LingBot-World-v2

Headlines · 2

1. 6d
   Oxford, Monash, Bristol teams unveil Oneira, an interactive video world model

   Teams from the University of Oxford, Monash University and the University of Bristol (among others) propose Oneira, an interactive video world model that separates "what happens in the world" from "how the picture is rendered": a Coding Agent maintains a World State Table recording each object's semantic label, 3D bounding box and interaction state, an Engine renders that table into a simplified condition video, and a video generation model fills in real appearance and motion detail. The authors also built InteractionBench, with 100 interaction cases and 50 memory cases, and compared Oneira against MiniMax-H3 Ref2VA, LingBot-World-V2, YUME 1.5 and AlayaWorld. They report Oneira leads the strongest baseline by 29, 19 and 29 percentage points on whether an interaction occurs, timing accuracy and correct target object; after the camera leaves and returns, PSNR and SSIM improve by 93% and 83% and state retention rises 18%, with VBench video quality at or near the best.

   机器之心 · 🔥 0
2. Oct 7
   QuantWM targets flicker in 2-bit video-world-model caches

   Researchers from iLearn-Lab at Harbin Institute of Technology (Shenzhen) and LV-Lab at the National University of Singapore propose QuantWM, a training-free framework for 2-bit KV Cache quantization in video world models. The team says its query-sensitive clustering and low-rank attention compensation improve visual stability while achieving up to 6.20x KV Cache compression across five evaluated models.

   机器之心 · 🔥 0

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