diff --git a/CLAUDE.md b/CLAUDE.md
index b06f380..bec8897 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -107,10 +107,11 @@ Recent completions:
 - Admin UX refinements: feedback consolidation, settings placement (#100–102)
 - ADR-0012 — ε-greedy v2 (D=12) — 2026-04-26 (now superseded by ADR-0013)
 - ADR-0014 complete: unified Profile schema + backfill, manifest plumbing, `/api/profile` read-through, registry-driven eligibility filter, inference framework + per-agent inference, legacy consent column drop — 2026-05-05
+- Rich per-agent inference for all four active agents (#112, #114, #115, #116) — 2026-05-06: quiet/peak hours (time-of-day), z-score baseline (momentum), p50 lateness + project realness (overdue-task), adaptive lookback + weekly/daily cycles (recent-patterns)
 
 Active work (M2):
-- Signal abstraction for multi-source support (#78)
 - Per-user feature freshness SLAs (#61, ADR-0011 phase B)
+- Embedding-based task clustering for focus-area inference (#97, #113)
 
 ## ADR-0014 endpoint map (as of step 6)
 
@@ -131,15 +132,18 @@ Lives in `ml/agents/inference/`. `run_inference(manifest, history)` evaluates al
 - `infer()` error → emit `cold_start_default` (never crashes)
 - Results written to `user_preferences` with `source='inferred'`; keys with `source='user'` are never overwritten
 
-All five agents are at v1.1.0. Per-agent inferred params:
-| Agent | Inferred param | Logic |
-|-------|---------------|-------|
-| `time-of-day` | `preferred_hour` | Mode done-hour from feedback history |
-| `momentum` | `engagement_trend` | Done-rate last 7d vs prior 7d |
-| `overdue-task` | `lateness_tolerance_days` | Snooze rate → 0/1/2 days |
-| `recent-patterns` | `window_days` | Event density → 7/14/30 days |
+All five agents are at v1.2.0. Per-agent inferred params (all live in `ml/agents/<name>.py`):
+
+| Agent | Inferred params | Notes |
+|-------|----------------|-------|
+| `time-of-day` | `preferred_hour`, `quiet_start`, `quiet_end`, `peak_hours`, `tz` | Quiet window = longest below-baseline hour run; peak = top-quartile done hours; tz cold-start only (from auth provider) |
+| `momentum` | `engagement_trend`, `baseline_completions_per_day`, `stdev` | Baseline = 28d rolling mean done/day; snippet uses z-score language |
+| `overdue-task` | `lateness_tolerance_days`, `project_realness` | Tolerance = p50 lateness from TaskCompletion history; realness = project median vs global median |
+| `recent-patterns` | `lookback_days`, `weekly_cycle`, `daily_cycle` | Lookback sized to ≥30 done events; cycles use peak-to-mean ratio; snippet hints when strength > 0.5 |
 | `focus-area` | *(none yet)* | Needs project-level feedback linkage (#78) |
 
+`UserHistory` carries both `events: list[FeedbackEvent]` and `task_completions: list[TaskCompletion]`. `AgentInferRequest` (ml/serving) accepts `task_completions: list[dict]` alongside `feedback_history`.
+
 ## What NOT to do
 
 - Don't copy Todoist's data into our DB. Store the OAuth token + computed features/derivatives we need, fetch raw on demand.