Frontier AI Research Digest

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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  • Post-Training Is the New Pre-Training: Why RL Training Loops Are Reshaping AI in 2026

    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…

  • The Year Alignment Got Empirical: When, Where, and for Whom Do Models Fail?

    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…

  • The Agent Stack Is Being Rewritten

    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…

  • The End of Uniform Scaling: How AI Architecture Learned to Think Dynamically

    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…

  • Multimodal AI: The Year We Stopped Gluing Encoders to LLMs

    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…

  • Beyond the One-Shot: How Dynamic Inference Compute Is Reshaping AI Reasoning

    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…

  • Why the LLM Scaling Era Is Over — and What Comes Next

    Why the LLM Scaling Era Is Over — and What Comes Next

    The year LLMs stopped getting bigger and started getting smarter. In May 2025, the AI research community was still buzzing about ever-larger models, ever-bigger training runs, and the seemingly inexorable march toward AGI fueled by GPU clusters the size of data centers. By May 2026, the conversation had fundamentally shifted. Not because scaling stopped working…