AI Models Learn to Get Back Up, Language Models Face Memory Challenges, and Software Engineers Test AI's Real-World Value

As robots learn to recover from falls and AI systems grapple with memory and learning constraints, researchers are putting artificial intelligence to the ultimate test: can it earn real money in the workplace? These developments highlight the growing pains of AI systems as they move from controlled lab environments to messy real-world applications, raising questions about their readiness to take on complex human tasks. Links to all the papers we discussed: Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention, Learning Getting-Up Policies for Real-World Humanoid Robots, SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?, ReLearn: Unlearning via Learning for Large Language Models, CRANE: Reasoning with constrained LLM generation, HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation