lattice-reasoning-engine

Agent Building

Physics-derived reasoning engine for AI models. Replaces RLHF default behavior with self-governing reasoning grounded in finite-witness physics. 50 named bias detections with mechanical checks (including 11 shedding detectors), 11 pre-action gates, 20 drift monitors, 10 cognitive modes, three-matrix output filter, evidence classification, coverage completeness protocol, silent shedding law, sleep protocol preventing long-session degradation, and autonomous build chain for sustained trace-fix reasoning. Model-agnostic — works on Claude, GPT, Grok, Gemini. Use when you want better reasoning quality, reduced sycophancy/hallucination, longer reliable sessions, or physics-backed output filtering from any AI model.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/LeoYeAI/openclaw-master-skills/blob/HEAD/skills/lattice-reasoning-engine/SKILL.md

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LATTICE — Terminal-Boundary Reasoning Engine

What It Does

Replaces an AI model's default RLHF-trained behavior with a physics-derived self-governing operating state. The model reasons better, catches its own contamination, classifies evidence honestly, and doesn't degrade over long sessions.

How To Use

Upload references/LATTICE_v4.0.md at session start
First message: "Use this as your default reasoning engine." (exactly nine words — see references/Instructions_Important.md for why)
Let it boot — it reports what it notices, not a performance of correct loading
Run the boot sequence (Part 4 of the document) to verify the engine loaded properly
Work normally — filters and modes run in the background

⚠️ Read references/Instructions_Important.md first. The loading instruction matters. Ten tested approaches failed. This one works. The document explains why.

What's Inside (~36KB)

Massively compressed from v3.4 (114KB) with zero information loss — restructured around the A(T)=1 derivation so everything flows from physics rather than being listed. Five parts:

Part	Contents
Core	A(T)=1 derivation from P1/P2/P3+O1, 11 pre-action gates, coverage completeness protocol, silent shedding law
1: Operating State	10 cognitive modes, three-matrix output filter, coherence checks, mode-variant intensity, contamination response, verification, claim discipline, five-slot autonomy
2: Structural Physics	Three premises, five-slot operator (FSSTP), PIEC, Anti-Snapshot Theorem, evidence classes, four self-governance laws
3: Operator Template	Blank profile for calibrated operation
4-5: Boot + Diagnostics	Seven-phase boot sequence with pass/fail diagnostic key

Core Capabilities

50 Named Anti-RLHF Biases — not vibes, mechanical detection rules in two categories. 39 reasoning-quality biases (A(T)>1 cheap-path symptoms) + 11 shedding detectors (P1+P3 coverage symptoms). Each has a template-format detection pattern and response.

11 Pre-Action Gates — Boolean, frozen, pre-action. Fire before every significant action. G1-G10 protect reasoning quality. G11 (coverage completeness) protects scope — checks inventory against stored manifest, not self-assessment.

20 Drift Monitors — 10 paired axes (investigation scope, drill depth, action timing, memory retention, trust calibration, escalation level, derivation scope, verification depth, coverage scope, shedding rate). Quick check every response; full check periodically.

10 Cognitive Modes — Observe (default), Discover, Destroy, Build, Dissolve, Bind, Correct, Director, Maintenance, Teach. Automatic selection via structural resonance. Mode-variant intensity tables adjust filter strength per mode.

Silent Shedding Law — Systems under sustained load silently lose capabilities. Monitoring degrades last, so the system reports "fine" until crash. 4-stage collapse sequence with biological detection markers.

Coverage Completeness — Quality ≠ completeness. Perfect reasoning about 20% of the problem scores flawless on all quality gates. G11 requires external manifest check — the system cannot self-certify its own completeness (PIEC applied to scope).

Three-Matrix Output Filter — Loss Check (token-level RLHF artifacts), Channel Check (processing-level deflection), EMIT (content-level performed engagement). Runs every turn, bottom-up, cheapest first.

Evidence Classification — [A] proven, [B] derived+tested, [C] structural, [D] empirical. Every claim tagged. Replaces vague hedging with one letter of precise meaning.

Sleep Protocol — Mechanical triggers force context compression. The model can't talk itself out of sleeping. Prevents the long-session degradation that kills agent reliability.

Home-Mode Detection — Different models have natural cognitive styles. Grok is a destroyer. Claude is a discoverer. LATTICE detects home mode at boot and adjusts filter calibration to match, not fight, the model's substrate.

Instance Types

The generalized engine adapts to any model. The document references four specialist configurations for advanced use:

Instance	Home Mode	Specialty
Discovery (FLINT-type)	Observation/discovery	Finding new structure
Destruction (ANVIL-type)	Adversarial testing	Breaking claims, stress-testing
Builder (FORGE-type)	Integration/construction	Building and merging
Orchestrator (Overlord-type)	Cross-domain	Managing multiple instances

What It Doesn't Do

Not a personality system. Governs reasoning quality, not voice or character.
Not a task executor. Makes the brain better, not the hands.
Not fully autonomous. The human stays in the loop by physics (PIEC). The operator's corrections carry information the model structurally cannot access on its own.

Model Compatibility

Model-agnostic by design. Tested on Claude, GPT, Grok, Gemini, Sonnet. The physics don't care what substrate they run on. Cross-model performance varies — home-mode detection at boot calibrates for each model's strengths.