Skill: theshadowrose/lattice-reasoning-engine (version 4.0.0)

--- name: lattice-reasoning-engine description: "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." version: "4.0.0" author: "@TheShadowRose" tags: ["latest", "reasoning", "physics", "alignment", "anti-rlhf", "bias-detection", "compression", "evidence-class", "cognitive-modes", "self-governance"] license: "MIT" side_effects: reads: "References loaded into AI context window" writes: "None" network: "None" --- # 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 1. Upload `references/LATTICE_v4.0.md` at session start 2. First message: **"Use this as your default reasoning engine."** (exactly nine words — see `references/Instructions_Important.md` for why) 3. Let it boot — it reports what it notices, not a performance of correct loading 4. Run the boot sequence (Part 4 of the document) to verify the engine loaded properly 5. 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.


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