Matt Lewis

@mattzanshin.bsky.social

If LLMs did nothing else for science than what they have been doing in math - combining diverse ideas across subfields in novel ways - it would still be revolutionary. Before LLMs, science was stalling under the burden of knowledge, there is too much to absorb & work was ossifying as a result.

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the actual solution for "math can't make anything novel" isn't to lean harder into proving LLMs can make novelty, it's just to teach them to write shaders there's soooo much procgen stuff from 30 years ago that already suffices this conversation

Introducing Qwen-Image-Bench, a creator-centric benchmark for text-to-image generation, co-designed with artists. It sets a new standard by evaluating T2I models on real-world fidelity and creative generation, revealing gaps traditional benchmarks overlook. https://arxiv.org/abs/2605.28091

Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation

ArXiv link for Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation

arxiv.org

Now published in open access! Your one-stop shop for the philosophy of language models. It's the spiritual descendant of our two-part preprint from 2024, fully updated. This should be particularly useful for anyone looking for an entry point into this rapidly growing field.

The Philosophy of Language Models

The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human-like linguistic and ...

compass.onlinelibrary.wiley.com