109 citations · 135 across the 10 of their papers we have counts for
11 papers
Learning Strategies to Break Judges
Guruprerana Shabadi, Aaditya Naik, Rajeev Alur +1
As AI agents surpass human performance, it becomes exceedingly hard for system designers to evaluate them directly and understand their failure modes. Consequently, agents themselv…
Do We Need Frontier Models to Verify Mathematical Proofs?
Aaditya Naik, Guruprerana Shabadi, Rajeev Alur +1
Advances in training, post-training, and inference-time methods have enabled frontier reasoning models to win gold medals in math competitions and settle challenging open problems.…
On Improving Neurosymbolic Learning by Exploiting the Representation Space
Aaditya Naik, Efthymia Tsamoura, Shibo Jin +2
We study the problem of learning neural classifiers in a neurosymbolic setting where the hidden gold labels of input instances must satisfy a logical formula. Learning in this sett…
The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Models
Adam Stein, Aaditya Naik, Neelay Velingker +2
Neuro-symbolic learning was proposed to address challenges with training neural networks for complex reasoning tasks with the added benefits of interpretability, reliability, and e…
Where's the Bug? Attention Probing for Scalable Fault Localization
Adam Stein, Arthur Wayne, Aaditya Naik +2
Ensuring code correctness remains a challenging problem even as large language models (LLMs) become increasingly capable at code-related tasks. While LLM-based program repair syste…
Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning
Aaditya Naik, Jason Liu, Claire Wang +4
Neurosymbolic learning enables the integration of symbolic reasoning with deep learning but faces significant challenges in scaling to complex symbolic programs, large datasets, or…
Towards Compositionality in Concept Learning
Adam Stein, Aaditya Naik, Yinjun Wu +2
Concept-based interpretability methods offer a lens into the internals of foundation models by decomposing their embeddings into high-level concepts. These concept representations…
LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
Sarah Fakhoury, Aaditya Naik, Georgios Sakkas +2
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, g…
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Aaditya Naik, Adam Stein, Yinjun Wu +2
Finding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the…
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Aaditya Naik, Yinjun Wu, Mayur Naik +1
Machine learning models can make critical errors that are easily hidden within vast amounts of data. Such errors often run counter to rules based on human intuition. However, rules…
Interactive Code Generation via Test-Driven User-Intent Formalization
Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik +8
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, w…