Important to read. China is committed to continuing its open, global ai approach.Vincent Chow: Key takeaways from President Xi's speech in his first ever appearance at…
Zhipu grew their ARR by 4x in ≈3-4 months$16B by Q1 2027 could be doable at this rate… but for the inability to provide so much…
really interesting experimentnewer LLMs are growing Transmetropolitan-pilled"eh. stuff happens. who cares about another cyberattack"Ziqian Zhong: @a_karvonen I feel the timeline we're living in is just weird…
one thing DeepSeek should lean into is multi-agentthey are uniquely unconstrained on inference nowtheir models are *cheap* and *fast* compared to all Chinese competitionV4-Pro@16 costs ≈the…
Google should take 3.5 Pro (as well as all 3rd gen models) out back and deal with them the way Meta did away with Behemoth. That…
This is true. They were talking of stealth research at the time. They chose to build a "fat V3" instead, to quickly catch up to the…
Kimi K3 is a very good model, but people are overindexing on an Arena score again (remember Llama 4?)ELO scores as judged by Arena users are…
Really different calibers of leaders, I'm sorry brosJMB 🧙♂️: the "urgent" trump speech tonight is wild.he's trying to sell us on the idea that "china rigged…
really coolFatescript: I’m a huge Hollow Knight fan working at Kimi. Before K3 was released, I used Hollow Knight assets to build a game where the…
Internalize one thingthe defining characteristic of Xi Jinping is that he's very persistenthe's not like Trump. if he's doing something, almost certainly he knows what he's…
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation,…
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains…
arXiv:2607.14563v1 Announce Type: cross Abstract: Estimating spatially heterogeneous elastic properties from low-resolution displacement measurements is a severely ill-posed inverse elasticity problem because low resolution obscures spatial…
arXiv:2510.16657v3 Announce Type: replace Abstract: Synthetic data has been increasingly used to train frontier generative models. However, recent studies raise key concerns that iteratively retraining a…
arXiv:2607.14516v1 Announce Type: cross Abstract: Interpreting optimizers as gradient-flow discretizations has motivated applying higher-order Runge-Kutta (RK) integrators to neural networks. We build a representative Adam variant…
arXiv:2412.20556v2 Announce Type: replace Abstract: We study distributionally robust optimization (DRO) for robust inference when the worst-case distribution is continuous, leading to significant computational challenges due…
arXiv:2607.14460v1 Announce Type: cross Abstract: We study the sample covariance error of centered Gaussians. A remarkable breakthrough [66] established the correct error scaling order and explicitly…
arXiv:2607.15229v1 Announce Type: cross Abstract: We develop data-driven algorithms for maintaining $N$ independent identical machines under a textit{block replacement policy}, in which each machine is replaced…
arXiv:2607.14371v1 Announce Type: cross Abstract: Large Language Models (LLMs) have revolutionized AI services, but a critical tension emerges: while personalization improves model performance, it consumes scarce…
arXiv:2607.14193v1 Announce Type: new Abstract: The Helmholtz equation governs time-harmonic wave propagation, and in dissipative media a complex modulus renders its squared wavenumber $kappa^2$ complex. Inferring…
arXiv:2607.14361v1 Announce Type: cross Abstract: We address fundamental challenges in representing and computing $mathbb{R}^{d}$-valued predictable square-integrable processes over $[0,T]$, collected in the space $mathcal{H}^2_T(mathbb{R}^{d})$. These processes…
arXiv:2603.26261v2 Announce Type: replace-cross Abstract: Contrastive learning produces coherent semantic feature embeddings by encouraging positive samples to cluster closely while separating negative samples. However, existing contrastive…
arXiv:2607.14297v1 Announce Type: cross Abstract: Mixture models which cluster skewed random matrices can often suffer from over-parameterization in the absence of performing dimension reduction. Even with…
arXiv:2602.05786v3 Announce Type: replace-cross Abstract: Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters.…