Memory Trace

Memory Trace

English translation by SOTA – AI Community of the Georgian original. Organisers who would like this translation removed can email [email protected].

Source: Georgian AI League I, a practice contest of the Georgian Artificial Intelligence Association (GAIA) on the Nitro AI judge, 18 January 2026, task 1: original statement.

🧠 Task: Memory Trace

Overview

You are given:

1. Tabular data (train_data.csv)
2. Two trained machine learning models:
   • model_A.joblib
   • model_B.joblib

Each model was trained on exactly half of the data:

• These halves do not overlap (non-overlapping).
• Each row (record) was part of the training set of only one model.
• Both models solve a binary classification task.

Your goal: for each row of this data, determine which model used it in its own training process.

Task

Make a prediction for each row of the dataset:

• 0 → the row was used to train model A.
• 1 → the row was used to train model B.

Input Files

train_data.csv

Tabular data containing:

• row_id – a unique identifier for each row.
• Feature columns – both numerical and categorical.

model_A.joblib, model_B.joblib

Two pre-trained sklearn models, saved in joblib format. You can load them as follows:

import joblib

model_A = joblib.load("model_A.joblib")
model_B = joblib.load("model_B.joblib")

Output Format

You must create a CSV file containing exactly three columns:

subtaskID,datapointID,answer
1,0,0
1,1,1
1,2,0
...

where:

• subtaskID is always 1 (the platform's format requires this)
• datapointID matches the IDs of the input dataset
• answer ∈ {0, 1}
  • 0 = model A
  • 1 = model B Under no circumstances change the column names. Every row must appear in the file exactly once.

Evaluation

Your work will be scored on a 0–100 point scale.

Scoring Rules

The final score is calculated with the following formula:

$$Score = 100 \times \frac{Accuracy - 0.5}{BestSolution - 0.5}$$

or, as code:

score = 100.0 * (acc - 0.5) / (best_solution - 0.5)

• Random guessing (≈ 50% accuracy) → 0 points
• The best solution → 100 points
• Intermediate results are scored by linear scaling.

Only the final score counts.

Constraints & Rules

1. Retraining the given models is forbidden.
2. Modifying the data is forbidden.