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. 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; just compare like-for-like, state which precision the baseline is, and apply the gate relative to that baseline rather than to an assumed BF16.

For SciCode, keep num_repeats: 1 to limit sandbox workload. If variance is a concern, run multiple independent matched baseline/candidate pairs instead of increasing repeats within one run.

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.
• Comparability verdict: comparable, not comparable, or inconclusive.
• Quantization feasibility verdict: acceptable, not acceptable, or inconclusive. Never report acceptable when external baseline sanity failed. An externally unverified baseline does not block acceptable; apply the candidate-delta gate and report the missing external corroboration.