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Saisha Kartik

dblp:404/0053 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0009-5405-9669ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%
Computer networks
1 paper
Edge and fog computing · 100%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › augmented reality
augmented reality assistance
0.912025
An XR Platform that Integrates Large Language Models with the Physical World · SenSys 2025

Methods — techniques the papers use, named apart from their topics

spatial reasoning · 2.6multimodal large language model · 2.6
YearPublicationVenuePosition
2025 An XR Platform that Integrates Large Language Models with the Physical World
abstract
As Artificial Intelligence (AI) and eXtended Reality (XR) evolve, integrating them effectively remains a challenge. Although multimodal large language models (MLLMs) offer powerful reasoning over text and images, they lack an inherent understanding of 3D space. Additionally, XR headsets are resource-constrained and cannot run these models locally. To address this gap, we introduce XaiR, a system that integrates MLLMs with XR to enable AI-driven spatial reasoning and interaction. XaiR employs a client-server architecture in which an XR headset (client) captures spatial data, generates 2D snapshots of the 3D environment, and renders augmented reality (AR) content, while a remote server runs multiple parallel MLLMs to generate contextually aware responses. Our demo showcases an XR cognitive assistant application that guides a user through a series of instructions. Deployed on a mobile AR headset, our system dynamically interprets user actions, tracks task progress in real time, and provides textual feedback and AR-guided assistance.
Sruti Srinidhi, Edward Lu, Akul Singh, Saisha Kartik, Audi Lin, Tarana Laroia, Anthony Rowe 0001
SenSys4