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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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…
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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…