Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.19376 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914052794482688 |
|---|---|
| author | Grofsky, Matthew |
| author_facet | Grofsky, Matthew |
| contents | We address temporal failures in RAG systems using two methods on cybersecurity data. A simple recency prior achieved an accuracy of 1.00 on freshness tasks. In contrast, a clustering heuristic for topic evolution failed (0.08 F1-score), showing trend detection requires methods beyond simple heuristics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Freshness in RAG: A Simple Recency Prior and the Limits of Heuristic Trend Detection Grofsky, Matthew Machine Learning Artificial Intelligence We address temporal failures in RAG systems using two methods on cybersecurity data. A simple recency prior achieved an accuracy of 1.00 on freshness tasks. In contrast, a clustering heuristic for topic evolution failed (0.08 F1-score), showing trend detection requires methods beyond simple heuristics. |
| title | Solving Freshness in RAG: A Simple Recency Prior and the Limits of Heuristic Trend Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.19376 |