13 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…
Consistency-based Abductive Reasoning over Perceptual Errors of Multiple Pre-trained Models in Novel Environments
Mario Leiva, Noel Ngu, Joshua Shay Kricheli +6
The deployment of pre-trained perception models in novel environments often leads to performance degradation due to distributional shifts. Although recent artificial intelligence a…
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…
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta +2
Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing cons…