Frontier AI Research Digest
Weekly curated intelligence from the cutting edge of AI research. LLMs, reasoning, multimodal, agents, alignment — explained and connected.
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Code & Math AI: When Proving Programs Correct Became Practical
The year AI stopped guessing and started proving — how agentic theorem proving, I/O-optimal attention, and domain specialization converged (56 papers surveyed, 33 miscategorized filtered out) — In May 2025, if you asked an LLM to write a program and formally verify it, you’d get back plausible-looking code that probably didn’t compile and definitely hadn’t…
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The Year Robots Learned to Learn — With 180 Demonstrations Instead of 10,000
How instrumentation, point tracking, and particle dynamics are rewriting the economics of robotic learning (53 papers surveyed, May 2025–May 2026) — Everyone knows the problem with robotics: robots can’t generalize. A factory arm that picks one part flawlessly for five years breaks when you move the bin six inches. A strawberry-picking robot that works in…
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Video & Image Generation Architecture Shift, 2025
37 papers surveyed | May 2025 – May 2026 — Video and image generation research hit a turning point this year. The field moved past the era of “more compute, better pixels” and discovered something more fundamental: the hardest problems in visual generation aren’t about rendering — they’re about planning what to render. The central…
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Post-Training Is the New Pre-Training: Why RL Training Loops Are Reshaping AI in 2026
51 papers analyzed | May 2025 – May 2026 — If you think the biggest AI breakthroughs come from bigger models trained on more data, you’re looking in the wrong place. The most important AI research of 2025–2026 didn’t happen during pre-training. It happened after — in the post-training phase where models learn to actually…
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The Year Alignment Got Empirical: When, Where, and for Whom Do Models Fail?
55 papers surveyed | May 2025 – May 2026 — For years, AI alignment lived in the realm of principles. Papers opened with “it is important that AI systems align with human values” and closed with hand-waved suggestions for future work. In 2025–2026, that changed. The field stopped asking is the model safe? and started…
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The Agent Stack Is Being Rewritten
Orchestration, skills, and security — the year agent research grew up. May 2025 – May 2026 | 37 papers surveyed — A year ago, if you wanted to build an AI agent, you picked a framework: LangGraph, CrewAI, AutoGen, Google ADK, OpenAI Agents SDK. These frameworks — collectively exceeding 290,000 GitHub stars — defined the…
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The End of Uniform Scaling: How AI Architecture Learned to Think Dynamically
53 papers surveyed | May 2025 – May 2026 For years, the AI scaling playbook was simple: build a bigger model, feed it more data, get better results. The returns were predictable enough to fuel an entire industry and dominate conference agendas. But something shifted in the last twelve months. The research coming out of…
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Multimodal AI: The Year We Stopped Gluing Encoders to LLMs
54 papers surveyed | May 2025 – May 2026 — For years, multimodal AI was mostly a wiring problem: take a vision encoder, glue it to an LLM, add a projection layer, and call it a day. In 2025-2026, that era ended. The field stopped asking “how do we connect vision to language?” and started…
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Beyond the One-Shot: How Dynamic Inference Compute Is Reshaping AI Reasoning
34 papers surveyed | A year of progress in reasoning and inference-time compute scaling (May 2025 – May 2026) — For most of the last decade, the AI inference pipeline looked the same: you train a model, deploy it, and every query costs the same amount of compute. A simple factual lookup gets the same…