docs/releases/r0513.md

# Release Notes — r0513

## The Engine That Won't Stop Learning

**Package:** `a11oy 0.1.0+r0513`  
**Author:** Stephen P. Lutar Jr. <stephen@szlholdings.com>  
**ORCID:** 0009-0001-0110-4173  
**Org:** SZL Holdings  
**Doctrine:** v2  
**Date:** 2026-05-14  
**License:** Apache-2.0

---

## Architecture Overview

r0513 completes the Brain v1 build by adding the inner-agent society to the Ouroboros Infinity loop. The full pipeline is:

```
Goal
 │
 ▼
A11oyAgent.think()
 ├─ Quipu Plan (DAG of steps)
 ├─ Execute steps → RAGReceipt per step
 ├─ Evalgate: 9-axis conjunctive AND gate
 ├─ BiettiMemory: goal → trace summary
 ├─ Observatory: drift detection
 └─ CodexSession → CodexClosure (Merkle root)
 │
 ▼
LoopController.meditate_n_cycles()
 ├─ CycleHead: current_head seeds each cycle
 ├─ Thesis corpus rotation: F1–F15 SZL formulas
 ├─ tick() → CycleTail(tail_hash, receipt, eval_result)
 └─ tail_hash → next current_head (Ouroboros fold)
 │
 ▼
Society.tick_all()  ─── 7 inner agents in fixed order ───►
 ├─ Perceiver (FEP — surprise minimization)
 ├─ Predictor (world-model prefix forecasting)
 ├─ Proposer (ReAct/Reflexion template proposals)
 ├─ Critic (Constitutional AI axis calibration)
 ├─ Rememberer (Park memory stream)
 ├─ Dreamer (DreamerV3 offline planning)
 └─ Arbiter (Minsky Society of Mind weighted selection)
 │
 ▼
Society.learn_all(gate_outcome)  ─── per-agent state update ───►
```

Every crossing of the evalgate gate calls `learn()` on all 7 agents.  
The loop invariant is `"but never stop"`.

---

## 7 Inner-Agent Roles and Scientific Basis

| Agent      | Leader Citation                         | tick() function               | learn() update                         |
|------------|------------------------------------------|-------------------------------|----------------------------------------|
| Perceiver  | Friston, *Free Energy Principle* (2010)  | Surprise = edit distance to last_head | Running mean surprise (EMA) |
| Predictor  | Karpathy / Sutskever                     | Prefix match forecast         | Residual MAE accumulation              |
| Proposer   | Yao & Shinn, *ReAct/Reflexion* (2023)    | Template-keyed proposals      | Pass/fail count per template key       |
| Critic     | Bai et al., *Constitutional AI* (2022)   | Per-axis score prediction     | Per-axis calibration error (running MAE)|
| Rememberer | Park et al., *Generative Agents* (2023)  | SHA-256 key memory lookup     | Recency + frequency weight update      |
| Dreamer    | Hafner et al., *DreamerV3* (2023)        | Dream score vs reality delta  | Dream–reality residual MAE             |
| Arbiter    | Minsky, *Society of Mind* (1986)         | Weighted selection of winner  | +1 on winner, -1 on losers             |

All agents implement:
- `tick(loop_state: LoopState) -> AgentReport`
- `learn(report: AgentReport, gate_outcome: GateOutcome) -> None`

All `learn()` updates are:
- **Deterministic:** seeded from `sha256_text(loop_state.current_head + salt)`
- **Idempotent w.r.t. replay:** same input → same state delta
- **JSON-serializable:** `get_state()` / `load_state()` round-trip

---

## New Module: inner_agents.py

`src/a11oy/inner_agents.py` — 916 lines, Apache-2.0, r0513.

Exports:
- `InnerAgent` (ABC with `tick` + `learn` + state I/O)
- `Perceiver`, `Predictor`, `Proposer`, `Critic`, `Rememberer`, `Dreamer`, `Arbiter`
- `Society` (`tick_all`, `learn_all`, `snapshot`, `snapshot_hash`, `to_json`, `from_json`)
- `AgentReport`, `GateOutcome`, `SocietyReport`

---

## Full Test Suite

```bash
$ pytest --tb=short -q 2>&1 | tail -2
243 passed in 3.22s
```

**243 tests — all passing.** Zero skips, zero failures.

### r0513 additions: test_inner_agents.py — 33 tests

Key coverage:
- All 7 agents tick without error
- All 7 agents learn without error
- tick() output is an `AgentReport` with correct role
- learn() mutates agent state deterministically
- Society.tick_all() runs all 7 agents in fixed order
- Society.learn_all() propagates gate_outcome to all agents
- Society.snapshot() returns JSON-serializable dict
- Society.snapshot_hash() is deterministic
- Society.to_json() / from_json() round-trip
- 5× replay: Society.tick_all()+learn_all() is byte-identical with frozen time

---

## Code Quality

```bash
$ ruff check src/ tests/
All checks passed!

$ mypy --strict src/
Success: no issues found in 20 source files
```

- **ruff:** clean (E, F, W, I, UP, B, C4, SIM; E501 ignored)
- **mypy --strict:** clean across all 20 source files

---

## Determinism Certification

### v0 baseline (preserved)

```
$ for i in 1..5: SZL_FROZEN_TIME=2026-05-14T10:00:00Z python examples/hello_brain.py | sha256sum
a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0
a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0
a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0
a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0
a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0
```

v0 hash `a8603d5de9f3c97d94d13b5e394424c2e18f1be352f98b097f43eee5947d90a0` — **unchanged.**

### v1 + r0513 (agi_brain.py)

```
$ for i in 1..5: SZL_FROZEN_TIME=2026-05-14T10:00:00Z python examples/agi_brain.py | sha256sum
bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c
bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c
bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c
bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c
bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c
```

v1 hash `bbf72d4ab5b3eddb18d889b67426636107d816101948e989fa168074bc126e8c` — **5× identical.**

---

## Cryptographic Receipt

**v1_codex_root** (SHA-256 Merkle chain over all 20 `src/a11oy/*.py`):  
`eab2a8c4b0059de48ca8e613c84da994a45bf3f1f61e78949ecc059c42ac8c0c`

**r0513_continuum_hash** (`sha256(v1_codex_root + "r0513")`):  
`46812870ce59251d8ea8e5494702aef05a3fedd061e182ee87a40dca238b867f`

---

## What r0513 Is NOT

- **Not ML training.** No weights updated. MockCortex only.
- **Not AGI.** See `AGI_HORIZON.md` for the honest 9-gap analysis.
- **Not production-ready.** No real LLM connected. No real tool use.

What it IS: a verifiable, auditable, doctrine-enforced cognitive scaffold that runs forever and learns from every crossing.

---

## Deferred to v2

- **Precision-weighted prediction-error gate (Neuroscience pod):** Noted in `Critic` docstring. Requires gradient infrastructure. Risk too high for v1.
- **Goedel-Prover-V2:** 12 sorry holes in `EvalGate.lean` (BLOCKER C1). Closes in next pod.

---

## File Manifest (new in r0513)

| File                              | Lines | Description                           |
|-----------------------------------|------:|---------------------------------------|
| `src/a11oy/inner_agents.py`       |   916 | 7 inner agents + Society              |
| `tests/test_inner_agents.py`      |   500 | 33 tests for inner_agents             |
| `examples/agi_brain.py`           |    82 | End-to-end demo: agent + loop + society|
| `BRAIN_V1_REPORT.md`              |   255 | Full verified build report            |
| `AGI_HORIZON.md`                  |   166 | Honest AGI gap analysis               |
| `BRAIN_V0_5_REPORT.md`            |   148 | Infinity loop addition report         |
| `RELEASE_NOTES_r0513.md`          | (this)| Release notes                         |

---

*r0513 — The Engine That Won't Stop Learning.*