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researcher

Manish Bhattarai

11 papers hereh-index 26 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author2
  • last author8

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI3
  • cs.CR2
  • cs.CL1
  • cs.CV1
same name
  • Manish Bhattarai — 24 papers, h 11
  • Manish Bhattarai — 6 papers, h 5
  • Manish Bhattarai — 6 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedTrustworthy Agentic AI Requires Deterministic Architectural Boundaries

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CV2025

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…

cs.LG2025

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.…

cs.AI2025

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…

cs.LG2025

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.