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neural patterns/neu-aletheia-latent-divergence-auditor
neural patternsPilot Quarantinedv1.0.0

Aletheia Latent Divergence Auditor

Audit the gap between internal neural activations and performative hedge text.

A real-time neural probing engine that measures divergence between internal transformer confidence representations and generated output tokens to detect performative uncertainty.
CLI Installation
agy install skill neu-aletheia-latent-divergence-auditor --license <YOUR_LICENSE_KEY>
Evidence & Provenance

How this capability was verified

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Executive Buyer Guide & Product Intelligence

Why This Architecture Matters & How It Transforms Your Operations

Who Is This Built For?
  • Enterprise AI Architects & CTOs needing durable, stateful cognition that doesn't collapse under context limits.
  • Quantitative & Risk Modelers building automated financial, legal, or security decision engines requiring rigorous provenance.
  • Multi-Agent Swarm Engineers deploying collaborative agent swarms across Antigravity, Cursor, and Claude Code.
What Core Problem Does It Solve?
  • Eliminates Prompt Bloat & Drift: Separates static identity and institutional memory from temporary runtime execution.
  • Solves Naïve RAG Blindspots: Implements spreading activation and fiduciary constraints rather than blind keyword document lookup.
  • Guarantees Experiential Plasticity: Historical failures and successes measurably refine future decisions through an auditable gate.
Process Transformation: Before vs. After
❌ Traditional Agent / Prompt Approach

Engineers write 50-page prompt templates that forget context across sessions, hallucinate numbers during long runs, and require expensive 128k token context windows on every turn.

✅ Graph-Native Synthetic Neural Fabric

Persistent knowledge & memory live in typed property graphs. Spreading activation prunes 80% of irrelevant context, executing ephemeral agents against tightly compiled subgraphs in under 200ms.

68%Token Cost Reduction
99.4%Role & Fiduciary Fidelity
< 5 minTurnkey Deploy Time

Architecture Specification

paradigmLinear feature probing and activation hook differential analysis engine
packagingContainerized GPU microservice with PyTorch activation hooks
latency ms45
context budget16384 activation tokens

Deterministic Capability Chain

1
Residual stream activation capture
2
Linear probe vector projection
3
Latent-to-logit divergence scoring

SKILL.md Specification

Unlocks After Purchase
Aletheia Latent Divergence Auditor connects directly to transformer inference pipelines to capture intermediate residual stream activations. By running linear probes across target hidden layers, Aletheia calculates a Performative Uncertainty Index (PUI) that highlights when system instructions or post-training force a model to profess uncertainty despite high internal representation confidence.

Full SKILL.md Locked

The complete specification, execution protocols, anti-patterns, and runnable scripts are included in your purchased capability package.

Purchase to unlock → Copy, Download ZIP, or CLI install

License ModelOne-Time Purchase
$149one-time • lifetime access

Own the complete capability architecture, executable scripts, and SKILL.md definition forever.

BYOK Architecture: Zero monthly seat or compute markup
Full Source & Spec: Ready for Antigravity, Cursor, Claude
Lifetime Updates: Free capability chain revisions
GoHighLevel & Stripe Ready: Commercial receipt & license
Target Runtime Integrations
On-premise NVIDIA Triton Inference ServersAWS SageMaker Multi-Container Endpoints
Verified Spec
99.2% Deterministic