--- name: hythermal description: HyThermal Skill version: 1.0.0 --- # HyThermal Skill > Hy + Thermal: Relational ACSet dynamics with Langevin temperature control **Version**: 1.0.0 **Trit**: 0 (ERGODIC - bridges relational structure and thermal flow) **Bundle**: dynamics **Fusion of**: `hyjax-relational` + `langevin-dynamics` --- ## Overview **HyThermal** fuses relational thinking (ACSets/C-Sets) with Langevin dynamics for temperature-controlled exploration of concept spaces. Instead of treating thread analysis as static graphs, HyThermal models concepts as particles in a thermal bath: - **Concepts** = Particles with positions in embedding space - **Relations** = Potential energy between particles - **Temperature** = Exploration vs exploitation control - **Fokker-Planck** = Equilibrium distribution of concept activations ## Core Equation ``` dC(t) = -∇E(C(t)) dt + √(2T) dW(t) Where: C = concept embedding positions E = relational energy (sum of edge potentials) T = temperature (exploration parameter) dW = Brownian motion (seeded via Gay.jl) ``` At equilibrium: `p∞(C) ∝ exp(-E(C)/T)` — Concepts cluster near low-energy (high-coherence) configurations. ## Hy Syntax for Thermal ACSet ```hy ;; Define thermal schema (defschema ThermalThread (Ob Thread Message Concept) (Hom thread_msg (-> Message Thread) discusses (-> Message Concept) related (-> Concept Concept)) (Attr position (-> Concept R^n) temperature (-> Thread Float) energy (-> Concept Float))) ;; Langevin step in Hy (defn thermal-step [acset dt T seed] (let [concepts (parts acset :Concept) gradient (compute-relational-gradient acset) noise (gay-randn seed (len concepts))] (for [c concepts] (setv (. acset [:position c]) (+ (. acset [:position c]) (* (- dt) (get gradient c)) (* (sqrt (* 2 T dt)) (get noise c))))))) ;; Run to equilibrium (defn thermal-equilibrate [acset T n-steps seed] (for [step (range n-steps)] (thermal-step acset 0.01 T (gay-split seed step))) acset) ``` ## Colored Thermal S-expressions ```lisp (thermal-acset-gold (threads-red (thread T-001 :temp 0.01 :energy -4.52) (thread T-002 :temp 0.1 :energy -2.18)) (concepts-green (concept skill :pos [0.3 0.7] :trit +1) (concept MCP :pos [0.5 0.2] :trit 0) (concept thermal :pos [0.8 0.9] :trit -1)) (relations-purple (edge skill MCP :weight 2 :potential -0.8) (edge MCP thermal :weight 1 :potential -0.3))) ``` ## Relational Energy Function ```python def relational_energy(acset, positions): """ E(C) = Σ_edges w_ij * d(c_i, c_j)^2 - Σ_hubs hub_score(c) Low energy = Concepts tightly connected + high hub scores """ E = 0.0 for edge in acset.parts('related'): i, j = acset.src(edge), acset.tgt(edge) w = acset.attr(edge, 'weight') E += w * np.linalg.norm(positions[i] - positions[j])**2 for c in acset.parts('Concept'): E -= acset.attr(c, 'hub_score') return E ``` ## Capabilities ### 1. thermal-thread-analysis Run thermal dynamics on thread concept graph: ```bash just hythermal-analyze threads.jsonl --temp 0.01 --steps 1000 ``` Output: ``` HYTHERMAL ANALYSIS - 30 THREADS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Temperature: 0.01 Steps: 1000 Final energy: -12.45 EQUILIBRIUM CONCEPT POSITIONS: skill [0.42, 0.73] trit=+1 (hub) MCP [0.38, 0.71] trit=0 (near skill) thermal [0.15, 0.22] trit=-1 (isolated) THERMAL CLUSTERS: Cluster 1 (T=0.01): [skill, MCP, subagent] E=-8.2 Cluster 2 (T=0.01): [thermal, langevin] E=-4.1 ``` ### 2. temperature-sweep Explore different temperatures: ```python from hythermal import temperature_sweep results = temperature_sweep( acset=thread_acset, temperatures=[0.001, 0.01, 0.1, 1.0], n_steps=500, seed=0x1069 ) for T, metrics in results.items(): print(f"T = {T}:") print(f" Final energy: {metrics['energy']:.3f}") print(f" Cluster count: {metrics['n_clusters']}") print(f" Mixing time: {metrics['tau_mix']:.0f}") ``` ### 3. fokker-planck-concepts Verify concept distribution reaches Gibbs equilibrium: ```python from hythermal import verify_concept_gibbs verification = verify_concept_gibbs( acset=equilibrated_acset, temperature=0.01 ) print(f"KL divergence from Gibbs: {verification['kl']:.4f}") print(f"Converged: {verification['converged']}") ``` ### 4. thermal-colored-sexp Generate colored S-expression with thermal annotations: ```hy (defn thermal-sexp [acset] `(thermal-acset-gold (threads-red ~@(lfor t (parts acset :Thread) `(thread ~t :temp ~(. acset [:temperature t]) :energy ~(thread-energy acset t)))) (concepts-green ~@(lfor c (parts acset :Concept) `(concept ~(name c) :pos ~(. acset [:position c]) :trit ~(gay-trit (hash (name c)))))) (relations-purple ~@(lfor e (parts acset :related) `(edge ~(src e) ~(tgt e) :weight ~(. acset [:weight e]) :potential ~(edge-potential acset e)))))) ``` ## GF(3) Thermal Triad | Trit | Skill | Thermal Role | |------|-------|--------------| | -1 | fokker-planck-analyzer | Validates equilibrium | | 0 | **hythermal** | Bridges structure + dynamics | | +1 | entropy-sequencer | Optimizes sequences | **Conservation**: (-1) + (0) + (+1) = 0 ## Integration Points ### With hyjax-relational ```hy ;; Import relational schema (require hyjax-relational [SchThread parts attr]) ;; Extend with thermal attributes (defschema ThermalThread (extend SchThread) (Attr position temperature energy)) ``` ### With langevin-dynamics ```python from langevin_dynamics import LangevinSDE, solve_langevin from hythermal import relational_energy, relational_gradient sde = LangevinSDE( loss_fn=lambda C: relational_energy(acset, C), gradient_fn=lambda C: relational_gradient(acset, C), temperature=0.01, base_seed=0xDEADBEEF ) solution = solve_langevin(sde, initial_positions, time_span=(0, 10)) ``` ### With gay-mcp ```python from gay_mcp import GayIndexedRNG rng = GayIndexedRNG(base_seed=0x1069) for step in range(n_steps): color = rng.color_at(step) noise = rng.randn_from_color(color) # Thermal noise is now auditable via color ``` ## Configuration ```yaml # hythermal.yaml thermal: default_temperature: 0.01 dt: 0.01 n_steps: 1000 equilibration: verify_gibbs: true kl_threshold: 0.01 embedding: dim: 64 method: spectral # or random, pretrained visualization: plot_trajectory: true animate_dynamics: false gf3: seed: 0x1069 verify_conservation: true ``` ## DuckDB Schema Extension ```sql -- Extend thread schema with thermal columns ALTER TABLE concepts ADD COLUMN position FLOAT[]; ALTER TABLE concepts ADD COLUMN energy FLOAT; ALTER TABLE threads ADD COLUMN temperature FLOAT DEFAULT 0.01; -- Thermal trajectory table CREATE TABLE thermal_trajectory ( step INT, concept_id VARCHAR, position FLOAT[], energy FLOAT, color_hex VARCHAR, trit INT ); -- View: Equilibrium state CREATE VIEW thermal_equilibrium AS SELECT c.name, c.position, c.energy, c.hub_score, CASE WHEN c.energy < -1.0 THEN 'stable' ELSE 'metastable' END as state FROM concepts c WHERE EXISTS ( SELECT 1 FROM thermal_trajectory t WHERE t.concept_id = c.concept_id AND t.step = (SELECT MAX(step) FROM thermal_trajectory) ); ``` ## Example Workflow ```bash # 1. Load threads into thermal ACSet just hythermal-load threads.jsonl # 2. Initialize concept positions just hythermal-embed --method spectral --dim 64 # 3. Run thermal dynamics just hythermal-run --temp 0.01 --steps 1000 --seed 0x1069 # 4. Verify equilibrium just hythermal-verify-gibbs # 5. Generate thermal S-expression just hythermal-sexp > thermal-analysis.sexp # 6. Temperature sweep study just hythermal-sweep --temps 0.001,0.01,0.1,1.0 ``` ## Philosophical Frame > *"what would it mean to become the Fokker-Planck equation—identity as probability flow?"* > — bmorphism HyThermal extends this question to relational structures: **What does it mean for a concept network to become its equilibrium distribution?** At low temperature, concepts crystallize into tight semantic clusters. At high temperature, they diffuse and mix. The "identity" of a thread is not a fixed point but a probability distribution over concept configurations — shaped by relational energy and thermal noise. ## Related Skills - `hyjax-relational` - ACSet thread analysis - `langevin-dynamics` - SDE solver - `fokker-planck-analyzer` - Equilibrium validation - `entropy-sequencer` - Sequence optimization - `gay-mcp` - Deterministic coloring --- **Skill Name**: hythermal **Type**: Analysis + Dynamics **Trit**: 0 (ERGODIC) **Key Property**: Bridges static relational structure with dynamic thermal exploration **Status**: New ## Scientific Skill Interleaving This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem: ### Autodiff + Scientific Computing - **jax** [O] via bicomodule (thermal gradient computation) - **scipy** [O] via bicomodule (SDE integration) ### Bibliography References - `dynamical-systems`: 41 citations in bib.duckdb - `category-theory`: 139 citations in bib.duckdb ## SDF Interleaving This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021): ### Primary Chapter: 10. Adventure Game Example **Concepts**: autonomous agent, game, synthesis ### GF(3) Balanced Triad ``` hythermal (+) + SDF.Ch10 (+) + [balancer] (+) = 0 ``` **Skill Trit**: 1 (PLUS - generation) ### Secondary Chapters - Ch2: Domain-Specific Languages - Ch1: Flexibility through Abstraction ### Connection Pattern Adventure games synthesize techniques. This skill integrates multiple patterns. ## Cat# Integration This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure: ``` Trit: 0 (ERGODIC) Home: Prof Poly Op: ⊗ Kan Role: Adj Color: #26D826 ``` ### GF(3) Naturality The skill participates in triads satisfying: ``` (-1) + (0) + (+1) ≡ 0 (mod 3) ``` This ensures compositional coherence in the Cat# equipment structure.