6 papers
LLM-ACES: Closed-Loop Discovery of Dynamical Systems with LLM-Guided Adaptive Search
Nikhil Abhyankar, Sha Li, Sanchit Kabra +3
Recovering governing Ordinary Differential Equations (ODEs) from data is a central challenge in modeling dynamical systems across scientific domains. Existing approaches cast disco…
LLM-AutoSciLab: Closed-Loop Scientific Discovery via Active Experimentation with LLMs
Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai +2
Scientific discovery is a closed-loop process in which hypotheses guide data acquisition and observations refine the hypothesis space. Yet most approaches reduce discovery to super…
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
Nikhil Abhyankar, Parshin Shojaee, Chandan K. Reddy
Automated feature engineering plays a critical role in improving predictive model performance for tabular learning tasks. Traditional automated feature engineering methods are limi…
LLEMA: Evolutionary Search with LLMs for Multi-Objective Materials Discovery
Nikhil Abhyankar, Sanchit Kabra, Saaketh Desai +1
Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present LLM-guided Evolution for MAterials…
RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tables
Nikhil Abhyankar, Purvi Chaurasia, Sanchit Kabra +3
Existing tabular reasoning benchmarks mostly test models on small, uniform tables, underrepresenting the complexity of real-world data and giving an incomplete view of Large Langua…
H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
Nikhil Abhyankar, Vivek Gupta, Dan Roth +1
Tabular reasoning involves interpreting natural language queries about tabular data, which presents a unique challenge of combining language understanding with structured data anal…