Offline Phase Architecture

This file documents REE’s offline consolidation phases.

2026-04-05 roadmap update: A minimal sleep-phase infrastructure (SD-017) is now V3 scope. The four full sub-phases (MECH-120-123) remain V4. See sd_017_sleep_phase_architecture.md for the V3 design.

V3 Minimal Offline Phases (SD-017)

Phase Name Function Status
SWS-analog Schema/slot consolidation Hippocampus-to-cortex replay; installs context templates (slot-formation, MECH-166) V3 required
REM-analog Causal attribution replay Fills context slots with co-correlational evidence; slot-filling phase (MECH-166) V3 required

Sub-phases

Phase Claim Biological Analog V3 Prerequisite
SWS denoising MECH-120 Synaptic homeostasis (Tononi SHY) MECH-094 tag
NREM replay MECH-121 SWR + spindles + slow oscillation MECH-092 replay
Spindles MECH-122 Thalamo-cortical spindle bursts MECH-089 ThetaBuffer (bidirectional)
REM recalibration MECH-123 REM precision prior (Hobson/Friston) MECH-094 tag, ARC-016

Phase Ordering Rationale and V4 Rewiring Specification (INV-045)

The V4 sub-phase sequence is not arbitrary. Each phase is in its position because of what must be true before it can run. This makes the ordering a V4 engineering constraint, not a design choice — and each phase directly specifies the rewiring the REE substrate requires.

Phase Claim Failure mode addressed Why this position Rewiring required
0: Sensory gate MECH-122 New input corrupts in-progress schema installation; context templates shift during consolidation Must be first — nothing downstream can run while perception is live Input gate on E1 latent stack; ThetaBuffer paused for new observations
1: SHY normalisation MECH-120 Replaying into a landscape dominated by recent high-salience experiences reinforces the dominant trace rather than consolidating diverse content Must precede replay — homeostasis must flatten attractors before replay repopulates them Normalisation pass over E1/E2/E3 weights decaying toward mean; dominant attractor suppression
2: NREM schema replay MECH-121 + MECH-165 You cannot fill context slots that do not exist (INV-044); world model only represents what was done, never what was possible Must precede REM — schema installation produces the stable attractors that attribution replay fills; replay diversity (MECH-165) must enter the schema now Hippocampus-to-cortex directed replay; balanced scheduler (forward + reverse + non-dominant paths); ContextMemory templates updated
3: Spindle coordination MECH-122 E1 state cannot be consolidated into E3 in reverse direction; ThetaBuffer is waking-only in V3 After schema is installed, bidirectional packaging transfers E1 updates for long-horizon integration ThetaBuffer gains reverse-direction mode; theta-packaged E1 updates transferred to hippocampus
4: REM recalibration MECH-123 Next waking cycle starts with priors calibrated to the previous episode; early evidence is over/under-weighted Must be last — resetting precision priors before replay would change how that evidence is weighted during replay z_beta suppressed (aminergic suppression); E1 runs unconstrained (no harm gate); commitment_threshold and precision_ema_alpha recalibrated from episode natural range

Key derivation: This sequence was not taken from sleep biology. It was derived from asking what an agent needs to reliably know what its actions mean across contexts. The biological NREM→REM sequence converges on the same order because the failure modes are the same. That convergence is evidence for both the biological account and the computational one.


Failure Mode

MECH-124: consolidation-mediated option-space contraction (Walker PTSD analog). Prevention requires balanced replay scheduling and MECH-094 tag throughout.

See Also

  • v3_v4_transition_boundary.md – V3 static setpoints and V4 dynamic mechanisms
  • default_mode.md – MECH-092 quiescent replay (V3 prerequisite for all sub-phases)
  • control_plane_heartbeat.md – MECH-089 ThetaBuffer (V3 scaffolding for MECH-122)
  • medications_dementia.md – INV-048, MECH-173-177: pharmacological pipeline disruption and disease-modifying predictions

REE is developed by Daniel Golden (Latent Fields). Apache 2.0.