feat: ε-greedy v1 as active policy; dwell-time reward inference; offline sim framework

- Promote egreedy-v1 to active serving policy (ADR-0007): /score/egreedy + /reward/egreedy
  replaces linucb-v1 endpoints after offline sim shows +10.7% mean reward (−0.548 vs −0.606)
- Replace explicit helpful/not_helpful feedback with dwell-time inferred reward (inferReward):
  dismiss=−1.0, snooze=+0.1, done<15s=−0.3, done 15s–2min=+1.0, done 2–10min=+0.6, done>10min=+0.3
- Add ml/serving ε-greedy endpoints: /score/egreedy, /reward/egreedy, /stats/egreedy/{user_id}
  with d=7 feature vector (base 5 + sin/cos day-of-week encoding)
- Add offline simulation framework (ml/experiments/sim): rule/LLM/claude-code judges,
  two-phase score+reward, synthetic personas, task generator; results stored in sim_runs/sim_events
- Add /admin/simulations page: start runs, live-poll status, reward curve SVG, action/persona tables
- Fix egreedy day_of_week training skew: reward endpoint now uses actual dow instead of hardcoded 0
- Fix runner.py proxy bypass: httpx.Client(trust_env=False) for localhost ML calls
- Add dwellMs to TipFeedbackEvent contract and bus.test.ts fixture
- Schema: sim_runs, sim_events tables; tip_feedback gains dwell_ms, reward_milli columns
- ADR-0006: admin console framework; ADR-0007: egreedy-v1 policy selection rationale

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

docs/adr/0007-egreedy-v1-active-policy.md

@@ -0,0 +1,47 @@
+# ADR-0007: ε-greedy v1 as the active recommendation policy
+
+## Status
+Accepted — 2026-04-16
+
+## Context
+
+M1 shipped LinUCB (d=5, α=1.0) as the first learned policy via `ml/serving /score`. After the M1 admin console landed, we ran an offline simulation to compare LinUCB against a new ε-greedy ridge-regression policy before deciding which to keep live.
+
+**ε-greedy v1 design:**
+- Ridge regression estimator, θ updated online (equivalent to LinUCB without the UCB bonus).
+- d=7 feature vector: base 5 (is\_overdue, task\_age\_days, priority, hour\_of\_day, bias) + sin/cos encoding of day\_of\_week.
+- ε=0.10 random exploration; 90% argmax(θ·x).
+- Separate per-user state files (`{user}_egreedy.json`), independent of LinUCB state.
+
+**Simulation setup (rule judge, seed=42):**
+- 5 synthetic personas × 20 rounds × 8 tasks/round = 100 judgments per policy.
+- Reward inferred from dwell-time (same `inferReward` logic as production): dismiss=−1, snooze=+0.1, done<15 s=−0.3, done 15 s–2 min=+1.0, done 2–10 min=+0.6, done>10 min=+0.3.
+- Both policies started from blank state (no warm-up).
+
+**Results:**
+
+| Policy | Total reward | Mean reward/pull | Pulls |
+|--------|-------------|-----------------|-------|
+| egreedy-v1 | −54.80 | −0.548 | 100 |
+| linucb-v1  | −60.60 | −0.606 | 100 |
+
+Winner: **egreedy-v1** (+10.7% mean reward).
+
+Both policies produce negative mean rewards under the dwell-time model — expected: most simulated users don't act in the 15s–2min magic zone on cold models. The gap widens from round 8 onward, consistent with LinUCB's UCB exploration bonus over-favouring high-uncertainty dimensions (is\_overdue, task\_age\_days) regardless of persona fit.
+
+## Decision
+
+Promote **egreedy-v1** to the active serving policy:
+- `POST /recommend` calls `/score/egreedy` instead of `/score`.
+- Feedback loop calls `/reward/egreedy`.
+- LinUCB (`/score`, `/reward`) remains deployed in `ml/serving` as a shadow-eligible fallback.
+
+The simulation does not replace online A/B testing; it is evidence that egreedy-v1 is worth promoting before collecting real-user signal. A future milestone will run live A/B once we have enough daily active users for statistical power.
+
+## Consequences
+
+- Recommendation calls and reward updates now hit the egreedy endpoints only.
+- LinUCB state is preserved on disk; re-activation is a one-line change.
+- `tip_scores.policy` will log `egreedy-v1` for new serves; historical rows remain `linucb-v1` or `random`.
+- The dwell-time reward model (`inferReward`) is now the canonical feedback signal for both online updates and simulation. Explicit helpful/not\_helpful signals are removed.
+- Next evaluation gate: once ≥500 real tips served with egreedy-v1, compare reward distribution to the LinUCB historical baseline in the admin Reward Analytics page before deciding on next policy iteration.