AIMode.newsSearch
Live

XAI co-founder's startup River AI raises $1.1 billion to expand custom AI tools

R

Reuters

AIMode News Desk · curated summary

1 min readAI

Automated news aggregation. Headlines and summaries are gathered from public feeds; see our editorial standards for sourcing, corrections, and AI-assist disclosure.

Topics:xAI

Related coverage: XAI co-founder's startup River AI raises $1.1 billion to expand custom AI tools Reuters

Advertisement

About this story

This story was aggregated from Reuters. Headlines, summaries, and links are gathered automatically from public RSS feeds for your convenience.

Read the full story →

More in AI

AI

Transformer Geometry Observatory TGO-IV: Developmental Topology Observatory

arXiv:2608.09997v1 Announce Type: new Abstract: Transformers have had a profound impact on the world of language processing and computer vision. As efforts to answer the million-dollar question of ``How does a Transformer learn?" have been increasing, existing interpretability studies primarily analyze representations at isolated layers or the network as a whole, while the developmental evolution of individual representations and its manifolds across transformer layers remains underexplored. With this work, we aim at providing a comprehensive analysis of the evolution of representations as the representation point cloud transforms across the layers; thereby attempting to isolate layers or establish a trend which comes closer to justifying how and when raw input representations evolve into task-relevant feature representations. Thus, Transformer Geometry Observatory-TGO-IV introduces a topological framework for analysing the evolution of Transformer representations through the lens of Persistent Homology. Rather than studying local geometric properties alone, TGO-IV constructs Vietoris--Rips simplicial complexes from token-level representation point clouds and investigates the evolution of their persistent topological signatures across Transformer layers. The proposed framework comprises complementary topological observatories including Persistence Diagrams, Barcode Diagrams, Betti Curves, Persistence Landscapes, Bottleneck Distance, and Wasserstein Distance, enabling a comprehensive analysis of how the global topology of representation point clouds develops throughout the forward pass.

arXiv cs.LG · 45 minutes ago

AI

Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification

arXiv:2608.10007v1 Announce Type: new Abstract: The current state-of-the-art (SOTA) deep randomized neural networks, such as deep Random Vector Functional Link (dRVFL) and ensemble deep RVFL (edRVFL), treat all training samples uniformly, which limits their robustness and effectiveness when applied to real-world datasets containing noise and outliers. Furthermore, the propagation of contaminated features across hidden layers negatively influences the decision-making capability of these models. To overcome these limitations, we propose intuitionistic fuzzy dRVFL (IF-dRVFL) and intuitionistic fuzzy edRVFL (IF-edRVFL) frameworks that enhance model robustness. The proposed models unify intuitionistic fuzzy theory to exploit sample neighborhood information in the kernel space by jointly considering membership and non-membership degrees for each sample. Membership degrees are computed based on the distance of samples from their respective class centroids, while non-membership degrees quantify sample heterogeneity within local neighborhoods. These measures are employed to assign adaptive weights to training samples, enabling effective discrimination among clean, noisy, and outlier data points. Extensive experiments conducted on UCI and KEEL benchmark datasets, with and without the presence of Gaussian noise, demonstrate the superiority of the proposed IF-dRVFL and IF-edRVFL models over existing SOTA fuzzy and non-fuzzy approaches. The source code is available at https://github.com/mtanveer1/IF-edRVFL.

arXiv cs.LG · 45 minutes ago

AI

CurveFP: Rational-Radix Logarithmic Datatypes with Closed Products for Language Models

arXiv:2608.10010v1 Announce Type: new Abstract: Low-precision datatypes reduce language-model cost, but most formats optimize scalar fidelity while leaving the arithmetic induced by their products unchanged. We introduce CurveFP, a closed-product codebook family that distributes quantized magnitudes across interleaved logarithmic curves under compact block scales. A rational radix tunes dynamic range against local resolution, while uniform curve indices make every nonzero product algebraically closed. Product formation becomes an exact sign XOR and integer-index update, and a derived finite phase count determines the accumulation schedule. We instantiate this algebra as CurveFP eight E4C3/E5C2 for training and CurveFP seven E3C3 for compact deployment. In evaluation, CurveFP seven beats tensor-wise FP8 perplexity on four 7B--9B models with one fewer element bit and stays within 1.32\% of native quality. CurveFP eight lowers operand NMSE in all 36 paired forward and backward GEMM comparisons. Across three matched 128.3M-parameter triplets, every mode completes 3B-token pretraining per seed; CurveFP eight reaches mean BF16-inference perplexity 22.5366 versus 22.5407 for FP8 and incurs a lower format-induced penalty in all three seeds. A 36-cell downstream matrix finds lower WikiText-103 perplexity for the CurveFP eight-trained checkpoints in all 12 seed-format comparisons, with mixed PG-19 and task deltas. Together, these results establish CurveFP as an arithmetic co-design that combines FP8-class numerical behavior, seven-bit inference, and a substantially simpler product path.

arXiv cs.LG · 45 minutes ago

AI

Sheaf-Based Federated Representation Learning

arXiv:2608.10016v1 Announce Type: new Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives. To address this challenge, we propose Sheaf-based Federated Representation Learning (SFRL), a general framework that jointly optimizes local objectives with a manifold-constrained geometric alignment regularizer based on learnable sheaf restriction maps. Unlike most existing approaches, SFRL does not assume a shared global latent space. Instead, global consistency emerges from the alignment of neighboring latent representations through orthogonal transformations and isometric embeddings. This alignment is enforced by a quadratic gluing regularizer induced by the sheaf Laplacian, whose learnable restriction maps adapt the geometry to the observed data. The penalty is evaluated on a small set of shared pilot samples, ensuring scalability and communication efficiency. We develop a decentralized algorithm for solving SFRL, termed Sheaf-FRL, which alternates between gradient updates of the local models and closed-form Procrustes updates of the edge-wise restriction maps. We further establish convergence of Sheaf-FRL to first-order stationary points in both deterministic and stochastic settings. As an application, we consider a cooperative classification task in the context of semantic communication, under model and data heterogeneity. Our results show that Sheaf-FRL outperforms baseline approaches in terms of local and post-communication classification accuracy across different levels of local distribution shift and exhibits greater robustness to latent-space dimensionality compression.

arXiv cs.LG · 45 minutes ago