Google deployed an agentic AI peer-reviewer at two top CS conferences — reviewing ~10,000 papers with 30-minute turnaround — and the new formal research paper shows it catches 34% more mathematical errors than zero-shot prompting; the precedent for AI-automated scientific review at conference scale is set and now formally documented. -- Source: https://arxiv.org/abs/2606.28277 submitted by /u/Justgototheeffinmoon [link] [Kommentare]

I’m pretty annoyed. We’re a small AI startup building a real-time coding agent. Our p95 latency requirements are tight (and self imposed, but thats the product). We need sustained high-throughput inference with ~1-2k tokens/second. Been on the Cerebras waitlist for months trying to get API access. We’re not doing training so don’t need a warehouse of H100s. We need fast, high-throughput ASIC inference for a specific production workload. Cerebras’ just went public and they basically have no compute how is that possible? Well turns out OpenAI and Cerebras for OpenAI to buy like $20b worth of these chips. This has effectively pre-allocated the vast majority of Cerebras’ near-term inference capacity to a single customer. I mean, none of us can compete with that The result is that this deal situation has made their API waitlist functionally infinite for anyone who isn’t a hyperscaler. Legit making me pull my hair out. submitted by /u/Kortopi-98 [link] [Kommentare]

At my pick, I had 1.6 million $. Now I have 0. I shouldve cashed out some. I should have listened to Michael Burry and Nouriel Roubini. F@&k Michal Sayler! These people give people false hope. They ruin peoples lives. My lifes destroyed.My life!!! submitted by /u/noreturn000 [link] [Kommentare]

There was a workshop in ICLR Recursive Self Improvement. Is this something worth pursing for a Phd topic? submitted by /u/Successful_Bowl2564 [link] [Kommentare]

Hey! The EML function made the rounds recently on the internet as a “cool trick” that allows for the representation of all elementary functions through composition. As a mathematical curiosity, we prove a universal approximation theorem for EML(-type) trees. Intuitively, one expects that if elementary functions can be presented by compositions of EMLs, then so too can polynomials, and polynomials are dense in other functional spaces (like continuous functions or certain Sobolev spaces), then one expects to be able to approximate (to desired accuracy) any function (in a reasonably general space) through an EML tree (with an upper bound on size and depth). One of the key steps in the proof (detailed in the appendix) is an explicit construction of EML(-type) representation of binary operations, polynomials, hyperbolic tangent, and approximate partitions of unity, and subsequently using them as “LEGO” blocks to get more complex functions. There are some technical difficulties that need to be dealt with in the proof, especially in what relates to the the ill-definedness of the natural logarithm for nonpositive inputs, which prompts us to do some “sign-based decompositions” in Theorem1.Step 5 and a suitable affine map in Corollary 1. Comments are welcome! Paper: https://arxiv.org/pdf/2606.23179 (Note: I use the term “EML(-type)” in the above description because, due to some theoretical and practical reasons detailed in the paper, we generalize the original EML function by adding some learnable parameters.) submitted by /u/JoeGermany [link] [Kommentare]