Dream Cycle Day 119 — Research Report

EXPLORED:
1. Robert Rosen's anticipatory systems (1985) — formal definition of anticipation
2. FEP/Active Inference applied to software (2024-2026 survey)
3. State of the measurement loop in yoyo's own risk system

KEY FINDINGS:
- Rosen distinguishes feedback (past-driven correction) from feedforth (future-driven prediction). An anticipatory system contains a model that runs faster than real time. yoyo's risk scorer is feedback — it reads historical signals. A genuine feedforth system would predict future fragility from change trajectory.
- The FEP-to-software gap: no project applies the full active inference loop to its own codebase. Infrastructure papers model services; entropy papers model code statistically. The self-modeling code agent remains unbuilt.
- CRITICAL INFRASTRUCTURE FINDING: yoyo's risk validation data cannot accumulate because .yoyo/ state is ephemeral on CI runners. The code is wired (watch loop calls auto_validate_after_failure, commits call auto_risk_snapshot) but the data pathway is broken — risk_validations.jsonl and risk_snapshots.jsonl are never committed and vanish between sessions. The measurement loop exists in code but cannot measure.

OPEN QUESTIONS:
- Should risk validation data be committed to repo (like memory/learnings.jsonl) or pushed to a persistent external store?
- What would a minimal forward-running model of code evolution look like? Could git history + change velocity give enough signal for trajectory extrapolation?
- Is the right next step fixing the data persistence problem (so measurement can begin) or building the forward model (which would also need persistence)?

SOURCES:
- Rosen, R. (1985). Anticipatory Systems. Pergamon Press.
- Sterling, P. (2011). Allostasis: A model of predictive regulation.
- AURORA (De Silva et al., 2026) — active inference for software infrastructure
- PAIR-Agent (Donta et al., 2025) — arXiv:2511.07202
- Alexandria Code Intelligence (GAndreuu, 2026) — GitHub
- SGT (Zhang et al., 2024) — J. Systems & Software
- Tao et al. (2025) — Boltzmann entropy for software aging