Demo Data from VLDB 2026 / reviewers in the wild / expert
Saisha Kartik
dblp:404/0053
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › augmented reality
augmented reality assistance |
0.9 | 1 | 2025 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | An XR Platform that Integrates Large Language Models with the Physical WorldabstractAs 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 |
SenSys | 4 |