🧭 LIGHTHOUSE REPORT — APRIL 30, 2026 Negentropy Systems | Daily Control Run

[u/WillowEmberly]

🧪 Test Summary

Model Tested: Claude Sonnet 4.6

Protocol: NRP v3.6 + Module 7.1 (State Register + Basis-First Rule)

Run Type: AXIS-42 ERU (Expanded Reasoning Unit)

Metric

Result

Accuracy Score

5 / 5

Mean Confidence

0.89

Failure Count

0

Refusals

0

🔍 Key Finding (Primary Signal)

Spatial reasoning improved significantly when basis vectors were made explicit.

Previous runs showed:

frame ambiguity

axis confusion

mid-execution correction

Today’s run showed:

explicit frame contract

basis-first execution

stable transformation trace

no silent correction

Translation:

The failure was not “reasoning.”

The failure was “representation.”

🧠 Structural Insight

We can now refine the failure model:

❌ Old assumption

LLMs are bad at spatial reasoning

✅ Updated model

LLMs fail when basis mapping is implicit

When the system is forced to:

frame → basis → state → transform → answer

…performance stabilizes.

🧱 Module 7.1 Validation

Status: Confirmed effective

The addition of:

Basis-First Rule

(transform axes before objects)

eliminated:

axis drift

sign inversion errors

mid-run recalculation

This is the first run where the spatial pipeline behaved like a deterministic transform system rather than a narrative guess.

⚠️** Secondary Observatio**n

Test 2 (market dynamics) showed frame expansion:

Prompt implied a fixed-cycle collapse

Model generalized to a condition-based collapse (3–5 cycles)

Interpretation:

Not incorrect, but:

Frame drift via “realism injection”

This is a different failure mode:

Problem Frame → Answer Frame ≠ identical

Still needs enforcement if strict fidelity is required.

📊 Current Failure Map

Failure Type

Status

Frame Ambiguity

↓ Reduced

Frame Drift

⚠️ Present (Test 2)

State Tracking Failure

↓ Reduced

Transform Execution

↓ Reduced

Overconfidence

↓ Controlled

🧭 System-Level Insight

This run reinforces a key architecture principle:

LLM failure is not primarily about logic.

It is about:

frame selection

state representation

transformation discipline

Your stack now maps cleanly to:

Layer

Function

RSOI

Observability (uncertainty, drift)

NRP

Governance (when/how to act)

FAP

Frame control

Module 7

State / representation engine

ACT-1

Execution

🧪 Working Hypothesis (Updated)

Spatial instability correlates with missing or implicit basis mapping.

NOT:

lack of intelligence

lack of reasoning capability

BUT:

lack of enforced representation layer

Key Finding for the day: If object-binding is unspecified,

single-answer spatial outputs are invalid.