SOURCE-LINKED INTELLIGENCE
Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval
Recent breakthroughs in LLM-based systems and their abilities in problem solving and coding have allowed progress in the AI for Science paradigm, potentially replacing human roles in machine learning (ML) research. However, while several frameworks of fully autonomous end-to-end ML research have been proposed, successful implementations of them are often limited to problems with narrow search spaces, like language modeling or biomedical ML benchmarks. In this paper, we explore how autonomous research can be adapted to solve open-ended, industry-grade ML problems, by considering a case study: t
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-11T17:09:15.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.