1 citations · 1 across the 8 of their papers we have counts for
15 papers
Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models
Mario Leiva, Yue Ma, Qinru Qiu +2
Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as m…
EntailLLM: Verifying LLM-Generated Vulnerability Discovery Paths with Domain Knowledge via Logic Programming
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Colton Payne +4
Large language models are increasingly used to reason about software vulnerabilities, but their outputs can silently violate domain knowledge, limiting their reliability in safety-…
Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
Joshua Shay Kricheli, Alexander Lawrence Reid, Soumajyoti Sarkar +2
Neural scaling laws approximate a language model's loss as a power-law function of parameter count and token count . Following Chinchilla-style compute-optimal training, man…
Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning
Kaustuv Mukherji, Jaikrishna Manojkumar Patil, Dyuman Aditya +5
We introduce Lattice Annotated Temporal (LAT) Logic, an extension of Generalized Annotated Logic Programs (GAPs) that incorporates temporal reasoning and supports open-world semant…
Error Detection and Correction for Interpretable Mathematics in Large Language Models
Yijin Yang, Cristina Cornelio, Mario Leiva +1
Recent large language models (LLMs) have demonstrated the ability to perform explicit multi-step reasoning such as chain-of-thought prompting. However, their intermediate steps oft…
Machine Learning Model Integration with Open World Temporal Logic for Process Automation
Dyuman Aditya, Colton Payne, Mario Leiva +1
Recent advances in Machine Learning (ML) have produced models that extract structured information from complex data. However, a significant challenge lies in translating these perc…