2026-10-03 03:45:22 +00:00
27 changed files with 3348 additions and 52 deletions
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -868,7 +868,7 @@ weeks.
 ### Which implementation is PRIMARY is now a config choice

 `classifier.mode` in the live deployment is currently `local_encoder`, set in
-`config.local.yaml` with `device: cuda` on the classifier host. That is the
+`config.local.yaml` with `device: cpu` on the classifier host. That is the
 mode answering real traffic right now. The explicit caveat is that a
 CPU-vs-CUDA latency and confidence comparison on the live classifier host is
 still pending; until that measurement exists, flipping the project default to
@@ -903,8 +903,8 @@ probability mass), brought that to 8 of 9 correct with confidence scores
 threshold and correctly fell through to the safe fallback rather than
 mis-routing.

-`classifier.mode` (`local_llm` default, `cloud_llm`, `local_encoder`) picks
-what answers a classification request — a peer concept to the cascade above,
+`classifier.mode` (`local_llm` default, `cloud_llm`, `local_encoder`,
+`local_decision`) picks what answers a classification request — a peer concept to the cascade above,
 **not a replacement for it**. Whichever mode is primary, a failure still
 walks the exact same cascade (stale session → session history →
 `cloud_fallback` → the static guess), unmodified.
@@ -929,6 +929,16 @@ walks the exact same cascade (stale session → session history →
  falls back to `fallback_tier` — a real limitation, not a bug. A
  below-threshold confidence is treated as a failure and cascades exactly
  like a local-LLM parse failure would.
+- **`local_decision`** asks a small generative model (configured in
+  `classifier.decision`) to pick a category from a set of natural-language
+  descriptions, then optionally classifies tier and — when enabled in its
+  decision config — runs the same A/B confidence check on the chosen label.
+  Requires a `classifier.decision:` block in config (confidence threshold,
+  base URL, model name); it is an opt-in overlay mode, not the default, and
+  the config load will refuse to start without it when the mode is selected.
+  Only produces `task_category` and an optional `task_tier` when the
+  decision block's `tier_classification.enabled` is true. Below-threshold
+  confidence cascades like a parse failure would.
 - **Neither is gated by `local_compute.enabled`** (gaming mode, below) the
  way `local_llm` is: `cloud_llm` never touches local hardware, and
  `local_encoder` is small enough to run on CPU, so neither competes for the
@@ -943,7 +953,9 @@ value (see [admin-portal](docs/admin-portal.md)).
 The default in `config.yaml` is still `local_llm`. `local_encoder` is
 intentionally an opt-in per-deployment choice rather than the repository
 default until the pending CPU-vs-CUDA comparison on the live classifier host is
-available.
+available. `local_decision` is also opt-in: it requires a `classifier.decision:`
+block with its own base URL, model, and confidence thresholds — the config load
+refuses to start without it when the mode is selected.

 ## Local dispatch model

--- a/admin/frontend/controls.html
+++ b/admin/frontend/controls.html
@@ -269,6 +269,7 @@ header.navbar{padding-top:2px!important;padding-bottom:2px!important}
                      <option value="local_llm">local_llm</option>
                      <option value="cloud_llm">cloud_llm</option>
                      <option value="local_encoder">local_encoder</option>
+<option value="local_decision">local_decision</option>
                    </select>
                  </div>
                  <div class="col-md-8" id="classifier-mode-fields"></div>
@@ -1183,6 +1184,54 @@ function classifierModeFieldsHtml(mode, data) {
        </div>
      </div>`;
  }
+  if (mode === 'local_decision') {
+    const dec = (data && data.decision) || {};
+    return `
+      <div class="row g-2">
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" id="classifier-decision-base-url"
+                 data-key="decision.base_url"
+                 placeholder="http://localhost:11434"
+                 value="${escapeHtml(dec.base_url || '')}">
+        </div>
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" id="classifier-decision-model"
+                 data-key="decision.model"
+                 placeholder="qwen3.5:4b"
+                 value="${escapeHtml(dec.model || '')}">
+        </div>
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" type="number" id="classifier-decision-num-ctx"
+                 data-key="decision.num_ctx"
+                 placeholder="8192"
+                 value="${dec.num_ctx != null ? dec.num_ctx : ''}">
+        </div>
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" type="number" id="classifier-decision-timeout-s"
+                 data-key="decision.timeout_s"
+                 placeholder="10"
+                 value="${dec.timeout_s != null ? dec.timeout_s : ''}">
+        </div>
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" type="number" step="0.1" id="classifier-decision-confidence-min"
+                 data-key="decision.confidence_min"
+                 placeholder="0.5"
+                 value="${dec.confidence_min != null ? dec.confidence_min : ''}">
+        </div>
+        <div class="col-md-4">
+          <input class="form-control form-control-sm" type="number" step="0.1" id="classifier-decision-coverage-min"
+                 data-key="decision.coverage_min"
+                 placeholder="0.3"
+                 value="${dec.coverage_min != null ? dec.coverage_min : ''}">
+        </div>
+      </div>
+      <div class="form-check form-switch mt-2">
+        <input class="form-check-input" type="checkbox" id="classifier-decision-tier-enabled"
+               data-key="decision.tier_enabled"
+               ${dec.tier_enabled ? 'checked' : ''}>
+        <label class="form-check-label" for="classifier-decision-tier-enabled" style="font-size:.78rem">tier_enabled</label>
+      </div>`;
+  }
  return `<span class="text-muted" style="font-size:.78rem">Today's default: the local model configured under <code>classifier.model</code> above.</span>`;
 }

@@ -1285,6 +1334,16 @@ async function loadClassifierConfig() {
  const badge = document.getElementById('classifier-mode-badge');
  badge.textContent = data.mode + (data.mode_source === 'overlay' ? ' (overlay)' : '');
  badge.className = data.mode === 'local_llm' ? 'badge bg-secondary' : 'badge bg-info';
+  if (data.mode === 'local_decision' && data.decision) {
+    const d = data.decision;
+    if (d.base_url) document.getElementById('classifier-decision-base-url').value = d.base_url;
+    if (d.model) document.getElementById('classifier-decision-model').value = d.model;
+    if (d.num_ctx) document.getElementById('classifier-decision-num-ctx').value = d.num_ctx;
+    if (d.timeout_s) document.getElementById('classifier-decision-timeout-s').value = d.timeout_s;
+    if (d.confidence_min) document.getElementById('classifier-decision-confidence-min').value = d.confidence_min;
+    if (d.coverage_min) document.getElementById('classifier-decision-coverage-min').value = d.coverage_min;
+    document.getElementById('classifier-decision-tier-enabled').checked = !!d.tier_enabled;
+  }
 }

 function collectClassifierConfigBody() {
@@ -1317,6 +1376,16 @@ function collectClassifierConfigBody() {
    // wants the 0.0-1.0 probability classify_zero_shot actually returns.
    if (thresholdPct !== '') encoder.confidence_min = parseFloat(thresholdPct) / 100;
    body.encoder = encoder;
+  } else if (mode === 'local_decision') {
+    body.decision = {
+      base_url: document.getElementById('classifier-decision-base-url')?.value || 'http://localhost:11434',
+      model: document.getElementById('classifier-decision-model')?.value || 'qwen3.5:4b',
+      num_ctx: parseInt(document.getElementById('classifier-decision-num-ctx')?.value) || 8192,
+      timeout_s: parseInt(document.getElementById('classifier-decision-timeout-s')?.value) || 10,
+      confidence_min: parseFloat(document.getElementById('classifier-decision-confidence-min')?.value) || 0.5,
+      coverage_min: parseFloat(document.getElementById('classifier-decision-coverage-min')?.value) || 0.3,
+      tier_enabled: document.getElementById('classifier-decision-tier-enabled')?.checked || false,
+    };
  }
  return body;
 }
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -680,22 +680,22 @@ local_dispatch_models:
    #   Q4_K_M @ 32k: 1.14s mean. Q8_0 @ 32k: 3.79s, because it needs 24GB and
    #   Ollama spills 17% to CPU (watch the "17%/83%" column in `ollama ps`).
    #
-    # num_ctx 32768 costs ~4GB of KV cache over 16384 (13GB -> 17GB resident).
-    # That is affordable here ONLY because the vision model was retagged to
-    # num_ctx 8192; together they are 23.2GB of 24GB, with ~800MB spare. On a
-    # smaller card, drop this to 16384 first -- KV cache is the cheapest GB to
-    # reclaim, and the classifier never needs it (classifier.max_input_chars
-    # clamps to 8000 chars, ~2.7k tokens).
+    # num_ctx 16384 measured (see plans/local-decision-classifier-results.md
+    # sections 7 and 9): the 14b at 32k is 16.5-17.8 GB resident, at 16k it is
+    # 12.26 GB. That headroom is what lets the local_decision classifier
+    # qwen3.5:4b (num_ctx 8192, 5.9 GB) coexist: 12.3 + 5.9 = ~19.9 of 24 GB
+    # (measured 2026-09-28). Local vision is off because its model no longer
+    # fits.
    #
    # Modelfile recipe (run on an Ollama host):
    #   ollama pull qwen2.5-coder:14b
-    #   printf 'FROM qwen2.5-coder:14b\nPARAMETER num_ctx 32768\n' > Modelfile.local-dispatch
+    #   printf 'FROM qwen2.5-coder:14b\nPARAMETER num_ctx 16384\n' > Modelfile.local-dispatch
    #   ollama create qwen2.5-coder-router:14b -f Modelfile.local-dispatch
    model_id: "qwen2.5-coder-router:14b"
    base_url: "http://localhost:11434/v1"
    api_key_env:
    timeout_seconds: 180
-    context_window: 32768
+    context_window: 16384
    max_output_tokens: 2048
    tier: 1
    # Restricted to file_summarization and diff_checking. The router's hard
--- a/docs/admin-portal.md
+++ b/docs/admin-portal.md
@@ -151,19 +151,32 @@ can't safely represent:

 - **Local Compute** — "gaming mode". See [Gaming mode](#gaming-mode) below.
 - **Classifier** — `classifier.mode`'s config (`cloud_llm` needs a pinned
-  primary or `auto`; `local_encoder` needs a model id). It shows the current
-  mode and, for `cloud_primary_auto`, the **live** resolved cheapest
-  candidate — computed with `routing.cheapest_classifier_candidate`, the same
-  function `classify()` calls, not a static echo of the config. An admin
-  panel that shows a config value instead of what dispatch actually does is a
-  real class of bug — the same lesson the profiles zero-admit badge above
-  already taught this project — and a "live" reading that is secretly stale
-  is worse than no reading at all. `POST /admin/api/classifier-config`
-  validates the same way config load does: an invalid combination (e.g.
-  `cloud_llm` with neither a pinned primary nor `auto`) is rejected before
-  anything reaches `config.local.yaml`, and mode plus its companion block are
-  written as one atomic change so an in-between invalid state is never even
-  written transiently.
+  primary or `auto`; `local_encoder` needs a model id; `local_decision` needs a
+  generative model id). It shows the current mode and, for `cloud_primary_auto`,
+  the **live** resolved cheapest candidate — computed with
+  `routing.cheapest_classifier_candidate`, the same function `classify()`
+  calls, not a static echo of the config. An admin panel that shows a config
+  value instead of what dispatch actually does is a real class of bug — the same
+  lesson the profiles zero-admit badge above already taught this project — and a
+  "live" reading that is secretly stale is worse than no reading at all.
+  `POST /admin/api/classifier-config` validates the same way config load does:
+  an invalid combination (e.g. `cloud_llm` with neither a pinned primary nor
+  `auto`) is rejected before anything reaches `config.local.yaml`, and mode
+  plus its companion block are written as one atomic change so an in-between
+  invalid state is never even written transiently.
+
+  When `local_decision` is selected the panel reveals a **Local Decision**
+  block with the fields from `LocalDecisionConfig` in `src/config.py`:
+
+  | field | type | default | meaning |
+  |---|---|---|---|
+  | `base_url` | string | `http://localhost:11434` | Ollama endpoint for the generative classifier |
+  | `model` | string | `qwen3.5:4b` | The small generative model that picks category by choice |
+  | `num_ctx` | int | `8192` | Context window passed to the model |
+  | `timeout_s` | int | `10` | Seconds before the request times out |
+  | `confidence_min` | float | `0.5` | Minimum logprob-derived confidence to accept the verdict (range [0.0, 1.0]); below this the classification cascades as a failure |
+  | `coverage_min` | float | `0.3` | Minimum total probability mass on option letters; below this `parse_logprobs` raises and the classification cascades |
+  | `tier_enabled` | bool | `false` | Whether the classifier may also decide `task_tier` (vs. only category) |
 - **Watchdog** — the watchdog's live state (`controls.html#watchdog-card`,
  `loadWatchdog()`): the last tick time (and a `no tick` badge before the
  first one), how many sessions the last tick saw, how many verdicts it
--- a/docs/local-models.md
+++ b/docs/local-models.md
@@ -329,6 +329,72 @@ CPU install:
 pip install -r requirements-encoder.txt
 ```

+### `local_decision`: read the first-token logprobs, never parse a generation
+
+`classifier.mode: local_decision` is a third answer to the same
+runaway-reasoning problem, and the most direct one. Instead of asking a small
+generative model to emit JSON and hoping it stops, it asks for a single
+lettered choice and reads the decision out of the **logprobs on the first
+output token**.
+
+Mechanism, a Jev-style first-token-logprob classifier:
+
+- The task text and a lettered list of candidate categories go into one prompt;
+  the model is asked to answer with one letter.
+- Ollama returns `logprobs` for that first position. `local_decision.parse_logprobs`
+  sums `exp(logprob)` per option letter (A through J), ignoring non-option
+  tokens and Ollama's `.` separator tokens, then normalizes the accumulated mass
+  into a confidence. The letter with the most mass is the predicted category.
+- There is **no JSON parse, no reasoning trace, and no multi-token generation
+  to run away**. `num_predict` is 1 and `think` is false, so a model that would
+  normally spend its budget thinking has nowhere to spend it. The whole failure
+  class that took `qwen3.5` down on 4 of 19 calls simply does not exist here.
+- Confidence is the chosen letter's share of total option mass. `coverage_min`
+  (default `0.3`) is the minimum total option mass for a call to be accepted at
+  all; below it, or when no logprobs come back, the call raises and walks the
+  same cascade a `local_llm` parse failure would. `confidence_min` (default
+  `0.5`) treats a below-threshold verdict as a failure rather than a
+  low-confidence answer, mirroring the encoder's semantics.
+- As with `local_encoder`, it produces only `task_category` unless
+  `classifier.decision.tier_enabled` is set, in which case it may also choose
+  `task_tier`; otherwise tier falls back to `classifier.fallback_tier`.
+
+Pull and enable it:
+
+```bash
+ollama pull qwen3.5:4b
+```
+
+```yaml
+# config.local.yaml
+classifier:
+  mode: "local_decision"
+  decision: {}
+```
+
+Every field in `classifier.decision` has a default, so `decision: {}` is enough
+to opt in. The one that matters for hardware is **`classifier.decision.num_ctx`**:
+it sets the context window the classifier loads at (default `8192`) and scales
+the KV cache directly, so it is the cheapest GB to reclaim when the pair below
+does not fit. Model, `base_url`, timeout, and the confidence gates all live
+under the same block.
+
+VRAM, and why this is tight on a 24 GB card:
+
+| model | role | num_ctx | resident |
+|---|---|---|---|
+| `qwen2.5-coder-router:14b` | classify + verify + dispatch | 16384 | 12.3 GB |
+| `qwen3.5:4b` | `local_decision` | 8192 | ~7 GB |
+| | | **total** | **~19.9 GB of 24 GB** |
+
+The pair fits, with about 4 GB spare, but **only because the 14b router runs at
+`num_ctx 16384`, not the 32768 it was originally tagged with**. At 32k the
+router alone measured 16.5 to 17.8 GB resident, and the two models then fit by
+about 1 GB or get evicted outright depending on load order. Cutting the router
+to 16k is what makes the coexistence robust; that is a change to the
+`qwen2.5-coder-router:14b` Modelfile, not to this config block. Local vision is
+off in this profile because its 5.6 GB model no longer fits alongside both.
+
 ### The latency tax, and what has and hasn't addressed it

 ~10s of local overhead per message was the original number on a reasoning
--- a/evals/heldout.yaml
+++ b/evals/heldout.yaml
@@ -0,0 +1,31 @@
+tasks:
+- {id: h1, category: coding_general, prompt: "Add a /healthz endpoint to the FastAPI app that returns the git sha and uptime in seconds."}
+- {id: h2, category: coding_general, prompt: "write me a bash script that watches ~/Downloads and moves any .pdf into ~/Documents/pdfs, creating the folder if needed"}
+- {id: h3, category: coding_general, prompt: "Implement a React hook useDebounce(value, delayMs) with a small test."}
+- {id: h4, category: coding_refactor, prompt: "dispatcher.py is 2700 lines. Split the classifier cascade out into its own module without changing behaviour; keep the public function names."}
+- {id: h5, category: coding_refactor, prompt: "Replace all these nested if/else chains in parse_config with a lookup table, same outputs."}
+- {id: h6, category: coding_refactor, prompt: "Rename the `usr` variable to `user` everywhere in the auth package and pull the duplicated token-validation code into one helper."}
+- {id: h7, category: debugging, prompt: "The service crash-loops on start with `KeyError: 'HF_HOME'` after the last deploy. Figure out why and fix it."}
+- {id: h8, category: debugging, prompt: "tests pass locally but test_router_timeout fails in CI about 1 in 5 runs. can you find the race"}
+- {id: h9, category: debugging, prompt: "Why does this return None for empty lists?\n\ndef first(xs):\n    for x in xs:\n        return x"}
+- {id: h10, category: docs_writing, prompt: "Write a README section explaining how to configure the classifier overlay, with an example config.local.yaml."}
+- {id: h11, category: docs_writing, prompt: "add docstrings to every public function in scoring.py, google style"}
+- {id: h12, category: docs_writing, prompt: "Draft the release notes for v0.9: new admin knobs, the circuit breaker fix, and the OpenRouter allowlist."}
+- {id: h13, category: summarization, prompt: "Here's the incident postmortem thread from Slack (pasted below). Give me a 5-bullet summary of what happened and the action items.\n\n[thread: 40 messages about a database failover at 03:12, replica lag, a bad DNS TTL, and follow-ups assigned to three people]"}
+- {id: h14, category: summarization, prompt: "TL;DR this paper abstract and intro for me in two sentences: Large language models are increasingly deployed as routers..."}
+- {id: h15, category: summarization, prompt: "Condense these meeting notes into decisions and open questions only."}
+- {id: h16, category: file_summarization, prompt: "Read src/watchdog.py and tell me what it does at a high level."}
+- {id: h17, category: file_summarization, prompt: "what's in docs/incidents.md? give me an overview of each incident"}
+- {id: h18, category: file_summarization, prompt: "Summarize the contents of config/config.yaml section by section."}
+- {id: h19, category: diff_checking, prompt: "Review this PR diff and flag anything risky:\n\n--- a/src/routing.py\n+++ b/src/routing.py\n@@ -40,7 +40,7 @@\n-    if cost > ceiling:\n+    if cost >= ceiling:\n         return None"}
+- {id: h20, category: diff_checking, prompt: "Compare the two versions of the migration and tell me what changed in the schema."}
+- {id: h21, category: diff_checking, prompt: "look over my staged changes before I commit, anything I forgot?"}
+- {id: h22, category: translation, prompt: "Translate the error messages in i18n/en.json into German, keep the keys."}
+- {id: h23, category: translation, prompt: "¿Puedes traducir este párrafo al inglés? El enrutador clasifica cada tarea antes de enviarla."}
+- {id: h24, category: translation, prompt: "Put this email into polite Japanese for a client."}
+- {id: h25, category: reasoning_math, prompt: "If energy is billed at $8/kWh and a request uses 2.7e-5 kWh, what does 40,000 requests cost?"}
+- {id: h26, category: reasoning_math, prompt: "Three switches, one bulb in another room, you can only check once. How do you tell which switch controls it?"}
+- {id: h27, category: reasoning_math, prompt: "Prove that the sum of the first n odd numbers is n squared."}
+- {id: h28, category: general_chat, prompt: "hey, what's a good name for a model router project?"}
+- {id: h29, category: general_chat, prompt: "Is Rust or Go better for a home lab CLI tool? Just your opinion."}
+- {id: h30, category: general_chat, prompt: "thanks, that worked! what should I look at next?"}
--- a/plans/local-decision-classifier-heldout.yaml
+++ b/plans/local-decision-classifier-heldout.yaml
@@ -0,0 +1,31 @@
+tasks:
+- {id: h1, category: coding_general, prompt: "Add a /healthz endpoint to the FastAPI app that returns the git sha and uptime in seconds."}
+- {id: h2, category: coding_general, prompt: "write me a bash script that watches ~/Downloads and moves any .pdf into ~/Documents/pdfs, creating the folder if needed"}
+- {id: h3, category: coding_general, prompt: "Implement a React hook useDebounce(value, delayMs) with a small test."}
+- {id: h4, category: coding_refactor, prompt: "dispatcher.py is 2700 lines. Split the classifier cascade out into its own module without changing behaviour; keep the public function names."}
+- {id: h5, category: coding_refactor, prompt: "Replace all these nested if/else chains in parse_config with a lookup table, same outputs."}
+- {id: h6, category: coding_refactor, prompt: "Rename the `usr` variable to `user` everywhere in the auth package and pull the duplicated token-validation code into one helper."}
+- {id: h7, category: debugging, prompt: "The service crash-loops on start with `KeyError: 'HF_HOME'` after the last deploy. Figure out why and fix it."}
+- {id: h8, category: debugging, prompt: "tests pass locally but test_router_timeout fails in CI about 1 in 5 runs. can you find the race"}
+- {id: h9, category: debugging, prompt: "Why does this return None for empty lists?\n\ndef first(xs):\n    for x in xs:\n        return x"}
+- {id: h10, category: docs_writing, prompt: "Write a README section explaining how to configure the classifier overlay, with an example config.local.yaml."}
+- {id: h11, category: docs_writing, prompt: "add docstrings to every public function in scoring.py, google style"}
+- {id: h12, category: docs_writing, prompt: "Draft the release notes for v0.9: new admin knobs, the circuit breaker fix, and the OpenRouter allowlist."}
+- {id: h13, category: summarization, prompt: "Here's the incident postmortem thread from Slack (pasted below). Give me a 5-bullet summary of what happened and the action items.\n\n[thread: 40 messages about a database failover at 03:12, replica lag, a bad DNS TTL, and follow-ups assigned to three people]"}
+- {id: h14, category: summarization, prompt: "TL;DR this paper abstract and intro for me in two sentences: Large language models are increasingly deployed as routers..."}
+- {id: h15, category: summarization, prompt: "Condense these meeting notes into decisions and open questions only."}
+- {id: h16, category: file_summarization, prompt: "Read src/watchdog.py and tell me what it does at a high level."}
+- {id: h17, category: file_summarization, prompt: "what's in docs/incidents.md? give me an overview of each incident"}
+- {id: h18, category: file_summarization, prompt: "Summarize the contents of config/config.yaml section by section."}
+- {id: h19, category: diff_checking, prompt: "Review this PR diff and flag anything risky:\n\n--- a/src/routing.py\n+++ b/src/routing.py\n@@ -40,7 +40,7 @@\n-    if cost > ceiling:\n+    if cost >= ceiling:\n         return None"}
+- {id: h20, category: diff_checking, prompt: "Compare the two versions of the migration and tell me what changed in the schema."}
+- {id: h21, category: diff_checking, prompt: "look over my staged changes before I commit, anything I forgot?"}
+- {id: h22, category: translation, prompt: "Translate the error messages in i18n/en.json into German, keep the keys."}
+- {id: h23, category: translation, prompt: "¿Puedes traducir este párrafo al inglés? El enrutador clasifica cada tarea antes de enviarla."}
+- {id: h24, category: translation, prompt: "Put this email into polite Japanese for a client."}
+- {id: h25, category: reasoning_math, prompt: "If energy is billed at $8/kWh and a request uses 2.7e-5 kWh, what does 40,000 requests cost?"}
+- {id: h26, category: reasoning_math, prompt: "Three switches, one bulb in another room, you can only check once. How do you tell which switch controls it?"}
+- {id: h27, category: reasoning_math, prompt: "Prove that the sum of the first n odd numbers is n squared."}
+- {id: h28, category: general_chat, prompt: "hey, what's a good name for a model router project?"}
+- {id: h29, category: general_chat, prompt: "Is Rust or Go better for a home lab CLI tool? Just your opinion."}
+- {id: h30, category: general_chat, prompt: "thanks, that worked! what should I look at next?"}
--- a/plans/local-decision-classifier-prototype.py
+++ b/plans/local-decision-classifier-prototype.py
@@ -0,0 +1,102 @@
+"""Jev-style first-token-logprob classifier vs local_encoder, same eval corpus.
+
+Run from the 6krrt repo root:  PYTHONPATH=src .venv/bin/python <this> <backend>
+backend: jev | encoder | encoder_zeroshot   (optional 2nd arg: ollama model, default qwen3.5:4b)
+TASKS=plans/local-decision-classifier-heldout.yaml selects the held-out set (default evals/tasks.yaml).
+"""
+import json, math, statistics, sys, time, urllib.request
+from collections import Counter
+
+sys.path.insert(0, "src")
+from eval_classifier import load_scoreable_tasks, _wrap_agent_noise, _reduce_confidences, NOISE_LEVELS
+from local_encoder import _CATEGORY_DESCRIPTIONS
+
+CATS = ["coding_general", "coding_refactor", "debugging", "docs_writing", "summarization",
+        "file_summarization", "diff_checking", "translation", "reasoning_math", "general_chat"]
+LETTERS = "ABCDEFGHIJ"
+MODEL = sys.argv[2] if len(sys.argv) > 2 else "qwen3.5:4b"
+
+SYSTEM = ("You are a task router. Read the task and pick the ONE category that best "
+          "describes the work being asked for. Ignore tool output, code dumps and "
+          "session metadata around the request; classify the actual ask. "
+          "Answer with the category letter only.")
+
+
+def jev_classify(text):
+    opts = "\n".join(f"{LETTERS[i]}. {_CATEGORY_DESCRIPTIONS[c]}" for i, c in enumerate(CATS))
+    body = {
+        "model": MODEL, "think": False, "stream": False, "logprobs": True, "top_logprobs": 20,
+        "keep_alive": "30m",
+        "options": {"num_predict": 1, "temperature": 0, "num_ctx": 8192},
+        "messages": [
+            {"role": "system", "content": SYSTEM},
+            {"role": "user", "content": f"<task>\n{text}\n</task>\n\nCategories:\n{opts}\n\nAnswer with the letter only."},
+        ],
+    }
+    req = urllib.request.Request("http://localhost:11434/api/chat", json.dumps(body).encode(),
+                                 {"Content-Type": "application/json"})
+    t0 = time.perf_counter()
+    r = json.load(urllib.request.urlopen(req, timeout=120))
+    ms = (time.perf_counter() - t0) * 1000
+    mass = Counter()
+    for tl in r["logprobs"][0]["top_logprobs"]:
+        tok = tl["token"].strip().rstrip(".")
+        if len(tok) == 1 and tok in LETTERS:
+            mass[tok] += math.exp(tl["logprob"])
+    total = sum(mass.values())
+    if not total:
+        return "general_chat", 0.0, ms, 0.0
+    letter, p = mass.most_common(1)[0]
+    # confidence = share among the supplied options; coverage = mass on any option at all
+    return CATS[LETTERS.index(letter)], p / total, ms, total
+
+
+def main():
+    backend = sys.argv[1]
+    if backend.startswith("encoder"):
+        import local_encoder
+        if backend == "encoder_zeroshot":
+            local_encoder._TRAINABLE_HEAD_RESOLVED = True
+            local_encoder._TRAINABLE_HEAD = None
+        def classify(text):
+            t0 = time.perf_counter()
+            cat, conf = local_encoder.classify_zero_shot(text, CATS, model_id="BAAI/bge-large-en-v1.5", device="cpu")
+            return cat, conf, (time.perf_counter() - t0) * 1000, 1.0
+    else:
+        classify = jev_classify
+
+    tasks = load_scoreable_tasks(__import__("os").environ.get("TASKS", "evals/tasks.yaml"))
+    classify("warm up the model")
+    per_level = {l: [0, 0] for l in NOISE_LEVELS}
+    lat, confs_ok, confs_bad, verdict_ok, misses = [], [], [], 0, Counter()
+    for t in tasks:
+        preds = []
+        for level in NOISE_LEVELS:
+            cat, conf, ms, _ = classify(_wrap_agent_noise(t["prompt"], level))
+            lat.append(ms)
+            ok = cat == t["category"]
+            per_level[level][0] += ok
+            per_level[level][1] += 1
+            (confs_ok if ok else confs_bad).append(conf)
+            if not ok:
+                misses[(t["category"], cat)] += 1
+            preds.append((cat, conf))
+        verdict_ok += _reduce_confidences(preds)[0] == t["category"]
+
+    n = len(tasks)
+    print(f"== {backend} {MODEL if backend == 'jev' else ''} :: {n} tasks x {len(NOISE_LEVELS)} noise levels")
+    for l, (ok, tot) in per_level.items():
+        print(f"  {l:6s} {ok}/{tot} = {ok/tot:.1%}")
+    allok = sum(v[0] for v in per_level.values())
+    print(f"  all    {allok}/{n*3} = {allok/(n*3):.1%}   majority-vote verdict {verdict_ok}/{n}")
+    q = statistics.quantiles(lat, n=20)
+    print(f"  latency ms: p50 {statistics.median(lat):.0f}  p95 {q[18]:.0f}  max {max(lat):.0f}")
+    if confs_ok:
+        print(f"  conf correct: mean {statistics.mean(confs_ok):.2f}", end="")
+    if confs_bad:
+        print(f"   conf wrong: mean {statistics.mean(confs_bad):.2f}", end="")
+    print()
+    print("  top confusions (gold -> predicted):", misses.most_common(6))
+
+
+main()
--- a/plans/local-decision-classifier-results.md
+++ b/plans/local-decision-classifier-results.md
@@ -0,0 +1,210 @@
+# Gate G — local_decision measurement results
+
+Status: done -- gate G measured; VRAM passes with the 14b at 16k and local vision off (section 9); accuracy and latency pass
+
+Date: 2026-09-28
+Plan: `plans/local-decision-classifier.md`
+Hardware: Quadro RTX 6000, 24 GB (24576 MiB total)
+Models: `qwen3.5:4b` (classifier), `qwen2.5-coder-router:14b` (router, resident at 32768 ctx = 17.76 GB)
+Ollama: `http://localhost:11434`
+
+## Headline
+
+| Criterion | Measured | Threshold | Verdict |
+|---|---|---|---|
+| Eval-set accuracy (`evals/tasks.yaml`) | **97.8%** (tuned) | ≥ 85% | PASS |
+| Held-out accuracy (`evals/heldout.yaml`) | **90.0%** (tuned) | ≥ 90% | PASS (at threshold) |
+| p95 latency (14b resident) | **502 ms** | ≤ 800 ms | PASS |
+| 14b router resident after 100 calls | **EVICTED** | must stay resident | **FAIL** |
+
+**Gate verdict: STOP.** The 14b router model is evicted whenever `qwen3.5:4b` is loaded —
+on this 24 GB card there is no `num_ctx` at which the two coexist. Per the plan, "STOP AND
+REPORT after G if ... the 14b router model was evicted. Do NOT start todos 4-17 in that case."
+Todos 4-17 are NOT started.
+
+---
+
+## 1. Accuracy per set
+
+Measured with the eval harness (`--backend decision --decision-base-url http://localhost:11434`),
+majority-vote overall accuracy over the clean/short/long noise variants of each task.
+
+### Baseline (original `_DECISION_DESCRIPTIONS`)
+
+| Set | clean | short | long | overall |
+|---|---|---|---|---|
+| `evals/tasks.yaml` (46) | 0.739 | 0.783 | 0.761 | **0.761** |
+| `evals/heldout.yaml` (30) | 0.800 | 0.933 | 0.900 | **0.967** |
+
+Baseline confusion (eval-set, dominant cells): `coding_general → debugging` (14) and
+`→ reasoning_math` (4); `file_summarization → debugging` (9). The `debugging` description
+was attracting both code-tracing and code-summary tasks.
+
+### After description tuning
+
+`_DECISION_DESCRIPTIONS` changes: widen `coding_general` to include "tracing what existing
+code returns" and "implementing a feature or algorithm"; narrow `debugging` to "fixing a bug";
+narrow `reasoning_math` to "pure math or logic word problem (no code)"; soften
+`file_summarization` to "summarizing or explaining".
+
+| Set | clean | short | long | overall |
+|---|---|---|---|---|
+| `evals/tasks.yaml` (46) | 0.978 | 0.978 | 0.978 | **0.978** |
+| `evals/heldout.yaml` (30) | 0.800 | 0.900 | 0.833 | **0.900** |
+
+Tuning lifted the eval-set from 76.1% → 97.8% (the confusion cells are gone: eval-set
+`coding_general` 27/27, `file_summarization` 18/18, `debugging` 18/18). It cost 6.7 points on
+held-out (96.7% → 90.0%), landing exactly on the 90% threshold. Held-out residual misses:
+`h2` coding_general→file_summarization, `h30` general_chat→file_summarization, `h9`
+debugging→general_chat — all low-confidence (0.33-0.49) edge cases the confidence cascade
+would absorb. Further tuning to chase these risks overfitting `evals/tasks.yaml` (the exact
+failure the plan warns about), so tuning stops here.
+
+## 2. Calibration (tuned)
+
+Mean confidence when correct vs incorrect (per-noise-level rows).
+
+| Set | correct mean | incorrect mean |
+|---|---|---|
+| `evals/tasks.yaml` | 0.798 | 0.350 |
+| `evals/heldout.yaml` | 0.802 | 0.413 |
+
+Calibrated in the right direction on both sets: the model is roughly twice as confident when
+right as when wrong. (Baseline was 0.833/0.438 eval-set, 0.815/0.300 heldout — the tuning
+moved both closer together but kept correct ≫ incorrect.)
+
+## 3. Latency (p50 / p95)
+
+Per-call latency of a single `classify_choice` over the eval-set, `qwen3.5:4b`, `num_ctx 8192`,
+with the 14b router resident (the production steady state).
+
+```
+single category call: n=46 p50=363ms p95=502ms p99=598ms mean=379ms
+```
+
+**p95 502 ms ≤ 800 ms — PASS.**
+
+## 4. Open question (a) — noise isolation
+
+Compare `classify_choice` on raw wrapped (noisy) text vs text passed through
+`local_encoder._isolate_task_text` first.
+
+| Set | no-isolation (short+long) | isolated (short+long) | clean baseline |
+|---|---|---|---|
+| `evals/tasks.yaml` | 0.772 | 0.739 | 0.739 |
+| `evals/heldout.yaml` | 0.917 | 0.800 | 0.800 |
+
+**Isolation HURTS** on both sets (−3.3 pt eval, −11.7 pt heldout). For `local_decision` the
+fenced-code/tool content `_isolate_task_text` strips is itself the signal (code-tracing,
+file-summary tasks). **Do NOT run `_isolate_task_text` before `classify_choice`; send raw text.**
+This overrides the prototype's earlier "noise cost about 2 points" note — for this backend
+isolation is strictly worse.
+
+## 5. Open question (b) — tier call strategy
+
+Measure separate-request latency vs a joint prompt reading two token positions.
+
+| strategy | p50 | p95 | reliability |
+|---|---|---|---|
+| category call (10 options) | 361 ms | 595 ms | — |
+| tier call (3 options, separate) | 326 ms | 553 ms | — |
+| separate, sequential total | 689 ms | 1154 ms | 100% |
+| joint prompt, two token positions | 434 ms | 635 ms | **5/30 = 17%** |
+
+The joint two-position read is faster (p50 434 vs 689 ms) but **unreliable**: only 5/30 (17%)
+prompts produced valid letter mass (>0.5) at BOTH token positions — the model does not
+reliably answer two questions with two letters in one generation.
+
+**Recommendation: two separate calls fired in parallel** (category + tier). Parallel wall-clock
+= max(category, tier) ≈ p95 ~595 ms, fully reliable, and matches the plan's component-4 design
+("fired in parallel ... so it adds no wall-clock time"). The joint cleverness is rejected on
+reliability, not speed.
+
+## 6. Open question (c) — num_ctx trade-off
+
+100-call run at each `num_ctx`, `/api/ps` before/after, `qwen3.5:4b` footprint and 14b residency.
+
+| num_ctx | p50 | p95 | qwen3.5:4b VRAM | 14b router resident? |
+|---|---|---|---|---|
+| 4096 | 361 ms | 588 ms | 6.15 GB | **NO** |
+| 8192 | 364 ms | 594 ms | 6.32 GB | **NO** |
+| 16384 | 364 ms | 591 ms | 6.65 GB | **NO** |
+
+Latency is essentially flat across `num_ctx` (the classification prompts are short), and VRAM
+grows 6.15 → 6.65 GB. If coexistence were possible, `num_ctx 4096` would be the best
+trade-off. It is not possible: see below.
+
+## 7. VRAM / router-model eviction (HARD CONSTRAINT — FAILS)
+
+`/api/ps` before and after a 100-call run at `num_ctx 8192`:
+
+```
+BEFORE: {'qwen2.5-coder-router:14b': 17.76}
+AFTER : {'qwen3.5:4b': 6.32}     # 14b router GONE
+14b router resident after 100 calls: False
+```
+
+Definitive coexistence check — 14b loaded at its production context (32768 = 17.76 GB), then a
+single `qwen3.5:4b` call at each `num_ctx`:
+
+```
+after qwen3.5@4096 : {'qwen3.5:4b': 6.15}                coexist? False
+after qwen3.5@8192 : {'qwen3.5:4b': 6.32}                coexist? False
+after qwen3.5@16384: {'qwen3.5:4b': 6.65}                coexist? False
+```
+
+Why: the RTX 6000 has 24 GB. The 14b router occupies 17.76 GB (Q4_K_M @ 32768 ctx) plus ~1.2 GB
+of other GPU processes → ~18.4 GB used, ~5.5 GB free. `qwen3.5:4b` needs ≥ 6.15 GB at any
+`num_ctx`, so loading it always exceeds free VRAM and Ollama evicts the 14b router. 17.76 + 1.2 +
+6.15 = 25.1 GB > 24 GB — it cannot physically fit at `num_ctx 4096`, let alone 8192.
+
+(The earlier plan note "the card sat at 23.4/24 GB with both resident at num_ctx 8192" was not
+reproducible — the 14b router here is 17.76 GB at its production 32768 context, and the two do
+not coexist.)
+
+## 8. Gate summary and next action
+
+- Eval-set 97.8% ≥ 85% — PASS
+- Held-out 90.0% ≥ 90% — PASS
+- p95 502 ms ≤ 800 ms — PASS
+- 14b router resident after 100 calls — **FAIL (evicted)**
+
+**The local_decision approach fails gate G on VRAM and must NOT proceed to todos 4-17.**
+The accuracy and latency bars are met and the description tuning is complete, but the classifier
+cannot run on this 24 GB card without evicting the 14b router model that local dispatch depends
+on. Options to revisit before re-running gate G (out of scope here): a smaller classifier model
+that coexists in the remaining ~5.5 GB (e.g. a ~3-4 GB 2-3B model at low num_ctx), or a model
+that is small enough that 14b + classifier + other procs fit in 24 GB.
+
+## 9. Re-measure with the 14b at 16k (reviewer, 2026-09-28, owner option A)
+
+The owner chose option A: drop the router 14b to `num_ctx 16384` and re-measure.
+Local vision fallback (`qwen3-vl-router:4b`, 5.6 GB) is being turned off: it has
+never fired (0 route decisions have ever selected a `qwen3-vl` model).
+
+Measured with a separate test tag (`FROM qwen2.5-coder:14b`, `PARAMETER num_ctx
+16384`), so the production tag was untouched. The tag was deleted afterwards.
+
+| state | `/api/ps` size_vram | nvidia-smi used |
+|---|---|---|
+| 14b @ 16k alone | 12.26 GB | 14.2 GB |
+| + `qwen3.5:4b` @ 4096 | 12.26 + 5.73 GB | 19.7 GB |
+| + `qwen3.5:4b` @ 8192 | 12.26 + 5.89 GB | 19.9 GB (about 4 GB spare) |
+
+**At 16k the two coexist, with about 4 GB of headroom.** The vision model (5.6 GB)
+would not also fit, which is why it is being turned off.
+
+**Gate G's "evicted at every num_ctx" is not stable.** During the latency run,
+production traffic reloaded its own 14b at 32k, and it stayed resident alongside
+the 4b: 16.54 + 5.89 GB, 22.9 of 24 GB used, about 1 GB spare. Ollama's eviction
+follows its own memory estimate, which varies with load order and other GPU
+processes (gate G saw the same 14b as 17.76 GB). At 32k the pair fits only by
+about 1 GB and can flip back to eviction; 16k is the robust setting.
+
+Latency, 100 calls of `classify_choice` (`num_ctx 8192`) with both models resident:
+p50 321 ms, p95 474 ms, max 2545 ms (the first cold call). The 14b stayed resident
+throughout. **p95 474 ms, PASS.**
+
+**Revised gate verdict:** PASS on every criterion **provided** the 14b router tag
+runs at `num_ctx 16384` and local vision is off. Both are production changes and
+the owner's call; todos 4-17 may proceed once they are made.
--- a/plans/local-decision-classifier.md
+++ b/plans/local-decision-classifier.md
@@ -0,0 +1,157 @@
+# `classifier.mode: local_decision` — a Jev-style classifier on the local GPU
+
+Status: planned -- plan reviewed 2026-09-28; gate G (tuning + measurement) decides whether the wiring proceeds
+Date: 2026-09-27
+Prototype: `plans/local-decision-classifier-prototype.py` (a benchmark you can run, NOT the
+implementation)
+Held-out prompts: `plans/local-decision-classifier-heldout.yaml`
+Background: TypeSafe's hosted "Jev" decision model and the OpenJev/SemIf open
+copies. We are copying the **technique**, not using either product.
+
+## Goal
+
+Add a fourth `classifier.mode`, `local_decision`. It asks a small local LLM
+(`qwen3.5:4b` on Ollama) one multiple-choice question and reads the answer
+from the **first output token's logprobs**, normalised over the option letters
+that were offered. There is no text generation, JSON or reasoning trace, so the
+runaway-reasoning failure that retired `local_llm` can't happen, and it is
+calibrated far better than `local_encoder`.
+
+## Evidence (2026-09-27, RTX 6000 24 GB, measured with the prototype)
+
+46 eval tasks and 30 held-out tasks, each at 3 noise levels (`_wrap_agent_noise`):
+
+| | `evals/tasks.yaml` | held-out | p50 / p95 ms |
+|---|---|---|---|
+| `local_decision` (`qwen3.5:4b`, GPU) | 71% | 100% | 340-385 / 510-610 |
+| `local_encoder` + trained head (live, CPU) | 100%\* | 47% | 80-115 / 100-360 |
+| `local_encoder` zero-shot (CPU) | 39% | 60% | 80-115 / 100-350 |
+
+\* The head was fitted on a synthetic corpus derived from `evals/tasks.yaml`, so
+this is leakage. On held-out prompts the head is **worse** than zero-shot.
+
+- **Calibration:** `local_decision` averages 0.96 confidence when right and 0.71 when wrong. The encoder
+  zero-shot sits at 0.2-0.3 on both.
+- **Eval-set misses:** 18 were `coding_general`→`reasoning_math` (algorithm tasks), 18 were
+  `file_summarization`→`debugging`/`reasoning_math`/`docs_writing`. Both look like
+  option-description problems (component 2).
+- **VRAM:** `qwen3.5:4b` holds about 7 GB at `num_ctx` 8192 next to `qwen2.5-coder-router:14b`.
+  The card sat at 23.4/24 GB. It's tight, so measure again when planning.
+- **Weak held-out set:** a single author wrote all 30 prompts and they are short and clean. Treat
+  100% as a ceiling, not a forecast.
+
+## Mechanism (proven by the prototype)
+
+Ollama 0.22 native `POST /api/chat`:
+`think: false`, `logprobs: true`, `top_logprobs: 20`,
+`options: {num_predict: 1, temperature: 0, num_ctx: N}`. The system prompt says
+"answer with the letter only". The options are `A.`–`J.` followed by a natural-language description.
+Parse `logprobs[0].top_logprobs`:
+- strip each token and remove a trailing `.`
+- sum the probability mass for each option letter (`"B"` and `" B"` both count)
+- confidence = winner's mass ÷ total option mass
+- coverage = total option mass
+
+Tokens like `<think>` show up in the top 20 and must be ignored.
+
+## Components
+
+### 1. `src/local_decision.py` (stdlib + `requests`, no new dependency)
+
+- **`classify_choice(text, options, *, base_url, model, num_ctx, timeout)`** returns
+  `(label, confidence, coverage)`. It is generic over the option list, so tier
+  reuses it.
+- **Coverage floor:** below a configurable `coverage_min` (the model didn't answer
+  with a letter), raise. Don't guess.
+- **Pure parse function:** split the logprob parsing into its own function so tests
+  can drive it with recorded responses and no Ollama.
+- **Noise:** decide whether to run `local_encoder._isolate_task_text` first. The prototype
+  sent the raw noisy text and noise cost about 2 points; measure both.
+
+### 2. Its own option descriptions
+
+Give it a separate `_DECISION_DESCRIPTIONS` map. **Do not edit**
+`local_encoder._CATEGORY_DESCRIPTIONS`, because that would shift the encoder and
+invalidate its head. Tune it against the two confusions above, and re-measure after
+every change on **both** sets so nobody overfits `evals/tasks.yaml` the way the head did.
+
+### 3. Wiring (follow how `local_encoder` was added)
+
+- **`src/config.py`:** add `"local_decision"` to the `mode` Literal (line ~1187). Add a
+  `classifier.decision` block (`base_url`, `model`, `num_ctx`, `timeout_s`,
+  `confidence_min`, `coverage_min`, `tier_enabled`) and a validator that requires
+  it when the mode is selected. Metering (line ~1712): this mode is an HTTP call to
+  Ollama, so it meters like `local_llm` via its own `base_url`, not like the in-process encoder.
+- **`src/dispatcher.py`:**
+  - add `_classify_via_local_decision` and a branch in `_classify_via_configured_mode`
+  - add a startup readiness check in `_ensure_classifier_mode_ready` (model is pulled and
+    logprobs come back)
+  - on success record `source="classifier"`
+  - treat below-threshold confidence as a failure that walks the **unchanged** cascade
+- **Gating:** it uses the GPU, so it obeys `_local_classifier_skip_reason()`
+  (`local_compute.enabled` plus the circuit breaker), the same as `local_llm`.
+- **Admin:** every new scalar knob gets a control on the Classifier card (North Star #1).
+  `tests/test_admin_knob_coverage.py` enforces this.
+
+### 4. Tier (behind `tier_enabled`, default false)
+
+- **How:** a second `classify_choice` call with three options (cheap/simple, general,
+  frontier/high-stakes), fired **in parallel** with the category call so it adds no
+  wall-clock time.
+- **Why it defaults off:** no gold tier labels exist yet. Until they do, tier keeps
+  falling back to `fallback_tier`, exactly as `local_encoder` does now.
+- **Output:** a gold tier set of about 30 labelled prompts, used to decide the default.
+
+### 5. Evaluation harness
+
+- **Backend flag:** add a `--backend encoder|decision` flag to `src/eval_classifier.py`
+  (latency is reported per call).
+- **Held-out set:** promote the held-out prompts to `evals/` and add a `--tasks` default for them.
+- **Head flag:** add a flag that runs the encoder with the head disabled, so the leakage
+  stays visible.
+- **Report:** accuracy for both sets, calibration (confidence correct vs wrong), and p50/p95.
+
+### 6. Docs
+
+- **CLAUDE.md:** the "Which implementation is PRIMARY" section, which also still says
+  the live encoder is `device: cuda` while the overlay says `cpu`. Fix that.
+- **Also:** `docs/local-models.md` and `docs/admin-portal.md`.
+
+## Acceptance
+
+- **Held-out accuracy:** at least 90% on the held-out set.
+- **Eval-set accuracy:** at least 85% on `evals/tasks.yaml` after component 2. **Stop and
+  report** if that needs more than description tuning.
+- **Latency:** p95 at or below 800 ms on the RTX 6000 with the 14b router model resident.
+  The router model must not be evicted: check `/api/ps` before and after a 100-call run.
+- **Failure paths:** Ollama down, model missing, coverage below the floor and low
+  confidence each walk the cascade, covered by tests with a fake Ollama.
+- **Tests:** the full suite is green, including `test_admin_knob_coverage.py`.
+
+## Out of scope
+
+- **Replacing gaming mode** with an automatic Ollama reachability check. It's a separate
+  lift, and when it lands this mode inherits it for free.
+- **What to do about the trained encoder head.** It's a separate decision, but the
+  evidence above is a strong argument for turning it off.
+- **Other routes:** hosted Jev (cloud-only, bans benchmarking, sends raw task text off-box),
+  OpenJev 27B (no FP8 on Turing, CC BY-NC, evicts the router model), and fine-tuning.
+- **Defaults:** making `local_decision` the `config.yaml` default. It's an overlay opt-in
+  until it has run on live traffic.
+
+## Guardrails
+
+- **Worktree:** do the work in its own worktree (North Star #3). There are 15 open ones,
+  and `feat/local-encoder-accuracy-rebuild` touches classifier code. Commit by explicit path.
+- **Live router:** exercise the admin portal on the 8081 sandbox, never on production 8080,
+  and don't edit `config.local.yaml` (see the `6krrt-ops` skill).
+- **Live database:** open `router.db` read-only.
+
+## Open questions for planning
+
+1. Isolate noise before calling or not (component 1)? Decide from a measurement.
+2. Should tier's second call be a separate request, or both questions in one prompt
+   read from two token positions? Separate is simpler; measure the latency before
+   choosing the clever option.
+3. What `num_ctx` gives the best VRAM trade-off against the 8000-char
+   `max_input_chars` clamp?
--- a/src/admin.py
+++ b/src/admin.py
@@ -513,6 +513,7 @@ _CONFIG_ALLOWLIST: dict[str, tuple[str, ...]] = {
    "session_cache.staleness_seconds": ("session_cache", "staleness_seconds"),
    "local_compute.enabled": ("local_compute", "enabled"),
    "verification.local_llm_enabled": ("verification", "local_llm_enabled"),
+    "local_vision.enabled": ("local_vision", "enabled"),
    "pinch.enabled": ("pinch", "enabled"),
    "pinch.prefix_probe": ("pinch", "prefix_probe"),
    "pinch.relevance.enabled": ("pinch", "relevance", "enabled"),
@@ -543,6 +544,7 @@ _CONFIG_GET_ORDER: list[str] = [
    "session_cache.staleness_seconds",
    "local_compute.enabled",
    "verification.local_llm_enabled",
+    "local_vision.enabled",
    "pinch.enabled",
    "pinch.prefix_probe",
    "pinch.relevance.enabled",
@@ -904,10 +906,11 @@ class _ClassifierConfigBody(_AdminBody):
    classes' field definitions here.
    """

-    mode: Literal["local_llm", "cloud_llm", "local_encoder"]
+    mode: Literal["local_llm", "cloud_llm", "local_encoder", "local_decision"]
    cloud_primary: Optional[dict] = None
    cloud_primary_auto: bool = False
    encoder: Optional[dict] = None
+    decision: Optional[dict] = None


 class _CloudFallbackBody(_AdminBody):
@@ -2671,6 +2674,7 @@ def build_router(
            "cloud_primary_auto": bool(classifier.get("cloud_primary_auto", False)),
            "cloud_fallback": classifier.get("cloud_fallback"),
            "encoder": classifier.get("encoder"),
+            "decision": classifier.get("decision"),
            "resolved_primary": resolved_primary,
        }

@@ -2737,6 +2741,8 @@ def build_router(
                mutations.append((("classifier", "cloud_primary"), body.cloud_primary))
        if body.mode == "local_encoder":
            mutations.append((("classifier", "encoder"), body.encoder or {}))
+        if body.mode == "local_decision":
+            mutations.append((("classifier", "decision"), body.decision or {}))

        try:
            _persist_many_to(
--- a/src/config.py
+++ b/src/config.py
@@ -1119,6 +1119,42 @@ class LocalEncoderConfig(StrictModel):
    tier_feature_fields: list[str] = Field(default_factory=list)


+class LocalDecisionConfig(StrictModel):
+    """A generative local model that picks a task category by choice.
+
+    Unlike the embedding+centroid ``LocalEncoderConfig``, this is a small
+    generative LLM asked to return one of a fixed set of category labels
+    (see ``classify_choice`` in local_decision.py). Confidence comes from the
+    model's logprobs on the chosen label, not from a similarity score.
+    """
+
+    base_url: str = "http://localhost:11434"
+    model: str = "qwen3.5:4b"
+    num_ctx: int = 8192
+    timeout_s: int = 10
+    # minimum logprob-derived confidence to accept the model's verdict.
+    # Below this threshold the classification is treated as a FAILURE
+    # rather than a low-confidence answer, mirroring the encoder's
+    # confidence_min semantics.
+    confidence_min: float = 0.5
+    # minimum total probability mass on option letters for one call;
+    # below this threshold parse_logprobs raises RuntimeError and the
+    # classification cascades as a failure.
+    coverage_min: float = 0.3
+    # whether the classifier may also decide task_tier (vs. only category).
+    tier_enabled: bool = False
+
+    @field_validator("confidence_min")
+    @classmethod
+    def confidence_min_in_range(cls, v: float) -> float:
+        if not (0.0 <= v <= 1.0):
+            raise ValueError(
+                f"classifier.decision.confidence_min must be in [0.0, 1.0], "
+                f"got {v!r}"
+            )
+        return v
+
+
 class ClassifierConfig(StrictModel):
    provider: str
    base_url: str
@@ -1184,7 +1220,7 @@ class ClassifierConfig(StrictModel):
    #
    # "local_llm" is every existing deployment's behavior, unchanged, and
    # stays the default so this feature costs nothing until opted into.
-    mode: Literal["local_llm", "cloud_llm", "local_encoder"] = "local_llm"
+    mode: Literal["local_llm", "cloud_llm", "local_encoder", "local_decision"] = "local_llm"
    # Only read when mode == "cloud_llm", and mutually exclusive with
    # cloud_primary_auto (RouterConfig validator enforces exactly one).
    # Reuses CloudFallbackConfig's shape verbatim rather than a near-copy —
@@ -1197,6 +1233,8 @@ class ClassifierConfig(StrictModel):
    cloud_primary_auto: bool = False
    # Only read when mode == "local_encoder".
    encoder: Optional["LocalEncoderConfig"] = None
+    # Only read when mode == "local_decision".
+    decision: Optional["LocalDecisionConfig"] = None


 class LocalComputeConfig(StrictModel):
@@ -1518,6 +1556,22 @@ class RouterConfig(StrictModel):
            )
        return self

+    @model_validator(mode="after")
+    def local_decision_mode_needs_decision_config(self) -> "RouterConfig":
+        """classifier.mode == 'local_decision' needs to know which model.
+
+        LocalDecisionConfig ships sensible defaults, so this never forces an
+        operator to write out every field -- but the block itself must exist,
+        the same way local_encoder.py refuses to guess a model id.
+        """
+        if self.classifier.mode == "local_decision" and self.classifier.decision is None:
+            raise ValueError(
+                "classifier.mode is 'local_decision' but classifier.decision is not "
+                "set. Add a classifier.decision block (its fields all have "
+                "defaults, so `classifier.decision: {}` is enough)."
+            )
+        return self
+
    @model_validator(mode="after")
    def gaming_mode_requires_a_cloud_classifier(self) -> "RouterConfig":
        """Local compute off with no cloud classifier is a worse router.
@@ -1724,18 +1778,26 @@ class RouterConfig(StrictModel):
            # silently stopped metering a GPU whose electricity is on this
            # machine's own bill -- while warning about a URL nothing calls.
            encoder_is_in_process = self.classifier.mode == "local_encoder"
+            classify_is_loopback = True if encoder_is_in_process else (
+                _is_loopback_host(self.classifier.decision.base_url)
+                if self.classifier.mode == "local_decision"
+                and self.classifier.decision is not None
+                else _is_loopback_host(self.classifier.base_url)
+            )
            sites = {
-                "classify": (
-                    True if encoder_is_in_process
-                    else _is_loopback_host(self.classifier.base_url)
-                ),
+                "classify": classify_is_loopback,
                "verify": _is_loopback_host(self.verification.base_url),
                "local_vision": _is_loopback_host(self.local_vision.base_url),
            }
            for name, loopback in sites.items():
                if not loopback:
                    section = {
-                        "classify": self.classifier,
+                        "classify": (
+                            self.classifier.decision
+                            if self.classifier.mode == "local_decision"
+                            and self.classifier.decision is not None
+                            else self.classifier
+                        ),
                        "verify": self.verification,
                        "local_vision": self.local_vision,
                    }[name]
--- a/src/dispatcher.py
+++ b/src/dispatcher.py
@@ -61,6 +61,7 @@ import admin
 import circuit_breaker
 import events
 import exploration
+import local_decision
 import local_encoder
 import local_energy
 import logs
@@ -570,13 +571,38 @@ def _ensure_classifier_mode_ready() -> None:
    not at the worst possible moment" reasoning StrictModel and this
    project's config validators already apply.
    """
-    if cfg.classifier.mode != "local_encoder":
-        return
-    try:
-        local_encoder.ensure_available(cfg.classifier.encoder.model, cfg.classifier.encoder.device)
-    except ImportError as exc:
-        logs.warning("classifier_mode_unavailable", mode="local_encoder", error=str(exc))
-        raise
+    if cfg.classifier.mode == "local_encoder":
+        try:
+            local_encoder.ensure_available(cfg.classifier.encoder.model, cfg.classifier.encoder.device)
+        except ImportError as exc:
+            logs.warning("classifier_mode_unavailable", mode="local_encoder", error=str(exc))
+            raise
+
+    if cfg.classifier.mode == "local_decision":
+        if not cfg.local_compute.enabled:
+            logs.info("classifier_mode_probe_skipped", mode="local_decision", reason="gaming_mode")
+            return
+
+        try:
+            # Make a single test call to verify the model responds with logprobs.
+            # We don't raise here because Ollama-down at boot time should not
+            # stop the router from starting — the cascade handles classification
+            # failures gracefully.
+            local_decision.classify_category(
+                "test task",
+                base_url=cfg.classifier.decision.base_url,
+                model=cfg.classifier.decision.model,
+                num_ctx=cfg.classifier.decision.num_ctx,
+                timeout_s=cfg.classifier.decision.timeout_s,
+                coverage_min=cfg.classifier.decision.coverage_min,
+            )
+        except Exception as exc:  # noqa: BLE001 — Ollama may be down at boot
+            logs.warning(
+                "classifier_mode_unavailable",
+                mode="local_decision",
+                error=str(exc),
+                suggestion="start Ollama or check classifier.decision.base_url",
+            )


 _ensure_classifier_mode_ready()
@@ -1058,6 +1084,216 @@ def _classify_via_local_encoder(task: str) -> Classification:
    )


+def _classify_via_local_decision(
+    system_prompt: str, user_content: str
+) -> Classification:
+    """classifier.mode == "local_decision": a generative model picks a category.
+
+    Unlike local_encoder (which scores the raw task text against every
+    category zero-shot), local_decision asks a generative Ollama model to
+    choose ONE lettered category, reading the confidence off the first-token
+    logprobs. When ``cfg.classifier.decision.tier_enabled`` is True the
+    function also fires a second call to classify task_tier; otherwise
+    tier falls back to ``cfg.classifier.fallback_tier`` with no extra call.
+    required_context_tokens is 0 for the same reason as the encoder:
+    chat_completions already measures the real conversation with
+    estimate_prompt_tokens and takes the larger value.
+
+    ``system_prompt`` is accepted for signature symmetry with the other
+    ``_classify_via_*`` functions, but local_decision supplies its own system
+    prompt (see local_decision.py); only ``user_content`` (the framed task
+    text) is classified. A below-threshold confidence is treated as a FAILURE
+    -- raising here means the caller cascades, exactly as it would for a
+    local-LLM parse failure or a low-confidence encoder verdict.
+    """
+    skip = _local_classifier_skip_reason()
+    if skip is not None:
+        logs.warning("classify_local_skipped", reason=skip)
+        raise _ClassifierSkipped(skip)
+
+    dec = cfg.classifier.decision
+    if cfg.local_energy.enabled and cfg.local_energy_call_sites.get("classify"):
+        try:
+            with local_energy.measure(
+                sample_interval_seconds=cfg.local_energy.sample_interval_seconds,
+                sampler=local_energy.sample_nvidia_smi,
+            ) as measurement:
+                tier = cfg.classifier.fallback_tier
+                if dec.tier_enabled:
+                    from concurrent.futures import ThreadPoolExecutor
+
+                    with ThreadPoolExecutor(max_workers=2) as pool:
+                        cat_future = pool.submit(
+                            local_decision.classify_category,
+                            user_content,
+                            base_url=dec.base_url,
+                            model=dec.model,
+                            num_ctx=dec.num_ctx,
+                            timeout_s=dec.timeout_s,
+                            coverage_min=dec.coverage_min,
+                        )
+                        tier_future = pool.submit(
+                            _classify_tier_via_local_decision, user_content
+                        )
+                        category, confidence, _coverage = cat_future.result()
+                        if confidence < dec.confidence_min:
+                            raise RuntimeError(
+                                f"local_decision confidence {confidence:.3f} "
+                                f"is below classifier.decision.confidence_min "
+                                f"({dec.confidence_min})"
+                            )
+                        try:
+                            tier = tier_future.result()
+                        except Exception:
+                            tier = cfg.classifier.fallback_tier
+                else:
+                    category, confidence, _coverage = (
+                        local_decision.classify_category(
+                            user_content,
+                            base_url=dec.base_url,
+                            model=dec.model,
+                            num_ctx=dec.num_ctx,
+                            timeout_s=dec.timeout_s,
+                            coverage_min=dec.coverage_min,
+                        )
+                    )
+                    if confidence < dec.confidence_min:
+                        raise RuntimeError(
+                            f"local_decision confidence {confidence:.3f} "
+                            f"is below classifier.decision.confidence_min "
+                            f"({dec.confidence_min})"
+                        )
+
+            _log_local_energy(
+                model_id=dec.model,
+                call_type="classify",
+                measurement=measurement,
+            )
+            return Classification(
+                task_category=category,
+                task_tier=tier,
+                required_context_tokens=0,
+                confidence=confidence,
+                source="classifier",
+            )
+        except Exception as exc:  # noqa: BLE001 — meter the real draw on ANY failure
+            if isinstance(exc, requests.RequestException):
+                _record_failure()
+            _log_local_energy(
+                model_id=dec.model,
+                call_type="classify",
+                measurement=measurement,
+            )
+            raise
+    # Run category and tier classification in parallel when tier_enabled.
+    # NOTE: RuntimeError (below confidence_min) and other non-transport errors
+    # must NOT call _record_failure() — the endpoint answered; opening the
+    # circuit on every unsure classification would skip the classifier for
+    # cooldown_seconds on healthy traffic.
+    try:
+        tier = cfg.classifier.fallback_tier
+        if dec.tier_enabled:
+            from concurrent.futures import ThreadPoolExecutor
+
+            with ThreadPoolExecutor(max_workers=2) as pool:
+                cat_future = pool.submit(
+                    local_decision.classify_category,
+                    user_content,
+                    base_url=dec.base_url,
+                    model=dec.model,
+                    num_ctx=dec.num_ctx,
+                    timeout_s=dec.timeout_s,
+                    coverage_min=dec.coverage_min,
+                )
+                tier_future = pool.submit(
+                    _classify_tier_via_local_decision, user_content
+                )
+                category, confidence, _coverage = cat_future.result()
+                if confidence < dec.confidence_min:
+                    raise RuntimeError(
+                        f"local_decision confidence {confidence:.3f} is below "
+                        f"classifier.decision.confidence_min ({dec.confidence_min})"
+                    )
+                try:
+                    tier = tier_future.result()
+                except Exception:
+                    tier = cfg.classifier.fallback_tier
+        else:
+            category, confidence, _coverage = local_decision.classify_category(
+                user_content,
+                base_url=dec.base_url,
+                model=dec.model,
+                num_ctx=dec.num_ctx,
+                timeout_s=dec.timeout_s,
+                coverage_min=dec.coverage_min,
+            )
+            if confidence < dec.confidence_min:
+                raise RuntimeError(
+                    f"local_decision confidence {confidence:.3f} is below "
+                    f"classifier.decision.confidence_min ({dec.confidence_min})"
+                )
+            tier = _classify_tier_via_local_decision(user_content)
+        return Classification(
+            task_category=category,
+            task_tier=tier,
+            required_context_tokens=0,
+            confidence=confidence,
+            source="classifier",
+        )
+    except Exception as exc:  # noqa: BLE001
+        if isinstance(exc, requests.RequestException):
+            _record_failure()
+        raise
+
+
+# Tier classification options for local_decision mode.
+# The option letters map to tiers: A→1, B→2, C→3.
+_TIER_OPTIONS: Final[dict[str, str]] = {
+    "A": "simple or small task (tier 1)",
+    "B": "medium complexity task (tier 2)",
+    "C": "complex or hard task (tier 3)",
+}
+
+
+def _classify_tier_via_local_decision(task_text: str) -> int:
+    """Ask the local decision model to classify task tier via Ollama.
+
+    When ``cfg.classifier.decision.tier_enabled`` is False the caller
+    skips tier classification entirely and returns
+    ``cfg.classifier.fallback_tier`` (fast path).  When enabled the
+    function calls ``local_decision.classify_choice()`` with the A/B/C
+    tier prompt; on any network or parse failure it falls back to
+    ``cfg.classifier.fallback_tier``.
+
+    Returns
+    -------
+    int
+        1, 2, or 3 for the classified tier, or ``fallback_tier`` on
+        failure.
+    """
+    if not cfg.classifier.decision.tier_enabled:
+        return cfg.classifier.fallback_tier
+
+    dec = cfg.classifier.decision
+    try:
+        tier_label, _confidence, _coverage = local_decision.classify_choice(
+            task_text,
+            _TIER_OPTIONS,
+            base_url=dec.base_url,
+            model=dec.model,
+            num_ctx=dec.num_ctx,
+            timeout_s=dec.timeout_s,
+            coverage_min=dec.coverage_min,
+        )
+        # Map option letter to tier: A→1, B→2, C→3
+        letter = tier_label.strip().upper()
+        mapping = {"A": 1, "B": 2, "C": 3}
+        return mapping.get(letter, cfg.classifier.fallback_tier)
+    except Exception:  # noqa: BLE001
+        logs.warning("classify_tier_failed", fallback=cfg.classifier.fallback_tier)
+        return cfg.classifier.fallback_tier
+
+
 def _classify_via_configured_mode(
    task: str, system_prompt: str, user_content: str
 ) -> Classification:
@@ -1073,6 +1309,8 @@ def _classify_via_configured_mode(
        return _classify_via_cloud_llm(system_prompt, user_content)
    if mode == "local_encoder":
        return _classify_via_local_encoder(task)
+    if mode == "local_decision":
+        return _classify_via_local_decision(system_prompt, user_content)
    return _classify_via_local_llm(system_prompt, user_content)


@@ -1391,6 +1629,7 @@ def classify(task: str, context: Optional[str]) -> Classification:
        TypeError,
        json.JSONDecodeError,
        RuntimeError,
+        requests.RequestException,
    ) as e:
        # A primary classifier that is slow, restarting, unreachable, or (for
        # local_encoder) unsure of itself must not take the caller down with
--- a/src/eval_classifier.py
+++ b/src/eval_classifier.py
@@ -36,6 +36,8 @@ plan-only run needs neither package present.
    python eval_classifier.py                    # every task, 3 noise levels
    python eval_classifier.py --noise clean      # clean prompts only
    python eval_classifier.py --categories coding_general,diff_checking
+    python eval_classifier.py --backend decision # local_decision backend
+    python eval_classifier.py --head-off         # encoder, trained head disabled
 """

 from __future__ import annotations
@@ -52,7 +54,7 @@ import yaml

 from config import load_config

-TASKS_PATH = "evals/tasks.yaml"
+TASKS_PATH = "evals/heldout.yaml"

 # The eval measures the scoreable task set. Tool-use tasks are excluded: they
 # are scored structurally by eval_proficiency and their category is
@@ -218,6 +220,22 @@ def _reduce_confidences(preds: list[tuple[str, float]]) -> tuple[str, float]:
    return best_cat, _mean(votes[best_cat]) or 0.0


+def _disable_trainable_head() -> None:
+    """Force the encoder onto its zero-shot path for the rest of the process.
+
+    ``local_encoder._get_trainable_head`` memoises the fitted logistic-regression
+    head once per process. Setting ``_TRAINABLE_HEAD_RESOLVED = True`` with
+    ``_TRAINABLE_HEAD = None`` makes the next call skip the filesystem load and
+    resolve to no head, so the encoder classifies against the plain centroid
+    similarities. This is the ``--head-off`` escape hatch that keeps the
+    (leaky) trained-head score visible against zero-shot on the same corpus.
+    """
+    import local_encoder
+
+    local_encoder._TRAINABLE_HEAD_RESOLVED = True
+    local_encoder._TRAINABLE_HEAD = None
+
+
 def run_eval(
    tasks: list[dict],
    categories: list[str],
@@ -225,10 +243,31 @@ def run_eval(
    model_id: str,
    device: str,
    noise_levels: tuple[str, ...],
+    backend: str = "encoder",
+    head_off: bool = False,
+    decision: Optional[dict] = None,
 ) -> dict:
-    """Run the encoder over *tasks* and return the full result structure."""
+    """Run the chosen classifier backend over *tasks*.
+
+    ``backend == "encoder"`` (default) drives ``local_encoder.classify_zero_shot``;
+    when ``head_off`` is set the trained head is disabled first (see
+    ``_disable_trainable_head``). ``backend == "decision"`` drives
+    ``local_decision.classify_choice`` over ``_DECISION_DESCRIPTIONS``, reading
+    its ``base_url``/``model``/``num_ctx``/``timeout_s``/``coverage_min`` from
+    the ``decision`` dict. ``local_decision`` is imported lazily so the module
+    can land independently (it is a sibling todo).
+    """
    from local_encoder import classify_zero_shot

+    if backend == "encoder" and head_off:
+        _disable_trainable_head()
+
+    local_decision = None
+    if backend == "decision":
+        import local_decision
+
+        decision = decision or {}
+
    # Per (task, noise-level) predictions, keyed enough to build everything.
    # gold_history[noise] = list of (task_id, gold, predicted, confidence)
    gold_history: dict[str, list[tuple[str, str, str, float]]] = {
@@ -249,12 +288,30 @@ def run_eval(
    for task_id, gold, _prompt in clean_tasks:
        level_preds: list[tuple[str, float]] = []
        for level in noise_levels:
-            predicted, confidence = classify_zero_shot(
-                wrapped[task_id][level],
-                categories,
-                model_id=model_id,
-                device=device,
-            )
+            if backend == "decision":
+                if decision is None:
+                    raise RuntimeError(
+                        "--backend decision but decision config is None"
+                    )
+                if local_decision is None:
+                    raise RuntimeError(
+                        "local_decision module not loaded"
+                    )
+                predicted, confidence, _coverage = local_decision.classify_category(
+                    wrapped[task_id][level],
+                    base_url=decision.get("base_url", "http://localhost:11434/api/chat"),
+                    model=decision.get("model", "qwen3.5:4b"),
+                    num_ctx=decision.get("num_ctx", 8192),
+                    timeout_s=decision.get("timeout_s", 120.0),
+                    coverage_min=decision.get("coverage_min", 0.0),
+                )
+            else:
+                predicted, confidence = classify_zero_shot(
+                    wrapped[task_id][level],
+                    categories,
+                    model_id=model_id,
+                    device=device,
+                )
            gold_history[level].append((task_id, gold, predicted, confidence))
            level_preds.append((predicted, confidence))
        best_cat, best_conf = _reduce_confidences(level_preds)
@@ -365,7 +422,7 @@ def _print_footer(

 # --- CLI ------------------------------------------------------------------

-def main() -> int:
+def _build_parser() -> argparse.ArgumentParser:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--tasks", default=TASKS_PATH)
    ap.add_argument(
@@ -385,7 +442,46 @@ def main() -> int:
        default=list(NOISE_LEVELS),
        help="noise levels to run (default: all)",
    )
+    ap.add_argument(
+        "--backend",
+        choices=["encoder", "decision"],
+        default="encoder",
+        help="classifier backend: encoder (local_encoder) or decision (local_decision)",
+    )
+    ap.add_argument(
+        "--head-off",
+        action="store_true",
+        help="encoder: disable the trained logistic-regression head (force zero-shot)",
+    )
+    ap.add_argument(
+        "--decision-base-url",
+        help="decision backend Ollama base URL (defaults to classifier.decision.base_url)",
+    )
+    ap.add_argument(
+        "--decision-model",
+        help="decision backend model id (defaults to classifier.decision.model)",
+    )
+    ap.add_argument(
+        "--decision-num-ctx",
+        type=int,
+        help="decision backend context window (defaults to classifier.decision.num_ctx)",
+    )
+    ap.add_argument(
+        "--decision-timeout",
+        type=int,
+        help="decision backend per-call timeout in seconds (defaults to classifier.decision.timeout_s)",
+    )
+    ap.add_argument(
+        "--decision-coverage-min",
+        type=float,
+        help="decision backend coverage floor (defaults to classifier.decision.coverage_min)",
+    )
    ap.add_argument("--dry-run", action="store_true")
+    return ap
+
+
+def main() -> int:
+    ap = _build_parser()
    args = ap.parse_args()

    cfg = load_config("config/config.yaml")
@@ -396,6 +492,26 @@ def main() -> int:
    if args.categories:
        categories = [c.strip() for c in args.categories.split(",")]

+    # Decision-backend config. ``classifier.decision`` is the config block
+    # local_decision's wiring adds; it may not exist yet on this branch, so
+    # read it defensively (getattr) and let the CLI flags override it.
+    decision: Optional[dict] = None
+    if args.backend == "decision":
+        dec = getattr(cfg.classifier, "decision", None)
+        decision = {
+            "base_url": args.decision_base_url
+            or getattr(dec, "base_url", "http://localhost:11434/api/chat"),
+            "model": args.decision_model
+            or getattr(dec, "model", "qwen3.5:4b"),
+            "num_ctx": args.decision_num_ctx
+            or getattr(dec, "num_ctx", 8192),
+            "timeout_s": args.decision_timeout
+            or getattr(dec, "timeout_s", 120),
+            "coverage_min": args.decision_coverage_min
+            if args.decision_coverage_min is not None
+            else getattr(dec, "coverage_min", 0.0),
+        }
+
    tasks = load_scoreable_tasks(args.tasks)
    if not tasks:
        print("no scoreable tasks", file=sys.stderr)
@@ -423,13 +539,27 @@ def main() -> int:
        print(f"  {cat:20s} {counts[cat]} tasks")

    if args.dry_run:
-        print("\n--dry-run: no encoder load, no transformers/torch")
-        print(f"  model={model_id} device={device}")
+        print("\n--dry-run: no classifier load, no transformers/torch")
+        print(f"  backend={args.backend} model={model_id} device={device}")
+        if args.backend == "decision" and decision is not None:
+            print(
+                f"  decision model={decision['model']} "
+                f"base_url={decision['base_url']}"
+            )
+        elif args.head_off:
+            print("  trainable head: OFF (--head-off)")
        print(f"  candidate categories ({len(categories)}): {', '.join(categories)}")
        return 0

    results = run_eval(
-        tasks, categories, model_id=model_id, device=device, noise_levels=noise_levels,
+        tasks,
+        categories,
+        model_id=model_id,
+        device=device,
+        noise_levels=noise_levels,
+        backend=args.backend,
+        head_off=args.head_off,
+        decision=decision,
    )
    gold_history = results["gold_history"]
    task_verdicts = results["task_verdicts"]
--- a/src/local_decision.py
+++ b/src/local_decision.py
@@ -0,0 +1,288 @@
+"""Parse logprobs from a local Ollama classifier response.
+
+The local classifier (Ollama) returns a response with top-level ``logprobs`` —
+an array of position logprob objects, each carrying a ``top_logprobs`` list.
+This module extracts per-option-letter confidence from those logprobs so the
+routing layer can decide whether the classifier's choice is trustworthy.
+
+The classifier prompts the local LLM with four lettered options (A--D).  The
+response looks like::
+
+    {
+      "model": "...",
+      "created_at": "...",
+      "message": { "role": "assistant", "content": "A" },
+      "logprobs": [
+        {
+          "token": "A",
+          "logprob": -0.01,
+          "top_logprobs": [
+            {"token": "A", "logprob": -0.01},
+            {"token": "B", "logprob": -3.2}
+          ]
+        },
+        {"token": ".", "logprob": -0.5, "top_logprobs": [...]}
+      ],
+      "done": true
+    }
+
+Ollama appends ``.`` (option separator) tokens between letters in some
+configs; the token key is stripped of its trailing dot before matching.
+Non-option tokens like ``thinking``, ``assistant``, ``response`` are ignored
+entirely.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any, Final, Optional
+
+import requests
+
+_DEFAULT_COVERAGE_MIN: float = 0.3
+
+# Decision-category descriptions tuned against the measured confusion
+# patterns (gate G, see plans/local-decision-classifier-results.md):
+# 1. coding_general → debugging  (code-tracing tasks like "what does this
+#    function return" were read as bug-fixing)
+# 2. coding_general → reasoning_math  (algorithm-implementation tasks were
+#    read as "algorithm analysis")
+# 3. file_summarization → debugging  (code-gotcha summaries were read as
+#    bug-fixing)
+#
+# Fixes: widen coding_general to include tracing/implementing, narrow
+# debugging to explicitly "fixing a bug", and narrow reasoning_math to pure
+# word problems (no code).
+#
+# `tool_use_agentic` is EXCLUDED — the local classifier only presents four
+# options to the LLM and this slot never appears in the decision prompt.
+_DECISION_DESCRIPTIONS: dict[str, str] = {
+    "coding_general": (
+        "writing code, implementing a feature or algorithm, or tracing what "
+        "existing code returns"
+    ),
+    "coding_refactor": (
+        "refactoring or restructuring existing code without changing its behavior"
+    ),
+    "debugging": "fixing a bug in code that is broken or produces wrong output",
+    "docs_writing": (
+        "writing documentation, API docs, or technical explanations (not code)"
+    ),
+    "summarization": "summarizing or condensing a long text",
+    "file_summarization": (
+        "summarizing or explaining what a single source file or code module "
+        "contains or does"
+    ),
+    "diff_checking": "reviewing a code diff or comparing code changes",
+    "translation": "translating text from one language to another",
+    "reasoning_math": (
+        "solving a pure math or logic word problem (no code to write or run)"
+    ),
+    "general_chat": "casual conversation or a general question",
+}
+
+_SYSTEM_PROMPT: Final[str] = "answer with the letter only"
+_ANSWER_SUFFIX: Final[str] = "Answer with the letter only."
+_NUM_PREDICT: Final[int] = 1
+_TEMPERATURE: Final[int] = 0
+_TOP_LOGBPROBS: Final[int] = 20
+
+
+def parse_logprobs(
+    response_json: dict[str, Any],
+    option_letters: list[str],
+    *,
+    coverage_min: float = _DEFAULT_COVERAGE_MIN,
+) -> tuple[str, float, float]:
+    """Extract the classifier's chosen letter and its confidence.
+
+    Parameters
+    ----------
+    response_json:
+        The raw Ollama ``/api/chat`` response dict.  The logprobs are read
+        from ``response_json["logprobs"]`` (top level).
+    option_letters:
+        The set of valid option labels, e.g. ``["A", "B", "C", "D"]``.
+    coverage_min:
+        Minimum total option-mass to accept the result.  Raised as a
+        :exc:`RuntimeError` when the combined ``exp(logprob)`` across all
+        option letters falls below this threshold.
+
+    Returns
+    -------
+    tuple[str, float, float]
+        ``(label, confidence, coverage)`` where *confidence* is the winning
+        letter's share of total option mass and *coverage* is the total mass.
+
+    Raises
+    ------
+    RuntimeError
+        When ``coverage < coverage_min`` or when no logprobs are found.
+    """
+    logprobs_list: list[dict[str, Any]] = response_json.get("logprobs", [])
+
+    if not logprobs_list:
+        raise RuntimeError(
+            "parse_logprobs: no logprobs found in classifier response"
+        )
+
+    # Accumulate exp(logprob) mass per option letter.
+    mass: dict[str, float] = {letter: 0.0 for letter in option_letters}
+
+    for position in logprobs_list:
+        for entry in position.get("top_logprobs", []):
+            raw_token: str = entry.get("token", "")
+            # Strip whitespace, then trailing dot (Ollama option separator).
+            token = raw_token.strip().rstrip(".")
+
+            if token not in mass:
+                # Ignore non-option tokens like "thinking", "assistant", "."
+                continue
+
+            logprob: Optional[float] = entry.get("logprob")
+            if logprob is None:
+                continue
+            mass[token] += math.exp(logprob)
+
+    total = sum(mass.values())
+
+    if total < coverage_min:
+        raise RuntimeError(
+            f"parse_logprobs: coverage {total:.4f} below "
+            f"minimum {coverage_min}"
+        )
+
+    # Winner is the option with the most accumulated mass.
+    label = max(mass, key=lambda k: mass[k])
+    confidence = mass[label] / total if total > 0 else 0.0
+    coverage = total
+
+    return (label, confidence, coverage)
+
+
+def classify_choice(
+    text: str,
+    options: dict[str, str],
+    *,
+    base_url: str,
+    model: str,
+    num_ctx: int,
+    timeout_s: float,
+    coverage_min: float = _DEFAULT_COVERAGE_MIN,
+) -> tuple[str, float, float]:
+    """Ask a local Ollama model to choose one of the given options.
+
+    Prompts the model with the task ``text`` and a lettered list of
+    ``options`` (e.g. ``{"A": "writing new code", "B": "refactoring"}``),
+    requesting a single-letter answer.  The model's first-token logprobs are
+    then parsed into ``(label, confidence, coverage)`` via
+    :func:`parse_logprobs`.
+
+    Parameters
+    ----------
+    text:
+        The task text to classify.
+    options:
+        Mapping of option letter (e.g. ``"A"``) to a human-readable
+        description of that option.
+    base_url:
+        Base URL of the Ollama server, e.g. ``http://localhost:11434``.
+    model:
+        Name of the Ollama model to call.
+    num_ctx:
+        Context window (in tokens) to use for the request.
+    timeout_s:
+        Timeout in seconds for the HTTP request.
+    coverage_min:
+        Minimum total option-mass to accept the result.  Passed through to
+        :func:`parse_logprobs`.
+
+    Returns
+    -------
+    tuple[str, float, float]
+        ``(label, confidence, coverage)``.
+
+    Raises
+    ------
+    requests.exceptions.RequestException
+        Propagated when the HTTP call fails; callers decide how to cascade.
+    RuntimeError
+        Raised by :func:`parse_logprobs` when coverage is too low or no
+        logprobs were returned.
+    """
+    option_letters = sorted(options.keys())
+    option_lines = "\n".join(
+        f"{letter}. {description}" for letter, description in options.items()
+    )
+    user_prompt = f"{text}\n\n<task>\n{option_lines}\n{_ANSWER_SUFFIX}"
+
+    payload: dict[str, Any] = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": _SYSTEM_PROMPT},
+            {"role": "user", "content": user_prompt},
+        ],
+        "options": {
+            "num_predict": _NUM_PREDICT,
+            "temperature": _TEMPERATURE,
+            "num_ctx": num_ctx,
+        },
+        "stream": False,
+        "think": False,
+        "logprobs": True,
+        "top_logprobs": _TOP_LOGBPROBS,
+    }
+
+    resp = requests.post(
+        base_url + "/api/chat",
+        json=payload,
+        timeout=timeout_s,
+    )
+    resp.raise_for_status()
+
+    return parse_logprobs(
+        resp.json(),
+        option_letters=option_letters,
+        coverage_min=coverage_min,
+    )
+
+
+def classify_category(
+    text: str,
+    *,
+    base_url: str,
+    model: str,
+    num_ctx: int,
+    timeout_s: float,
+    coverage_min: float = _DEFAULT_COVERAGE_MIN,
+) -> tuple[str, float, float]:
+    """Ask a local Ollama model to classify the task into a known category.
+
+    Wraps :func:`classify_choice`: assigns letters (A, B, ...) to
+    ``_DECISION_DESCRIPTIONS`` in its iteration order, calls
+    ``classify_choice()`` with the lettered dict, and maps the winning
+    letter back to the original category name.
+
+    Returns
+    -------
+    tuple[str, float, float]
+        ``(category_name, confidence, coverage)``.
+    """
+    letters = [chr(ord("A") + i) for i in range(len(_DECISION_DESCRIPTIONS))]
+    options = dict(zip(letters, _DECISION_DESCRIPTIONS.values()))
+    letter, confidence, coverage = classify_choice(
+        text,
+        options,
+        base_url=base_url,
+        model=model,
+        num_ctx=num_ctx,
+        timeout_s=timeout_s,
+        coverage_min=coverage_min,
+    )
+    # Map the winning letter back to the category name.
+    for i, letter_name in enumerate(letters):
+        if letter_name == letter:
+            category = list(_DECISION_DESCRIPTIONS.keys())[i]
+            return (category, confidence, coverage)
+    # Fallback (should never happen if classification succeeded).
+    return (letter, confidence, coverage)
--- a/tests/fixtures/real_ollama_response.json
+++ b/tests/fixtures/real_ollama_response.json
@@ -0,0 +1,32 @@
+{
+  "model": "qwen3.5:4b",
+  "created_at": "2026-09-28T12:00:00Z",
+  "message": {
+    "role": "assistant",
+    "content": ""
+  },
+  "logprobs": [
+    {
+      "token": "A",
+      "logprob": -0.012,
+      "top_logprobs": [
+        {"token": "A", "logprob": -0.012},
+        {"token": "B", "logprob": -3.84}
+      ]
+    },
+    {
+      "token": ".",
+      "logprob": -0.45,
+      "top_logprobs": [
+        {"token": ".", "logprob": -0.45},
+        {"token": ",", "logprob": -1.92}
+      ]
+    }
+  ],
+  "done": true,
+  "total_duration": 4200000000,
+  "load_duration": 15000000,
+  "prompt_eval_count": 847,
+  "eval_count": 1,
+  "eval_duration": 380000000
+}
--- a/tests/test_admin_classifier_config.py
+++ b/tests/test_admin_classifier_config.py
@@ -167,6 +167,22 @@ def test_get_reports_no_resolution_when_pinned_not_auto(tmp_path):
    assert body["cloud_primary"]["model"] == "deepseek-v4-flash"


+def test_get_classifier_config_includes_decision_block(tmp_path):
+    """GET /admin/api/classifier-config includes decision block when mode is local_decision."""
+    client, _config_yaml, _local_yaml = _client(tmp_path)
+    resp = client.post(
+        "/admin/api/classifier-config",
+        json={"mode": "local_decision", "decision": {"coverage_min": 0.42}},
+    )
+    assert resp.status_code == 200
+
+    resp = client.get("/admin/api/classifier-config")
+    body = resp.json()
+    assert body["mode"] == "local_decision"
+    assert body.get("decision") is not None
+    assert body["decision"]["coverage_min"] == 0.42
+
+
 # --- POST: validated the same way config load is ------------------------


@@ -292,3 +308,26 @@ def test_post_response_names_a_restart_is_required(tmp_path):
    client, _config_yaml, _local_yaml = _client(tmp_path)
    resp = client.post("/admin/api/classifier-config", json={"mode": "local_llm"})
    assert "restart" in resp.json()["message"].lower()
+
+
+def test_post_local_decision_mode_returns_200(tmp_path):
+    """local_decision mode with an empty decision block is accepted."""
+    client, _config_yaml, local_yaml = _client(tmp_path)
+    resp = client.post(
+        "/admin/api/classifier-config",
+        json={"mode": "local_decision", "decision": {}},
+    )
+    assert resp.status_code == 200
+    written = yaml.safe_load(local_yaml.read_text())
+    assert written["classifier"]["mode"] == "local_decision"
+    assert written["classifier"]["decision"] == {}
+
+
+def test_post_invalid_mode_returns_422(tmp_path):
+    """A mode that is not in the Literal is rejected."""
+    client, _config_yaml, _local_yaml = _client(tmp_path)
+    resp = client.post(
+        "/admin/api/classifier-config",
+        json={"mode": "invalid_mode"},
+    )
+    assert resp.status_code == 422
--- a/tests/test_admin_config.py
+++ b/tests/test_admin_config.py
@@ -97,6 +97,7 @@ def test_config_GET_returns_allowlisted_values(client):
        "session_cache.enabled",
        "session_cache.staleness_seconds",
        "verification.local_llm_enabled",
+        "local_vision.enabled",
        "objective.incumbent_cache_pricing",
        "objective.incumbent_challenger_cache_rate",
        "pinch.enabled",
@@ -186,6 +187,42 @@ def test_config_pinch_prefix_probe_is_allowlisted_at_the_right_path(client):
    assert _CONFIG_ALLOWLIST["pinch.prefix_probe"] == ("pinch", "prefix_probe")


+def test_config_local_vision_enabled_round_trips_through_the_real_validator(client):
+    """local_vision.enabled persists to the temp overlay and reloads as a bool.
+
+    Same reasoning as the ``pinch.prefix_probe`` round-trip: the value that
+    reaches disk has to be one the config loader will take back on the next
+    boot. ``local_vision.enabled`` is read per request in the dispatcher, so a
+    write that lands as the string ``"false"`` would silently keep the fallback
+    on. Assert a real ``False`` survives the merged reload.
+    """
+    tc, config_yaml = client
+    local_yaml = config_yaml.with_name("config.local.yaml")
+
+    before = tc.get("/admin/api/config").json()
+    assert before["local_vision.enabled"] == {"value": True, "source": "base"}
+
+    resp = tc.post("/admin/api/config/local_vision.enabled", json={"value": False})
+    assert resp.status_code == 200
+    assert resp.json()["value"] is False
+
+    after = tc.get("/admin/api/config").json()
+    assert after["local_vision.enabled"] == {"value": False, "source": "overlay"}
+
+    # The base file never carries an admin write.
+    assert yaml.safe_load(config_yaml.read_text())["local_vision"]["enabled"] is True
+    assert yaml.safe_load(local_yaml.read_text())["local_vision"]["enabled"] is False
+
+    # And the merged result parses through RouterConfig, which is what the
+    # service does on its next boot.
+    loaded = load_config(str(config_yaml), include_overlay=True)
+    assert loaded.local_vision.enabled is False
+    # The rest of the local_vision block is untouched by the overlay merge.
+    base_vision = yaml.safe_load(config_yaml.read_text())["local_vision"]
+    assert loaded.local_vision.model == base_vision["model"]
+    assert loaded.local_vision.base_url == base_vision["base_url"]
+
+
 def test_config_incumbent_knobs_round_trip_through_the_real_validator(client):
    """Both Wave 2 knobs survive a write as genuine Python types.

--- a/tests/test_admin_frontend.py
+++ b/tests/test_admin_frontend.py
@@ -707,3 +707,39 @@ def test_render_board_reads_decision_outcomes_and_render_live_keeps_verdict_mix(
    assert " ${share}: " not in board
    live = _function_body(html, "renderLive")
    assert "verdict_mix" in live
+
+
+def test_controls_html_has_local_decision_fields():
+    """controls.html contains all 7 local_decision field data-key attributes.
+
+    The classifierModeFieldsHtml function must render fields for base_url,
+    model, num_ctx, timeout_s, confidence_min, coverage_min, and tier_enabled
+    when mode is local_decision.
+    """
+    html = (ROOT / "admin" / "frontend" / "controls.html").read_text()
+    assert 'data-key="decision.base_url"' in html
+    assert 'data-key="decision.model"' in html
+    assert 'data-key="decision.num_ctx"' in html
+    assert 'data-key="decision.timeout_s"' in html
+    assert 'data-key="decision.confidence_min"' in html
+    assert 'data-key="decision.coverage_min"' in html
+    assert 'data-key="decision.tier_enabled"' in html
+
+
+def test_controls_html_local_decision_field_rendering():
+    """classifierModeFieldsHtml has a local_decision case rendering 7 fields.
+
+    The local_decision block must exist in the function and must reference
+    data.decision for field values.
+    """
+    html = (ROOT / "admin" / "frontend" / "controls.html").read_text()
+    fields = _function_body(html, "classifierModeFieldsHtml")
+    assert "mode === 'local_decision'" in fields
+    assert "data && data.decision" in fields
+    assert "dec.base_url" in fields
+    assert "dec.model" in fields
+    assert "dec.num_ctx" in fields
+    assert "dec.timeout_s" in fields
+    assert "dec.confidence_min" in fields
+    assert "dec.coverage_min" in fields
+    assert "dec.tier_enabled" in fields
--- a/tests/test_admin_knob_coverage.py
+++ b/tests/test_admin_knob_coverage.py
@@ -46,10 +46,14 @@ against. Two clauses cut it to the knobs an operator would plausibly turn:
   definitions), ``profiles`` (its own CRUD surface, not a scalar knob),
   ``local_energy`` (machine-specific, lives in the gitignored overlay by
   design), ``tiers`` / ``tiering`` / ``proficiency`` / ``context`` (catalog and
-   scoring structure), ``local_vision``, and ``classifier`` — the last has its
+   scoring structure), and ``classifier`` — the last has its
   own dedicated admin card and endpoint pair (``/admin/api/classifier-config``,
   ``/admin/api/cloud-fallback-config``), so measuring it against the GENERIC
   allowlist would report every field as missing while the card covers it.
+   ``local_vision`` is reached now that ``local_vision.enabled`` has a persisted
+   control; the rest of the section (``base_url``, ``api_key_env``, ``model``)
+   is deployment wiring out by clause 2, and the timeout/image limits are
+   excused in ``DELIBERATELY_NOT_IN_ADMIN``.
   The known limitation: a brand-new section with no control at all is out of
   scope and unchecked. The first control added under it drags every one of its
   knobs into scope at once, which is the intended moment to decide.
@@ -215,6 +219,21 @@ DELIBERATELY_NOT_IN_ADMIN: dict[str, str] = {
        "relevance-path shape internal to a covered master switch "
        "(pinch.relevance.enabled)."
    ),
+    # --- local_vision: the master switch (local_vision.enabled) is covered;
+    # --- the timeout and image limits are tuning for the configured local
+    # --- Ollama vision model, changed with the deployment it points at.
+    "local_vision.timeout_seconds": (
+        "tuning for the configured local vision model; the operator's decision "
+        "is whether the fallback runs at all (local_vision.enabled, covered)."
+    ),
+    "local_vision.max_images": (
+        "request-shape limit for the local vision fallback; tuning for the "
+        "configured local model, changed with the deployment."
+    ),
+    "local_vision.max_image_bytes": (
+        "request-shape limit for the local vision fallback; tuning for the "
+        "configured local model, changed with the deployment."
+    ),
    # --- measured constants, re-derived rather than dialled ------------------
    "objective.assumed_cache_rate": (
        "a measured property of this deployment's traffic (0.917, token-weighted "
--- a/tests/test_chat_completions.py
+++ b/tests/test_chat_completions.py
@@ -735,6 +735,24 @@ def test_no_vision_model_uses_local_fallback_when_enabled(router, monkeypatch):
    ), "the image_url part must survive into the local call"


+def test_no_vision_model_with_local_fallback_off_is_a_422(router, monkeypatch):
+    """local_vision.enabled off: no cloud vision candidate 422s, no local call."""
+    client, calls, db_path = router
+    _drop_cheap_by_tier(dispatcher, db_path)
+    monkeypatch.setattr(dispatcher.cfg.local_vision, "enabled", False)
+    _local_vision_fake(monkeypatch, calls, content="must not be used")
+
+    resp = client.post(
+        "/v1/chat/completions",
+        json={"model": "auto", "messages": _image_messages()},
+    )
+
+    assert resp.status_code == 422
+    assert "vision" in resp.json()["detail"]
+    local = [c for c in calls if c.get("local")]
+    assert not local, "the local vision fallback must not run when disabled"
+
+
 def test_local_fallback_skips_when_json_mode_is_requested(router, monkeypatch):
    """Local vision answers in prose; a json_object request must not use it.

--- a/tests/test_classifier_modes_config.py
+++ b/tests/test_classifier_modes_config.py
@@ -177,3 +177,88 @@ def test_local_encoder_unaffected_by_the_cloud_llm_validator(raw):
    cfg["classifier"]["mode"] = "local_encoder"
    cfg["classifier"]["encoder"] = {}
    RouterConfig(**cfg)  # must not raise
+
+
+# --- local_decision: needs the decision block -----------------------------
+
+
+def test_local_decision_config_loads_with_defaults(raw):
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    cfg["classifier"]["decision"] = {}
+    loaded = RouterConfig(**cfg)
+    assert loaded.classifier.decision.base_url == "http://localhost:11434"
+    assert loaded.classifier.decision.model == "qwen3.5:4b"
+    assert loaded.classifier.decision.num_ctx == 8192
+    assert loaded.classifier.decision.timeout_s == 10
+    assert loaded.classifier.decision.confidence_min == 0.5
+    assert loaded.classifier.decision.coverage_min == 0.3
+    assert loaded.classifier.decision.tier_enabled is False
+
+
+def test_local_decision_config_rejects_unknown_fields(raw):
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    cfg["classifier"]["decision"] = {"bogus_field": 1}
+    with pytest.raises(ValidationError, match="extra_forbidden"):
+        RouterConfig(**cfg)
+
+
+def test_local_decision_config_rejects_invalid_confidence_min(raw):
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    cfg["classifier"]["decision"] = {"confidence_min": 1.5}
+    with pytest.raises(ValueError, match=r"must be in \[0.0, 1.0\]"):
+        RouterConfig(**cfg)
+
+
+def test_missing_decision_block_rejected_for_local_decision_mode(raw):
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    with pytest.raises(ValueError, match="classifier.decision is not"):
+        RouterConfig(**cfg)
+
+
+def test_local_decision_is_valid_mode(raw):
+    """local_decision must be accepted as a classifier.mode literal alongside
+    local_llm/cloud_llm/local_encoder -- the entrypoint for the new mode."""
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    cfg["classifier"]["decision"] = {}
+    loaded = RouterConfig(**cfg)
+    assert loaded.classifier.mode == "local_decision"
+
+
+# --- local_energy metering uses the right base_url per mode ---------------
+
+
+def test_local_decision_metering_uses_decision_base_url(raw):
+    """local_decision mode meters classifier.decision.base_url, not
+    classifier.base_url."""
+    # --- loopback → metered ---
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_decision"
+    cfg["classifier"]["decision"] = {"base_url": "http://localhost:11434"}
+    cfg["local_energy"] = {"enabled": True, "meter": "nvidia_smi", "tariff_usd_per_kwh": 0.10}
+    loaded = RouterConfig(**cfg)
+    assert loaded._sites_cache["classify"] is True
+
+    # --- remote → not metered ---
+    cfg2 = copy.deepcopy(raw)
+    cfg2["classifier"]["mode"] = "local_decision"
+    cfg2["classifier"]["decision"] = {"base_url": "http://remote:11434"}
+    cfg2["local_energy"] = {"enabled": True, "meter": "nvidia_smi", "tariff_usd_per_kwh": 0.10}
+    loaded2 = RouterConfig(**cfg2)
+    assert loaded2._sites_cache["classify"] is False
+
+
+def test_local_encoder_metering_unconditional(raw):
+    """local_encoder runs in-process, so classify is always metered
+    regardless of base_url."""
+    cfg = copy.deepcopy(raw)
+    cfg["classifier"]["mode"] = "local_encoder"
+    cfg["classifier"]["encoder"] = {}
+    cfg["classifier"]["base_url"] = "http://remote:11434/v1"
+    cfg["local_energy"] = {"enabled": True, "meter": "nvidia_smi", "tariff_usd_per_kwh": 0.10}
+    loaded = RouterConfig(**cfg)
+    assert loaded._sites_cache["classify"] is True
--- a/tests/test_classifier_modes_dispatch.py
+++ b/tests/test_classifier_modes_dispatch.py
@@ -12,12 +12,18 @@ signal. An intentionally configured cloud primary is not degraded.
 """
 from __future__ import annotations

+import contextlib
+import time
+
 from types import SimpleNamespace
 from unittest.mock import MagicMock

 import pytest
+import requests

 import dispatcher
+import local_decision
+import local_energy
 import local_encoder
 import session_cache

@@ -336,6 +342,104 @@ def test_local_encoder_below_threshold_uses_the_real_cascade(monkeypatch):
    assert got.task_category == "coding_refactor"


+# --- mode: local_decision ---------------------------------------------------
+
+
+def _decision_conf(confidence_min=0.5, coverage_min=0.3, tier_enabled=False):
+    return SimpleNamespace(
+        base_url="http://localhost:11434",
+        model="stub-model",
+        num_ctx=8192,
+        timeout_s=10,
+        confidence_min=confidence_min,
+        coverage_min=coverage_min,
+        tier_enabled=tier_enabled,
+    )
+
+
+def _logprob_response(label="coding_refactor"):
+    return {
+        "logprobs": [
+            {
+                "token": label,
+                "logprob": -0.01,
+                "top_logprobs": [{"token": label, "logprob": -0.01}],
+            }
+        ]
+    }
+
+
+def test_local_decision_success_records_source_classifier(monkeypatch):
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf(confidence_min=0.5)
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+
+    calls = []
+    monkeypatch.setattr(
+        local_decision,
+        "classify_category",
+        lambda *a, **k: calls.append((a, k))
+        or ("coding_refactor", 0.83, 0.9),
+    )
+
+    got = dispatcher._classify_via_local_decision("sys", "refactor this function")
+
+    assert got.source == "classifier"
+    assert got.task_category == "coding_refactor"
+    assert got.confidence == pytest.approx(0.83)
+    assert calls[0][0][0] == "refactor this function"
+    assert calls[0][1]["base_url"] == "http://localhost:11434"
+
+
+def test_local_decision_uses_fallback_tier(monkeypatch):
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 3)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf(confidence_min=0.5)
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+    monkeypatch.setattr(
+        local_decision,
+        "classify_category",
+        lambda *a, **k: ("general_chat", 0.9, 0.9),
+    )
+
+    got = dispatcher._classify_via_local_decision("sys", "hello")
+    assert got.task_tier == 3
+
+
+def test_local_decision_below_confidence_cascades(monkeypatch):
+    """A below-confidence local_decision verdict is a failure, not a guess."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf(confidence_min=0.6)
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+    monkeypatch.setattr(
+        local_decision,
+        "classify_category",
+        lambda *a, **k: ("general_chat", 0.2, 0.9),
+    )
+
+    with pytest.raises(RuntimeError, match="confidence"):
+        dispatcher._classify_via_local_decision("sys", "do a thing")
+
+
+def test_local_decision_skip_reason_respected(monkeypatch):
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf(confidence_min=0.5)
+    )
+    monkeypatch.setattr(
+        dispatcher, "_local_classifier_skip_reason", lambda: "gaming_mode"
+    )
+
+    with pytest.raises(dispatcher._ClassifierSkipped, match="gaming_mode"):
+        dispatcher._classify_via_local_decision("sys", "do a thing")
+
+
 # --- startup: local_encoder mode must fail loudly, not on the first request -


@@ -385,3 +489,527 @@ def test_startup_check_surfaces_a_missing_dependency_loudly(monkeypatch):

    with pytest.raises(ImportError, match="pip install -r requirements-encoder.txt"):
        dispatcher._ensure_classifier_mode_ready()
+
+
+# --- startup: local_decision mode must warn, not fail, if Ollama is down ----
+
+def _decision_startup_conf():
+    return _decision_conf(confidence_min=0.5, coverage_min=0.3)
+
+
+def test_startup_check_validates_decision_mode(monkeypatch):
+    """A healthy local_decision mode makes one classify_category call
+    and passes."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "decision", _decision_startup_conf())
+    calls = []
+    monkeypatch.setattr(
+        local_decision,
+        "classify_category",
+        lambda *a, **k: calls.append((a, k)) or ("coding_general", 0.9, 0.9),
+    )
+    dispatcher._ensure_classifier_mode_ready()  # must not raise
+    assert len(calls) == 1
+    assert calls[0][0][0] == "test task"
+    assert calls[0][1]["base_url"] == "http://localhost:11434"
+    assert calls[0][1]["model"] == "stub-model"
+
+
+def test_startup_check_warns_on_decision_failure(monkeypatch, caplog):
+    """Ollama down at boot: warn and continue — the cascade handles failures."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "decision", _decision_startup_conf())
+
+    def fake_classify_category(*a, **k):
+        raise ConnectionError("Ollama is not running")
+
+    monkeypatch.setattr(local_decision, "classify_category", fake_classify_category)
+
+    caplog.set_level("WARNING", logger="llm_router")
+    dispatcher._ensure_classifier_mode_ready()  # must NOT raise
+
+    assert any(
+        "classifier_mode_unavailable" in r.getMessage()
+        and "mode=local_decision" in r.getMessage()
+        and "Ollama is not running" in r.getMessage()
+        for r in caplog.records
+    ), "a local_decision startup failure must be logged as classifier_mode_unavailable"
+
+
+def test_startup_check_is_noop_for_non_decision_modes(monkeypatch):
+    """Encoder mode still fires ensure_available; decision path is untouched."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_encoder")
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier,
+        "encoder",
+        SimpleNamespace(model="stub-model", device="cpu", confidence_min=0.5),
+    )
+    calls = []
+    monkeypatch.setattr(
+        local_encoder, "ensure_available", lambda model, device: calls.append((model, device))
+    )
+    monkeypatch.setattr(
+        local_decision,
+        "classify_choice",
+        lambda *a, **k: pytest.fail("classify_choice called for a non-decision mode"),
+    )
+    dispatcher._ensure_classifier_mode_ready()
+    assert calls == [("stub-model", "cpu")]
+
+
+def test_startup_check_skipped_when_local_compute_off(monkeypatch):
+    """When local_compute.enabled is False (gaming mode), the local_decision
+    startup probe must not call Ollama — it would load the 4b model onto the GPU.
+    """
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "decision", _decision_startup_conf())
+    monkeypatch.setattr(dispatcher.cfg.local_compute, "enabled", False)
+
+    def probe_should_not_be_called(*a, **k):
+        pytest.fail("classify_category must NOT be called when local_compute.enabled is False")
+
+    monkeypatch.setattr(local_decision, "classify_category", probe_should_not_be_called)
+
+    dispatcher._ensure_classifier_mode_ready()
+
+
+# --- tier classification in local_decision mode ------------------------------
+
+
+def test_local_decision_tier_enabled_false_no_tier_calls(monkeypatch):
+    """When tier_enabled is False (the default) classify_category fires
+    only once — for category — and tier equals fallback_tier."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf(confidence_min=0.5)
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+
+    calls = []
+    monkeypatch.setattr(
+        local_decision,
+        "classify_category",
+        lambda *a, **k: calls.append(1) or ("coding_refactor", 0.83, 0.9),
+    )
+
+    got = dispatcher._classify_via_local_decision("sys", "refactor this")
+
+    assert got.task_tier == 2
+    assert len(calls) == 1, "classify_category should fire only once for category"
+
+
+def test_local_decision_tier_enabled_true_makes_tier_call(monkeypatch):
+    """When tier_enabled is True classify_category fires once for
+    category, and classify_choice fires once for tier via the tier
+    function, and the returned tier matches the tier prompt's
+    option letter mapping."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 1)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=True),
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+
+    call_count = 0
+
+    def fake_classify_category(*a, **k):
+        nonlocal call_count
+        call_count += 1
+        return ("coding_refactor", 0.83, 0.9)
+
+    def fake_classify_choice(*a, **k):
+        nonlocal call_count
+        call_count += 1
+        # Return B → tier 2
+        return ("B", 0.75, 0.85)
+
+    monkeypatch.setattr(local_decision, "classify_category", fake_classify_category)
+    monkeypatch.setattr(local_decision, "classify_choice", fake_classify_choice)
+
+    got = dispatcher._classify_via_local_decision("sys", "refactor this")
+
+    assert got.task_tier == 2
+    assert got.task_category == "coding_refactor"
+    assert call_count == 2, (
+        "classify_category once for category + classify_choice once for tier"
+    )
+
+
+def test_local_decision_tier_failure_falls_to_fallback(monkeypatch):
+    """If the tier call raises (network/model error) tier falls to
+    fallback_tier with zero disruption to the category result."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 3)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=True),
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+
+    call_count = 0
+
+    def fake_classify_category(*a, **k):
+        nonlocal call_count
+        call_count += 1
+        return ("coding_refactor", 0.83, 0.9)
+
+    def fake_classify_choice(*a, **k):
+        nonlocal call_count
+        call_count += 1
+        # Second call: simulate network/model failure
+        raise ConnectionError("Ollama is not running")
+
+    monkeypatch.setattr(local_decision, "classify_category", fake_classify_category)
+    monkeypatch.setattr(local_decision, "classify_choice", fake_classify_choice)
+
+    got = dispatcher._classify_via_local_decision("sys", "refactor this")
+
+    assert got.task_tier == 3, "tier should fall to fallback on failure"
+    assert got.task_category == "coding_refactor"
+    assert call_count == 2
+
+
+def test_local_decision_tier_runs_in_parallel_with_category(monkeypatch):
+    """Category and tier calls execute concurrently when tier_enabled is
+    True. Both must hit a barrier before either completes — proving they
+    run in separate threads inside a shared ThreadPoolExecutor."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=True),
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+
+    import threading
+    import time
+
+    barrier = threading.Barrier(2, timeout=2)
+
+    def fake_classify_category(*a, **k):
+        time.sleep(0.05)
+        barrier.wait()
+        return ("coding_refactor", 0.83, 0.9)
+
+    def fake_classify_choice(*a, **k):
+        time.sleep(0.05)
+        barrier.wait()
+        return ("B", 0.75, 0.85)
+
+    monkeypatch.setattr(local_decision, "classify_category", fake_classify_category)
+    monkeypatch.setattr(local_decision, "classify_choice", fake_classify_choice)
+
+    start = time.time()
+    got = dispatcher._classify_via_local_decision("sys", "refactor this")
+    elapsed = time.time() - start
+
+    assert got.task_category == "coding_refactor"
+    assert got.task_tier == 2
+    # With two 50 ms sleeps and a barrier, sequential code takes ≥0.2 s,
+    # parallel code ≈0.05 s.  Give generous slack but fail on sequential.
+    assert elapsed < 0.25, f"took {elapsed:.3f}s — calls ran sequentially, not in parallel"
+
+
+def test_local_decision_metered_logs_correct_duration_power(monkeypatch):
+    def fake_nvidia(*a, **k):
+        return 100.0
+
+    def fake_post(*a, **k):
+        time.sleep(0.2)
+        return SimpleNamespace(
+            json=lambda: {
+                "model": "qwen3.5:4b",
+                "message": {"role": "assistant", "content": "B"},
+                "logprobs": [{
+                    "top_logprobs": [
+                        {"token": "B", "logprob": -0.1},
+                        {"token": "A", "logprob": -2.0},
+                        {"token": "C", "logprob": -3.0},
+                    ]
+                }],
+            },
+            raise_for_status=lambda: None,
+        )
+
+    def capture_log(*a, measurement=None, **k):
+        measurement_sent.append(SimpleNamespace(
+            duration_seconds=measurement.duration_seconds,
+            avg_power_watts=measurement.avg_power_watts,
+        ))
+
+    measurement_sent = []
+
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=False),
+    )
+    monkeypatch.setattr(dispatcher.cfg.local_energy, "enabled", True)
+    monkeypatch.setattr(
+        dispatcher, "cfg",
+        SimpleNamespace(
+            classifier=dispatcher.cfg.classifier,
+            local_energy=SimpleNamespace(
+                enabled=True,
+                sample_interval_seconds=0.01,
+                call_sites={"classify": True},
+                tariff=0.08,
+            ),
+            local_energy_call_sites={"classify": True},
+        ),
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+    monkeypatch.setattr(dispatcher.local_energy, "sample_nvidia_smi", fake_nvidia)
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+    monkeypatch.setattr(dispatcher, "_log_local_energy", capture_log)
+
+    got = dispatcher._classify_via_local_decision("sys", "refactor this")
+    assert got.task_category == "coding_refactor"
+
+    assert len(measurement_sent) == 1, "expected one _log_local_energy call"
+    sent = measurement_sent[0]
+    assert sent.duration_seconds >= 0.15, \
+        f"expected >= 0.15, got {sent.duration_seconds}"
+    assert sent.avg_power_watts == 100.0, \
+        f"expected 100.0, got {sent.avg_power_watts}"
+
+
+# --- HTTP-level tests: letter→category mapping via mocked requests.post ------
+# These tests fake local_decision.requests.post (the lowest HTTP boundary)
+# so they exercise classify_choice → parse_logprobs → letter→category mapping
+# end-to-end, NOT just the return value of a stubbed classify_category.
+
+
+def make_response(letter="B", top_logprobs=None):
+    """Create a mock response object like requests.post() returns.
+
+    ``top_logprobs`` defaults to three options with B as clear winner.
+    """
+    if top_logprobs is None:
+        top_logprobs = [
+            {"token": letter, "logprob": -0.1},
+            {"token": "A", "logprob": -2.0},
+            {"token": "C", "logprob": -3.0},
+        ]
+    return SimpleNamespace(
+        json=lambda: {
+            "model": "qwen3.5:4b",
+            "message": {"role": "assistant", "content": letter},
+            "logprobs": [{"top_logprobs": top_logprobs}],
+        },
+        raise_for_status=lambda: None,
+    )
+
+
+def test_classify_category_maps_letter_to_category(monkeypatch):
+    """When requests.post returns top token 'B', classify_category maps it
+    to the second key of _DECISION_DESCRIPTIONS ('coding_refactor')."""
+    from src.local_decision import _DECISION_DESCRIPTIONS, classify_category
+
+    second_key = list(_DECISION_DESCRIPTIONS.keys())[1]
+
+    captured_calls = []
+
+    def fake_post(*a, **k):
+        captured_calls.append((a, k))
+        return make_response(letter="B")
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    result = classify_category(
+        "test task",
+        base_url="http://localhost:11434",
+        model="qwen3.5:4b",
+        num_ctx=8192,
+        timeout_s=10,
+        coverage_min=0.3,
+    )
+
+    label, confidence, coverage = result
+    assert label == second_key, f"Expected {second_key!r}, got {label!r}"
+    assert len(captured_calls) == 1
+    assert 0.0 < confidence <= 1.0
+    assert coverage > 0.3
+
+
+def test_dispatcher_returns_category_from_letter(monkeypatch):
+    """When the model returns letter 'B', _classify_via_local_decision
+    returns the correct category ('coding_refactor') with source='classifier',
+    proving the full requests.post → classify_choice → letter→category mapping
+    works through the dispatcher."""
+    from src.local_decision import _DECISION_DESCRIPTIONS
+
+    second_key = list(_DECISION_DESCRIPTIONS.keys())[1]
+
+    captured_calls = []
+
+    def fake_post(*a, **k):
+        captured_calls.append((a, k))
+        return make_response(letter="B")
+
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=False),
+    )
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    result = dispatcher._classify_via_local_decision("sys", "test task")
+
+    assert result.task_category == second_key, \
+        f"Expected {second_key!r}, got {result.task_category!r}"
+    assert result.source == "classifier"
+    assert len(captured_calls) == 1
+
+
+def test_tier_enabled_false_is_default(monkeypatch):
+    """tier_enabled defaults to False so the normal local_decision path
+    never fires extra HTTP calls without explicit config."""
+    from config import LocalDecisionConfig
+
+    assert LocalDecisionConfig().tier_enabled is False
+
+
+def test_unreachable_ollama_walks_the_cascade(monkeypatch):
+    """When local_decision Ollama is unreachable, classify() returns a
+    degraded fallback Classification instead of raising (c06bc36)."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision", _decision_conf()
+    )
+    dispatcher.cfg.classifier.decision.base_url = "http://127.0.0.1:1"
+    monkeypatch.setattr(dispatcher, "_local_classifier_skip_reason", lambda: None)
+    monkeypatch.setattr(
+        local_decision.requests, "post", lambda *a, **k: (_ for _ in ()).throw(
+            requests.ConnectionError("Connection refused")
+        )
+    )
+
+    got = dispatcher.classify("refactor this function", None)
+
+    assert got.source == "fallback"
+    assert got.confidence == 0.0
+    assert got.required_context_tokens == 0
+    assert got.task_tier == dispatcher.cfg.classifier.fallback_tier
+    assert got.task_category == dispatcher.cfg.classifier.fallback_category
+
+
+def test_local_decision_connection_error_opens_circuit_unmetered(monkeypatch):
+    """Transport failure in unmetered local_decision opens the classifier
+    backoff and blocks subsequent calls until the circuit closes."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=False),
+    )
+    # Make metering off so we hit the unmetered path
+    monkeypatch.setattr(dispatcher.cfg.local_energy, "enabled", False)
+    monkeypatch.setattr(dispatcher.cfg.local_compute, "enabled", True)
+
+    call_count = [0]
+    def fake_post(*a, **k):
+        call_count[0] += 1
+        raise requests.ConnectionError("Connection refused")
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    # First call — should record failure and return fallback
+    got1 = dispatcher.classify("refactor this function", None)
+    assert got1.source == "fallback"
+    assert dispatcher._last_classifier_failure > 0.0
+    assert dispatcher._classifier_backoff_active() is True
+
+    # Second call — should be SKIPPED (post called exactly once total)
+    got2 = dispatcher.classify("another task", None)
+    assert call_count[0] == 1, f"post called {call_count[0]} times, expected 1"
+    assert got2.source == "fallback"
+
+    # _last_classifier_failure unchanged by second call
+    assert dispatcher._last_classifier_failure > 0.0  # still set from first call
+
+
+def test_local_decision_connection_error_opens_circuit_metered(monkeypatch):
+    """Transport failure in metered local_decision opens the classifier
+    backoff and blocks subsequent calls until the circuit closes."""
+    call_count = [0]
+
+    def fake_nvidia(*a, **k):
+        return 100.0
+
+    def fake_post(*a, **k):
+        call_count[0] += 1
+        raise requests.ConnectionError("Connection refused")
+
+    def capture_log(*a, measurement=None, **k):
+        pass  # just swallow — we don't care about the measurement
+
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=False),
+    )
+    monkeypatch.setattr(dispatcher.cfg.local_energy, "enabled", True)
+    monkeypatch.setattr(dispatcher.cfg.local_energy, "sample_interval_seconds", 0.01)
+    monkeypatch.setattr(dispatcher.cfg, "_sites_cache", {"classify": True})
+    monkeypatch.setattr(dispatcher.cfg.local_compute, "enabled", True)
+    monkeypatch.setattr(dispatcher.local_energy, "sample_nvidia_smi", fake_nvidia)
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+    monkeypatch.setattr(dispatcher, "_log_local_energy", capture_log)
+
+    # First call — should record failure and return fallback
+    got1 = dispatcher.classify("refactor this function", None)
+    assert got1.source == "fallback"
+    assert dispatcher._last_classifier_failure > 0.0
+    assert dispatcher._classifier_backoff_active() is True
+
+    # Second call — should be SKIPPED (post called exactly once total)
+    got2 = dispatcher.classify("another task", None)
+    assert call_count[0] == 1, f"post called {call_count[0]} times, expected 1"
+    assert got2.source == "fallback"
+
+
+def test_local_decision_low_confidence_does_not_open_circuit(monkeypatch):
+    """A successful classify with low confidence is a real answer, not a
+    transport failure — the circuit must stay closed."""
+    monkeypatch.setattr(dispatcher.cfg.classifier, "mode", "local_decision")
+    monkeypatch.setattr(dispatcher.cfg.classifier, "fallback_tier", 2)
+    monkeypatch.setattr(
+        dispatcher.cfg.classifier, "decision",
+        _decision_conf(confidence_min=0.5, tier_enabled=False),
+    )
+    monkeypatch.setattr(dispatcher.cfg.local_compute, "enabled", True)
+
+    def fake_post(*a, **k):
+        # Return a response where the winning token has very low logprob
+        # → confidence below 0.5 → below confidence_min → RuntimeError
+        return SimpleNamespace(
+            json=lambda: {
+                "model": "qwen3.5:4b",
+                "message": {"role": "assistant", "content": "Z"},
+                "logprobs": [{
+                    "top_logprobs": [
+                        {"token": "Z", "logprob": -5.0},  # very low confidence
+                        {"token": "A", "logprob": -5.1},
+                        {"token": "B", "logprob": -5.2},
+                    ]
+                }],
+            },
+            raise_for_status=lambda: None,
+        )
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    # classify() should cascade to fallback because confidence < confidence_min
+    got = dispatcher.classify("refactor this function", None)
+    assert got.source == "fallback"  # cascaded
+
+    # But _last_classifier_failure must be 0.0 — this was NOT a transport error
+    assert dispatcher._last_classifier_failure == 0.0
+    assert dispatcher._classifier_backoff_active() is False
--- a/tests/test_eval_classifier.py
+++ b/tests/test_eval_classifier.py
@@ -0,0 +1,181 @@
+"""Tests for the eval_classifier CLI backends (Todo 18).
+
+Covers the ``--backend encoder|decision`` and ``--head-off`` flags:
+flag parsing, the ``--head-off`` effect on the encoder's trainable-head
+globals, and the decision backend dispatching through
+``local_decision.classify_choice`` over ``_DECISION_DESCRIPTIONS``.
+
+``local_decision`` does not exist on this branch yet (it is a sibling todo),
+so the decision-backend tests stub the module in ``sys.modules``. Nothing here
+touches the network, transformers or torch.
+"""
+
+import sys
+from unittest import mock
+
+import pytest
+
+import local_encoder
+from eval_classifier import (
+    _build_parser,
+    _disable_trainable_head,
+    run_eval,
+)
+
+
+def _sample_tasks():
+    return [
+        {"id": "t1", "category": "coding_general", "prompt": "write a function"},
+        {"id": "t2", "category": "debugging", "prompt": "fix this crash"},
+    ]
+
+
+CATS = ["coding_general", "debugging"]
+
+
+# --- flag parsing ---------------------------------------------------------
+
+def test_backend_defaults_to_encoder():
+    args = _build_parser().parse_args([])
+    assert args.backend == "encoder"
+    assert args.head_off is False
+
+
+def test_backend_accepts_decision():
+    args = _build_parser().parse_args(["--backend", "decision"])
+    assert args.backend == "decision"
+
+
+def test_backend_rejects_unknown_choice():
+    with pytest.raises(SystemExit):
+        _build_parser().parse_args(["--backend", "bogus"])
+
+
+def test_head_off_flag_is_store_true():
+    args = _build_parser().parse_args(["--head-off"])
+    assert args.head_off is True
+
+
+def test_decision_override_flags_parse():
+    args = _build_parser().parse_args([
+        "--backend", "decision",
+        "--decision-base-url", "http://x:11434/api/chat",
+        "--decision-model", "qwen3.5:4b",
+        "--decision-num-ctx", "8192",
+        "--decision-timeout", "60",
+        "--decision-coverage-min", "0.5",
+    ])
+    assert args.decision_base_url == "http://x:11434/api/chat"
+    assert args.decision_model == "qwen3.5:4b"
+    assert args.decision_num_ctx == 8192
+    assert args.decision_timeout == 60
+    assert args.decision_coverage_min == 0.5
+
+
+# --- --head-off -----------------------------------------------------------
+
+def test_disable_trainable_head_sets_globals():
+    local_encoder._TRAINABLE_HEAD = object()
+    local_encoder._TRAINABLE_HEAD_RESOLVED = False
+    try:
+        _disable_trainable_head()
+        assert local_encoder._TRAINABLE_HEAD is None
+        assert local_encoder._TRAINABLE_HEAD_RESOLVED is True
+    finally:
+        local_encoder._TRAINABLE_HEAD = None
+        local_encoder._TRAINABLE_HEAD_RESOLVED = False
+
+
+def test_run_eval_encoder_head_off_disables_head():
+    # --head-off with the encoder backend must set the globals before scoring.
+    local_encoder._TRAINABLE_HEAD = object()
+    local_encoder._TRAINABLE_HEAD_RESOLVED = False
+    try:
+        with mock.patch(
+            "local_encoder.classify_zero_shot",
+            return_value=("coding_general", 0.9),
+        ):
+            run_eval(
+                _sample_tasks(), CATS,
+                model_id="x", device="cpu", noise_levels=("clean",),
+                backend="encoder", head_off=True,
+            )
+        assert local_encoder._TRAINABLE_HEAD is None
+        assert local_encoder._TRAINABLE_HEAD_RESOLVED is True
+    finally:
+        local_encoder._TRAINABLE_HEAD = None
+        local_encoder._TRAINABLE_HEAD_RESOLVED = False
+
+
+def test_run_eval_encoder_default_does_not_touch_head():
+    # Without --head-off the encoder backend leaves the globals alone.
+    local_encoder._TRAINABLE_HEAD = None
+    local_encoder._TRAINABLE_HEAD_RESOLVED = False
+    try:
+        with mock.patch(
+            "local_encoder.classify_zero_shot",
+            return_value=("coding_general", 0.9),
+        ):
+            run_eval(
+                _sample_tasks(), CATS,
+                model_id="x", device="cpu", noise_levels=("clean",),
+                backend="encoder", head_off=False,
+            )
+        assert local_encoder._TRAINABLE_HEAD_RESOLVED is False
+    finally:
+        local_encoder._TRAINABLE_HEAD = None
+        local_encoder._TRAINABLE_HEAD_RESOLVED = False
+
+
+# --- decision backend -----------------------------------------------------
+
+def _fake_decision_module():
+    mod = mock.Mock()
+    mod._DECISION_DESCRIPTIONS = {
+        "coding_general": "writing or editing code",
+        "debugging": "finding and fixing bugs",
+    }
+    mod.classify_category.return_value = ("coding_general", 0.95, 1.0)
+    return mod
+
+
+def test_run_eval_decision_uses_classify_category():
+    fake = _fake_decision_module()
+    decision = {
+        "base_url": "http://x:11434/api/chat",
+        "model": "qwen3.5:4b",
+        "num_ctx": 8192,
+        "timeout_s": 60,
+        "coverage_min": 0.0,
+    }
+    with mock.patch.dict(sys.modules, {"local_decision": fake}):
+        results = run_eval(
+            _sample_tasks(), CATS,
+            model_id="x", device="cpu", noise_levels=("clean",),
+            backend="decision", decision=decision,
+        )
+
+    assert fake.classify_category.called
+    _, kwargs = fake.classify_category.call_args
+    assert kwargs["base_url"] == decision["base_url"]
+    assert kwargs["model"] == decision["model"]
+    assert kwargs["num_ctx"] == decision["num_ctx"]
+    assert kwargs["timeout_s"] == decision["timeout_s"]
+    assert kwargs["coverage_min"] == decision["coverage_min"]
+
+    # The returned category name is used directly as the predicted verdict.
+    tv = results["task_verdicts"]
+    assert tv["t1"][1] == "coding_general"
+    assert tv["t2"][1] == "coding_general"
+
+
+def test_run_eval_decision_defaults_coverage_min():
+    fake = _fake_decision_module()
+    with mock.patch.dict(sys.modules, {"local_decision": fake}):
+        run_eval(
+            _sample_tasks(), CATS,
+            model_id="x", device="cpu", noise_levels=("clean",),
+            backend="decision", decision={"base_url": "u", "model": "m"},
+        )
+    _, kwargs = fake.classify_category.call_args
+    assert kwargs["coverage_min"] == 0.0
--- a/tests/test_eval_heldout.py
+++ b/tests/test_eval_heldout.py
@@ -0,0 +1,62 @@
+"""Tests that evals/heldout.yaml is a loadable, scoreable task set.
+
+The held-out set is a fixed benchmark: 30 tasks across 10 categories, with
+tool-use tasks excluded from the scoreable set (as in the plan). These tests
+guard the loader contract against a malformed or drifted file.
+"""
+
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src"))
+
+from eval_classifier import EXCLUDED_CATEGORIES, load_scoreable_tasks
+
+HELDOUT_PATH = Path(__file__).resolve().parent.parent / "evals" / "heldout.yaml"
+
+# The 10 categories in the held-out set, one per 3-task group.
+EXPECTED_CATEGORIES = [
+    "coding_general",
+    "coding_refactor",
+    "debugging",
+    "docs_writing",
+    "summarization",
+    "file_summarization",
+    "diff_checking",
+    "translation",
+    "reasoning_math",
+    "general_chat",
+]
+
+
+def test_heldout_file_exists():
+    assert HELDOUT_PATH.exists(), "evals/heldout.yaml must be committed alongside the loader"
+
+
+def test_heldout_has_30_scoreable_tasks():
+    tasks = load_scoreable_tasks(str(HELDOUT_PATH))
+    assert len(tasks) == 30
+
+
+def test_heldout_categories_match_expected():
+    tasks = load_scoreable_tasks(str(HELDOUT_PATH))
+    categories = [t["category"] for t in tasks]
+    # The held-out set is grouped: three tasks per category, in order.
+    expected = [c for c in EXPECTED_CATEGORIES for _ in range(3)]
+    assert categories == expected
+
+
+def test_heldout_tasks_have_id_and_prompt():
+    tasks = load_scoreable_tasks(str(HELDOUT_PATH))
+    for t in tasks:
+        assert t["id"]
+        assert t["prompt"]
+
+
+def test_heldout_excludes_tool_use():
+    # The scoreable set must never admit tool-use tasks (per the plan).
+    tasks = load_scoreable_tasks(str(HELDOUT_PATH))
+    cats = {t["category"] for t in tasks}
+    assert not (cats & set(EXCLUDED_CATEGORIES))
--- a/tests/test_local_decision_descriptions.py
+++ b/tests/test_local_decision_descriptions.py
@@ -0,0 +1,161 @@
+"""Structural tests for local_decision._DECISION_DESCRIPTIONS.
+
+Verifies:
+- All 10 expected category keys are present.
+- `tool_use_agentic` is excluded.
+- The dict is NOT a shallow copy of local_encoder._CATEGORY_DESCRIPTIONS.
+- local_encoder.py is NOT modified by this change (git diff is empty).
+- classify_choice would work with these descriptions (they're valid option values).
+"""
+
+import pathlib
+import subprocess
+
+
+def _encoder_descriptions():
+    """Import and return local_encoder._CATEGORY_DESCRIPTIONS."""
+    import local_encoder
+    return local_encoder._CATEGORY_DESCRIPTIONS
+
+
+def _decision_descriptions():
+    """Import and return local_decision._DECISION_DESCRIPTIONS."""
+    import local_decision
+    return local_decision._DECISION_DESCRIPTIONS
+
+
+# Expected keys: all 11 minus tool_use_agentic = 10
+_EXPECTED_KEYS = sorted([
+    "coding_general",
+    "coding_refactor",
+    "debugging",
+    "docs_writing",
+    "summarization",
+    "file_summarization",
+    "diff_checking",
+    "translation",
+    "reasoning_math",
+    "general_chat",
+])
+
+
+# --- Key presence -----------------------------------------------------------
+
+
+def test_decision_descriptions_has_exactly_10_keys():
+    """_DECISION_DESCRIPTIONS has exactly 10 keys."""
+    d = _decision_descriptions()
+    assert len(d) == 10
+
+
+def test_decision_descriptions_keys_match_expected():
+    """All 10 expected category keys are present, no extras."""
+    d = _decision_descriptions()
+    assert sorted(d.keys()) == _EXPECTED_KEYS
+
+
+def test_tool_use_agentic_excluded():
+    """tool_use_agentic must NOT appear in _DECISION_DESCRIPTIONS."""
+    d = _decision_descriptions()
+    assert "tool_use_agentic" not in d
+
+
+# --- Not a shallow copy of encoder descriptions ------------------------------
+
+
+def test_decision_descriptions_not_same_object_as_encoder():
+    """_DECISION_DESCRIPTIONS is a distinct dict, not local_encoder's."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d is not e
+
+
+def test_decision_descriptions_differ_from_encoder():
+    """At least one value differs — must not be a shallow copy."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    # They must NOT be equal at the value level
+    assert d != e
+
+
+def test_coding_general_differ():
+    """coding_general was tuned — should include 'tracing' wording."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d["coding_general"] != e["coding_general"]
+    assert "tracing" in d["coding_general"]
+
+
+def test_debugging_differ():
+    """debugging was tuned — should include 'fixing a bug' wording."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d["debugging"] != e["debugging"]
+    assert "fixing a bug" in d["debugging"]
+
+
+def test_reasoning_math_differ():
+    """reasoning_math was tuned — should be a pure word problem, no code."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d["reasoning_math"] != e["reasoning_math"]
+    assert "no code" in d["reasoning_math"]
+
+
+def test_file_summarization_differ():
+    """file_summarization was tuned — should emphasize 'single source file'."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d["file_summarization"] != e["file_summarization"]
+    assert "single source file" in d["file_summarization"]
+
+
+def test_docs_writing_differ():
+    """docs_writing was tuned — should include '(not code)' qualifier."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d["docs_writing"] != e["docs_writing"]
+    assert "(not code)" in d["docs_writing"]
+
+
+# --- Keep-same keys still differ from encoder --------------------------------
+# Even categories marked "keep" differ because the encoder has 11 keys
+# and decision has 10 — the decision dict is a new dict, so `is` must fail.
+
+
+def test_unmodified_keys_still_distinct_object():
+    """summarization key value is the same text but the dict itself differs."""
+    d = _decision_descriptions()
+    e = _encoder_descriptions()
+    assert d is not e
+
+
+# --- local_encoder.py is NOT modified ----------------------------------------
+
+
+def test_local_encoder_file_unmodified():
+    """git diff src/local_encoder.py must be empty — no encoder edits."""
+    repo_root = pathlib.Path(__file__).parent.parent.parent
+    result = subprocess.run(
+        ["git", "diff", "src/local_encoder.py"],
+        capture_output=True,
+        text=True,
+        check=False,
+        cwd=str(repo_root),
+    )
+    assert result.stdout == "", (
+        f"local_encoder.py was modified! Diff:\n{result.stdout}"
+    )
+
+
+# --- classify_choice compatibility -------------------------------------------
+
+
+def test_decision_descriptions_keys_can_be_used_as_options():
+    """The 10 keys are valid option identifiers for classify_choice."""
+    d = _decision_descriptions()
+    # Keys should all be valid strings suitable for option labels
+    for key, value in d.items():
+        assert isinstance(key, str)
+        assert isinstance(value, str)
+        assert len(value) > 0, f"{key!r} has empty description"
--- a/tests/test_local_decision_parse.py
+++ b/tests/test_local_decision_parse.py
@@ -0,0 +1,582 @@
+"""Tests for local_decision.parse_logprobs and local_decision.classify_choice.
+
+Covers (parse_logprobs):
+- Basic option-letter extraction from Ollama logprobs.
+- Token stripping: trailing dots, leading whitespace.
+- Non-option tokens are silently ignored.
+- Confidence = winner_mass / total_option_mass.
+- Coverage = total_option_mass (sum of all option letter masses).
+- RuntimeError on coverage below minimum.
+- RuntimeError on zero total mass.
+- RuntimeError on missing logprobs.
+- Multiple positions and overlapping top_logprobs entries.
+
+Covers (classify_choice):
+- POSTs to the Ollama native /api/chat endpoint with the right body.
+- timeout_s maps to the requests timeout.
+- Returns (label, confidence, coverage) from parse_logprobs.
+- Propagates requests.exceptions.RequestException.
+- Forwards coverage_min to parse_logprobs.
+"""
+
+import json
+import math
+from typing import Any
+
+import pytest
+
+from local_decision import parse_logprobs
+
+# --- Fixtures ----------------------------------------------------------------
+
+
+def _make_logprob(
+    token: str,
+    logprob: float,
+    top_logprobs: list[dict[str, Any]],
+) -> dict[str, Any]:
+    """Convenience builder for a logprob position entry."""
+    return {
+        "token": token,
+        "logprob": logprob,
+        "top_logprobs": top_logprobs,
+    }
+
+
+def _make_response(
+    positions: list[dict[str, Any]],
+) -> dict[str, Any]:
+    """Build a minimal Ollama-style response dict from position entries."""
+    return {"logprobs": positions}
+
+
+# --- Basic parsing -----------------------------------------------------------
+
+
+def test_simple_two_option_a_wins():
+    """A has much higher mass than B -> (A, ~1.0, total)."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.01,
+            [
+                {"token": "A", "logprob": -0.01},
+                {"token": "B", "logprob": -3.0},
+            ],
+        ),
+        _make_logprob(
+            ".",
+            -0.5,
+            [{"token": ".", "logprob": -0.5}],
+        ),
+    ])
+    label, confidence, coverage = parse_logprobs(response, ["A", "B"])
+    assert label == "A"
+    assert confidence > 0.95
+    assert coverage > 0
+
+
+def test_simple_two_option_b_wins():
+    """B has much higher mass than A -> (B, ~1.0, total)."""
+    response = _make_response([
+        _make_logprob(
+            "B",
+            -0.02,
+            [
+                {"token": "A", "logprob": -4.0},
+                {"token": "B", "logprob": -0.02},
+            ],
+        ),
+    ])
+    label, confidence, _ = parse_logprobs(response, ["A", "B"])
+    assert label == "B"
+    assert confidence > 0.98
+
+
+def test_four_options_a_wins():
+    """A wins among A, B, C, D."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.05,
+            [
+                {"token": "A", "logprob": -0.05},
+                {"token": "B", "logprob": -2.0},
+                {"token": "C", "logprob": -3.0},
+                {"token": "D", "logprob": -4.0},
+            ],
+        ),
+    ])
+    label, confidence, _ = parse_logprobs(response, ["A", "B", "C", "D"])
+    assert label == "A"
+    assert confidence > 0.82
+
+
+# --- Token stripping ---------------------------------------------------------
+
+
+def test_trailing_dot_stripped():
+    """Ollama sends 'A.' as the option separator token — must match 'A'."""
+    response = _make_response([
+        _make_logprob(
+            "A.",
+            -0.01,
+            [
+                {"token": "A.", "logprob": -0.01},
+            ],
+        ),
+    ])
+    label, _, _ = parse_logprobs(response, ["A"])
+    assert label == "A"
+
+
+def test_leading_whitespace_token_ignored():
+    """Token ' thinking' — after stripping whitespace it is not an option."""
+    response = _make_response([
+        _make_logprob(
+            " thinking",
+            -0.1,
+            [
+                {"token": " thinking", "logprob": -0.1},
+                {"token": " response", "logprob": -0.2},
+            ],
+        ),
+    ])
+    # Should not raise; no option letters found so coverage < 0.3.
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_thinking_token_does_not_pollute_mass():
+    """'thinking' and 'response' tokens are ignored, not counted."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.01,
+            [
+                {"token": "A", "logprob": -0.01},
+                {"token": "thinking", "logprob": -0.5},
+                {"token": "response", "logprob": -0.6},
+            ],
+        ),
+    ])
+    label, confidence, _ = parse_logprobs(response, ["A", "B"])
+    assert label == "A"
+    # Only A's mass contributes — confidence should be 1.0
+    assert confidence == pytest.approx(1.0)
+
+
+# --- Confidence and coverage -------------------------------------------------
+
+
+def test_confidence_is_normalized_mass_ratio():
+    """confidence = winner_mass / total_option_mass."""
+    # Use -ln(2) ≈ -0.693 so exp ≈ 0.5 for A, and -ln(1.5) ≈ -0.405 for B
+    logprob_a = math.log(2.0)  # ≈ 0.693, exp = 0.5
+    logprob_b = math.log(1.5)  # ≈ 0.405, exp = 0.667
+    # A's mass = 0.5, B's mass = 0.667 → total = 1.167, confidence(A) = 0.5/1.167
+    response = _make_response([
+        _make_logprob(
+            "B",
+            -logprob_b,
+            [
+                {"token": "A", "logprob": -logprob_a},
+                {"token": "B", "logprob": -logprob_b},
+            ],
+        ),
+    ])
+    label, confidence, coverage = parse_logprobs(response, ["A", "B"])
+    assert label == "B"
+    expected_confidence = math.exp(-logprob_b) / (math.exp(-logprob_a) + math.exp(-logprob_b))
+    assert confidence == pytest.approx(expected_confidence, rel=1e-9)
+    assert coverage == pytest.approx(math.exp(-logprob_a) + math.exp(-logprob_b), rel=1e-9)
+
+
+def test_coverage_is_total_mass():
+    """Coverage = sum of exp(logprob) across all option letters."""
+    logprob_val = -0.1
+    response = _make_response([
+        _make_logprob(
+            "A",
+            logprob_val,
+            [
+                {"token": "A", "logprob": logprob_val},
+                {"token": "B", "logprob": logprob_val},
+            ],
+        ),
+    ])
+    _, _, coverage = parse_logprobs(response, ["A", "B"])
+    expected = math.exp(logprob_val) + math.exp(logprob_val)
+    assert coverage == pytest.approx(expected, rel=1e-9)
+
+
+# --- Multiple positions ------------------------------------------------------
+
+
+def test_multiple_positions_accumulate_mass():
+    """Logprobs from multiple positions add up per option."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.1,
+            [{"token": "A", "logprob": -0.1}, {"token": "B", "logprob": -0.2}],
+        ),
+        _make_logprob(
+            "B",
+            -0.05,
+            [{"token": "A", "logprob": -0.3}, {"token": "B", "logprob": -0.05}],
+        ),
+    ])
+    label, _, coverage = parse_logprobs(response, ["A", "B"])
+    assert label == "B"
+    # A mass = exp(-0.1) + exp(-0.3), B mass = exp(-0.2) + exp(-0.05)
+    expected_a = math.exp(-0.1) + math.exp(-0.3)
+    expected_b = math.exp(-0.2) + math.exp(-0.05)
+    assert coverage == pytest.approx(expected_a + expected_b, rel=1e-9)
+
+
+def test_mixed_positions_with_non_option_tokens():
+    """Mix of option letters and noise tokens across multiple positions."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.01,
+            [
+                {"token": "A", "logprob": -0.01},
+                {"token": "thinking", "logprob": -1.0},
+            ],
+        ),
+        _make_logprob(
+            " response",
+            -0.3,
+            [
+                {"token": "B", "logprob": -0.3},
+                {"token": "assistant", "logprob": -2.0},
+            ],
+        ),
+    ])
+    label, _, _ = parse_logprobs(response, ["A", "B"])
+    assert label == "A"  # A: exp(-0.01) ≈ 0.99 > B: exp(-0.3) ≈ 0.74
+
+
+# --- Coverage threshold errors -----------------------------------------------
+
+
+def test_runtime_error_on_low_coverage():
+    """Coverage below minimum raises RuntimeError."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -10.0,  # very low probability -> very small mass
+            [{"token": "A", "logprob": -10.0}],
+        ),
+    ])
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A", "B"], coverage_min=0.3)
+
+
+def test_runtime_error_on_zero_coverage():
+    """No option letters in logprobs → zero coverage → raise."""
+    response = _make_response([
+        _make_logprob(
+            "thinking",
+            -0.1,
+            [
+                {"token": "thinking", "logprob": -0.1},
+                {"token": "response", "logprob": -0.2},
+            ],
+        ),
+    ])
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_runtime_error_on_empty_logprobs():
+    """Empty logprobs list raises RuntimeError about missing logprobs."""
+    response: dict[str, Any] = {"logprobs": []}
+    with pytest.raises(RuntimeError, match="no logprobs"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_runtime_error_on_missing_logprobs_key():
+    """Missing logprobs key → default to [] → raise."""
+    response: dict[str, Any] = {}
+    with pytest.raises(RuntimeError, match="no logprobs"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_custom_coverage_min():
+    """Higher coverage_min requires stronger evidence."""
+    logprob_val = -0.1
+    response = _make_response([
+        _make_logprob(
+            "A",
+            logprob_val,
+            [{"token": "A", "logprob": logprob_val}],
+        ),
+    ])
+    # Default min (0.3) should pass since exp(-0.1) ≈ 0.905
+    _, _, _ = parse_logprobs(response, ["A"])
+    # Very high min should fail
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A"], coverage_min=10.0)
+
+
+# --- Edge cases --------------------------------------------------------------
+
+
+def test_single_option_letter():
+    """Single option letter in the list — must still work."""
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.01,
+            [{"token": "A", "logprob": -0.01}],
+        ),
+    ])
+    label, confidence, _ = parse_logprobs(response, ["A"])
+    assert label == "A"
+    assert confidence == pytest.approx(1.0)
+
+
+def test_option_letters_not_present_in_response():
+    """When none of the option letters appear in top_logprobs, coverage is 0."""
+    response = _make_response([
+        _make_logprob(
+            "X",
+            -0.5,
+            [{"token": "X", "logprob": -0.5}],
+        ),
+    ])
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_dot_token_only_is_ignored():
+    """A bare '.' token (not a stripped option) should be ignored."""
+    response = _make_response([
+        _make_logprob(
+            ".",
+            -0.5,
+            [{"token": ".", "logprob": -0.5}],
+        ),
+    ])
+    with pytest.raises(RuntimeError, match="coverage"):
+        parse_logprobs(response, ["A", "B"])
+
+
+def test_empty_top_logprobs_list():
+    """Position with an empty top_logprobs list is skipped gracefully."""
+    response = _make_response([
+        _make_logprob("A", -0.1, []),
+        _make_logprob(
+            "A",
+            -0.01,
+            [{"token": "A", "logprob": -0.01}],
+        ),
+    ])
+    label, _, _ = parse_logprobs(response, ["A"])
+    assert label == "A"
+
+
+def test_missing_top_logprobs_key():
+    """Position entry missing top_logprobs key does not crash."""
+    response = _make_response([
+        {"token": "A", "logprob": -0.1},  # no top_logprobs key
+        _make_logprob(
+            "B",
+            -0.01,
+            [{"token": "B", "logprob": -0.01}],
+        ),
+    ])
+    label, _, _ = parse_logprobs(response, ["A", "B"])
+    assert label == "B"
+
+
+def test_missing_logprob_entry_is_skipped():
+    response = _make_response([
+        _make_logprob(
+            "A",
+            -0.01,
+            [
+                {"token": "A", "logprob": -0.01},
+                {"token": "B"},  # no logprob key
+            ],
+        ),
+    ])
+    label, confidence, _ = parse_logprobs(response, ["A", "B"])
+    assert label == "A"
+    # Only A's mass contributes — B's missing logprob is skipped
+    assert confidence == pytest.approx(1.0)
+
+
+def test_fixture_real_response_parses():
+    import pathlib
+
+    fixture_path = pathlib.Path(__file__).parent / "fixtures" / "real_ollama_response.json"
+    with open(fixture_path) as f:
+        response = json.load(f)
+
+    label, confidence, coverage = parse_logprobs(response, ["A", "B", "C", "D"])
+    assert label == "A"
+    assert confidence > 0.9
+    assert coverage > 0
+
+
+# --- classify_choice --------------------------------------------------------
+
+
+class _FakeResponse:
+    def __init__(self, payload: dict[str, Any]) -> None:
+        self._payload = payload
+
+    def json(self) -> dict[str, Any]:
+        return self._payload
+
+    def raise_for_status(self) -> None:
+        return None
+
+
+def test_classify_choice_posts_native_api_and_parses(monkeypatch):
+    """classify_choice POSTs to /api/chat and returns parse_logprobs result."""
+    import local_decision
+
+    captured: dict[str, Any] = {}
+
+    def fake_post(url, *, json, timeout):
+        captured["url"] = url
+        captured["json"] = json
+        captured["timeout"] = timeout
+        return _FakeResponse(_make_response([
+            _make_logprob(
+                "A",
+                -0.01,
+                [
+                    {"token": "A", "logprob": -0.01},
+                    {"token": "B", "logprob": -3.0},
+                ],
+            ),
+        ]))
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    label, confidence, coverage = local_decision.classify_choice(
+        "Write a function",
+        {"A": "writing new code", "B": "refactoring"},
+        base_url="http://ollama:11434",
+        model="qwen3.5:4b",
+        num_ctx=8192,
+        timeout_s=120,
+    )
+
+    assert captured["url"] == "http://ollama:11434/api/chat"
+    assert captured["timeout"] == 120
+
+    payload = captured["json"]
+    assert payload["model"] == "qwen3.5:4b"
+    assert payload["stream"] is False
+    assert payload["think"] is False
+    assert payload["logprobs"] is True
+    assert payload["top_logprobs"] == 20
+    assert payload["options"] == {
+        "num_predict": 1,
+        "temperature": 0,
+        "num_ctx": 8192,
+    }
+    assert payload["messages"][0] == {"role": "system", "content": "answer with the letter only"}
+    user_content = payload["messages"][1]["content"]
+    assert "Write a function" in user_content
+    assert "A. writing new code" in user_content
+    assert "B. refactoring" in user_content
+    assert user_content.endswith("Answer with the letter only.")
+
+    assert label == "A"
+    assert confidence > 0.95
+    assert coverage > 0
+
+
+def test_classify_choice_sorted_option_letters(monkeypatch):
+    """option_letters passed to parse_logprobs are sorted."""
+    import local_decision
+
+    captured: dict[str, Any] = {}
+
+    def fake_post(url, *, json, timeout):
+        captured["url"] = url
+        captured["json"] = json
+        captured["timeout"] = timeout
+        return _FakeResponse(_make_response([
+            _make_logprob(
+                "B",
+                -0.02,
+                [
+                    {"token": "A", "logprob": -4.0},
+                    {"token": "B", "logprob": -0.02},
+                ],
+            ),
+        ]))
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    # Unsorted option insertion order; parse must sort to A, B.
+    label, _, _ = local_decision.classify_choice(
+        "Refactor",
+        {"B": "second", "A": "first"},
+        base_url="http://ollama:11434",
+        model="m",
+        num_ctx=4096,
+        timeout_s=30,
+    )
+    assert label == "B"
+
+
+def test_classify_choice_propagates_request_exception(monkeypatch):
+    """requests.exceptions.RequestException propagates out of classify_choice."""
+    import requests as real_requests
+
+    import local_decision
+
+    def fake_post(url, *, json, timeout):
+        raise real_requests.exceptions.ConnectionError("boom")
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    with pytest.raises(real_requests.exceptions.RequestException):
+        local_decision.classify_choice(
+            "x",
+            {"A": "a"},
+            base_url="http://ollama:11434",
+            model="m",
+            num_ctx=4096,
+            timeout_s=30,
+        )
+
+
+def test_classify_choice_coverage_min_passthrough(monkeypatch):
+    """coverage_min is forwarded to parse_logprobs."""
+    import local_decision
+
+    captured: dict[str, Any] = {}
+
+    def fake_post(url, *, json, timeout):
+        captured["timeout"] = timeout
+        return _FakeResponse(_make_response([
+            _make_logprob(
+                "A",
+                -10.0,  # very low mass -> fails high coverage_min
+                [{"token": "A", "logprob": -10.0}],
+            ),
+        ]))
+
+    monkeypatch.setattr(local_decision.requests, "post", fake_post)
+
+    with pytest.raises(RuntimeError, match="coverage"):
+        local_decision.classify_choice(
+            "x",
+            {"A": "a"},
+            base_url="http://ollama:11434",
+            model="m",
+            num_ctx=4096,
+            timeout_s=30,
+            coverage_min=10.0,
+        )