Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness

Robbyant, Ant Group’s embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0) . It is a causal video generation model that behaves as an interactive world simulator. It is how the team attacks two failure modes: long-horizon drift and interactive latency.

An interactive world model generates video frame by frame , conditioned on a stream of user actions. Each state depends only on past frames and current input. The research team formalizes this as a causal factorization:

Here x_t is the visual state at time t. The action a_t combines a camera pose and a text prompt. Camera pose uses Plücker embeddings, injected through adaptive layer normalization (AdaLN). Text enters as chunk-wise prompts through cross-attention.

The primary model is 14B. A lightweight 1.3B counterpart is described as deployable on a single GPU.

The core contribution is the Mixture of Bidirectional and Autoregressive (MoBA) Attention Mask . It explains the drift.

Standard autoregressive video training uses a teacher forcing mask. Each noisy frame attends to itself and its clean context. The research team found a failure here. As context grows, the model leans on that context instead of predicting future frames. The result is overfitting and visual quality degradation.

MoBA appends a bidirectional full-attention block to the teacher forcing mask. That block acts as a regularizer. It also helps the model handle flexible-length generation.

The cross-attention mask mirrors the split. The autoregressive component attends to a background prompt along with chunk-wise prompts in a lower-triangular pattern. That prevents future semantics from leaking backward. The bidirectional component attends to one global prompt.

Pre-training optimizes a conditional flow-matching objective with rectified-flow interpolation. Post-training then compresses the multi-step teacher into a few-step student:

The important detail sits in the DMD. The research team applies it over long self-rollout trajectories, not only teacher-forced states. The student is therefore optimized on the state distribution its own predictions induce. That is the stated mechanism behind anti-drift.

A frame predictor does not play itself. The Robbyant research team wraps the generator in a Director-Pilot Co-Simulation Framework .

As described in the research paper, a Vision-Language Model is the Director . It governs macroscopic semantic rules and causal reasoning. The Diffusion Transformer video generator is the Pilot. It simulates low-level physical dynamics and renders transitions.