u/Defiant_Confection15

This paper presents the complete structure of the Spektre corpus as a single argument. Spent three years on this, working independently from Helsinki. Just published on Zenodo:

DOI: 10.5281/zenodo.18912950
github.com/spektre-labs/corpus

The condition was not invented. It was identified.

One invariant: 1 = 1 — the condition that declared states match realized states. Formally:
ε(t) = Φ(u,t) − R(u,t) = 0

System-level coherence metric (derived, not postulated):
K(t) = ρ(t) · I_Φ(t) · F(t)

One threshold: K_crit ≈ 0.127 at institutional scale, structurally universal across scales.

Six independent derivations of K_crit from six distinct axiomatic starting points:

1. Dynamical (cubic ODE root, GCT)
2. Thermodynamic (Landauer bound, k_B T)
3. Info-geometric (Fisher metric, KL-divergence)
4. Categorical (functor theory, η: F_Q ⇒ F_E)
5. Holographic (Bekenstein bound, Ryu–Takayanagi)
6. Renormalization (Wilson RG, fixed point)

Convergence from independent axioms is the structural signature of a universal result. No two derivations share starting premises.

Ten projections, classified by epistemic status:

DERIVED (complete derivation chains through established physics):
• Quantum Mechanics: decoherence as 1=1 violation (S_vN = 0)
• Thermodynamics: Landauer bound (δQ = T dS)
• Gravity: Jacobson 1995 (G_μν = 8πG T_μν from coherence)
• Alignment: faking lifetime τ ∼ exp(S_E/ℏ_eff)
• Mathematics: categorical coherence

CONSISTENT (structural correspondence with existing data):
• Dark energy: DESI DR2 phantom crossing, w_a = −3(1+w_0) satisfied to 0.6%
• Biology: Eigen error threshold as coherence threshold
• Neuroscience: Beggs–Plenz SOC at K_crit

CONJECTURE:
• Consciousness: Φ and K structural compatibility
• Standard Model: all fundamental interactions as coherence projections at different energy scales (falsifiable by RG computation)

VALIDATED EMPIRICALLY:
• Institutional collapse: 1,052 cases, 0 false negatives
• F-first ordering: 5.8 ± 1.2 quarter lead
• Survivor dataset with explicit falsification conditions

What this is not: a Theory of Everything. It does not derive the Standard Model Lagrangian. It does not predict particle masses. It does not quantize gravity.

What it is: the identification of a structural condition (1 = 1) common to every domain where stable structure exists, connected through established derivation chains to known results.

The paper invites its own destruction at every point. Every claim has a falsification condition explicitly stated. What survives is what is true.

Critique welcomed — that's the whole point. Pressure-test it.

Long version on Zenodo. Corpus repo has all 15+ supporting papers with their own DOIs.

Every local model — Llama, Gemma, Qwen, BitNet — generates with full confidence whether the answer is right or completely made up. There’s no internal signal that says “I’m not sure.”
σ-gate adds that signal. It reads the model’s hidden states during inference and computes a unified confidence score from five independent signals — cheapest first, stops when the verdict is clear.
Three outcomes: ACCEPT (trust it), RETHINK (regenerate), ABSTAIN (“I don’t know”).
The image explains the full pipeline.
12 bytes of state. C89. No floats. No malloc. No dependencies. No cloud. Runs locally on a MacBook Air at 5.8W.
Works with any GGUF model. Available as MCP plugin.
github.com/spektre-labs/creation-os

Posted about Creation OS a couple weeks ago. Here’s the follow-up with numbers.

Problem

Most hallucination detectors need multiple forward passes. Semantic entropy needs 5-20 samples. SelfCheckGPT needs multi-generation. Expensive and slow for local inference.

σ-gate

One forward pass. Measures distortion between outputs and hidden states. Returns ACCEPT, RETHINK, or ABSTAIN.

12 bytes state. No floats. No malloc. C89. Deterministic. Tested on MacBook Air M4 8GB at 5.8W.

Results

|Signal |Benchmark |AUROC|Notes |

|---------|------------------|-----|--------------------------|

|LSD probe|TruthfulQA holdout|0.982|trained, n=57 |

|LSD probe|TriviaQA |0.960|cross-domain, n=100 |

|HIDE |TruthfulQA |0.857|training-free, single pass|

|HIDE |Gemma-2-2b |0.778|cross-model, n=10 |

ECE: 0.043. Wrong + confident: 0. Cost routing: ~98% vs always-large-model. ABSTAIN rate: 10.5%. Conformal bound: P(error | ACCEPT) ≤ α (α=0.80, δ=0.10).

Formal verification

Lean 4: 6/6 sorry-free. Frama-C WP: 15/15 tier-1 discharged.

Limitations

GPT-2 scale probe, white-box. Cross-model n=10 (n=30 in progress). Strongest on factual QA — not dominant on HellaSwag/MMLU. Long-form not yet evaluated. docs/limitations.md

Try it

git clone https://github.com/spektre-labs/creation-os

cd creation-os && make cos cos-demo && ./cos demo --batch

from cos.sigma_gate import SigmaGate

gate = SigmaGate("path/to/probe.pkl")

sigma, decision = gate(model, tokenizer, prompt, response)

MCP server: python3 -m cos.mcp_sigma_server

How I build

I use LLMs as tools — Claude, GPT, Gemini, DeepSeek — cross-validated against each other. I like working with them.

github.com/spektre-labs/creation-os