
Plurai raises $13 million to simulate and tune AI agents
The seed round, led by Team8, funds digital twins that post-train small open-weight models for customer workflows.
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The seed round, led by Team8, funds digital twins that post-train small open-weight models for customer workflows.

NVIDIA says GR00T 2 hit 71% average success across 50 tasks with one demonstration and 95% with five.
On 11 DexJoCo tasks success rises from 49.4% to 60.5%; three real-robot tasks reach 58.3% versus 50.0%.
Authors say the method beats supervised fine-tuning and reinforcement learning with 3,000 samples while keeping general video quality.
Authors report 83.4% mean success on all 50 RoboTwin 2.0 tasks without gradient updates, over 20 points above the strongest trained policy.
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War game reveals geopolitical tensions and disinformation risks in international responses to solar geoengineering technologies.
Hugging Face published an Allen Institute for AI post; the available item gives only the project title.

Physicl repurposes its 3D rendering tech to generate high-fidelity simulations for training physical AI models.
PyRUA-DataFarm cuts robot data costs to $21 per task using an LLM agent in simulation.
KierstonBench benchmark tests language models on 125,000-resident Allentown simulation for record linkage and investigation tasks.

The author urges measuring reporting speed and response, not only click rates, as AI-written lures improve.
AgenticMobility is an open-source platform studying how AI agents interact in urban transportation simulations over repeated experience.
Machine learning model predicts physical impacts from cyber compromises in water networks using hydraulic simulations.
Frontier models score an average reward of 0.06 and fail every RoboWorld test task; several teaching methods raise performance.
The Rust simulator targets selected STM32, ESP32, and RP2040 boards so agents can test firmware without flashing hardware.
A research preview goes to trusted testers first, then Google AI Ultra subscribers in Project Genie and early Gemini API partners.
A simulated twin matched the real arm's policy ranking exactly, though simulation overestimated success.
Among 12 models, only GPT-6-Astra discovered laws, at a reported rate of 16.7 percent.
A 27B vision-language model fine-tuned only in simulation matches GPT-6 Astra and beats MolmoAct2's 33.3% in the same harness.
From Qwen3-VL-8B-Instruct, the paper reports PhysBench Throwing up 8.8 and ContPhy Goal-Pit up 24.4 points.
The authors report mean gains of 34 points on 28 training benchmarks and 26 points on 18 unseen benchmarks.
Navigation policies trained on its scenes beat policies trained on the same number of real scans in held-out scans.
No post body was available, so only the sim-to-real Microduck and Isaac Lab 3.0 topic is known.
The post gives no asset count, license terms, or release date beyond the announcement itself.
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