/compare-results

name: compare-results
description: Establish baseline-vs-candidate evaluation plans, delegate missing evaluations, compare validated results, and decide quantization feasibility. Use when the user asks to compare baseline vs quantized runs, explain an accuracy drop/regression, verify whether a quantized checkpoint is acceptable, or compare NEL/MLflow evaluation outputs. Do NOT use for generic single-model evaluation without comparison intent (use evaluation), live NEL status/debugging (use launching-evals), or generic MLflow browsing without a comparison goal (use accessing-mlflow).
license: Apache-2.0

Compare Results

Use this to plan and complete a baseline-vs-candidate comparison. The baseline is the reference checkpoint, and the candidate is the checkpoint whose accuracy change is being measured, typically a further quantized version of the baseline.

Workflow

1. Establish the candidate checkpoint/run and the matching baseline. Infer the baseline from the PTQ source model/checkpoint in the workspace or config used to create the candidate. If it cannot be inferred, ask the user for the baseline checkpoint or an existing baseline invocation/run path. 2. If a required baseline or candidate evaluation is missing, delegate to the evaluation skill to create, run, and verify it. The companion evaluation config should match benchmark versions, task configs, serving args, token limits, dataset setup, credentials, cluster, and container as closely as possible; change only the model/checkpoint and checkpoint-specific serving or quantization flags. 3. Fetch the baseline and candidate task list, configs, score artifacts, and logs. If the user provides MLflow runs or invocation IDs, use the accessing-mlflow skill to fetch configs and artifacts. 4. Confirm each run passed evaluation Step 9, "Verify completed evaluation run", before comparing scores. If not, validate logs, server health, judge/code-execution status, sample accounting, and reasoning parsing before computing deltas. 5. For each task, use the canonical score field from the matching evaluation skill task recipe, `recipes/tasks/<task>.md`, under **Score Extraction**. 6. Use the evaluation skill's `references/run-validation.md` to perform the **External Baseline Sanity Check**. Record each source URL, protocol difference, and task status before applying the candidate-delta gate. A failed baseline blocks a success verdict; correct and rerun it first. If no credible comparable reference exists, label the baseline externally unverified rather than claiming the check passed, then continue using the validated measured baseline. 7. Compute exact deltas outside the chat context when there are multiple tasks or repeated runs. 8. Report comparability, external baseline sanity, and quantized-feasibility verdicts before interpreting the delta as model quality. If the user did not provide an acceptance threshold, report feasibility as inconclusive instead of inventing one.

Comparability Checklist

Before treating a baseline-vs-quantized delta as a model quality result, verify the validated runs are comparable:

1. Prompt text, system prompt, chat template, and rendered messages match. 2. Task name, benchmark version, dataset split, container, harness, and task fragment match. 3. Generation settings match, including temperature, top_p, top_k, max tokens, stop strings, chat-template kwargs, reasoning mode/budget, and task-specific overrides. 4. Reasoning traces are enabled, disabled, parsed, stripped, or ignored consistently between runs. 5. The number of evaluated and scored samples/repeats matches for each task and split. 6. Judge-backed or simulator-backed tasks use the same judge/user model, endpoint class, prompt, and scoring config. 7. The same accuracy metric and score field is used for both runs. 8. Timeout policies, effective limits, and failure scoring/exclusions match. Apply **Timeout and Output-Limit Accounting** in the evaluation skill's `references/run-validation.md` to both runs; matching limits alone cannot rule out serving-speed effects on scores. 9. **Baseline precision matches the gate.** A `<1pp vs BF16` gate requires a true full-precision (BF16) baseline. Many models ship *natively quantized* (e.g. INT4 `W4A16` or block-wise FP8) with no BF16 release — a quant-to-quant comparison against the released precision (e.g. INT4 vs NVFP4, as for Kimi-K2.6) is still a valid result. **State which precision the baseline is** and apply only an acceptance criterion explicitly defined for that precision. If the requested gate is relative to BF16 and no BF16 baseline is available, report that gate as inconclusive; do not reinterpret it as an FP8/INT4 gate.

For SciCode, keep `num_repeats: 1` and require **at least 8 runs per side**, comparing the two means — see the evaluation skill's `recipes/tasks/aa/scicode.md`. Fewer than 8 valid runs on a side is `INDETERMINATE`, not a delta.

If any item differs, either rerun with matched settings or label the result as not an apples-to-apples quantization comparison.

These checks compare the baseline and candidate to each other. The external baseline check in the evaluation skill's `references/run-validation.md` separately tests whether the baseline's absolute score is credible; both guards must be reported.

Report Format

Include:

• Baseline and candidate identifiers.
• Per-task metric path, baseline score, candidate score, delta, and stderr if

available.

• Per-task external reference score, source URL, known protocol differences,

percentage-point difference, and sanity status (`verified`, `failed`, or `externally unverified`).

• Comparability status for prompt/template, generation settings, sample counts,

reasoning handling, judge/simulator setup, and score field.

• Per-task timeout and output-limit counts/rates for both runs, explicit

denominators, te