Invarians

SOLUTIONS

Structural measurement framework for blockchain infrastructure

Invarians is built on finalized, public, on-chain data only.

No mempool reconstruction. No price feeds. No sentiment data.
Each signal is observable, reproducible, and falsifiable.

01 Two Independently Measured Layers

Invarians reads two distinct dimensions of blockchain state simultaneously. A chain can be structurally stressed without elevated demand, or under heavy demand without structural degradation. Their cross-reading defines the state.

Layer 1 — Structural

DimensionWhat it measuresBehavior
Block timing Block arrival pace relative to the chain's own historical baseline Inertial — evolves slowly. Elevated when blocks arrive late relative to historical pace.
Chain continuity Sequence integrity relative to the chain's own historical baseline Drops when the block sequence shows gaps or interruptions.

Layer 2 — Demand

DimensionWhat it measuresAvailability
Block saturation Block fullness relative to baseline ETH · POL · AVA — gas-physical measurement
Block weight Block size relative to baseline All 4 chains
Transaction volume Transaction count relative to baseline. Solana: user transactions only. All 4 chains
Key principle

Each chain measures itself against its own history. There is no cross-chain normalization. A structural ratio of 1.05 means "5% slower than this chain's own baseline" — the meaning of that number depends entirely on the chain's historical dynamics.

Calibration is derived from multi-year historical data per chain. Thresholds are empirically computed from each chain's own distribution, not fixed constants.

02 State Classification

Four states, derived from the simultaneous state of both layers. The classification is discrete and unambiguous — each moment maps to exactly one state.

S1D1
structure ≈1 · demand ≈1
structural nominal rhythm · normal continuity demand nominal — within historical baseline
S1D2
structure ≈1 · demand >1
structural nominal rhythm · normal continuity demand elevated — above historical percentiles
S2D2
structure >1 · demand >1
structural slowed rhythm · degraded continuity demand elevated — both layers converge
S2D1
structure >1 · demand ≈1
structural slowed rhythm · degraded continuity demand nominal — invisible to fee monitors
State Structural layer Demand layer Meaning
S1D1 Within bounds Within bounds Chain operating within its established historical norms. Both layers healthy.
S1D2 Within calibrated bounds Elevated beyond calibrated historical percentiles High-activity period. Demand elevated above historical baseline. Structural layer absorbing the load.
S2D2 Stressed above calibrated historical percentiles Elevated beyond calibrated historical percentiles Both layers above historical baseline simultaneously. Structural strain coinciding with elevated demand.
S2D1 Stressed above calibrated historical percentiles Within bounds Infrastructure structurally stressed without elevated demand. This signal is invisible to fee-based monitors.
On S2D1

S2D1 is the state with the highest diagnostic value. It captures infrastructure events — validator behavior, timing anomalies, network-layer issues — that do not manifest in fees or transaction volumes. A simple fee monitor would show S1D1 during an S2D1 period.

This is the core of the two-layer approach: structural stress without demand pressure is only detectable when the two layers are read independently.

03 Divergence Signal

Beyond binary classification, the instrument exposes a divergence metric between the structural layer and the demand layer. This signal captures the degree to which the two layers move in the same or opposite directions.

Conceptual meaning

When structural metrics and demand metrics diverge, the divergence scalar encodes the relative movement between layers — direction and magnitude.

The divergence value is included in the API response as divergence.f3_delta. It is a continuous signal — state classification remains the primary output for agent decisions.

04 Calibration

Every threshold is derived empirically — not set by assumption.

Data source
Multi-year historical calibration per chain
Calibration covers multiple market cycles, network upgrades, and structural regimes per chain. No cross-chain normalization.
Method
Empirical threshold calibration
Regime classification is derived from each dimension's position relative to its historical distribution. Regime frequency is validated against target distributions.
Baseline formation
EMA-based dynamic baseline
Each ratio is computed against an exponential moving average of the signal — the chain's structural baseline adapts over time. The instrument does not need recalibration as market conditions shift.
Falsifiability
Verifiable against on-chain data
All input data is public and reproducible. Any party with access to the chain can independently verify whether a given state classification was warranted.
05 Per-Chain Coverage
Chain Regime classification Structural layer Demand layer Beacon chain
Ethereum S1D1 · S1D2 · S2D2 · S2D1 Full (2 dims) Full (3 dims — gas physical) Participation + epoch
Polygon S1D1 · S1D2 · S2D2 · S2D1 Full (2 dims) Full (3 dims — gas physical)
Avalanche S1D1 · S1D2 · S2D2 · S2D1 Full (2 dims) Full (3 dims — gas physical)
Solana S1D1 · S1D2 · S2D2 · S2D1 Full (2 dims) Partial (2 dims — size + user tx, vote filtered)
06 Design Principles
Finalized blocks only
Post-PBS compatible. No mempool dependency.
No narrative
Measurement, not interpretation or advice.
Chain measures itself
Each chain calibrated against its own history.
No price feeds
Structural, not economic. Independent of market.
Reproducible
From same public data → same state output.
Falsifiable by design
The instrument accepts its own failure modes.