对话 IDEA 张磊：「不以动作为输入条件，就不叫世界模型」

TL;DR - An in-depth interview with IDEA chief scientist / 视启未来 founder Zhang Lei (IEEE Fellow, DINO/Grounding DINO/DINO-X author) arguing that a system only qualifies as a "world model" if it is action-conditioned, cutting through the definitional chaos in China's ~¥30–90B world-model funding wave in H1 2026.

• Core definition claim: world models come from model-based RL and must do action-conditioned next-state prediction, not just next-state prediction; pure video generators like early Sora aren't world models because they never model the agent's action, and World Action Models (WAM) are "effect-before-cause" (generate future frames, then infer action) so they can't serve RL.
• Motivation for the hype: VLA imitation learning plateaus, and RL on real robots is bottlenecked by slow data collection and unacceptable failure costs (broken dishes, crashes); a world model gives a virtual sandbox to "imagine" trials before acting.
• Route debate: pixel vs. latent is a false dichotomy since Stable Diffusion/Sora also compress to latents — the real question is what to discard. Latent risks representation collapse; pixel realism just flatters human eyes, so evaluation should target physical plausibility, not image fidelity.
• His differentiator vs. LeCun's JEPA line: inject object structure into the latent space (objects as the basic unit of prediction/planning, starting pragmatically from masks, extendable to non-rigid things like water/sand), leveraging DINO-X open-world object perception; plus "action alignment" via 3D keypoints to map human-hand 2D video, 2-finger grippers, and dexterous hands into one latent action space.
• Bottleneck view: the brain, not the body — current robots hit 70–80% success in single scenes (1 in 5 failures), lack causal reasoning/common sense, and still depend on heavy off-board compute.