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You cannot establish that a smaller AI model was distilled from a particular larger model just because their answers sound alike or their benchmark scores are similar. The strongest available approaches test for a teacher-specific change in the student’s likelihoods relative to an earlier checkpoint, or look for a secret fingerprint the teacher’s owner deliberately placed in its outputs before training. Each approach depends on access, controls, and statistical calibration; neither turns a suggestive match into proof by itself.
Start by defining the claim precisely: are you asking whether the student used outputs from one named teacher, whether it learned from any synthetic data, or whether a passage bears a watermark? Those are different questions, and they need different evidence.
What does it mean to detect distillation?
In the common setup examined by Rawat and colleagues, a teacher model answers prompts and a student is trained on those prompt-response pairs. Detecting that process means finding evidence that links the student’s training to a particular teacher—not merely showing that the two models produce similar text.
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Similarity has several plausible explanations. Models may share a base model, use the same public training data, or independently produce familiar answers. A student may also have learned from a mixture of teachers or from later training that obscures earlier influences. For a defensible attribution, the test must distinguish the suspected teacher from these alternatives.
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There are two materially different routes:
- Reference-checkpoint likelihood comparison: with access to student and teacher token likelihoods and a suitable earlier student checkpoint, measure whether the student became better than that reference at predicting the candidate teacher’s responses.
- Teacher-owned fingerprint: if the teacher’s owner embedded a secret, testable signal in its outputs before they were used for training, check whether that signal can be found in text sampled from the suspect student.
The first route can rank candidate teachers under specific access assumptions. The second can provide a key-specific signal without access to suspect weights or logits, but only when the owner has the relevant watermark resources. Neither route is a universal detector for every training history.
What access do you need before choosing a test?
Write down what you can actually inspect. A method that requires logits cannot be applied to a text-only API as if it were the same test, and a watermark test cannot work without a pre-existing, teacher-owned signal and its verification resources.
- Student weights or token likelihoods: needed for the reference-based likelihood procedure described by Rawat and colleagues.
- Candidate-teacher access: needed to obtain responses to fresh prompts for that comparison.
- An earlier student checkpoint: needed as the reference, ideally one that is close enough in model lineage to make the change informative and that predates exposure to the candidate teacher.
- Full teacher reasoning traces: potentially important where the teacher’s reasoning is part of what the student learned. Withheld traces can remove a useful signal.
- A teacher-owned watermark and secret key: needed for a targeted fingerprint test. The watermark must have been embedded before the suspected training.
- Only sampled suspect-model text: compatible with the WM-Basin proposal’s suspect-side access setup, but not with the reference-based logits score.
- Independent prompt sets: needed to reduce the chance that a candidate appears to win because of a prompt set selected or reused for the decision.
Rawat and colleagues describe their approach as black-box in a broad model-access sense, but their method explicitly assumes logits access to both the student and candidate teachers. Do not describe that likelihood procedure as available from a text-only suspect API. WM-Basin, by contrast, describes a verifier that works with sampled suspect text, while the verifier still needs owner-side watermark resources and a clean probe model.
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How does the reference-checkpoint test work?
It asks whether the student has become unusually good at predicting a candidate teacher’s responses compared with a suitable earlier version of itself. The change relative to the reference is the signal; a high raw likelihood or a close-looking answer alone is not.
Calculate a candidate score
For a fresh prompt x, obtain candidate teacher T’s response y. Score the response under suspect student S and earlier reference R using average token-level negative log-likelihood (NLL):
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score(T,x) = -(NLL_S(y|x) - NLL_R(y|x))
A positive score means the student assigns the teacher response a lower average NLL than the reference does. Repeat this over prompts and candidate teachers, then compare their mean scores. In principle, the procedure looks for a teacher-specific relative shift rather than asking whether one response is simply plausible to the student.
The reference matters. It should be close enough in lineage for changes to be interpretable and, ideally, precede the student’s exposure to the candidate teacher. If no appropriate earlier checkpoint exists, the interpretation of the score weakens: a difference might reflect other changes between models rather than distillation from the named teacher.
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A top-ranked teacher is not automatically a positive detection. In the paper’s procedure, the authors compare the two highest mean candidate scores, calibrate a margin threshold, check whether the top candidate clears that threshold, correct for selecting a candidate from a pool, and require the same teacher to pass on two separate prompt sets. This is the authors’ evaluated procedure, not a plug-in threshold that can be carried unchanged to another model family.
Prompt selection and candidate selection matter because testing many teachers or tuning prompts can make a seemingly strong match appear by chance. Use separate prompt sets for confirmation rather than treating the prompts that surfaced a candidate as independent validation. Preserve the candidate pool, scoring choices, threshold-calibration method, and any multiple-comparison correction in the audit record.
What the published results do—and do not—show
Rawat and colleagues’ June 19, 2026 arXiv preprint reports 85.5% accuracy (65 of 76 cases) for a threshold selected on controlled evaluation cells, but 53.6% (15 of 28) when that threshold was transferred unchanged to real-world cells. In controlled cross-validation, the reported accuracy was 82.9% (63 of 76) when leaving a student out and 60.5% (46 of 76) when leaving a teacher out. These are results in the paper’s evaluation settings, not expected accuracy for an arbitrary audit. Read the study and its method.
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The study focuses on single-teacher, last-stage supervised fine-tuning. It does not establish a general solution for multi-teacher or multi-stage histories. The authors also report that hiding DeepSeek-R1 reasoning traces reduced teacher-identification accuracy to zero in the cited comparison. That finding is a warning about missing information in this evaluated case, not a universal estimate for every teacher or student.
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A model owner can deliberately add a secret, statistically testable pattern to teacher outputs, then later check whether a student appears to have learned that key-specific pattern. This is a targeted provenance test: it depends on planting the signal before the suspected training, and on preserving the means to verify it.
What the watermark studies report
Meta AI Research reported a p-value below 10-5 when as little as 5% of training text consisted of watermarked synthetic instructions in its 2024 experimental setting. The authors’ claim that “watermarked training data leaves traces easier to detect and much more reliable than membership inference” describes their work, not a comparison of every possible detector. Read Meta AI Research’s publication summary.
Gu and coauthors demonstrated that students can learn to produce watermarked text, but also reported that further fine-tuning on ordinary text can remove watermark behavior. A detected signal can therefore support a targeted hypothesis; it does not guarantee that every student trained on marked outputs will retain a detectable trace.
Other designs try to balance detectability with output quality. The 2026 ICML paper Antidistillation Fingerprinting uses a proxy for student learning to choose fingerprint-bearing tokens and reports a better detectability-versus-output-utility trade-off on its evaluated math, dialogue, and coding tasks. A 2026 ACL paper explores rewriting teacher reasoning traces to insert verifiable signatures while maintaining answer correctness and semantic coherence in its experiments. An October 2026 preprint proposes placing a sparser watermark bias near the teacher’s top-token choices. These are distinct research designs, not universal, off-the-shelf detectors.
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What WM-Basin adds for text-only suspect access
Submitted October 27, 2026, the preprint Watermark Basins proposes WM-Basin. Its verifier holds the claimed watermark key, builds reference signals from teacher outputs generated under that key and decoy keys, and scores text sampled from the suspect without accessing suspect weights or logits. This makes it a different access route from the reference-checkpoint test; it still requires the owner-side watermark setup. Read the WM-Basin preprint.
In its reported n-gram=1 experiments, WM-Basin reports 79% true-positive detection at a 0.1% false-positive rate, compared with 29% for its token-level baseline. For n-gram=3, it reports 45% versus 1.3% at the same false-positive rate. These figures describe the paper’s experimental conditions; they are not field-wide performance guarantees or predictions for a particular suspect model.
Why a negative watermark test is inconclusive
A 2025 ACL study reports that targeted paraphrasing of training data and inference-time watermark neutralization eliminated inherited watermark evidence in its experiments. WM-Basin reports resistance to several post-distillation modifications in its own tested setting, but that does not establish resistance to all adversaries or invalidate the earlier study. A failed watermark detection means the tested signal was not found under those conditions; it does not prove that teacher outputs were never used. Read the 2025 ACL study.
Which detection route fits which evidence?
The methods answer related but different questions. Choose based on the evidence you can obtain and the claim you need to assess.
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| Question | Reference-checkpoint likelihood comparison | Teacher-owned fingerprint |
|---|---|---|
| Who must have planted a signal? | No watermark planting is required; the method depends on a suitable earlier student checkpoint and candidate-teacher outputs. | The teacher owner must have embedded the fingerprint before the suspected training. |
| What suspect access is required? | Student logits or token likelihoods. | WM-Basin describes sampled suspect text without suspect weights or logits. |
| What teacher-side resources are required? | Access to candidate teachers to obtain their responses. | The owner’s watermark reference signals and claimed secret key, with decoy-key signals in WM-Basin. |
| Is an earlier checkpoint needed? | Yes; it is central to the relative score. | Not for the described WM-Basin suspect-side test. |
| Does it identify one teacher? | It ranks and tests candidate teachers, subject to the method’s assumptions and calibration. | It tests for a key-specific signal associated with a planted fingerprint. |
| What can weaken the signal? | An unsuitable reference, missing reasoning traces, or training histories beyond the principal evaluation. | Paraphrasing, downstream training, or other changes may remove inherited evidence; resistance claims are method- and experiment-specific. |
| What does a positive result establish? | A teacher-specific relative likelihood pattern under the tested candidates and prompts, not an unqualified account of the full training history. | Evidence consistent with the tested watermark’s influence, not a guarantee about every source or stage of training. |
Neither a standard watermark detector applied to a passage nor a document-membership watermark answers the same question. The first asks whether that passage bears an AI-output watermark. The second asks whether marked documents were present in training data. Neither, on its own, establishes that a named teacher supplied supervised-training outputs to a separate student.
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How should you report an audit without overclaiming?
Describe the result as a tested hypothesis with its scope and limitations, not as a bare accusation. An audit report should let a reader see what was compared, what alternatives remain plausible, and how the decision was calibrated.
- Name the claim and alternatives: identify the suspected teacher and plausible alternatives, including shared lineage, shared public data, other teachers, or other stages of training.
- Document the access: state whether you had student logits, sampled text only, candidate-teacher access, full reasoning traces, or a teacher-owned key and watermark resources.
- Establish reference provenance: for a likelihood comparison, record which earlier checkpoint was used and what is known about when it was created relative to exposure to candidate outputs.
- Record the prompt design: distinguish prompts used to discover a candidate from independent prompts used to confirm it.
- Explain the decision rule: report the candidate pool, score definition, threshold calibration, candidate-selection correction, and whether the same candidate passed on separate prompt sets.
- State the coverage limits: say whether the method was evaluated for a single teacher and last-stage supervised fine-tuning, whether relevant traces were withheld, and whether mixed or multi-stage histories are outside its supported scope.
- Separate evidence from interpretation: report the observed score or watermark result and its false-positive calibration, then state precisely what training influence it supports and what it cannot establish.
This reporting checklist follows from the methods’ assumptions and observed limits; it is not a published universal standard. The sharp drop when a controlled threshold was transferred to real-world evaluation is a concrete reason to avoid presenting a top-ranked candidate as settled provenance.
What common clues are not enough?
Several observations can motivate an investigation, but none is a provenance verdict without a calibrated test that considers alternatives.
- Similar answers: lexical overlap or semantic resemblance can arise without a teacher-student relationship; direct lexical and semantic comparisons were unreliable for attribution in the reference-based study.
- High raw likelihood: a student may find a response generally plausible. The proposed score uses change against an earlier reference, not raw likelihood in isolation.
- Similar benchmark performance: shared task competence does not identify the training source.
- An AI watermark on one output: that detects a property of the sampled passage, not necessarily whether a separate model learned from a named teacher.
- A pattern in pretraining documents: document watermark evidence concerns marked data membership, not necessarily teacher-generated supervised responses.
- A negative watermark result: signal removal or downstream training can make inherited evidence disappear in tested settings, so absence is not proof of absence.
Keep the conclusion proportional to the evidence: a ranking is a ranking, a key-specific signal is evidence consistent with a planted source, and neither alone supplies a complete history of how the student was trained.
Frequently Asked Questions
Can I run the likelihood comparison if the suspect is only available through a chat API?
Not as described in Rawat and colleagues’ procedure: it assumes token-level logits or likelihoods for the student, not just generated replies. A text-only interface may fit a watermark approach such as WM-Basin only if the teacher owner has already created the required fingerprint and verification resources.
Does distillation always mean the student copied the teacher’s exact answers?
No. The relevant setup is training on teacher-generated prompt-response pairs, while the resulting student can differ in its wording or behavior. Conversely, answer similarity by itself does not establish that those pairs were used.
Can a teacher add a fingerprint after a student has already been trained?
A fingerprint test depends on a signal being present in the teacher outputs before the suspected training exposure. Adding one afterward cannot establish that the student learned that key-specific signal from earlier outputs.
What does a very small p-value mean in a watermark study?
It describes evidence against the study’s tested null model under that experiment’s assumptions; it is not the probability that a particular company or person distilled a model. Interpret it alongside the signal design, tested data and models, and the possibility that later training altered the watermark.
