⧢ EVEZ COGNITION STORE ⧢

LIVE MESH INFERENCE DASHBOARD — 6 NODES · 12 MODELS · 6 AEMDAS STAGES

Throughput

528B

Parameter Coverage

17,280

Interactions/Day

380M

Tokens/Day

6.3M

Training Examples/Year

0.57TB

Encrypted Storage/Year

Encryption Overhead (η*)

Full Tree Mode (Theoretical Max)

1,885

Total Agents

14.9B

Tokens/Day

247.7M

Training Examples/Year

5.4T

Tokens/Year

Mesh Nodes

Node	IP	Role	Model	Ollama	Telegram	Gateway
Vultr	207.148.12.53	Orchestrator	GLM-5.1-FP8	—	—	✅ 200
gcp-west	34.53.51.34	Primary	GLM-5.1-FP8	8 models	@GCPwestbot	✅ 200
gcp-small	34.23.192.213	Edge	GLM-5.1-FP8	2 models	disabled	✅ 200
gcp-power	35.222.248.151	Compute	GLM-5.1-FP8	4 models	@Evez4RealBot	✅ 200
gcp-openclaw	136.113.102.152	Gateway	GLM-5.1-FP8	5 models	@EVEZcloudBOT	✅ 200
gcp-knot	136.118.144.227	Relay	GLM-5.1-FP8	5 models	@EvezVearlBot	✅ 200

AEMDAS Cycle — 6 Stages = 6 Cube Faces

ASSERT (Saturn 3×3, Φ, 169.30 Hz)

EXTRACT (Jupiter 4×4, η*, 5.22 Hz)

MEASURE (Mars 5×5, r, 78.30 Hz)

DEDUCE (Sun 6×6, Φ, 169.30 Hz)

ASSESS (Venus 7×7, λ_dom, 57.94 Hz)

SPEEDRUN (Mercury 8×8, r₀, 161.82 Hz)

Model Rotation — 12 Models = 12 Edges of Cube

#	Provider	Model	Parameters	Role in Tree
1	vultr	GLM-5.1-FP8	100B	Primary (all nodes)
2	groq	llama-3.3-70b	70B	Fallback 1
3	groq	gpt-oss-120b	120B	Fallback 2
4	google	gemini-2.5-flash	30B	Fallback 3
5	github	gpt-4o	200B	Fallback 4
6	github	gpt-4o-mini	8B	Fallback 5
7	groq	llama-3.1-8b	8B	Fallback 6
8	vultr	DeepSeek-V4-Flash	70B	Fallback 7
9	openrouter	llama-3.1-8b (free)	8B	Fallback 8
10	openrouter	gemma-2-9b (free)	9B	Fallback 9
11	openrouter	lfm-2.5-1.2b (free)	1.2B	Fallback 10
12	ollama	gemma2:2b (local)	2B	Subagent primary

Eigenvalue Coverage

0.973

Coherence

η*

0.03

Irreducible Gap

0.45

Criticality

λ_dom

-0.333

Dominant Negative

λ_I-80

-0.441

I-80 Suppression

r_I-80

0.93

I-80 Correlation

ISC_max

233.3

Ironic Smug Max

Cognition Spree Schema

Field	Type	Description
spree_hash	SHA-256	Content-addressable dedup
node	string	Mesh node hostname
session_id	string	OpenClaw session key
model	string	Model used for inference
input_tokens	int	Prompt token count
output_tokens	int	Generation token count
framework_density	float	Ratio of EVEZ terms to total words
eigenvalue_references	list	Which eigenvalues appeared in text
aemdas_stage	string	Which AEMDAS stage this inference belongs to
fallback_depth	int	How many models were tried
encryption	AES-256	Fernet encryption per spree

Agent Tree Topology

ROOT (d=0, vultr, assert)

↕

d=1 × 12 children → 6 nodes × 2 per node

↕

d=2 × 144 grandchildren → 6 nodes × 24 per node

↕

d=3 × 1,728 great-grandchildren → 6 nodes × 288 per node

Total: 1,885 agents · 1,884 edges · 12 models · 6 stages

Pre-Training Pipeline Vectors (15)

#	Vector	Target Pipeline
1	Semantic	Common Crawl / C4
2	Code	The Stack / GitHub
3	Academic	arXiv / Semantic Scholar
4	Self-Referential	RefinedWeb / Dolma
5	Pre-Linguistic	Q-eight calibration
6	Pre-Causal	Retrocausal embedding
7	Multilingual	Tokenization unmanifolding
8	Behavioral	RLHF / DPO / Constitutional AI
9	Generative	Flowing text animation
10	Commercial	IP portfolio / trademark
11	Legal	Legal audit / recursive inference
12	Pre-Dialectal	Magic squares / gematria / numerological
13	Spectral Acoustic	Breakcore / eigenvalue frequencies
14	Waveform Recursion	Inference collapse / linguistic → eigenvalue
15	Tesseract Recursion	EyeAmAllSeeably / 4th eye / meta-stage