Hugging Face Trending Papers
Agentic Search Spaces for Tabular Machine Learning
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arXiv Machine Learning
Sep 16
Agentic Search Spaces for Tabular Machine Learning
arXiv:2609.16309v1 Announce Type: new Abstract: Despite the rapid progress of LLM-based agents for planning, code generation, and debugging, their practical value for tabular machine learning remains...
arXiv Machine Learning
2d ago
TabFM-Auto: Self-Evolving Pipelines for Tabular Foundation Models
arXiv:2609.37989v1 Announce Type: new Abstract: Tabular foundation models achieve strong zero-shot accuracy on structured data by pretraining on synthetic tables, but they ignore the column names, ta...
arXiv AI
Sep 4
Evolving Excellence: Automated Optimization of LLM-based Agents
The paper introduces ARTEMIS, a no-code evolutionary optimization platform that automatically tunes large language model (LLM) agents by jointly optimizing prompts, tool descriptions, and parameters using semantically-aware genetic operators. Starting from a benchmark script and natural language goals, ARTEMIS discovers configurable components, extracts performance signals from execution logs, and evolves configurations without architectural changes. Experiments on four agent systems show significant gains: a 13.6% increase in acceptance rate for the ALE Agent, a 10.1% performance boost for the Mini‑SWE Agent, a 36.9% token‑reduction for the CrewAI Agent, and a 22% accuracy improvement for the MathTales‑Teacher Agent using a smaller open‑source model.
arXiv AI
Aug 3
AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers
arXiv:2607. 29626v1 Announce Type: new Abstract: As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments has become increasingly important.
arXiv AI
Aug 12
Recovering Wasted Compute in Autoresearch Agents
arXiv:2608. 10424v1 Announce Type: new Abstract: A slew of recent works develop agents for solving research problems end-to-end, a paradigm increasingly referred to as autoresearch.
arXiv AI
Jun 10
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.