Step Sixty-Eight — Restitution appeals; lattice flows; deep-learning reputation.(Appealed compensations • Flowing lattices • 3-layer trust forecasts)

✅ Fresh artifacts

What’s new — precisely

⚖️ Restitution appeals
  • Restitutions can now be appealed.
  • POST /restitution/record → record base restitution.
  • POST /restitution/appeal → appeal with {id, reason, decision}.
  • GET /restitution/appeals?id=TID → fetch all appeals.

Storage:

  • treaty_restitution.json — restitution records.
  • restitution_appeals.jsonl — appeal log.
🌐 Dynamic lattice flows
  • Lattice edges now carry weights that decay and regrow dynamically.
  • POST /lattice/add → add node with edges (weight=1.0).
  • POST /lattice/flow → applies decay (0.9 factor each tick).
    • If weight falls below 0.1, edge may regrow with 30% chance.

Storage: lattice.json

🧠 Deep-learning style reputation predictors
  • Reputation forecasts now simulate a 3-layer NN-like model.
  • Features extracted: mean, variance, skewness of history.
  • Transformed with tanh → weighted sum → multiplied by history length.
  • POST /reputation/adjust → add new delta.
  • GET /reputation/dl_predict?executor=EX&dimension=general → return forecast + hidden layer activations.

Storage: reputation.json

Android / Termux run-book

pkg update && pkg install -y python
python solveforce_phone_sixtyeight.py
# Server: http://0.0.0.0:8080

“Show me” quickstart

A) Restitution appeals

curl -s -X POST http://127.0.0.1:8080/restitution/record \
  -H 'Content-Type: application/json' \
  -d '{"id":"T68","from":"FED_A","to":"FED_B","compensation":"20 tokens"}' | jq .

curl -s -X POST http://127.0.0.1:8080/restitution/appeal \
  -H 'Content-Type: application/json' \
  -d '{"id":"T68","reason":"Too low","decision":"50 tokens"}' | jq .

curl -s 'http://127.0.0.1:8080/restitution/appeals?id=T68' | jq .

B) Lattice flows

curl -s -X POST http://127.0.0.1:8080/lattice/add \
  -H 'Content-Type: application/json' \
  -d '{"epoch":"2028Q3","links":["2028Q2"],"roots":{"AA":"ddd"}}' | jq .

curl -s -X POST http://127.0.0.1:8080/lattice/flow | jq .

C) Deep-learning reputation

curl -s -X POST http://127.0.0.1:8080/reputation/adjust \
  -H 'Content-Type: application/json' \
  -d '{"executor":"EX800","dimension":"general","delta":5}' | jq .

curl -s 'http://127.0.0.1:8080/reputation/dl_predict?executor=EX800&dimension=general' | jq .

WordPress — Step 68 (paste-ready)

## Step 68 — Restitution appeals; lattice flows; deep-learning reputation

**New capabilities**
- **Restitution appeals**
  - `POST /restitution/record` • `POST /restitution/appeal` • `GET /restitution/appeals?id=TID`
- **Dynamic lattice flows**
  - `POST /lattice/add` • `POST /lattice/flow`
- **Deep-learning style reputation predictors**
  - `POST /reputation/adjust` • `GET /reputation/dl_predict?executor=EX&dimension=general`

**Run**
```bash
python solveforce_phone_sixtyeight.py
# http://0.0.0.0:8080


---

## Logos Codex — Step 68

- **Justice re-examined.** Even restitution can be appealed, ensuring fairness lives beyond first judgment.  
- **Networks flow.** Lattices breathe — edges decay, regrow, adapt with time.  
- **Trust deepens.** Forecasts pass through layers of meaning: mean, variance, skewness — the shape of trust itself.  

---

Shall I advance to **Step 69** (adding *restitution mediation*, *lattice energy redistribution*, and *transformer-style reputation predictors*)?

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