MALMAS is a memory-augmented multi-agent LLM system that generates diverse, high-quality features for tabular data via agent decomposition, routing, and iterative memory-guided refinement.
Deep feature synthesis: Towards automating data science endeavors
2 Pith papers cite this work, alongside 416 external citations. Polarity classification is still indexing.
2
Pith papers citing it
416
external citations · external index
years
2026 2verdicts
UNVERDICTED 2representative citing papers
OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.
citing papers explorer
-
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
MALMAS is a memory-augmented multi-agent LLM system that generates diverse, high-quality features for tabular data via agent decomposition, routing, and iterative memory-guided refinement.
-
OpenRFM: Dissecting Relational In-Context Learning
OpenRFM combines a relational transformer backbone with a batch-level ICL layer and homophily-aware synthetic-plus-real pre-training to improve relational in-context learning by ~30% over prior open models and surpass KumoRFMv1.