---
title: "LingBot-World | Interactive World Model | world-models.io"
canonical: "https://world-models.io/en/models/lingbot-world/"
description: "LingBot-World is a native interactive world model evaluated by WBench. Review LingBot-World: evidence, scope, limitations, and primary sources."
author: "Tyler D."
generated: "2026-10-23"
---

New: the Timeline is live. Track world model releases, papers, and benchmark updates in real time.

The Knowledge Hub for AI World Models

# LingBot-World

LingBot-World is a native interactive world model evaluated by WBench.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the [world models database](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## Key Attributes

Static HTML snapshot of the model record for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Model | LingBot-World |
| Lab / Organization | Ant |
| Category | Generative World Model |
| Subtype | Versatile Interactive World Model |
| World Model Type | Versatile Interactive World Model |
| Primary Domain | Interactive Video |
| Architecture | Autoregressive video world model with camera, action, and event interaction |
| Modality | Image + Camera/Actions -> Interactive Video |
| Training Method | Autoregressive interactive world modeling |
| Status | emerging |
| Year | 2026 |

## About LingBot-World

Main editorial body preserved directly in static HTML.

LingBot-World generates an evolving environment conditioned on camera input. It is included as a native interactive system rather than a text-to-video model wrapped in WBench's chained-context protocol.

LingBot-World is a versatile interactive world model developed by Ant Group in 2026 for interactive video.

## Editorial Snapshot

Short extractable facts for answer engines and no-JS readers.

| Signal | Value |
| --- | --- |
| Definition | LingBot-World is a versatile interactive world model developed by Ant Group in 2026 for interactive video. |
| Short Description | LingBot-World is a native camera-conditioned interactive world model evaluated by WBench. |
| Benchmark Rows | 1 |
| FAQ Entries | 1 |
| Related Models | 3 |
| Related Guides | 0 |
| Related Research Topics | 0 |
| Last Updated | 2026-08-31 |

## Notable Features

Key capabilities associated with this model.

- • Versatile interactions
- • Infinite-world positioning
- • Open source

## Use Cases

Representative applications attached to this model record.

Interactive environment simulation

World-model evaluation

Interactive Video

## Strengths and Limitations

Balanced assessment surfaced in static HTML.

### Strengths

- • Versatile interactions
- • Infinite-world positioning

### Limitations

- • Score represents v2 fast variant
- • Variant results must remain versioned

## Benchmarks

Published benchmark evidence attached to this model record.

| Benchmark | Metric | Result | Source |
| --- | --- | --- | --- |
| WBench | Five-dimension average 79.4 /100 | 79.4 | [Source](https://meituan-longcat.github.io/WBench/) |

## References and Citations

Primary references preserved in static HTML for citation extraction.

| Reference | Link |
| --- | --- |
| Ant Group. LingBot-World. Primary project source. | [Open source](https://arxiv.org/abs/2607.07534) |
| Ying et al., 2026. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation. | [Open source](https://arxiv.org/abs/2512.14614) |

## Related Models

Nearby models linked from the current editorial record.

| Model | Category | World Model Type | Index v1.1 |
| --- | --- | --- | --- |
| [HY-World 1.5](https://world-models.io/en/models/hy-world-1-5/) | Generative World Model | Camera-Conditioned Interactive World Model | N/A |
| [Matrix-Game 2.0](https://world-models.io/en/models/matrix-game-2/) | Generative World Model | Open-source action-conditioned streaming video world model | N/A |
| [Genie 3](https://world-models.io/en/models/genie-3/) | Generative World Model | Real-time interactive 3D environment generation | 89/100 |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### Why is LingBot-World included?

LingBot-World exposes native camera conditioning and generates an evolving interactive environment; WBench evaluates that interface directly.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- LingBot-World is a versatile interactive world model developed by Ant Group in 2026 for interactive video.
- Use this page when you need a fast read on how LingBot-World fits into the generative world model landscape, then validate the details in the benchmarks, citations, and related pages.
- A key strength surfaced in the editorial record is versatile interactions.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**Published by** world-models.io editorial board.

**Lead editor** Tyler D. - Technical editor, methodology and benchmark analysis.

This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-08-31.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

Primary sources only

Last reviewed date visible

Methodology documented

Source links included

Correction channel published

Review date visible

## External Sources

Primary model and lab sources embedded in static HTML.

- [LingBot-World paper](https://arxiv.org/abs/2607.07534) Ant Group primary research source
- [LingBot-World official page](https://github.com/Robbyant/lingbot-world-v2) Ant Group official page
- [LingBot-World cited source](https://arxiv.org/abs/2512.14614) Ying et al., 2026. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation.

## References

1. [[1] Ant Group. LingBot-World. Primary project source.](https://arxiv.org/abs/2607.07534)
2. [[2] Ying et al., 2026. WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation.](https://arxiv.org/abs/2512.14614)