Papers

I had been colllecting and reading academic papers over the years. I tried to pick a few of the highlights. Here they are. Not a random internet search. These are the ideas that are actually shaping AI -the efficiency breakthroughs, and the moments where someone said "what if we did this completely differently?" and it worked.

There is an idea that runs through all of them. One paper introduced the architecture that powers every major AI today. Another proved that training smarter matters more than building bigger. A team cut off from the best hardware built one of the world's strongest models for a fraction of the cost — because they had to. A model learned to reason on its own, with no one showing it how. And someone asked whether the dominant architecture is even the right one.

The pattern isn't brute force. It's elegance — doing more with less, rethinking assumptions, and finding leverage where others see limitations. That's the story these papers tell.

Academic papers can be brutal to get through if you're not directly in the field — dense notation, assumed context, and writing that seems designed to keep people out rather than invite them in. Each summary here breaks down the big idea in plain language, walks through the technical architecture so you actually understand what they built, covers the key results and why they matter, and gives you an honest look at the limitations.

These are a solid way to get up to speed, but nothing beats sitting down with the source paper itself. Every summary includes a direct link to the original — I'd encourage you to read the ones that grab you.