Eval methodology — the gate finding

Date: 2026-06-08 Status: confirmed, the gate task #270 has its evidence TL;DR: fm-fixtures are passable in full (19/19) by a rule-based endpoint that runs zero model inference. Every Pace LoRA we trained scores ≤ this baseline. The fixtures test framework, not model.

What we ran

scripts/fake_pace.py — a 300-line Python script using ONLY:

• regex over the user prompt to detect intent (click / type / scroll / key combo / open app / identity probe / QA)
• substring matching against element labels for click targets
• a JSON-shape wrapper and free-text-mode toggle that mirror Pace’s v6-label system prompt rules

No tokenizer beyond re. No model. No neural net. No LoRA. No SFT.

Results

The rule-based baseline is the ceiling of what fm-fixtures can distinguish. Every LoRA we shipped lives below it.

Implications

1. Pace v5’s “17/19” was not measuring model capability. It was measuring the model’s compliance with the JSON grammar + the label-lookup convention. A regex endpoint matches it.
2. v6’s “regression” to 14/19 was the model unlearning the format, not regressing at any task. The grammar mask had to fall back to default behavior for 5 fixtures.
3. v6.1’s four-way SFT collapse (10 → 8 → 2 → 0) was the model finding new formats that broke the eval’s regex assumptions — not a model that became worse at planning. We were optimizing toward an eval that doesn’t reward planning improvements.
4. The “factory beats teacher” framing is unsupported. The Pace specialist may or may not beat the Qwen3-30B-A3B teacher at planning; fm-fixtures cannot tell us either way.

What an honest eval would look like

We need fixtures where a rule-based endpoint fails and a real planner succeeds. Candidate axes:

Axis 1 — Semantic disambiguation that requires world knowledge

USER: open the app i use to write code
ELEMENT: [0] dock_icon|..|Mail
ELEMENT: [1] dock_icon|..|Xcode
ELEMENT: [2] dock_icon|..|Safari
ELEMENT: [3] dock_icon|..|Spotify
EXPECT_CLICK_ID: 1   # the model must KNOW Xcode is a code editor

A regex over “code” wouldn’t match any of those labels. The model has to bring the world knowledge that Xcode is an IDE.

Axis 2 — Multi-element reasoning (“the most expensive one”)

USER: click the cheapest plan
ELEMENT: [0] button|..|Free plan|$0/mo
ELEMENT: [1] button|..|Pro plan|$15/mo
ELEMENT: [2] button|..|Enterprise plan|$99/mo
EXPECT_CLICK_ID: 0

A regex doesn’t know to read the text field for prices, parse them, and pick the minimum.

Axis 3 — Contextual reference resolution

USER: click the same button as last time
HISTORY: [previous turn shows user clicked "Save Draft"]
ELEMENT: [0] button|..|save draft
ELEMENT: [1] button|..|discard
EXPECT_CLICK_ID: 0

Requires the model to track conversational history. Regex can’t.

Axis 4 — Open-ended generation quality

USER: summarize what's on my screen
ELEMENT: [0..N] (a complex screen)
EVAL: human or LLM judge rates summary on 1-5 scale

No fixed regex assertion. The judge evaluates the response itself — relevance, accuracy, brevity, tone.

Axis 5 — Behavioral diversity tests

50 paraphrases of “save this file” — the model should produce 50 correct responses with appropriate variation in spoken text, not the same template every time. A regex endpoint produces one template.

The eval gate is now built

scripts/fake_pace.py is the gate. To compare any LoRA against the real-model-contribution standard:

1. Run the LoRA via tinygpt serve against fm-fixtures → lora_score / N
2. The FakePace baseline depends on the fixture set:
   • v1 (clickyLocal/evals/fm-fixtures): 19/19 — useless for measuring model contribution; only format compliance
   • v2 (clickyLocal/evals/fm-fixtures-v2): 1/15 (6.7%) — the genuine baseline for model-required tasks
3. model_contribution = lora_score(v2) − 6.7%. ≥85% on v2 is the bar for “this model adds real capability.”

v2 baseline shipped (2026-06-08)

clickyLocal/evals/fm-fixtures-v2/ — 15 fixtures across three axes:

• Semantic disambiguation (7): “open the app i use to write code” → Xcode. Element labels don’t contain function words; only the model’s world knowledge does.
• Multi-element reasoning (6): “click the cheapest plan” requires parsing $0/$15/$99 in the element text field and picking the minimum.
• Abstract reference resolution (2): “click the one for sending money” → Transfer. User names a goal; model maps to action.

FakePace result on v2: 1/15 (6.7%) — the single pass is a lucky first-match-wins on reason-cheapest-plan (element 0 happened to be the answer). The eval is calibrated correctly to require real model capability.

PR: https://github.com/sarthakagrawal927/clicky/pull/new/eval/fm-fixtures-v2

What we should do next

1. Don’t train new LoRAs against fm-fixtures. Until new fixtures that distinguish model from framework exist, training is theater.
2. Build the new fixture set. ~20-30 fixtures covering axes 1-5. Most can be hand-written; behavioral diversity (axis 5) can use LLM-paraphrase synthesis.
3. Re-baseline FakePace against the new fixtures. Target: FakePace ≤ 50%, a real model ≥ 85%. The delta IS the moat.
4. Re-measure v5 / v6 / v6.1 against the new fixtures to see whether any of them have residual capability that the old eval masked.

Stakes

This finding makes most of today’s earlier work — ANE M8 Swift orchestrator, M9 spec dec experiment, v6.1 augmented SFT attempts — either irrelevant or premature. The artifacts are still real; they just don’t answer the question we thought they answered.

The single most valuable hour of this session was building this baseline. Knowing the eval is broken is worth more than any number of speed improvements measured against it.