AI Research Gets a New Testing Ground, Language Models Face Graduate-Level Exams, and Code Generation Takes a Leap Forward

Today we explore how artificial intelligence is being put through increasingly rigorous academic challenges, from specialized research tasks to graduate-level coursework across hundreds of disciplines. While current AI models show promise in finding better solutions to existing problems, they still struggle with generating truly novel ideas or matching human-level expertise across specialized fields - raising important questions about the real capabilities and limitations of these powerful systems. Links to all the papers we discussed: MLGym: A New Framework and Benchmark for Advancing AI Research Agents, SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines, SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features, How Much Knowledge Can You Pack into a LoRA Adapter without Harming LLM?, S*: Test Time Scaling for Code Generation, Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning