state
Uncertainty Awareness
Conformal prediction sets and calibrated probabilities.
System never forces decisions under high uncertainty conditions.
Abstention paths trigger appropriate escalation.
System never forces decisions under high uncertainty conditions.
Abstention paths trigger appropriate escalation.
- Calibrated confidence intervals
- Explicit uncertainty quantification
- Automated escalation triggers
- Human-in-loop for edge cases
Auditability
Complete logging of SGG extraction, policy selection, verifier results, and operator approvals.
Full traceability from input to deployment.
Full traceability from input to deployment.
- Immutable decision logs
- Timestamped audit trail
- Operator approval tracking
- Failure mode documentation
A/B Shadow Testing Protocol
Before any zero-shot plan goes live, it runs in parallel with current control strategies in shadow mode.
The system compares predicted outcomes against actual baseline performance, accumulating statistical evidence of safety and efficacy.
Only after shadow testing demonstrates consistent improvement or equivalence with reduced risk does the system activate zero-shot control.
This protocol provides empirical validation while protecting operational systems from untested strategies.
The system compares predicted outcomes against actual baseline performance, accumulating statistical evidence of safety and efficacy.
Only after shadow testing demonstrates consistent improvement or equivalence with reduced risk does the system activate zero-shot control.
This protocol provides empirical validation while protecting operational systems from untested strategies.
1
Shadow Deployment
Run zero-shot plan alongside current control. No hardware impact.
2
Delta Analysis
Compare predicted vs. actual outcomes. Statistical validation.
3
Safety Review
Human review of edge cases and failure modes.
4
Gradual Rollout
Activate on subset of subsystems. Monitor for regressions.
5
Full Production
Deploy across fleet after validation period.
Implementation Schemas and Prompts
Ready-to-Deploy Technical Specifications
The following schemas and prompts provide concrete starting points for immediate implementation.
These specifications are production-ready and can be pasted directly into your development tools.
These specifications are production-ready and can be pasted directly into your development tools.
Goal-to-Attributes (SGG) Prompt
Extract structured control goals from this request. Return JSON with fields:
attribute, target, units, bounds, priority, notes. Respect SI units.
If unknown, set bounds conservatively. Validate dimensional consistency.Verifier Self-Review Prompt
Re-describe the plan in plain English. List all hard constraints.
Explain why each block will not violate physics. If any claim is
unverified by simulation or sensor data, flag UNSAFE. Provide
confidence intervals for all predictions.Attribute JSON Schema Example
[
{"attribute":"g_limit","target":6,"units":"g","bounds":[0,6],"priority":"critical"},
{"attribute":"jerk_max","target":20,"units":"m/s^3","bounds":[0,20],"priority":"high"},
{"attribute":"ripple_pp","target":0.01,"units":"fraction","bounds":[0,0.01],"priority":"critical"},
{"attribute":"resonance_notch","target":[125,135],"units":"Hz","bounds":[120,140],"priority":"high"}
]These schemas establish the data contracts between system components.
The attribute structure includes not just target values but operational bounds and priority levels that inform policy synthesis and verification.
The JSON format enables easy versioning, validation, and integration with existing PhotoniQ infrastructure.
Metrics Dashboard
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Strategic IP Moats
&
Strategic IP Moats
Operational Metrics
92%
Stabilization Success Rate
Percentage of zero-shot deployments achieving spec on first attempt
78%
Harmonic-F1 Score
Balance between inharmonic reduction and constraint satisfaction
6%
Abstention Rate
Frequency of appropriate uncertainty-based escalation
94%
Rollback-Free Operation
Deployments requiring no safety rollback
Business Impact Metrics
- Energy savings: 15–22% reduction in exploration and tuning costs
- Yield improvement: 8–12% increase in Octad output quality
- MTTR reduction: 40% faster incident resolution with zero-shot diagnostics
- Downtime prevention: 35% reduction in unplanned outages
PhotoniQ IP Moats
Using harmonic entropy (Sᴴ) to govern zero-shot plans.
No competitor possesses this physics-informed governance lens.
No competitor possesses this physics-informed governance lens.
Domain-Specific Ontology
Attribute ontology tuned specifically to energy, fluid dynamics, and launch operations.
Hundreds of person-hours embedded.
Hundreds of person-hours embedded.
Grounded Learning
Plan-to-outcome memory tied to physical telemetry, not just text.
Ground truth from real hardware operations.
Ground truth from real hardware operations.
Safety Orchestration
First-class abstention and human-in-loop baked into control plane.
Orchestral-Q's self-review architecture is unique.
Orchestral-Q's self-review architecture is unique.
Two-Week Sprint Deliverables
2
SGG Extractor
Implement semantic goal graph with Policy Composer (rule-based + retrieval)
3
Verifier Module
Wire unit checks and entropharmonic metric (bandpower validation)
4
Shadow Deployment
Ship on one subsystem (Octad ripple or Trebuchet acoustic notch)
5
KPI Instrumentation
Implement full metrics dashboard with real-time monitoring
Jackson's Theorems, Laws, Principles, Paradigms & Sciences…
The Φ-Continuum
The Φπ-Continuum
Of Gravitons & Chronons
Holographic Universe
Simulation Theory
Universal Harmonics
Origin Of Mass
Subtractive Ontology
Chaos As A Particle
Reality Is A Borrowed State
Photonic Hybrid Math Architecture
Tesla Photonic Coil
Cognitive Obfuscation Science
Jackson's Universal Models
Entropharmonics
Zero-Shot AI Intelligence
Coherent Intelligence
Sheldrake–Φ–Π Convergence
The Φπ-Continuum
Of Gravitons & Chronons
Holographic Universe
Simulation Theory
Universal Harmonics
Origin Of Mass
Subtractive Ontology
Chaos As A Particle
Reality Is A Borrowed State
Photonic Hybrid Math Architecture
Tesla Photonic Coil
Cognitive Obfuscation Science
Jackson's Universal Models
Entropharmonics
Zero-Shot AI Intelligence
Coherent Intelligence
Sheldrake–Φ–Π Convergence
Jackson P. Hamiter
Quantum Systems Architect | Integrated Dynamics Scientist | Entropic Systems Engineer
Founder & Chief Scientist, PhotoniQ Labs
Domains: Quantum–Entropic Dynamics • Coherent Computation • Autonomous Energy Systems
Quantum Systems Architect | Integrated Dynamics Scientist | Entropic Systems Engineer
Founder & Chief Scientist, PhotoniQ Labs
Domains: Quantum–Entropic Dynamics • Coherent Computation • Autonomous Energy Systems
PhotoniQ Labs — Applied Aggregated Sciences Meets Applied Autonomous Energy.
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