Google says Gemini 4 Argon scores 87% on its own embodied-reasoning benchmark and found new fusion-plasma solutions

Google DeepMind reports that Gemini 4 Argon scored 87% on the Embodied Reasoning Question Answering (ERQA) benchmark, a test of physical-world reasoning for robots, without using external tools. Google says that result beats every other frontier model. The Google Developers Blog presents Argon as "a general-purpose physical reasoning model" with four functions: robot control, Real2Sim (turning real footage into physics simulations), physical understanding of video, and research and self-improvement.

The benchmark needs context: Google DeepMind created ERQA itself when it launched Gemini Robotics in 2025, so the 87% figure is a vendor result on a vendor-built test, not an independent replication. The demonstrations follow the same pattern. According to The Decoder, Google showed mobile manipulation practiced in MuJoCo, zero-shot robot control, and simulation twins rebuilt from robot camera feeds, egocentric human video or hand-drawn sketches. Google also says Argon helped Apptronik's Apollo humanoid land seven consecutive cornhole shots after watching video of a person playing. One review described reliability as unclear.

The research claim concerns stellarator physics. According to AI Post, Argon agents helped researchers find new exact solutions for 3D magnetic plasma configurations, with promising candidates appearing within hours. Google says its team and University of Maryland professor Matt Landreman found a broader class of counterexamples to Grad's conjecture. That 1967 hypothesis held that smooth 3D plasma equilibria require special symmetries. The solutions can serve as reference cases for checking stellarator simulation codes.

Credit for the September results is contested. Javier Gómez-Serrano, Lukas Liehr and Mitchell Taylor posted rigorous counterexamples on September 21. The Neuron reported that Landreman's explicit families from September were found with help from OpenAI's GPT-6 Astra Pro. Google's announcement describes a further class found with Argon. Readers should treat the Argon contribution as an extension of work already underway, not as the original breakthrough.