Apoth3osis
<_RESEARCH/INFORMATION_THEORY

Epiplexity Program

VERIFIED3 PHASES23 MODULES80+ THEOREMS0 SORRY4 MENTAT CERTS

The Central Question

How complex does a distribution look to a computationally bounded observer? Epiplexity answers this by decomposing the observer's optimal description into structure (code length S_T) and noise (cross-entropy H_T). A CSPRNG output has maximum epiplexity — no efficient observer can distinguish it from uniform, so all its information looks like noise. Conversely, one-way permutations create asymmetric complexity: easy to compute forward, hard to invert.

Original Researchers

Jonathan Finzi
Researcher
Epiplexity framework: time-bounded MDL complexity connecting information theory and cryptographic hardness.
Chris Olah
Anthropic
Neural network interpretability pioneer. Information-theoretic perspectives on representation learning.
Neel Nanda
Google DeepMind
Mechanistic interpretability. Grokking phenomena and phase transitions in neural network learning.

Scientific Context

Classical Kolmogorov complexity measures the shortest program that produces a given string — but is uncomputable. Epiplexity introduces a time bound T, restricting to programs that run in at most T steps. This makes the measure computable (though potentially intractable) and connects it to cryptographic hardness assumptions.

The MDL decomposition: For any T-bounded program P describing distribution X, the total cost is MDL_T(X) = S_T(X) + H_T(X). The optimal program minimizes this total cost, with ties broken by code length. S_T measures extractable structure; H_T measures residual noise.

What formalization provides: Every theorem in the paper — nonnegativity bounds, CSPRNG security reduction, OWP factorization hardness — is machine-checked. The formalization also extends to emergence detection: a sharp drop in epiplexity signals that new structure has become computationally accessible.

Project Phases

PHASE 1VERIFIEDLean 4 Formal Proofs

Complete formalization across 23 modules: MDL cost, optimal programs, epiplexity S_T, time-bounded entropy H_T, nonnegativity bounds, CSPRNG security reduction, OWP factorization hardness, and emergence detection.

  • MDLT_eq_mdlCost: MDL_T = S_T + H_T
  • ST_nonneg, HT_nonneg: nonnegativity bounds
  • csprng_high_epiplexity: CSPRNG → high S_T
  • owp_factorization_hardness: OWP → asymmetric S_T
PHASE 2VERIFIEDC & Rust Transpilation

Faithful translation to verified C (gcc -Wall -Wextra -Werror: 0 warnings) and safe Rust (cargo test: 4/4 pass). Prog struct, MDL cost, and security property checks faithfully translated.

  • gcc -std=c11 -Wall -Wextra -Werror: PASS
  • cargo build: 0 warnings
  • cargo test: 4/4 pass
  • Prog struct matches Lean OptimalProg specification
PHASE 3PENDINGIPFS Permanent Storage

All artifacts content-addressed and pinned to IPFS. Lean proofs, verified C, safe Rust archives with CIDv1 identifiers.

  • Lean archive: 6 key source files (20.9KB)
  • C archive: 1 translation unit (1.1KB)
  • Rust archive: 1 source file + Cargo.toml (1KB)
  • SHA-256 content hashes for all archives

MENTAT Contribution Certificates

MENTAT-CA-005|MCR-EP-001
2024-01-01

MENTAT Contribution Record

IDEA

Conceptual Contribution

CONTRIBUTION LEVEL: IDEA

Ontological Engineer

Epiplexity: Time-Bounded Complexity via MDL Decomposition

Contributor

Jonathan Finzi, Chris Olah, Neel Nanda

Anthropic / Google DeepMind

Core conceptual insight: the complexity of a distribution can be decomposed into structure (code length S_T) and noise (cross-entropy H_T) under a time bound T. Epiplexity S_T(X) measures how much structure a T-bounded observer can extract — high epiplexity means the distribution looks random to any efficient observer. This connects information theory, cryptography, and computational complexity.

MENTAT · Mesh-Encrypted Network for Trusted Autonomous TransactionsImmutable · Content-Addressed · Tamper-Proof
MENTAT-CA-005|MCR-EP-002
2024-01-01

MENTAT Contribution Record

THEORY

Mathematical Foundation

CONTRIBUTION LEVEL: THEORY

Ontological Engineer

Full MDL Framework: Programs, Distributions, Security Reductions

Contributor

Jonathan Finzi, Chris Olah, Neel Nanda

Anthropic / Google DeepMind

Full mathematical framework: (1) Programs with code length and distribution output, (2) Feasibility under time bound T, (3) MDL cost as code length + cross-entropy, (4) Optimal programs with tie-breaking by code length, (5) CSPRNG security reduction — computationally indistinguishable from uniform implies high epiplexity, (6) OWP factorization — one-way permutations create asymmetric epiplexity.

Builds Upon

MCR-EP-001
MENTAT · Mesh-Encrypted Network for Trusted Autonomous TransactionsImmutable · Content-Addressed · Tamper-Proof
MENTAT-CA-005|MCR-EP-003
2026-03-13

MENTAT Contribution Record

PROOF

Formally Verified

CONTRIBUTION LEVEL: PROOF

Lean 4 Formalization — 23 Modules, 80+ Theorems, 0 Sorry

Contributor

Apoth3osis Labs

R&D Division

Machine-checked Lean 4 formalization of the Epiplexity framework. 23 modules covering MDL cost, optimal programs, epiplexity S_T, time-bounded entropy H_T, nonnegativity bounds, CSPRNG security reduction, OWP factorization hardness, and emergence detection. All theorems proved without sorry/admit.

Builds Upon

MCR-EP-001MCR-EP-002
MENTAT · Mesh-Encrypted Network for Trusted Autonomous TransactionsImmutable · Content-Addressed · Tamper-Proof
MENTAT-CA-005|MCR-EP-004
2026-03-13

MENTAT Contribution Record

KERNEL

Computationally Verified

CONTRIBUTION LEVEL: KERNEL

Epiplexity Verified Kernel — C & Rust Transpilation

Contributor

Apoth3osis Labs

R&D Division

Verified C and safe Rust transpilations of the core Epiplexity modules. C: gcc -std=c11 -Wall -Wextra -Werror, 0 warnings. Rust: cargo build 0 warnings, cargo test 4/4 pass. Prog struct, mdl_cost, epiplexity_st, csprng_implies_high_epiplexity, and owp_factorization_hardness faithfully translated.

Builds Upon

MCR-EP-003
MENTAT · Mesh-Encrypted Network for Trusted Autonomous TransactionsImmutable · Content-Addressed · Tamper-Proof

Governed by MENTAT-CA-001 v1.0 · March 2026