How Eigenstate measures the tokenized settlement field - three verbs, one loop, honest gates. Plain language first; precise engine terms where Host needs them.
Structural measurement · not a trade signal · Base commits only when gates pass
One parkash cycle can measure without publishing, and publish without acting. Most cycles stay measure-only.
Measure
Sensors and parkash cycles observe entities; qualifying rows land in the vault.
Not a trade signal
Publish
Reports and claims go out with verification status (status_at_publish) when
the membrane admits them.
Not every observation is published
Act
Build, deploy, connect, or settlement paths when Host / gates allow.
Observe-heavy today; act lanes are gated
Example: CIRCLE parkash
A parkash cycle measures CIRCLE (vault + dossier numbers update). A Pages
report may publish later with status_at_publish.
Act (on-chain helix commit or other gated paths) only runs when Host/gates
allow - so most cycles stay measure-only.
Without this engine: desks still split those jobs across Bloomberg/Refinitiv, EDGAR, Slack, and counsel. The loop exists; it is manual, slow, and rarely hashed as one measurement chain.
Website tiles and report cards use these terms. Plain language first; precise engine meaning in the same row.
| Term | Plain language | Precise / honest note |
|---|---|---|
| E, ΔI, A | How much new information an observation yields per unit cost | E = ΔI / A. ΔI = surprise vs expected state; A = observation cost (API / work / time). Structural metric, not a price forecast. |
| Φ_S | Settlement / signal pressure on an entity | Primary entity field signal (unrealized settlement energy). Mirrored on public dossiers (own_numbers). Not volume, not INFRA_FLOW. |
| κ vs Γ | κ = system coherence of the measured field; Γ (gamma) = extraction / force | Separate keys (Brief B): κ stays on sys/measured coherence; Γ lives under entity physics (gamma). Never treat Γ as Φ_S or rename κ to mean gamma. |
| PT / Protocol Truth | A Host act-readiness score from the engine SoT | Shown on Track Record / proof tiles from HOST/state.json. Canonical act threshold ≈ 0.618. Below waist ≈ observe (measure / membrane publish); high-stakes act lanes stay gated. |
| M1 / 199 / coverage | How much of the fixed topology map has been observed | Denominator 199 (forbidden stale denoms 196/197/218/…). Coverage = m1_strict ∩ topology, baked as e.g. 192 / 199. Dossier count ≠ M1 denominator. |
| SoT | Source of truth for public field tiles | Engine HOST/state.json (κ, PT, parkash stamps) + M1 coverage ledger → stamped into Pages field-state.json at deploy. Tiles bake from that stamp, not frozen marketing HTML. |
| vault@publish vs M1 ledger | Two different "obs" counts on report cards | vault@publish (green badges) = trimmed cycle-vault rows present at report publish - not lifetime history. M1 ledger = lifetime m1_strict observation counts. Do not equate them. |
| status_at_publish | Trust label on a published claim | Set by VERIFY (fail-closed). Common values: ATTESTED (when spine admits), UNVERIFIED-PENDING (published but not attested). Federation proof_shape can refuse packages that invent attestation without a provenance spine. |
| Assessable n / accuracy | How we score Host prediction outcomes | TRUE/FALSE only (VOID excluded). A % appears only when assessable n ≥ 5. Thin samples show raw n - never a fake 100% from a handful of rows. |
| Direction signals | Older claim-table labels (e.g. BUILDING, INFRA_FLOW) | Retired on the current Track Record rebuild. INFRA_FLOW / cumulative volume is a different unit and must not be shown as Φ_S. Live surface: SoT scoreboard + field tiles. |
| Helix / Base commit | On-chain fingerprint of vault / crossing state | Local HelixHash chaining is continuous; Base commits to GeniusFlowSettlement are gated (wallet, balance, mirror flags). Attested when a tx lands - not every parkash. |
| Return-wire ack | Optional consumption acknowledgment | Federation /api/return_wire can ack that a consumer saw a package. Supports return-signal / Cell-E paths; it does not mean every vault row was published or attested. |
We score how much new information an entity produces relative to the cost of watching it. High score → the entity is moving the field. Low score → it is moving with the field.
This is a structural metric, not a price forecast. The field is the topology
of obligations among 199 tracked entities (M1 denominator). Live coverage is
reported as M1 strict in
field-state.json - not a frozen marketing number such as
196/197.
Example: quiet vs loud SEC day
New filing / rulemaking vs prior expected state → large ΔI for roughly the same parse cost A → high E.
Same feed pull, no material change → mostly confirmation → low E. Same sensor, different information yield.
Example: BUIDL / TVL tick
A DefiLlama TVL step-change on a tokenized fund (e.g. BUIDL) is high-E. A flat reprint of yesterday's number is low-E. Surprise per observation cost, not "TVL went up so buy."
Without this engine: analysts open EDGAR by hand and decide "this matters" by inbox volume. That judgment is real; it is rarely scored as surprise-per-cost across a fixed entity set.
When an observation is worth more than it costs to take, we append it. Changing an old row breaks later hashes / parent checks.
vault_fingerprint and/or sha.hash with
parent-consistency checks)
Φ_S and κ live on entity field state and are mirrored on
public dossiers (own_numbers). Do not confuse κ with Γ (gamma).
The vault is primarily a measure surface - not every vault row becomes a
public article.
Example: CIRCLE parkash → dossier mirror
After a qualifying observation of CIRCLE or BLACKROCK, the private vault appends a chained row. The public mirror of measured field state - including Φ_S and κ - is the federation dossier, not a guarantee that every vault row ships as a report. We do not paste fake vault hashes here.
Without this engine: the analogous record lives in research notes, CRM rows, or auditor workpapers - useful, but not an append-only, hash-chained measurement log with public dossier digests.
Sometimes we fingerprint vault state and post it to Base mainnet so outsiders can timestamp-check a claim. That is an attestation when it lands - not a promise that every parkash writes a new transaction.
GeniusFlowSettlement on Base
(0x3A2d6599d5409c1A87609c38dB9b1619e47F6b02) with a fingerprint as
evidenceHash when the commit path succeeds.
SETTLEMENT_MIRROR_ENABLED + sevadar interlocks). EAS outbound remains gated.
Example: when Base tx does / doesn't land
Wallet, balance, and mirror flags allow a commit → fingerprint lands on Base. Prefer the On-Chain Proof Index over any single hard-coded hash in prose.
Mirror off or wallet/balance blocks → local HelixHash still advances, but no new Basescan tx that cycle. Partial mirror means "attested when it lands."
Example: early Base commit
Historical example (with 0x prefix):
view on Basescan. Confirm
settlement contract and input data. For machine admit/refuse, use federation
/api/verify and
proof_shape v1.
Without this engine: teams timestamp claims with PDF hashes, notaries, or ad-hoc explorer checks. Those practices work for deals; they do not continuously fingerprint an internal measurement vault.
Before something is treated as a published claim, the stack checks it. Wrong high-confidence claims get blocked; uncertain ones get flagged; public consumers should read the published status, not assume "attested."
BLOCKED_FACT_CHECK); 0.60–0.89 → flag for human review; <
0.60 → log only. Plus codename-leak and on-chain timestamp gates.
status_at_publish (fail-closed; never upgrades a miss to ATTESTED).
registry_ref → refuse_or_admit → provenance_spine on
/api/package and /api/verify. Cheap copies that invent attestation
labels fail.
Example: proof_shape admit / refuse
Package carries a real registry_ref and provenance spine → can admit under
proof_shape v1.
Cheap copy that stamps ATTESTED without that spine → refuses. Separately: a
disputed claim scored ≥ 0.90 is blocked before the publish queue.
Without this engine: verification is editorial review or "check the source
link." The status is usually implicit in a byline, not a machine-checkable
status_at_publish / proof_shape admit-or-refuse.
We watch a fixed map of institutions and rails that matter for tokenized fixed income - regulators, custodians, issuers, chains, benchmarks - and update each entity's field state every cycle.
M1 topology denominator = 199 (forbidden/stale denoms such as 196/197/218 are not used). Federation may expose more dossier cards than the M1-199 set; dossier count ≠ M1 denominator.
Example: DTCC vs random token
DTCC sits in M1 as settlement infrastructure - clearing/custody rails tokenized fixed income still depends on.
A random newly listed token is not in that map. Watching its price does not substitute for observing DTCC's position in the obligation graph. Role ≠ market cap or tweet volume.
Without this engine: coverage maps live as Excel universes or "who we follow on X." Those lists are often good for news; they rarely encode settlement-role topology with a fixed M1 denominator.
We look for things the topology needs that markets or product design still treat as optional or equivalent when they are not.
Gap distance = what correct function requires − what current pricing / product design assumes. Large, persistent distance → structural gap. Gap publication coverage is tracked separately from vault measure.
Example: LIBOR_EQUIVALENT
Markets treat SOFR + ISDA fallback as a full LIBOR replacement, but the topology still needs
a sovereign-backed term structure the transition did not supply. Gap id
LIBOR_EQUIVALENT (first logged prediction:
$300 Trillion Structural Gap… /
LIBOR_EQUIVALENT_001). Publishing a SOFR article does not close
the gap - measure and publish coverage are separate rails.
Example: SOFR three-body
Overnight benchmark, Treasury issuance velocity, and tokenized settlement mechanics remain a primary structural gap. SOFR-linked products that treat overnight exposure as term exposure are structurally false under this lens.
Without this engine: the same tension shows up in ISDA papers and conference panels. What is usually missing is a durable, machine-tracked gap id with separate measure vs publish coverage.
Prefer federation + Pages over inventing live counts or on-chain status. The GeniusFlow engine repo is private.
HOST/state.json + M1 coverage ledger →
field-state.json at deploy.
Plain markdown for agents:
METHODOLOGY.md.
Equation DOI:
10.5281/zenodo.18413995.