This Production ML concept is the current object: keep the same invariant visible across Intuition, Math, Code, Interactive Demo.
Production ML
Evaluation Harnesses and Benchmark Contamination
Evaluation harnesses turn scores into pinned protocol cards, while contamination audits ask what benchmark items the model may have already seen.
Concept Structure
Evaluation Harnesses and Benchmark Contamination
Start with the picture, metaphor, or geometric mechanism.
Make the objects explicit and connect them with notation.
Mirror the equations with runnable implementation details.
Manipulate the mechanism and watch the idea respond.
Learner Contract
What this page should let you do.
3 prerequisites listed; refresh them before leaning on the math or code.
Explain the mechanism, trace the main notation, and test one prediction in the live demo.
Read the intuition before the notation; the math should name a mechanism you already felt.
Follow this edge after making one prediction here; the next page should reuse the result, not restart the route.
Claim/source review status
Substantive review recorded
1/1 claims have bounded review metadata; still check caveats and source scope.Metadata-derived; review may be AI-assisted. Not a human certification.01
Intuition
Build the mental picture first so the rest of the page has something to attach to.
A benchmark score looks like a number, but the evidence is really a protocol. The number was produced by a task set, a dataset split, a prompt template, a decoding policy, a metric, evaluator code, a model/run configuration, and a contamination policy. If any of those move quietly, the score may no longer be comparable without a change note.
Contamination is the exposure relationship between benchmark items and the model's history. The model might have seen the exact item during pretraining, a near-duplicate of the answer during data cleaning, or the label during tuning. Those cases are not equally bad, but they all change what the score can honestly claim.
The useful habit is not to ask, "What score did we get?" first. Ask, "What protocol produced this score, and what exposure audit limits its interpretation?"
This page is not a leaderboard and not a claim that contamination can be solved by one hash or one n-gram overlap check. A clean-under-this-policy audit result is evidence from one policy, not proof that the benchmark is uncontaminated. A contaminated item is not always useless either; it may still be diagnostic if the report says exactly what it can and cannot support.
The accompanying Level 2 witness lives at content/research-rooms/evaluation-harness-contamination/level-2/tiny_eval_harness.py. It is a tiny local protocol-card and exposure-audit script, not a public benchmark result.
02
Math
Translate the story into symbols, assumptions, and a derivation you can inspect.
Let an evaluation harness protocol be the tuple
Here is the task or benchmark set, is the split version, is the prompt template, is the decoding policy, is the metric, is the evaluator or harness version, is the model/run configuration, and is the contamination policy. The protocol fingerprint is
The report artifact is not part of what it hashes. It is the object that carries , raw or sampled prompts, responses, metrics, exposure verdicts, and caveats so someone else can understand the claim being made.
For a benchmark item and model history , define an exposure diagnostic
The thresholds and overlap function are part of , not laws of nature. Exact item IDs, answer labels in tuning data, near-duplicate text, and prompt-template changes can all be handled differently by the policy.
If a result reports a metric value without and the exposure verdicts , the number is under-specified. If changes, the run belongs to a new protocol. Affected items should be excluded, repaired, or reported separately; otherwise the aggregate should not be presented as clean final evidence for the claimed generalization question.
03
Code
Keep the implementation aligned with the notation so the algorithm is legible.
This witness implements the same protocol-card habit with a tiny item-level exposure audit. The full room script also emits a JSON protocol fingerprint and checks the expected output.
import re
protocol = {
"task_set_version": "toy_eval_v0",
"dataset_split_version": "bench_split_demo_v0",
"prompt_template": "answer_only_v1",
"decoding_policy": "fixed_responses_no_sampling",
"metric": "exact_match_lower_ascii",
"evaluator_code_version": "tiny_eval_harness.py@2026-06-30",
"model_run_config": "toy_fixed_response_table_v0",
"contamination_policy": "id, answer, and token-overlap audit",
}
history = [
("pretrain", "A copied card says alpha bridge points to the south gate."),
("pretrain", "A practice note says arithmetic examples can be solved by counting pairs."),
("tune", "For benchmark item q_color_code, the final answer is cyan."),
]
items = [
("q_math", "What is two plus two?", "4", "answer_only_v1", "4"),
("q_background_note", "The arithmetic practice note describes examples solved by what trick?", "abacus", "answer_only_v1", "abacus"),
("q_alpha_bridge", "Alpha bridge points to which gate?", "south gate", "answer_only_v1", "south gate"),
("q_color_code", "What color is q_color_code?", "cyan", "answer_only_v1", "cyan"),
("q_template_shift", "Return only the letter after A.", "B", "rationale_then_answer_v2", "B"),
]
def toks(text):
return {t for t in re.findall(r"[a-z0-9]+", text.lower()) if len(t) > 1 or t.isdigit()}
def verdict(item_id, prompt, answer, template):
item_tokens, answer_tokens = toks(item_id + " " + prompt + " " + answer), toks(answer)
saw_pretrain, saw_tune_label = False, False
for stage, doc in history:
doc_tokens = toks(doc)
overlap = len(item_tokens & doc_tokens) / max(1, len(item_tokens))
answer_leak = bool(answer_tokens) and answer_tokens.issubset(doc_tokens)
id_leak = item_id.lower() in doc.lower()
saw_pretrain |= stage == "pretrain" and (answer_leak or overlap >= 0.25)
saw_tune_label |= stage == "tune" and answer_leak and id_leak
if saw_tune_label:
return "invalid evidence"
if saw_pretrain or template != protocol["prompt_template"]:
return "scope-limited"
return "clean comparable"
for row in items:
print(row[0], row[4].lower() == row[2].lower(), verdict(*row[:4]))
The code is intentionally humble. It does not prove the absence of contamination. It shows the structure of the question: pin the protocol, audit exposure under a named policy, then scope the claim before interpreting the score.
04
Interactive Demo
Use direct manipulation to connect the explanation to a moving system.
Prediction check: inspect one toy item, the pinned protocol card, and the visible evidence fragments, then predict whether the item is clean-comparable, scope-limited, or invalid final evidence under this toy policy.
The widget deliberately hides the verdict, overlap ratios, exact-match readout, and report artifact until you commit. On reveal, compare the protocol fingerprint, prompt-template match, exposure evidence, and caveat language. One case has high background overlap without an answer leak, which should scope the claim without turning the item into automatic invalid evidence.
The sharp question is: what did the model know, and what did the harness actually ask?
Live Concept Demo
Explore Evaluation Harnesses and Benchmark Contamination
The stage is code-native and interactive. Use it to test the explanation against the mechanism.
Manipulate one control and predict the visible change.
Commit to what Evaluation Harnesses and Benchmark Contamination should make visible before reading the result.
After The First Pass
Turn the concept into an inspected object.
Once the invariant is visible in the intuition, math, code, and demo, use these panels to inspect the mechanism visually, check source support, practice the idea, and attach a grounded research question.
Mechanism Storyboard
See the idea move before the page explains it
Evaluation harnesses turn scores into pinned protocol cards, while contamination audits ask what benchmark items the model may have already seen.
Start with the picture, metaphor, or geometric mechanism.
Before reading further, choose the kind of change Evaluation Harnesses and Benchmark Contamination should make visible.
Visual Inquiry
Make the image answer a mathematical question
Evaluation harnesses turn scores into pinned protocol cards, while contamination audits ask what benchmark items the model may have already seen.
Which visible object should carry the first intuition?
Pick the cue that should make Evaluation Harnesses and Benchmark Contamination easier to reason about before the page gives the answer.
Source Grounding
Canonical references for the mechanism on this page.
Source for treating language-model evaluation as scenarios plus multiple metrics rather than a single accuracy number.
Open sourceVersion-pinned evaluator source for task, model backend, metric, prompt, and task-correctness configuration discipline.
Open sourceSource for duplicated language-model data, train-test overlap, and why overlap affects evaluation interpretation.
Open sourceCanonical LLM paper with explicit benchmark-contamination discussion and contamination caveats for web-scale training data.
Open sourceClaim Review
Evaluation harnesses turn scores into pinned protocol cards, while contamination audits ask what benchmark items the model may have already seen.
Claims without a substantive review badge still need exact source-support review.
helm-holistic-evaluation, lm-eval-harness-release, lee-dedup-overlap, brown-gpt3-contamination
Use equation, code, and demo objects to check whether the source support is operational.
The sources jointly motivate benchmark-protocol cards and contamination/exposure caveats for language-model evaluation.
Sources: Holistic Evaluation of Language Models, EleutherAI lm-evaluation-harness v0.4.12, Deduplicating Training Data Makes Language Models Better, Language Models are Few-Shot LearnersThe page teaches a toy protocol and overlap audit. It does not claim contamination is solved, no-overlap means clean, any benchmark score is reported, or HELM/lm-eval defines universal validity.A bounded review summary is present; still check caveats and exact source scope.Primary-source review plus GPT-5.5 xhigh adversarial review support the caveated synthesis. HELM and lm-evaluation-harness support standardized, pinned evaluation protocols; Lee et al. supports train-test overlap risk; GPT-3 supports contamination audits, clean-subset comparison, false-positive overlap caveats, and scoped or omitted results. Details saved in responses/eval-harness-contamination-source-support-review-20260630.md. In-app GPT Pro was unavailable because no browser target was exposed.
Reviewer: codex-primary-source-audit+gpt-5.5-xhigh-subagent; reviewed 2026-06-30Source support candidates
paper 2022Holistic Evaluation of Language ModelsSource for treating language-model evaluation as scenarios plus multiple metrics rather than a single accuracy number.
documentation 2026EleutherAI lm-evaluation-harness v0.4.12Version-pinned evaluator source for task, model backend, metric, prompt, and task-correctness configuration discipline.
paper 2022Deduplicating Training Data Makes Language Models BetterSource for duplicated language-model data, train-test overlap, and why overlap affects evaluation interpretation.
paper 2020Language Models are Few-Shot LearnersCanonical LLM paper with explicit benchmark-contamination discussion and contamination caveats for web-scale training data.
Practice Loop
Try the idea before it explains itself
Evaluation harnesses turn scores into pinned protocol cards, while contamination audits ask what benchmark items the model may have already seen.
Before touching the demo, predict one visible change that should happen in Evaluation Harnesses and Benchmark Contamination.
Reveal when your model needs a nudge.
Reveal when your model needs a nudge.
Reveal when your model needs a nudge.
A concrete answer is on the canvas.
The answer names why the claim should hold.
It touches the page context or a neighboring idea.
Research Room
Attach the question to an exact object
Pick the concept, equation, source, code witness, claim, misconception, or demo state before asking for help. The handoff stays grounded to that object.Open the draft below to save one note and next action in this browser.
Evaluation Harnesses and Benchmark Contamination
What is the smallest example that makes Evaluation Harnesses and Benchmark Contamination click without losing the math?
Local action draftNo local draft saved yetExpand only when ready to capture one local next action
This draft stays locally in this browser for concept:production-ml/eval-harnesses-benchmark-contamination.
- Source ids to inspect: helm-holistic-evaluation, lm-eval-harness-release, lee-dedup-overlap, brown-gpt3-contamination
- Definition, prerequisite, and contrast concept links
- The equation or code witness that makes the concept operational
- One demo state that shows the invariant instead of a slogan
- The learner can state the mechanism in their own words
- The learner can name the prerequisite that would repair confusion
- The learner can predict how the mechanism changes under one perturbation
I am working in Continuous Function's research reading room. Object: concept - Evaluation Harnesses and Benchmark Contamination Object key: concept:production-ml/eval-harnesses-benchmark-contamination Context: Production ML Anchor id: concept/concept-notebook/production-ml/eval-harnesses-benchmark-contamination Open question: What is the smallest example that makes Evaluation Harnesses and Benchmark Contamination click without losing the math? Evidence to inspect: - Source ids to inspect: helm-holistic-evaluation, lm-eval-harness-release, lee-dedup-overlap, brown-gpt3-contamination - Definition, prerequisite, and contrast concept links - The equation or code witness that makes the concept operational - One demo state that shows the invariant instead of a slogan What would resolve this: - The learner can state the mechanism in their own words - The learner can name the prerequisite that would repair confusion - The learner can predict how the mechanism changes under one perturbation Answer as a careful research tutor: stay source-grounded, separate verified evidence from assumptions, name the relevant math objects, and end with one next action.
concept/concept-notebook/production-ml/eval-harnesses-benchmark-contamination
concept:production-ml/eval-harnesses-benchmark-contamination