MLE-Bench Lite
PulseAugur coverage of MLE-Bench Lite — every cluster mentioning MLE-Bench Lite across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New World Model Predicts ML Experiment Outcomes, Boosting AI Agent Efficiency
Researchers have developed MLE-World, a novel world model fine-tuned from Qwen3.5-4B, designed to predict the outcomes of machine learning experiments without direct execution. This model aims to make test-time scaling …
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Evolutionary Ensemble Search method constructs ML procedures via guided evolution · 2 sources tracked
Researchers have introduced Evolutionary Ensemble Search (EES), a novel method for constructing machine-learning procedures through expert-guided program evolution. EES utilizes a specialized council to translate task e…
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Frontis-MA1 AI model shows recursive self-improvement capabilities
Researchers have introduced Frontis-MA1, a 35 billion parameter AI model designed for recursive self-improvement in machine learning engineering. The model, trained using the OpenMLE system, demonstrated significant imp…
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HASTE system improves ML engineering agent efficiency with hierarchical skill transfer
Researchers have developed HASTE, a hierarchical multi-agent system designed to improve the efficiency of ML engineering agents. By organizing knowledge into global, domain, and competition-specific tiers, HASTE allows …
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AI framework Arbor enables autonomous scientific research
Researchers have developed Arbor, a novel AI framework designed for autonomous scientific research. Arbor utilizes a persistent knowledge tree called Hypothesis Tree Refinement (HTR) to link hypotheses, evidence, and in…
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DataMaster framework automates ML data engineering for improved model performance
Researchers have developed DataMaster, a novel framework designed to automate the data engineering process for machine learning. This system aims to improve ML model performance by optimizing data selection, composition…