🔥︎Delighted, thrilled, happy and please to share torchcurves - parametric curves in pure PyTorch. I believe it's mature enough to share here. Inspired by a two-year old paper from Yahoo. code: github.com/alexshtf/torchcur… docs: torchcurves.readthedocs.io paper that inspired it: openreview.net/forum?id=M422… Use: pip install torchcurves or my personal preference: uv add torchcurves A small library of vectorized, differentiable B-spline and Legendre curves with learnable coefficients. Handy for continuous embeddings of numerical features in your recommender system, shape-constrained models (e.g. monotonicity via non-decreasing control points) in your ad surplus optimization work, and KAN-style layers. I set out to keep it small, pure PyTorch, readable, and easy to maintain, with only modest speed ambitions. It is reasonably fast for such a library, and the performance comes mainly from leaning on PyTorch’s vectorization and efficient algorithms (Cox-De Boor for the splines, Clenshaw for the polynomials). The vectorized code can evaluate several curves at a batch of points in one call. Gradients flow to both the inputs and the coefficients via custom autograd. It’s not the fastest implementation out there, but it stays simple and transparent. Would love to that it works for you, and what would make it better. Have fun using it!

I dont understand the backlash against Terra based on terminal bench. Who cares? Remember that its also a language model. You know, for tasks like understanding human language and generating human language. Something OpenAI models tend to do better than Anthropic's. Not to mention vision - Opus cant see what old good Luna sees.

Yes. And new, weird and unusual ML stuff. Because it's not in your AI's training data.

So when will model-providers start publishing slop-scores in their model cards, i.e., benchmarks with multiple episodes, where a model is punished for creating complexity in first episodes that affects the resources required for success in the future episodes?

So - has Argon solved it?

What I can understand from the latest discourse on AI for math is this - 𝑂 hacking is all you need.

LaTeX rendering in @zeddotdev would make it truly the best environment for scientific computing and ML workflows. Please make this PR happen :) github.com/zed-industries/ze…

Not bad :)

This year's ICLR I got no papers to review about LLMs or transformers. Great success! Finally - no more LLM / Transformer mumbo jumbo!

Hot take: to stop AI code slop, model providers should stop focusing on "bigger and better agentic tasks" but rather make their models produce less slop. RL environments should simulate sequences of related tasks in a row, and give bad reward when many tokens or tool calls are spent on tasks far in the sequence. It means that code early in the sequence produced incomprehensible slop, that later in the sequence is hard to solve. We don't need super-intelligent models that can deal with slop. We need super-intelligent models that are trained to avoid it in the first place.

OK ok.. maybe Grok got a bit too excited :)