# Which Paper Is Newer?

*English translation by SOTA – AI Community of the Russian and Kazakh original. Licensed CC BY-SA 3.0, like the original. Organisers who would like this translation removed can email sota.ai.community@gmail.com.*

*Day 2 of the final of AI Olymp, the Republican Olympiad in Artificial Intelligence of Kazakhstan (Federation of Sports Programming of Kazakhstan and DSML.KZ), 2025. This is the Overview tab of the Kaggle competition "Kazakhstan Respa Final - Day 2 (afterparty)" (subtitle: "Find the most recent papers"), the public late-submission copy of the Day 2 contest, open from 7 May to 1 June 2025. Original: [kaggle.com/competitions/kazakhstan-respa-final-day-2-late-competition](https://www.kaggle.com/competitions/kazakhstan-respa-final-day-2-late-competition). The Overview and Description are bilingual on Kaggle, with Russian and Kazakh versions side by side; both columns are translated below. The Evaluation section is in Russian only. The Data tab is translated in a separate file.*

## Overview

| Russian version | Kazakh version |
|----------------|-----------------|
| Welcome to the final round of AI Olymp! | Welcome to the final stage of the AI Olymp competition! |
| In the qualification round you determined the topic of a scientific paper from a pair of sentences. | In the qualifying stage you determined the topic of a paper from two sentences in it. |
| In the homework assignment you predicted how many papers would be published in the future. | In the homework assignment you had to predict how many papers will be published in the future. |
| Today it is something in between! | Today's task is halfway between these two! |
| We give you two papers, and you need to determine which of them is newer. | You are given two papers, and you must determine which of them came out later. |
| This is no longer just classification, and not quite a time series: it is a step towards teaching AI to understand progress in science through language. | This is neither plain classification nor pure time-based forecasting. It is a step towards understanding progress in science through language. |
| Good luck! | Good luck! |

## Description

| Russian version | Kazakh version |
|----------------|-----------------|
| In this task you work with real data from arXiv, the world's largest open-access repository of scientific papers. | In this task you work with arXiv data, the world's largest open database of scientific papers. |
| You have the text of the first sentences of scientific papers, and the task is to work out which of two papers was published later. | You have the first sentences of scientific papers, and the task is to determine which of the two papers came out later. |
| The model must learn to recognise signs of time: trends, fashionable terms, references to old or new methods. | The model must be able to distinguish signs of time: trends, references to new or old methods, terms. |
| The training set is made up of the first two sentences of papers selected across 140 topics, with no fewer than 10 papers per year for each topic. | The training set consists of the first two sentences of papers, with at least 10 papers selected every year for each of the 140 topics. |
| Moreover, one paper may belong to several topics at once, which makes the task more realistic. | Each paper may relate to several topics; this makes the model more complex and more realistic. |
| In the test set, 400 papers were chosen at random for each topic, uniformly over the entire period from 2000 to 2025. | In the test set, 400 papers were selected from each topic, at random and evenly distributed (over the years 2000–2025). |
| From this pool the papers were randomly combined into pairs so that the publication dates within each pair are at least 2 years apart. | Random pairs were formed from these papers, and within each pair the publication times differ by at least 2 years. |

## Evaluation

Accuracy - the accuracy of binary classification

## **Final points**
Benchmarks (on the public leaderboard)
55 per cent - 30 points
60 per cent - 60 points
65 per cent - 90 points
70 per cent - 120 points

## **Positional points** 
(on the private leaderboard)
- 1st place - 120 points
- 2nd place - 100 points
- 3rd place - 90 points
- 4th place - 85 points
- places 5 - 10 - 60 - 80 points (100 - 4x)
- places 11 - 20 - 20 - 57 points (80 - 3x)
- places 21 - 40 - 0 - 19 points (40 - x)

To be eligible for places 1-9 you must beat at least one benchmark
In the event of a tie in points, your place is taken to be the lowest position of the group

### Example: 
- Participant 1 - 1st place - 56 per cent - receives 30 points for the benchmark and 120 points for first position
- Participant 2 - 2nd place - 54 per cent - receives 0 points for the benchmark and 80 points for tenth position