1 citations · 1 across the 9 of their papers we have counts for
11 papers
Inner Momentum for Differentially Private Muon
Bishnu Bhusal, Minh Vu, Ben Southworth +3
Differentially private training clips each per-example gradient before adding noise. This clipping is radial for each example, yet unequal clipping factors can distort the relative…
One Model, Two Physical Stories: Auditing Misalignment in Multi-Modal World Modeling
Geigh Zollicoffer, Minh Vu, Rajiv Ranasinghe +1
World models, systems that generate what happens next given current environmental conditions, are increasingly being implemented with multi-modal generation in mind. However, gener…
Sanity Checks for Long-Form Hallucination Detection
Geigh Zollicoffer, Minh Vu, Hongli Zhan +2
Hallucination detection methods for large language models increasingly operate on chain-of-thought reasoning traces, yet it remains unclear whether they evaluate the reasoning itse…
Trustworthy Agentic AI Requires Deterministic Architectural Boundaries
Manish Bhattarai, Minh Vu
Current agentic AI architectures are fundamentally incompatible with the security and epistemological requirements of high-stakes scientific workflows. The problem is not inadequat…
Privacy Enhanced PEFT: Tensor Train Decomposition Improves Privacy Utility Tradeoffs under DP-SGD
Pradip Kunwar, Minh Vu, Maanak Gupta +1
Fine-tuning large language models on sensitive data poses significant privacy risks, as membership inference attacks can reveal whether individual records were used during training…
PAS : Prelim Attention Score for Detecting Object Hallucinations in Large Vision--Language Models
Nhat Hoang-Xuan, Minh Vu, My T. Thai +1
Large vision-language models (LVLMs) are powerful, yet they remain unreliable due to object hallucinations. In this work, we show that in many hallucinatory predictions the LVLM ef…
HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling
Minh Vu, Brian K. Tran, Syed A. Shah +3
Large Language Models (LLMs) exhibit impressive reasoning and question-answering capabilities. However, they often produce inaccurate or unreliable content known as hallucinations.…
MTRE: Multi-Token Reliability Estimation for Hallucination Detection in VLMs
Geigh Zollicoffer, Minh Vu, Manish Bhattarai
Vision-language models (VLMs) now rival human performance on many multimodal tasks, yet they still hallucinate objects or generate unsafe text. Current hallucination detectors, e.g…
Topological Signatures of Adversaries in Multimodal Alignments
Minh Vu, Geigh Zollicoffer, Huy Mai +3
Multimodal Machine Learning systems, particularly those aligning text and image data like CLIP/BLIP models, have become increasingly prevalent, yet remain susceptible to adversaria…
Towards Faster Matrix Diagonalization with Graph Isomorphism Networks and the AlphaZero Framework
Geigh Zollicoffer, Kshitij Bhatta, Manish Bhattarai +4
In this paper, we introduce innovative approaches for accelerating the Jacobi method for matrix diagonalization, specifically through the formulation of large matrix diagonalizatio…
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization
Kshitij Bhatta, Geigh Zollicoffer, Manish Bhattarai +4
This paper introduces a novel framework for matrix diagonalization, recasting it as a sequential decision-making problem and applying the power of Decision Transformers (DTs). Our…