3 citations · 3 across the 20 of their papers we have counts for
21 papers
Consilience: Conformally Calibrated Communication Control for Hidden-Profile Multi-Agent Reasoning
Abhijith Babu, Ramneet Kaur, Vishal Pramanik +7
Multi-agent LLM systems can improve reasoning by pooling diverse perspectives, but their effectiveness depends on coordinating communication, particularly in hidden-profile setting…
Closed-Loop Neural Activation Control in Vision-Language-Action Models
Abhijith Babu, Ramneet Kaur, Nathaniel D. Bastian +5
Vision-Language-Action (VLA) models can be steered at test time by intervening on semantically meaningful internal directions, but existing methods use a fixed steering coefficient…
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Trilok Padhi, Ramneet Kaur, Krishiv Agarwal +9
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capabi…
Do Diffusion Models Dream of Electric Planes? Discrete and Continuous Simulation-Based Inference for Aircraft Design
Aurelien Ghiglino, Daniel Elenius, Anirban Roy +7
In this paper, we generate conceptual engineering designs of electric vertical take-off and landing (eVTOL) aircraft. We follow the paradigm of simulation-based inference (SBI), wh…
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
Noah Schwartz, Chandra Kanth Nagesh, Sriram Sankaranarayanan +3
We present a generalized framework for the range verification of neural networks featuring non-linear activation functions. Our approach first constructs an ``optimized piecewise a…
Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs
Ayush Gupta, Ramneet Kaur, Anirban Roy +3
We propose a novel inference-time out-of-domain (OOD) detection algorithm for specialized large language models (LLMs). Despite achieving state-of-the-art performance on in-domain…
Privacy Preserving In-Context-Learning Framework for Large Language Models
Bishnu Bhusal, Manoj Acharya, Ramneet Kaur +5
Large language models (LLMs) have significantly transformed natural language understanding and generation, but they raise privacy concerns due to potential exposure of sensitive in…
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations
Chandra Kanth Nagesh, Sriram Sankaranarayanan, Ramneet Kaur +2
We study the problem of learning neural network models for Ordinary Differential Equations (ODEs) with parametric uncertainties. Such neural network models capture the solution to…
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Colin Samplawski, Adam D. Cobb, Manoj Acharya +2
Despite their widespread use, large language models (LLMs) are known to hallucinate incorrect information and be poorly calibrated. This makes the uncertainty quantification of the…
TOGA: Temporally Grounded Open-Ended Video QA with Weak Supervision
Ayush Gupta, Anirban Roy, Rama Chellappa +3
We address the problem of video question answering (video QA) with temporal grounding in a weakly supervised setup, without any temporal annotations. Given a video and a question,…
Backpropagation-Free Metropolis-Adjusted Langevin Algorithm
Adam D. Cobb, Susmit Jha
Recent work on backpropagation-free learning has shown that it is possible to use forward-mode automatic differentiation (AD) to perform optimization on differentiable models. Forw…
On the Evaluation of Engineering Artificial General Intelligence
Sandeep Neema, Susmit Jha, Adam Nagel +6
We discuss the challenges and propose a framework for evaluating engineering artificial general intelligence (eAGI) agents. We consider eAGI as a specialization of artificial gener…
Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding
Trilok Padhi, Ramneet Kaur, Adam D. Cobb +7
We introduce a novel approach for calibrating uncertainty quantification (UQ) tailored for multi-modal large language models (LLMs). Existing state-of-the-art UQ methods rely on co…
Safety Monitoring for Learning-Enabled Cyber-Physical Systems in Out-of-Distribution Scenarios
Vivian Lin, Ramneet Kaur, Yahan Yang +6
The safety of learning-enabled cyber-physical systems is compromised by the well-known vulnerabilities of deep neural networks to out-of-distribution (OOD) inputs. Existing literat…
AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models
Colin Samplawski, Adam D. Cobb, Susmit Jha
Computer-aided design (CAD) is a promising application area for emerging artificial intelligence methods. Traditional workflows for cyberphysical systems create detailed digital mo…
TeleLoRA: Teleporting Model-Specific Alignment Across LLMs
Xiao Lin, Manoj Acharya, Anirban Roy +1
Mitigating Trojans in Large Language Models (LLMs) is one of many tasks where alignment data is LLM specific, as different LLMs have different Trojan triggers and trigger behaviors…
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +8
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban…
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
Ramneet Kaur, Colin Samplawski, Adam D. Cobb +8
In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Mode…
Non-Markovian Quantum Control via Model Maximum Likelihood Estimation and Reinforcement Learning
Tanmay Neema, Susmit Jha, Tuhin Sahai
Reinforcement Learning (RL) techniques have been increasingly applied in optimizing control systems. However, their application in quantum systems is hampered by the challenge of p…
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Ramneet Kaur, Kaustubh Sridhar, Sangdon Park +4
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applicatio…