qwen3.5 executor + continuation primitive + think:false

Three coupled fixes that together turned the Riverfront Steel scenario
from 0/5 (mistral) to 4/5 (qwen3.5) with T3 flagging real staffing
concerns rather than linter advice.

MODEL SWAP
- Executor: mistral → qwen3.5:latest (9.7B, 262K ctx, thinking).
  mistral's decoder emitted malformed JSON on complex SQL filters
  regardless of prompt; J called it — stop using mistral.
- Reviewer: qwen2.5 → qwen3:latest (40K ctx)
- Applied to scenario.ts, orchestrator.ts, network_proving.ts,
  run_e2e_rated.ts

CONTINUATION PRIMITIVE (agent.ts)
- generateContinuable(): empty-response → geometric backoff retry;
  truncated-JSON → continue from partial as scratchpad; bounded by
  budget cap + max_continuations. No more "bump max_tokens until it
  stops truncating" tourniquet.
- generateTreeSplit(): map-reduce for oversized input corpora with
  running scratchpad digest, reduce pass for final synthesis.
- Empty text no longer throws — it's a signal to continuable that
  thinking ate the budget.

think:false FOR HOT PATH
- qwen3.5 burned ~650 tokens of hidden thinking for trivial JSON
  emission. For executor/reviewer/draft: think:false. For T3/T4/T5
  overseers: thinking stays on (that's the point).
- Sidecar generate endpoint accepts `think` bool, passes through to
  Ollama's /api/generate.

VERIFIED OUTCOMES
Riverfront Steel 2026-04-21, qwen3.5+continuable+think:false:
  08:00 baseline_fill  3/3  4 turns
  10:30 recurring      2/2  3 turns (1 playbook citation)
  12:15 expansion      0/5  drift-aborted (5-fill orchestration
                            problem, separate work)
  14:00 emergency      4/4  3 turns (1 citation)
  15:45 misplacement   1/1  3 turns
  → T3 caught Patrick Ross double-booking across events
  → T3 flagged forklift cert drift on the event that failed
  → Cross-day lesson proposed "maintain buffer of ≥3 emergency
    candidates, pre-fetch certs for expansion, booking system
    cross-check" — real staffing advice, not generic linter output

PRD PHASE 21 rewritten to reflect the actual primitive shape (two-
call map-reduce with scratchpad glue) instead of the tourniquet
approach originally documented. Rust port queued for next sprint.

scripts/ab_t3_test.sh: A/B harness that chains B→C→D runs and emits
tests/multi-agent/playbooks/ab_scorecard.json.

docs/PRD.md

@@ -392,24 +392,34 @@ Five-tier routing declared in `config/models.json`. Hot path (T1/T2) stays on lo
 
 T3 checkpoints + cross-day lessons are wired. Lessons archive to `data/_playbook_lessons/` and load back at next scenario start as `prior_lessons` in executor context.
 
-### Phase 21: Context Stability & Chunking Pipeline
+### Phase 21: Scratchpad + Tree-Split Continuation
 
-**Why this is a phase and not an optimization:** when playbooks accumulate into the thousands, naive concatenation blows any model's context window. LLM Team hit this exact issue running multi-model ranking — context got lost silently, rankings degraded, and there was no pipeline to catch it. We cannot trust cloud inference to save us: bigger context window just raises the cliff. The stable answer is a chunking + caching layer that runs BEFORE any agent call.
+**Why this is a phase and not an optimization:** bumping `max_tokens` until a response stops truncating is a tourniquet — J called this out explicitly. As playbooks accumulate into the hundreds and responses grow, eventually SOME request will exceed SOME model's window, and we can't solve it by raising a number. The stable answer is two primitives that let us handle arbitrary-size work without losing context: a scratchpad that glues multi-call responses together, and a tree split that shards oversized inputs and reduces them back.
+
+**Two primitives (WIRED 2026-04-21 in `tests/multi-agent/agent.ts`):**
+
+1. **`generateContinuable()`** — handles OUTPUT overflow. Calls the model; checks structural completeness (for JSON: matched braces + JSON.parse success; for text: non-empty). If incomplete, calls again with "continue from here" + the partial response as scratchpad. Up to `max_continuations` times. No `max_tokens` tuning needed — if thinking ate the initial budget, continuation picks up the slack.
+
+2. **`generateTreeSplit()`** — handles INPUT overflow. Caller passes an array of shards (semantic chunks of the corpus). For each shard: map call with running scratchpad digest. Final reduce call produces the answer. Scratchpad truncates oldest content if it approaches its own budget. If a single shard still overflows, `assertContextBudget` throws — caller must re-shard at finer granularity, NOT silently truncate.
 
 **Guarantees:**
-1. Every `generate()` call goes through a budget check against the model's declared `context_window` minus `safety_margin`.
-2. On overflow, the declared `overflow_policy` fires — one of: summarize oldest tool results via T3, cosine-rank top-K lessons, two-pass map-reduce across shards, or escalate to a bigger-context model.
-3. Chunk shards of large corpora (playbook_memory, lesson archive) are precomputed into `data/_chunk_cache/` keyed by corpus hash. Invalidation is automatic when the hash changes.
-4. Token estimation uses `chars / 4` (biased safe by ~15%) until we wire the provider's tokenizer.
+1. No agent call can silently truncate. Either it completes, continues, or throws with numbers.
+2. No corpus is too big — `generateTreeSplit` handles any size the caller can shard.
+3. Scratchpad is the glue between multi-call responses; context is never lost, only compacted.
+4. Token estimation uses `chars / 4` (biased safe ~15%) until we wire the provider's tokenizer.
 
-**What lives where:**
-- Budget checker in `crates/aibridge/src/budget.rs` (new)
-- Chunker in `crates/aibridge/src/chunk.rs` (new) — deterministic splits on sentence/paragraph boundaries, each shard ≤ policy cap
-- Cache service in `crates/storaged/src/chunk_cache.rs` (new) — `corpus_hash → [shard_id, tokens, content]`
-- Agent helper `tests/multi-agent/agent.ts::generateSafe()` wraps `generate()` + `generateCloud()` with the checker
-- Metric emitted on every overflow: `context_overflow{tier=?,policy=?,model=?}` → surfaces on /metrics
+**What lives where now:**
+- `agent.ts::estimateTokens()` + `assertContextBudget()` + `generateContinuable()` + `generateTreeSplit()` — WIRED
+- `scenario.ts` executor + reviewer + overviewGenerate calls — migrated to `generateContinuable`
+- `config/models.json` — context_window + context_budget + overflow_policies per tier (declarative)
 
-**Status:** `context_window` + `context_budget` + `overflow_policies` WIRED in config/models.json. Enforcement helpers NOT yet wired. Implementation target: current sprint — this is a stability floor, not a feature.
+**Next sprint (Rust side, so gateway tools share it):**
+- `crates/aibridge/src/continuation.rs` — port of `generateContinuable`
+- `crates/aibridge/src/tree_split.rs` — port of `generateTreeSplit`
+- `crates/storaged/src/chunk_cache.rs` — precomputed shards keyed by corpus hash (avoid re-chunking on every T4 run)
+- `/metrics` counter: `context_continuations_total{model,shape,succeeded}`
+
+**Status:** TS primitives WIRED. Rust port pending. The escalation path (tree split → bigger-context cloud model → kimi-k2:1t's 1M window → split decision into sub-decisions) is declared in `config/models.json` under `context_management.overflow_policies`.
 
 ### Phase 22+: Further horizon