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cs.PL2025
Active Learning for Neurosymbolic Program Synthesis
Celeste Barnaby, Qiaochu Chen, Ramya Ramalingam +2
The goal of active learning for program synthesis is to synthesize the desired program by asking targeted questions that minimize user interaction. While prior work has explored ac…
cs.PL2025
Quasar: A Programming Language Specialized for LLM Code Actions
Stephen Mell, Botong Zhang, David Mell +6
Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use complex control flow such as condition…
cs.PL2024
Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction
Ramya Ramalingam, Sangdon Park, Osbert Bastani
Machine learning has become an effective tool for automatically annotating unstructured data (e.g., images) with structured labels (e.g., object detections). As a result, a new pro…