Research-Stack/4-Infrastructure/infra/embedded_surface/omni_lut

Dynamic Omnitoken LUT Slotter

The older ISO/precompression notes already define the pattern:

Stage 0 classifier -> workload/domain table -> Pass 1/1.5 symbol basis

This harness applies that to Omnitoken. A tiny token does not carry every ISO, RFC, token-layer, or chain table. It carries a compact workload slot selector:

u8 lut_slot
u8 domain
u8 scalar

Before a selected slot expands, the harness runs a tiny S3C partial-computation gate over (slot, domain, scalar). The gate uses shell decomposition, two contacts, and a bounded score to decide whether enough structure exists to expand the LUT. This lets tiny nodes do the cheapest possible mountain/slot selection before paying for a real table.

The selected slot decides which compressed LUT bank is active for the next admission step. Examples:

• angry_sphinx (default)
• recovery
• standards_registry
• crypto_mev_research
• ibmii_ethernet
• iso_prepass

angry_sphinx is the default profile. Unknown workloads do not expand into a large table. They enter the frustration range and receive a proof-of-defense challenge/quarantine token until a hosted registry admits a more specific slot.

This is research infrastructure, not live trading logic. MEV-related profiles classify and route surfaces for analysis; execution remains a separate GCL admission decision.

Sequence Surface LUT

sequence_surface_lut.py adds a small biological/synthetic sequence selector for GCL compression work. DNA, RNA, mRNA, Hachimoji, and generic XNA are treated as related substrate surfaces with a common four-byte token:

u8 surface_id
u8 alphabet_id
u8 role_flags
u8 op_flags

The sequence payload is then bit-packed by alphabet width:

Surface	Symbols	Bits/symbol	Role
DNA	ACGT	2	archival heredity
RNA	ACGU	2	catalytic/regulatory
mRNA	ACGU	2	transient executable transcript
Hachimoji	ACGTZPSB	3	expanded hereditary alphabet
XNA	16-symbol generic lane	4	synthetic backbone/alphabet lane

This gives the nanokernel/GCL edge a cheap first-pass answer to two questions:

1. Which surface family should receive the computation?
2. How many bits are needed to carry its local symbol stream?

Example:

python3 infra/embedded_surface/omni_lut/sequence_surface_lut.py \
  --surface hachimoji \
  --sequence ACGTZPSBACGTZPSB \
  --complement

The result includes the token, packed payload, roundtrip decode, complement when defined, and a simple reduction estimate against ASCII sequence storage.

Possibility-Space Probe

possibility_space_probe.py lets the math expose the useful LUT regions. It enumerates known and synthetic alphabet/role/operator candidates, extracts a small metaprobe signature, then runs a coarse RGFlow pass. Candidates are ranked only when their compactness, complement closure, operation density, and frame efficiency remain useful under coarse-graining.

python3 infra/embedded_surface/omni_lut/possibility_space_probe.py \
  --max-alphabet 16 \
  --window-symbols 256 \
  --steps 4 \
  --top 12

For machine use:

python3 infra/embedded_surface/omni_lut/possibility_space_probe.py \
  --jsonl \
  --output out/sequence_surface_possibility_space.jsonl

This is the intended flow:

possibility space -> metaprobe signature -> RGFlow persistence -> LUT candidate

So DNA/RNA/mRNA/Hachimoji/XNA are not privileged by name. They survive when the features that make them computationally useful remain stable across scale.

GCL Motif And Informaton Surfaces

gcl_motif_lut.py adds the existing GCL/Omnitoken motifs to the same LUT family:

Motif	Surface role
gcl_control	finite OT0 control codons
gcl_admission	RGFlow admit/refuse gate
gcl_compression	Delta GCL/PTOS/manifest compression
gcl_route	carrier-independent route/refuse
gcl_manifest	manifest/fragment hash conservation
gcl_attest	provenance and hash-chain attestation
gcl_recovery	recovery/snapshot/mark-good/rollback
informaton_genome	6D RGFlow genome/address surface
informaton_bind	lawful/cost/invariant bind witness
ms3c_reduction_gear	Matroska-S3C nested route-prior gear

The possibility probe imports these motifs automatically. That means the math can rank biological sequence substrates, synthetic binary lanes, GCL control motifs, and informaton surfaces in one shared possibility space.

matroska_s3c_reduction_gear.py emits the MS3C-RG codon used by that motif:

python3 infra/embedded_surface/omni_lut/matroska_s3c_reduction_gear.py 12345

It computes the corrected S3C split, signed contra-rotation route pressure, a bounded shear score, and the required GCL/FAMM wrapping fields.

Unified Nanokernel Compression Route

unified_compression_route.py combines the sequence LUT, MS3C route-prior codon, motif LUT, and RGFlow persistence probe into one bounded selector:

payload
-> payload metaprobe
-> MS3C/S3C route-prior codon
-> GCL motif candidate
-> RGFlow persistence
-> nanokernel tuple

Example:

python3 infra/embedded_surface/omni_lut/unified_compression_route.py \
  "ACGTACGTACGTACGT"

The returned tuple is descriptive, not authoritative:

surface + motif + witness + compressor

The embedded surface exposes the same selector as WebSocket op 11 (plan_route). GCL still must admit/refuse through the normal receipt path.