> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pathprotocol.finance/llms.txt
> Use this file to discover all available pages before exploring further.

# Signal Mesh

> The prediction engine behind Path: multi-horizon directional and continuous outputs with confidence scoring.

Signal Mesh is Path's prediction engine. It continuously synthesises
on-chain protocol health, prediction-market signals, and macro
indicators into confidence-scored predictions for every monitored
pool.

## Signal inputs

<CardGroup cols={2}>
  <Card title="On-chain protocol health" icon="heart-pulse">
    Utilisation curves, supply and borrow caps, oracle staleness,
    reserve factors, kink slopes, interest-accrual fragility.
  </Card>

  <Card title="TVL flow + cohort behaviour" icon="users">
    Institutional vs. retail cohort flow, multi-window TVL deltas,
    address-level rebalance event clustering.
  </Card>

  <Card title="Prediction-market + sentiment" icon="chart-mixed">
    Prediction-market-derived risk premia, sentiment on
    protocol-specific tickers, governance-vote dispersion.
  </Card>

  <Card title="Macro indicators" icon="globe">
    Stablecoin supply trajectories, central-bank rate prints, funding
    rates, category-level capital flow.
  </Card>

  <Card title="Per-protocol specialists" icon="microscope">
    Mechanism-specific signals: AMM-yield discount evolution,
    vault-level curator action, parameter-drift detection, governance
    activity.
  </Card>

  <Card title="Cross-protocol correlation" icon="diagram-project">
    Pair-wise APY co-movement matrix, regime-flip detection, leader
    and laggard graph from a rolling correlation window.
  </Card>
</CardGroup>

## Capability surface

Signal Mesh emits predictions on four horizons. Each horizon is a
gradient-boosted classifier trained on the data shape that horizon
answers best.

<AccordionGroup>
  <Accordion title="Sub-day direction">
    Short-horizon directional signal for tight-cadence rebalance
    triggers. The right horizon when utilisation can shift inside a
    single trading day and rebalance cost is low.
  </Accordion>

  <Accordion title="1-day direction">
    The primary directional model. Calibrated for confidence-score
    interpretability and used as the headline directional signal on
    product surfaces. Output works as a binary trigger or a
    confidence-weighted scaler.
  </Accordion>

  <Accordion title="7-day APY estimate">
    Continuous APY forecast for the 7-day forward window, decomposed
    into base yield, reward yield, and demand components per protocol
    family. The decomposition lets the estimator handle protocols
    with structurally different rate mechanics (lending,
    AMM-style fixed yield, governance-set rates) inside one stack.
  </Accordion>

  <Accordion title="30-day stability">
    Three-class signal: trending up, trending down, stable. The
    long-horizon allocation context. Surfaces pools where the
    short-horizon signal is noise around a stable mean rather than a
    real directional move.
  </Accordion>
</AccordionGroup>

Three structural layers sit alongside the horizon stack:

* **Per-mechanism specialists.** AMM-style yield runs through its
  own specialist because the underlying pricing is mechanically
  different from utilisation-curve lending. Specialists outperform
  the generalist on their own protocol family.
* **Regime detection.** A separate model surfaces correlation
  regime flips, so the directional heads can be re-weighted when
  cross-protocol relationships break.
* **Hybrid composite score.** Combines the directional output, the
  continuous APY estimator, native risk signals, and liquidity-impact
  context into the single score that drives the allocation ranking.

## Confidence calibration

Every directional output ships with a calibrated confidence score on
a 0 to 100 scale. The calibration step is run as a separate stage
after model training, so confidence values map to empirical
hit-rates and can be used as a gating threshold by downstream
consumers. A live calibration verifier monitors drift between the
score and observed forward outcomes; calibration is the class of
silent bug a model pipeline is most exposed to, and the verifier
keeps that loop closed.

## Explainability

Every prediction carries a per-signal attribution vector showing
which inputs contributed most to the directional and confidence
output. Allocators see the top contributing signals alongside the
recommendation, so the model output is auditable at the level of
a single decision rather than as a black-box score.

## How predictions surface

Every prediction is published with:

* The horizon (sub-day, 1 day, 7 days, 30 days)
* A direction class (up, down, stable)
* A calibrated confidence score (0 to 100)
* A continuous APY estimate
* A per-signal attribution vector
* A liquidity-impact context that answers "what would my deposit do
  to this pool's APY"

The liquidity-impact context is documented in detail under
[Liquidity impact](/core-concepts/liquidity-impact).