Pith. sign in

REVIEW 3 cited by

V-IRL: Grounding Virtual Intelligence in Real Life

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.03310 v3 pith:V24OSFX4 submitted 2024-02-05 cs.AI cs.CV

Jihan Yang , Runyu Ding , Ellis Brown , Xiaojuan Qi , Saining Xie This is my paper · ORCID
classification cs.AIcs.CV
keywords agentsrealdigitalenvironmenthumansinhabitplatformreal-world
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created. To develop AI agents that can sense, think, and act as flexibly as humans in real-world settings, it is imperative to bridge the realism gap between the digital and physical worlds. How can we embody agents in an environment as rich and diverse as the one we inhabit, without the constraints imposed by real hardware and control? Towards this end, we introduce V-IRL: a platform that enables agents to scalably interact with the real world in a virtual yet realistic environment. Our platform serves as a playground for developing agents that can accomplish various practical tasks and as a vast testbed for measuring progress in capabilities spanning perception, decision-making, and interaction with real-world data across the entire globe.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MentalThink: Shaping Thoughts in Mental SVG World

    cs.AI 2026-07 conditional novelty 7.0 of 10

    MLLMs that generate and render SVG sketches as multi-turn intermediate reasoning steps reach 55.1% on VSIBench and 76.0% on MindCube, far above the Qwen2.5-VL-7B backbone.

  2. E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes

    cs.CV 2026-04 conditional novelty 6.0 of 10

    E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.

  3. Embodied Web Agents: Bridging Physical-Digital Realms for Integrated Agent Intelligence

    cs.AI 2025-06 conditional novelty 6.0 of 10

    A new benchmark integrates AI2-THOR, Google Street View, and functional websites to test agents that must combine physical actions with online information retrieval.

Pith tools