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Checklist AICC 2025 Round 0 · Task 3

Latent Model Classification

Decide, for each input–output pair, which of two partially known neural networks produced the observed logits, without any source labels.

  • Tabular
  • Unsupervised source attribution of model outputs

The task

Each sample's 5-dimensional logits Y were produced by one of two hidden models, A or B. Both map 100-dimensional inputs to 10-dimensional embeddings with different learned transformations and then apply a hidden final linear head.

Contestants receive the two models up to, but not including, the final layer, together with the inputs X and the observed logits Y. The source of each row is not provided; the problem is intentionally unlabelled.

Contestants predict, for each row, whether model A or model B generated the logits.

Abridged by SOTA from the official materials. The official statement has the exact rules, and it wins wherever this summary differs.

At a glance

You get
train.csv (ID, X_1–X_100, Y_1–Y_5) and modelA_penultimate.pth, modelB_penultimate.pth (weights up to the 10-dimensional penultimate layer).
You submit
submission.csv with columns ID and Source ('A' or 'B').
Scoring
Accuracy of the predicted source labels.
Rules
  • Individual participation (maximum team size 1); at most 15 submissions per day.
  • AICC contest rules (stated on each Kaggle rules page, not enforceable): no use of LLMs for writing code or getting task ideas; no internet use other than official library documentation and the contest platform; no communication with anyone during the contest; clarifications only via the #clarification-requests channel on the AICC Discord server.
Format
AICC Round 0, online on Kaggle, 4 Oct 2025 18:00 UTC – 5 Oct 2025 18:00 UTC.

Details

Year
2025, Online (Kaggle)
Round
Round 0 · Task 3
Language
English
License
Varies by task: Deceptive Points and Latent Model Classification — MIT; Find Brain Tumors — CC BY-NC-SA 4.0 (Kaggle competition licences). Solutions repository: MIT., as stated by the source