Autonomous Research for Open-Ended Problems: A Case Study on Telecom Ticket Retrieval
Case study shows autonomous LLM research reaches 90% of SOTA on telecom ticket retrieval in 10 weeks versus 10 months human work.
The paper explores adapting autonomous research to open-ended, industry-grade ML problems through a telecom ticket retrieval case study with commercial and open-source agents. Autonomous research reached 90% of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in 10 weeks versus 10 months of human work, at up to $200 per Cursor campaign. The authors find agents excel at narrow hyperparameter optimization but lack human-like intuition, recommending human-agent collaboration.
- Autonomous research achieved 0.34 vs. 0.38 Recall@1 SOTA on telecom ticket retrieval
- 10 weeks of agent work versus 10 months of human effort, up to $200 per campaign
- Agents excel at hyperparameter optimization but lack human-like intuition and creativity
Full article200 words · extracted from arxiv.org · click to collapse
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: telecom ticket retrieval, an open-ended task with degrees of freedom in representation, architecture, and training data generation. We discover that autonomous research for open-ended problems with commercial and open-source agents shows both promise and limitations: while autonomous research can excel in narrow hyperparameter optimization, it lacks human-like intuition and creativity and requires operational overhead. Even with minimal human supervision, autonomous research can reach $90\%$ of state-of-the-art performance (0.34 vs. 0.38 Recall@1) in a much shorter time period (10 weeks vs. 10 months of human work) at a modest cost (up to \$200 per Cursor campaign). Our empirical evidence recommends that human researchers and autonomous research frameworks work together for best results in ML research.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.13073