19 citations · 26 across the 10 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data
Aditya Kumar, Cassandra A. Cohen, William W. Cohen
Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this…
cs.LG2026
Learning to Construct Practical Agentic Systems
Aditya Kumar, Zhihan Lei, Jerry Yan +6
Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns. However…
cs.AI2026
Localizing and Correcting Errors for LLM-based Planners
Aditya Kumar, William W. Cohen
Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning tasks. Our studies, as well as p…