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Sigh, it's a bit of a mess. Let me just give you guys the full nuance in one stream of consciousness since I think we'll continue to get partial interpretations that confuse everyone. All the little things I post need to always be put together in one place. First, I have long

btw i forgot to say that “under-reporting” might not be the most fair characterization hence. a lot of this is “the community’s current mental model of what LLMs even are is off” rather than “the researcher is under-reporting some baseline” it isn’t those researchers’ fault…

What if a Transformer could render? Not text → image. But mesh → image — with global illumination. No rasterizers. No ray-tracers. Just a Transformer without per-scene training. RenderFormer does exactly that. #SIGGRAPH2025 🔗microsoft.github.io/renderformer

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Yu Yang Hi one exciting thing is: we haven’t observed an upper limit yet — we’re still training and continuing to increase the number of steps!

LLM Coding made easier HardTests: synthesizing high-quality test cases for LLM coding to improve code evaluation and LLM post-training huggingface.co/papers/2505.24…

the use of "verifier" in CS: - theory/PL: a tool for checking or generating a mathematical proof - ML: a tool that returns a real number

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“It’s a bummer that we need to pretend stuff didn’t exist every time it’s commercialized at scale.”

Linguists are so mad

Too many think the problem with LLMs is that they’re not human enough. But the problem with LLMs is that they’re not computer enough. We’re used to a standard of reliability from computer programs that LLMs so far don’t live up to. But making them human-like doesn’t fix that!

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