# client-prediction-replay-granularity Specification

## Purpose

Define the contract that client-side replay of pending movement inputs MUST use fixed-step accumulation matching the server authoritative movement cadence, not a single accumulated duration, so that replay trajectory matches live prediction trajectory for the same input sequence.

## Requirements

### Requirement: Replay uses fixed-step accumulation matching server cadence

The controlled-client prediction replay path SHALL consume each pending `PredictedMoveStep` by applying its input in fixed-duration substeps equal to the server authoritative movement cadence, regardless of the step's total `SimulatedDurationSeconds`. The replay accumulation shape MUST be identical to the live `FixedUpdate` prediction path for the same input values.

#### Scenario: Replay produces same trajectory as live prediction for steady input
- **WHEN** the client replays a `PredictedMoveStep` with turn=0, throttle=1, duration=0.15s using a 0.05s server cadence
- **THEN** the replay applies 0.05s + 0.05s + 0.05s substeps in sequence
- **THEN** the final predicted position matches the position that would result from three consecutive FixedUpdate predictions of 0.05s each with the same input

#### Scenario: Replay produces same trajectory as live prediction for turn-and-move input
- **WHEN** the client replays a `PredictedMoveStep` with turn=0.5, throttle=1, duration=0.10s using a 0.05s server cadence
- **THEN** the replay applies two 0.05s substeps where each substep's heading affects the next substep's forward direction
- **THEN** the final predicted heading and position match the live prediction path for the same input sequence

#### Scenario: Replay handles non-multiples of cadence interval
- **WHEN** the client replays a `PredictedMoveStep` with duration=0.12s using a 0.05s cadence
- **THEN** the replay applies 0.05s + 0.05s + 0.02s substeps sequentially
- **THEN** no remaining duration is lost or double-counted

### Requirement: Replay trajectory determinism is verifiable

The client prediction system SHOULD provide a deterministic way to verify that replay and live prediction produce identical trajectories for a given input sequence, enabling regression coverage.

#### Scenario: Replay and live prediction produce identical results
- **WHEN** a controlled client records a `MoveInput` sequence during live play
- **AND** the client triggers reconciliation and replays those same inputs
- **THEN** the final predicted pose after replay equals the predicted pose that would result from live FixedUpdate simulation for the same input sequence
- **THEN** the result is stable across multiple replays of the same input sequence