The paper examines the commonly used structure‑disjoint split for evaluating molecular machine learning models, which relies on single‑linkage clustering with the Maximum Common Edge Subgraph (MCES) distance at a threshold of 10. The authors show that this criterion, originally calibrated on 29,000 molecules, has never been tested on the current public libraries of 227,690 molecules due to its NP‑hard complexity. They propose an optimized algorithm that reduces the number of required integer programs from $2.6 imes10^{10}$ to $1.15 imes10^{7}$, enabling lossless partitioning at scale, and reveal that the resulting graph percolates—over 95 % of the library lies in a single component, making held‑out compounds systematically larger and more complex than training ones.
"whyItMatters":"The study highlights that large‑library benchmarks using the traditional split conflate structural novelty with extrapolation to larger, more complex chemistry, potentially misleading assessments of model generalization."
By Tomo Oga, Devesh Shah, Cailum M. K. Stienstra, Gabriel Asher, Antonio H. de O. Fonseca, Niall O'Connor, Michael Widrich
arXiv:2606. 24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters.
By Peter Toth
The paper presents a method for distributing large language model inference across multiple Intel AI PCs by splitting the model into pipeline shards, each pre‑compiled into an OpenVINO graph. Three key techniques—beam_idx Gather to enable GPU optimizations, speculative decoding on stateful models, and interleaved micro‑batching—allow a two‑node Llama 3.1 8B INT4 pipeline to serve two users at 1.79× the throughput of a single‑node model, while a four‑node deployment can run a 70B model that no single PC can hold. The authors provide code, benchmark logs, and reproduction scripts on GitHub.
By Tate Berenbaum, Muthaiah Venkatachalam
The paper introduces Group-Query Latent Attention (GQLA), a variant of Multi-head Latent Attention that shares group‑sharded up‑projections to enable a joint latent representation and a shardable expanded cache. GQLA supports both MQA‑absorb and GQA execution using the same weights and existing kernels, and roofline analysis shows how absorption trades traffic for computation while GQA shards KV groups. Experiments on DeepSeek‑V3.1‑Base and other models demonstrate that GQLA achieves comparable training losses to MLA, maintains high accuracy after checkpoint conversion, and improves output throughput by up to 35% on eight H20‑3e GPUs, while also allowing larger batch sizes due to a smaller per‑rank cache.
By Fanxu Meng, Zhixin Pan
The paper investigates how to optimally partition streaming inference models into megakernels and conventional graph components. It introduces a framework that predicts the best megakernel composition without full end‑to‑end runs, showing that multiple megakernels or a fully conventional graph often outperform a single megakernel across various models and batch sizes. The study defines four model categories based on megakernel stability and demonstrates significant latency and throughput gains on an H100 NVL GPU using a three‑megakernel design.
By Aditya Chatterjee, Anand Joseph X M, Sanjay Singh Chauhan
The paper introduces ERASE, a training schedule that detaches subgraphs in a neural network to launch their backward passes early on separate CUDA streams, thereby overlapping backward and forward computations. By applying this technique to a lightweight transformer and a large‑scale click‑through‑rate model, the authors demonstrate up to a 9.51% increase in training throughput while maintaining performance metrics close to the baseline.
By Ergan Shang, Flavio Sales Truzzi