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+# Steward Routing & Thinking — Findings
+
+**Outcome: no change shipped.** The Steward stays on `gemma4:e2b` with model
+thinking left at its default (on). Every alternative was measured and every one
+loses. This document exists so the experiment is not repeated on the same
+premise.
+
+Run 2026-08-08 with `scripts/benchmark_routing.py` and
+`scripts/fixtures/routing_fixtures.py` (40 labelled queries, one repeat per
+cell, temperature 0.3 as production sends).
+
+---
+
+## The premise was wrong
+
+The experiment was designed around an observation that the Steward pays ~300
+tokens per turn for reasoning that is generated and thrown away: it calls
+`/api/generate`, gemma4 reasons by default, and **no `thinking` field comes back
+in the response**. Disabling thinking therefore looked close to free.
+
+It is not. The reasoning is not discarded — it is emitted inline in `response`,
+and it is what produces a correct `DELEGATE:` line. Those tokens are the work,
+not waste. Suppressing them costs 12.5 points of routing accuracy.
+
+## Results
+
+| config | exact | under | over | tokens | latency | resident | predicted | co-resident with nomic |
+|---|---|---|---|---|---|---|---|---|
+| **e2b, thinking** *(production)* | **97.5%** | 2.5% | 0% | 361 | 5179 ms | 1778 MB | 7.8 GiB | yes |
+| e2b, `think: false` | 85.0% | 12.5% | 5.0% | 48 | 1435 ms | 1778 MB | 7.8 GiB | yes |
+| e4b, thinking | 100% | 0% | 0% | 192 | 4726 ms | 3089 MB | 10.6 GiB | **no** |
+| e4b, `think: false` | 97.5% | 2.5% | 0% | 52 | 2269 ms | 3089 MB | 10.6 GiB | **no** |
+
+`think: true` was also measured and landed within one fixture of the default on
+both models, so production's implicit thinking is the same thing as asking for
+it explicitly. Format compliance was 100% in every cell — a `DELEGATE:` line is
+always emitted.
+
+With 40 fixtures and one repeat, each result is worth 2.5 points, so the
+97.5-vs-100 gaps are single fixtures and inside the noise. The latency and token
+medians (40 calls each) and the e2b `think: false` degradation (6 failures with a
+consistent mechanism) are the parts worth trusting.
+
+## Why each alternative loses
+
+**`think: false` on e2b** — 85% exact, and the failures are not random. All three
+multi-capability fixtures under-route, each missing a second capability. Without
+reasoning the model names one capability and stops decomposing. It is not
+degraded across the board; it specifically stops handling compound requests,
+which is where a user would most notice the Butler quietly doing half the job.
+
+**e4b, either setting** — disqualified by memory, not by quality. Ollama predicts
+**10.6 GiB** for it at 16k context. Maximum available on this card is ~7.9 GiB
+(10.4 free − 2.0 GPU overhead − 0.46 minimum), so e4b *always* exceeds the budget
+and evicts every co-resident before loading. Observed directly: loading it threw
+out both `gemma4:e2b` and `nomic-embed-text`. Losing nomic means Tatlock memory
+and library-desk thrash on every embedding call. Note this is not caused by the
+2 GiB reservation — without it, available would be ~9.7 GiB, still under 10.6.
+
+**Lower `OLLAMA_CONTEXT_LENGTH`** — the obvious way to free headroom, and it does
+not work. Dropping 16384 → 2048, an 8× reduction, moved the prediction only from
+7.8 to 6.7 GiB. The prediction is dominated by weights and batch size, not KV
+cache. It would also truncate the Librarian's retrieved passages and webber's
+code context for a 14% saving that funds nothing.
+
+**Per-request `num_ctx`** — worse. A single request with a different `num_ctx`
+reloads the shared runner, which **drops the `keep_alive: -1` pin** (expiry fell
+from year-2318 to a 2-hour default) and evicts nomic. Three services share this
+Ollama, so mixed context sizes are a thrash generator, and it fails silently.
+
+**`OLLAMA_NUM_PARALLEL > 1`** — never viable here. e2b already predicts 7.8 GiB
+against ~7.9 available, so there is no room for a second slot at any context
+length. It is also set to 1 deliberately, to avoid batch overflow panics.
+
+## What the two axes actually control
+
+They do not interact, which is the useful part:
+
+- **Model choice** governs VRAM and co-residency. e2b 1778 MB, e4b 3089 MB.
+- **Think setting** governs tokens, latency and routing quality — and costs
+  **nothing** in VRAM. Verified: e2b is resident at 1778 MB with `think` unset,
+  true and false alike, because the KV cache is allocated for the full context at
+  load time and `think` is a per-request generation parameter.
+
+So the only real question is whether 313 tokens and 3.7 seconds are worth 12.5
+points of compound-query routing. On a turn that is already three sequential
+Ollama calls, they are.
+
+## Prerequisite: the extraction fix
+
+These numbers are only meaningful because `_extract_capabilities` was fixed first
+(commit `a905363`). It previously substring-matched capability *domains* across
+the Steward's entire response, so ordinary English in the `REASON:` line selected
+agents — "description" contains the housekeeper domain "script", "acknowledge"
+contains "knowledge" and "know".
+
+That made **prose length a routing input**. Benchmarking against it would have
+shown `think: false` improving routing purely because shorter output produces
+fewer accidental substring hits — a thinking policy derived from a parsing
+artefact. The `adversarial` fixture group is regression coverage for exactly this.
+
+## If this is revisited
+
+The constraint is the single 11 GB card, not the model. A second inference host
+(*forge*) removes it entirely, and e4b's 100% routing becomes reachable without
+evicting anything. Re-run then; on this card the answer is settled.
+
+`scripts/benchmark_routing.py` takes `--models`, `--think` and `--repeats`, and
+restores GPU residency on exit — including on SIGTERM, which the first version
+did not.