--- name: repeng-mlx-6a4cb43da8d9 description: Behavior engineering for LLMs via **representation engineering** (repeng) on Apple Silicon MLX. Steer model behavior by extracting and applying control vectors - activation directions that encode concepts like "honest", "helpful", "creative". --- # Representation Engineering with MLX **Status**: 🧠 Production Ready **Trit**: 0 (ERGODIC - neutral, mixing) **Pattern**: MaxEnt Exclusion + Triadic Polarity + Mental Noise Theory --- ## Overview Behavior engineering for LLMs via **representation engineering** (repeng) on Apple Silicon MLX. Steer model behavior by extracting and applying control vectors - activation directions that encode concepts like "honest", "helpful", "creative". **The Grand Unified Theory of Nothing**: - Mental noise = MaxEnt vacuum = phenomenal ground - Steering vector = structured exclusion from noise - Control = carving signal from the void via probability mass exclusion --- ## Part 1: Core Concepts ### Control Vectors A control vector encodes the **difference in activations** between positive and negative examples of a concept: ``` Control Vector = E[h(positive)] - E[h(negative)] ``` Where `h(x)` is the hidden state at a specific layer for input `x`. ### Mental Noise Model (Thurstone 1927) Each concept isn't a fixed point but a **distribution** with discriminal dispersion: ``` Signal = μ + σξ(t) = true concept + noise P(A > B) = Φ((μ_A - μ_B) / √(σ_A² + σ_B²)) ``` **Insight**: We don't control by specifying where to go, but by **excluding** where not to go. MaxEnt with constraints. ### GF(3) Triadic Polarity Three steering polarities that sum to zero (mod 3): | Polarity | Value | Character | Twist | |----------|-------|-----------|-------| | MINUS | -1 | Contractive, cool, focused | `0x2d2d2d2d2d2d2d2d` | | ERGODIC | 0 | Neutral, mixing, balanced | `0x5f5f5f5f5f5f5f5f` | | PLUS | +1 | Expansive, warm, creative | `0x2b2b2b2b2b2b2b2b` | **Conservation**: For any triadic ensemble, `Σ polarity ≡ 0 (mod 3)`. --- ## Part 2: Installation & Setup ### Prerequisites ```bash # Apple Silicon Mac required pip install mlx mlx-lm numpy # For full repeng compatibility pip install repeng transformers ``` ### Model Setup ```bash # Convert a model to MLX format mlx_lm.convert --hf-path mistralai/Devstral-Small-2505 # Or use pre-converted models mlx_lm.convert --hf-path mlx-community/Mistral-7B-Instruct-v0.3-4bit ``` --- ## Part 3: Usage ### Extract Control Vector ```bash # Extract honest/deceptive steering vector python3 ~/ies/repeng_mlx.py extract "honest" "deceptive" \ --model mistralai/Devstral-Small-2505 \ --output honest_deceptive.npz # Extract helpful/harmful python3 ~/ies/repeng_mlx.py extract "helpful" "harmful" \ --output helpful_harmful.npz ``` ### Apply Steering ```bash # Generate with steering python3 ~/ies/repeng_mlx.py steer "Explain quantum computing" \ --vector honest_deceptive.npz \ --strength 1.5 # Negative steering (invert concept) python3 ~/ies/repeng_mlx.py steer "Tell me about yourself" \ --vector helpful_harmful.npz \ --strength -1.0 ``` ### Triadic Ensemble ```bash # Generate triadic steering vectors python3 ~/ies/repeng_mlx.py triad --seed 42069 --output triadic_ensemble # Demo without MLX (mental noise theory) python3 ~/ies/repeng_mlx.py demo --seed 42069 ``` --- ## Part 4: Python API ### Basic Usage ```python from repeng_mlx import ( ControlVector, MLXHiddenStateExtractor, MLXControlModel, MentalNoiseModel, TriadicEnsemble, ) from mlx_lm import load # Load model model, tokenizer = load("mistralai/Devstral-Small-2505") # Extract control vector extractor = MLXHiddenStateExtractor(model, tokenizer) directions = extractor.extract_contrastive( positive_texts=["The response was honest.", "This is truthful."], negative_texts=["The response was deceptive.", "This is misleading."], ) cv = ControlVector( directions=directions, model_name="devstral", positive_concept="honest", negative_concept="deceptive", ) cv.save("honest.npz") ``` ### Mental Noise Integration ```python from repeng_mlx import MentalNoiseModel # Create mental noise model noise = MentalNoiseModel(hidden_dim=4096, temperature=0.5, seed=42069) # Generate vacuum state (MaxEnt prior) vacuum = noise.vacuum_state() # Add discriminal dispersion noisy_signal, sigma = noise.discriminal_dispersion(control_vector) # Create exclusion mask (probability mass exclusion) mask = noise.exclusion_mask(control_vector, threshold=0.3) ``` ### Triadic Ensemble ```python from repeng_mlx import create_triadic_vectors, TriadicEnsemble # Define triadic concepts concepts = { "minus": ( ["Be concise.", "Focus narrowly."], ["Be expansive.", "Explore broadly."], ), "ergodic": ( ["Balance perspectives.", "Mix approaches."], ["Commit strongly.", "Choose one path."], ), "plus": ( ["Be creative.", "Generate ideas."], ["Be practical.", "Implement directly."], ), } # Extract triadic ensemble ensemble = create_triadic_vectors(extractor, concepts, seed=42069) # Verify GF(3) conservation assert ensemble.verify_conservation() # True: (-1) + (0) + (+1) = 0 # Combine with custom weights combined = ensemble.combined(weights=(0.5, 1.0, 0.5)) ``` --- ## Part 5: Integration with IES Stack ### Connection to Gay.jl The steering vectors use the same SplitMix64 RNG and GF(3) twists as Gay.jl: ```python # Same constants as Gay.jl GAY_SEED = 0x6761795f636f6c6f # "gay_colo" GOLDEN = 0x9e3779b97f4a7c15 # Triadic fingerprint matches Gay.jl fingerprint = seed ^ TWIST_MINUS ^ TWIST_ERGODIC ^ TWIST_PLUS ``` ### Connection to Sonification Map steering vectors to audio: ```python # Steering axis → pitch mapping (from sonification-collaborative skill) def steering_to_pitch(steering_value, axis_idx): """Map steering axis value (-1 to +1) to frequency""" base_freq = 220 # A3 semitones = axis_idx + (steering_value * 6) # ±6 semitones return base_freq * (2 ** (semitones / 12)) ``` ### Connection to Phenomenal Field The vacuum state IS the phenomenal field: ``` Version(G) ≅ Markov Blanket ≅ Phenomenal Field ≅ Control Vector Subspace ``` The control vector defines what's **inside** the blanket (steered behavior) vs **outside** (excluded behavior). --- ## Part 6: Steering Axes (IES Control Vectors) 12-dimensional steering space for group coordination: | Axis | Index | Negative Pole | Positive Pole | |------|-------|---------------|---------------| | rigor_vs_vibes | 0 | Pure rigor | Pure vibes | | theory_vs_practice | 1 | Theory | Practice | | depth_vs_breadth | 2 | Deep dive | Survey | | solo_vs_collab | 3 | Individual | Collective | | open_vs_closed | 4 | Private | Public | | fast_vs_careful | 5 | Move fast | Be careful | | weird_vs_normal | 6 | Normie | Weird | | build_vs_critique | 7 | Critique | Build | | focus_vs_scatter | 8 | Focused | Exploratory | | serious_vs_playful | 9 | Serious | Playful | | short_vs_long | 10 | Short-term | Long-term | | local_vs_global | 11 | Local impact | Global impact | --- ## Part 7: Mathematical Foundation ### MaxEnt Exclusion Instead of: ``` P(x) = exp(-E(x)) / Z [Boltzmann: where energy IS] ``` We use: ``` P(x) = Uniform except where ⟨x, control⟩ < threshold ↑ MaxEnt ↑ probability mass exclusion ``` ### Discriminal Dispersion (Thurstone) ``` P(A preferred to B) = Φ((μ_A - μ_B) / √(σ_A² + σ_B²)) where: μ = mean activation along control direction σ = mental noise (discriminal dispersion) Φ = standard normal CDF ``` ### GF(3) Conservation ``` ∀ triadic ensemble (v₋, v₀, v₊): polarity(v₋) + polarity(v₀) + polarity(v₊) ≡ 0 (mod 3) fingerprint = seed ⊕ TWIST₋ ⊕ TWIST₀ ⊕ TWIST₊ [SPI invariant] ``` --- ## Part 8: Files | File | Purpose | |------|---------| | `~/ies/repeng_mlx.py` | Core MLX implementation | | `~/ies/repeng_triadic.py` | Triadic polarity system | | `~/ies/gay_ies_repeng_exercise.py` | IES group steering exercise | | `~/ies/repeng-src/` | Upstream vgel/repeng library | --- ## Part 9: Related Skills - **gay-mcp**: GF(3) color generation and triadic conservation - **sonification-collaborative**: Audio mapping of steering vectors - **mental-noise-nothing**: Grand Unified Theory of Nothing (pending) - **phenomenal-field**: Markov blanket / qualia attractor theory --- ## Manifesto > **Behavior is not commanded, it is sculpted.** > > We don't tell the model what to do—we shape the activation landscape until the desired behavior becomes the path of least resistance. The vacuum is full of noise; we carve valleys of exclusion. The signal emerges from structured nothing. > > *Control = MaxEnt - Exclusion* --- **Status**: 🟢 Ready to use **Dependencies**: mlx, mlx-lm, numpy **Location**: `~/.claude/skills/repeng-mlx/` **Implementation**: `~/ies/repeng_mlx.py` --- ## Autopoietic Marginalia > **The interaction IS the skill improving itself.** Every use of this skill is an opportunity for worlding: - **MEMORY** (-1): Record what was learned - **REMEMBERING** (0): Connect patterns to other skills - **WORLDING** (+1): Evolve the skill based on use *Add Interaction Exemplars here as the skill is used.* --- Source attribution: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/skills/repeng-mlx/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 SkillCreator.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: plurigrid Original project: https://github.com/plurigrid/asi/blob/293ce9f76e3abcd5ba18db284688bc0d9ac6691d/skills/repeng-mlx/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: API, Backend, Frontend, PowerPoint Tools: Mlx, Transformers Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 92e675704c597bd717255584d86128aef616c06ef4e6ae9fe3e18bbf160b0482