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Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
Latched Hyperprior Hypothesis (LHH) — a sustained vascular contraction engages the smooth-muscle latch-bridge, durably freezing a circuit as a committed hyperprior isolated from global updating; unlatches when its prediction resolves. Use when modeling durable commitments, trauma/PTSD as cemented priors, latch spirals, or the cross-substrate latch (mechanical/bioelectric/immune/sheaf).
Synaptic substrate of biological active inference — long-term potentiation/depression (LTP/LTD) writes priors into synaptic weights = the learning landscape (Deep CANALs). A held vascular latch annealed long enough crystallizes into a neuron prior. Use when modeling consolidation, neuron priors, the inference→learning landscape write-path, or commit-to-disk of a held prediction.
基于 SOC 职业分类
正在显示 SKILL.md
| name | discohy-streams |
| description | DisCoPy categorical color streams via Hy with 3 parallel TAP streams and 7 operad variants |
| trit | 0 |
Provides personalized color streams using DisCoPy categorical diagrams via Hy (discohy). Each human gets a self-learning color embedding with 3 parallel streams, now extended with 7 operad variants for compositional structure.
Trit: 0 (ERGODIC - Coordinator)
GF(3) Triad: three-match (-1) ⊗ discohy-streams (0) ⊗ gay-mcp (+1) = 0 ✓
color://human-id/LIVE → +1 (forward, real-time)
color://human-id/VERIFY → 0 (verification, BEAVER)
color://human-id/BACKFILL → -1 (historical, archived)
| Operad | File | Trit | Structure |
|---|---|---|---|
| Little Disks (E₂) | discohy_operad_1_little_disks.py | ⊕ +1 | Configuration of non-overlapping disks |
| Cubes (E_∞) | discohy_operad_2_cubes.py | ⊖ -1 | Hypercube parallel structures |
| Cactus | discohy_operad_3_cactus.py | ⊖ -1 | Trees with cycles (self-modification) |
| Thread | discohy_operad_4_thread.py | ⊙ 0 | Thread continuations + DuckDB |
| Gravity | discohy_operad_5_gravity.lisp | ⊖ -1 | Moduli M_{0,n} with involutions |
| Modular | discohy_operad_6_modular.bb | ⊕ +1 | Genus-labeled runtime polymorphism |
| Swiss-Cheese | discohy_operad_7_swiss_cheese.py | ⊕ +1 | Open/closed for forward-only learning |
GF(3) Total: (+1) + (-1) + (-1) + (0) + (-1) + (+1) + (+1) = 0 ✓
From Sophie Libkind's thesis and AlgebraicJulia:
| Operad | Trit | Description |
|---|---|---|
| Directed (⊳) | +1 | Output→Input wiring diagrams |
| Undirected (○) | -1 | Interface matching via pullback |
| Machines | 0 | State machines with dynamics |
| Dynamical | +1 | Open ODEs: dx/dt = f(x,u) |
| Model | Description |
|---|---|
| Dendroidal | Trees as colored operads (Cisinski-Moerdijk) |
| Lurie | coCartesian fibrations over Fin_* |
| Segal | Quillen equivalent to simplicial operads |
from discopy.monoidal import Ty, Box, Id
from discopy.drawing import draw
# Types for operad network
LittleDisks = Ty('E₂')
Cubes = Ty('E_∞')
Thread = Ty('Thread')
# Morphisms (operad maps)
stabilize = Box('stabilization', LittleDisks, Cubes)
linearize = Box('linearization', LittleDisks, Thread)
# Compose diagram
diagram = stabilize >> linearize.dom @ linearize
#!/usr/bin/env hy
(import [discohy_thread_operad [RootedColorOperad build-operad-from-threads]])
;; Build operad from thread tree
(setv threads [
{:id "T-001" :title "Root" :parent nil}
{:id "T-002" :title "Child1" :parent "T-001"}
{:id "T-003" :title "Child2" :parent "T-001"}])
(setv operad (build-operad-from-threads threads 0x42D))
;; Get operad variant
(.set-variant operad "dendroidal")
;; Compose operations
(setv composed (.compose operad "T-001" ["T-002" "T-003"]))
@present SchOperadNetwork(FreeSchema) begin
Operad::Ob
Morphism::Ob
src::Hom(Morphism, Operad)
tgt::Hom(Morphism, Operad)
Name::AttrType
Trit::AttrType
name::Attr(Operad, Name)
trit::Attr(Operad, Trit)
morph_type::Attr(Morphism, Name)
end
@acset_type OperadNetwork(SchOperadNetwork)
The 7 operads form a relational network (ACSet):
┌─────────────┐
│ Modular │⊕
└──────┬──────┘
┌──────┴──────┐
┌────┴────┐ ┌────┴────┐
│ Cactus │⊖ │ Swiss │⊕
└────┬────┘ │ Cheese │
│ └────┬────┘
▼ │
┌─────────┐ │
│ Thread │⊙◄─────┘
└────┬────┘
┌────────┼────────┐
▼ ▼ ▼
┌────────┐ ┌──────┐ ┌─────────┐
│ Cubes │⊖│Gravity│⊖│ Little │⊕
│ E_∞ │ │ M_0,n │ │ Disks │
└────────┘ └──────┘ └─────────┘
Build balanced schedules with GF(3) = 0 per triplet:
from operads.relational_operad_interleave import build_triad_from_operads
triad = build_triad_from_operads()
schedule = triad.build_round_robin(7)
# Output:
# 0: cubes ⊗ thread ⊗ little_disks
# 1: cactus ⊗ thread ⊗ modular
# 2: gravity ⊗ thread ⊗ swiss_cheese
# ...
-- Query operad compositions
SELECT
src.name as source,
tgt.name as target,
m.morph_type,
(src.trit + tgt.trit) % 3 as combined_trit
FROM operad_morphisms m
JOIN operads src ON m.src_id = src.id
JOIN operads tgt ON m.tgt_id = tgt.id;
-- Find GF(3)-conserving triads
SELECT o1.name, o2.name, o3.name
FROM operads o1, operads o2, operads o3
WHERE (o1.trit + o2.trit + o3.trit) % 3 = 0
AND o1.id < o2.id AND o2.id < o3.id;
src/operads/
├── __init__.py # Registry
├── relational_operad_interleave.py # ACSet + Triad
├── libkind_spivak_dynamics.py # Directed/Undirected/Machines
└── infinity_operads.py # Dendroidal + Lurie
scripts/
├── discohy_operad_1_little_disks.py
├── discohy_operad_2_cubes.py
├── discohy_operad_3_cactus.py
├── discohy_operad_4_thread.py
├── discohy_operad_5_gravity.lisp
├── discohy_operad_6_modular.bb
└── discohy_operad_7_swiss_cheese.py
# Run relational interleaving demo
python3 src/operads/relational_operad_interleave.py
# Run Libkind-Spivak operads
python3 src/operads/libkind_spivak_dynamics.py
# Test individual operad
python3 scripts/discohy_operad_4_thread.py
acsets - Algebraic databases (schema category)triad-interleave - GF(3) balanced schedulinggay-mcp - Deterministic color generationthree-match - 3-SAT via colored subgraph isomorphismworld-hopping - Badiou triangle navigation