.@entropyIO deployer just raised the io:ANTH open-interest cap from $45M to $50M likely because the market was already using roughly 89% of its previous capacity. It doesn’t necessarily mean they expect the price to rise - it more likely signals expectations of stronger trading demand and ensures new positions aren’t rejected as OI approaches the cap and for token2049 attendees they registered new promo page for side event - https://entropy[.]io/shardingxtoken2049 but you'd better use PROFILE code for a 100% fee discount for those in Singapore -

ERRC: Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication PyTorch Foundation Ambassador Abdulsalam Bande will present a poster on ERRC (Entropy-Reinvested Residual Correction for Tensor-Parallel LLM Communication) at PyTorch Conference North America 2026. ERRC focuses on optimizing GPU data exchange during LLM inference. By compressing inter-GPU communication and using the saved bandwidth to mitigate quantization errors, ERRC makes multi-GPU inference significantly more efficient. Register and join us to San Jose, October 20-21: #PyTorchCon# @abdulsalambande

Anthropic jumps ~6% on Entropy on news that its IPO is already several times oversubscribed, with the company and its selling shareholders seeking up to $90.4 billion across the public offering and a private placement, per Bloomberg.

Did softmax and cross-entropy have numerical bugs nobody actually fixed? A new paper from Google DeepMind says yes. Title: A Fix for Softmax and Cross-Entropy URL: ❓ What is the problem? Subtracting the max logit prevents overflow, but two bugs remain. In Softmax Collapse, the target gradient rounds to zero once the correct logit runs far ahead. In Softmax Cancellation, distillation subtracts nearly equal probabilities. ❓ How is it fixed? By rewriting in log space. The max logit is set to 0, the loss is softplus(-m), and the target gradient is defined as the negated sum of the others, so gradients always sum to zero. The distillation side uses expm1 and log1p to avoid the subtraction. Short JAX code is a drop-in replacement. ❓ Does it help? On a²+b² mod 97 grokking, standard test accuracy drops from 61% to 23% at the precision cliff, while the fix reaches 85%. In soft-target distillation, the fix keeps KL at exactly 0 in every precision. ❓ What did we learn? Slingshot spikes and grokking failures have a numerical cause, and the margin ceiling is about log(1/ε) from Adam's ε and weight decay. The experiments are small-scale, though, and large-scale impact is untested. #DeepLearning# #MachineLearning#

Big week 👀 🔷 Entropy integration, access more Hyperliquid perp markets including pre-IPO perps. 🔷 0% swap fees on staked and wrapped assets. 🔷 Ondo OIP rewards, now live. (next week looks huge)