“GPA...enables a single autoregressive model to flexibly perform TTS, ASR, and VC without architectural modifications.”

“These findings demonstrate that modern LLMs internalize psychologically grounded trust signals without explicit supervision, offering a representational foundation for designing credible, transparent, and trust-worthy AI systems in the web ecosystem.”

“The article highlights the efforts of AWS CCoE to drive the internal adoption of AI.”

“LLM is 'AI that generates and explores text,' and the diffusion model is 'AI that generates images and data.'”

“Databricks 基盤モデルAPIは多種多様なLLM APIを提供しており、Llamaのようなオープンウェイトモデルもあれば、GPT-5.2やClaude Sonnetなどのプロプライエタリモデルをネイティブ提供しています。”

“The article discusses fine-tuning LLMs and the use of methods like LoRA.”

“The AI partnerships allow companies to access the org's content, like Wikipedia, at scale.”

“Editor’s note: This article is a part of our series on visualizing the foundations of machine learning.”

“MedGemma 1.5, small multimodal model for real clinical data MedGemma […]”

“Collective Communication (CC) is at the core of data exchange between multiple accelerators.”

“The core of the problem is the resource strain and the lack of ethical considerations when scraping data at scale.”

“The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions.”

“Apple and Google have embarked on a non-exclusive, multi-year partnership that will involve Apple using Gemini models and Google cloud technology for future foundational models.”

“"Physical AIのChatGPTモーメントが到来した"”

“Liquid AI has introduced LFM2.5, a new generation of small foundation models built on the LFM2 architecture and focused at on device and edge deployments.”

“It’s built to power reliable on-device agentic applications: higher quality, lower latency, and broader modality support in the ~1B parameter class.”

“AEF-based models generally exhibit strong performance on all tasks and are competitive with purpose-built RS-ba”

“Current audio evaluation faces three major challenges: (1) audio evaluation lacks a unified framework, with datasets and code scattered across various sources, hindering fair and efficient cross-model comparison”

“By unifying these diverse AI components into a single, easy-to-adapt platform”

“N/A (Source is a Reddit post with no direct quotes)”

“N/A - Article content not directly provided.”

“Unable to extract a direct quote from the provided context.”

“I'm looking for resources to study the following: -statistics and probability -calculus (for applications like optimization, gradients, and understanding models) ... I don't want to study the entire math courses, just what is necessary for AI/ML.”

“The South Korean government funded the Sovereign AI Foundation Model Project, and the five selected teams released their initial models and presented on December 30, 2025. ... all 5 teams "presented robust open-source policies so that foundation models they develop and release can also be used commercially by other companies, thereby contributing in many ways to expansion of the domestic AI ecosystem, to the acceleration of diverse AI services, and to improved public access to AI."”

“DLMs augmented with polynomial-length chain-of-thought (CoT) can simulate any parallel sampling algorithm using an optimal number of sequential steps.”

“FoundationSLAM achieves superior trajectory accuracy and dense reconstruction quality across multiple challenging datasets, while running in real-time at 18 FPS.”

“mHC restores the identity mapping property while incorporating rigorous infrastructure optimization to ensure efficiency.”

“Learning curves can better capture the effects of multi-task learning and their multi-task extensions can delineate pairwise and contextual transfer effects in foundation models.”

“The model achieves 12% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38% error).”

“OneRec Foundation (1.7B and 8B), a family of models establishing new state-of-the-art (SOTA) results across all tasks in RecIF-Bench.”

“The article quotes Xue Guirong, the technical director of the scientific model overall team at Zhejiang Lab, who points out that LLMs are limited by the 'boundaries of language' and cannot truly understand high-dimensional, multi-type scientific data, nor can they independently complete verifiable scientific discoveries. The article also highlights the 'AI Manhattan Project' as a major initiative in the application of AI in science.”

“The article quotes the founder, Su Wen, emphasizing the importance of building their own models and the unique approach of AutoCoder.cc, which doesn't provide code directly, focusing instead on deployment.”

“BandiK employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits.”

“The discussion revolves around the future of "world models," with researchers offering predictions on breakthroughs in areas like geometric foundation models, self-supervised learning, and the development of 4D/5D/6D AIGC.”

“The paper establishes error bounds for the reconstructed solution and the source term without requiring classical source conditions, and derives an expected convergence rate for the source error in a weaker topology.”

“HOLOGRAPH provides rigorous mathematical foundations while achieving competitive performance on causal discovery tasks.”

“The paper introduces an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM).”

“The paper introduces "Semantic Lookout", a camera-only, candidate-constrained vision-language model (VLM) fallback maneuver selector that selects one cautious action (or station-keeping) from water-valid, world-anchored trajectories under continuous human authority.”

“The paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot.”

“Foundation Models trained on collider data can help improve the prediction of cosmological parameters and to predict halo and galaxy velocities in different datasets from CosmoBench.”

“Alibaba Tongyi Lab have released MAI-UI—a family of foundation GUI agents. It natively integrates MCP tool use, agent user interaction, device–cloud collaboration, and online RL, establishing state-of-the-art results in general GUI grounding and mobile GUI navigation, surpassing Gemini-2.5-Pro, Seed1.8, and UI-Tars-2 on AndroidWorld.”

“SeedFold outperforms AlphaFold3 on most protein-related tasks.”

“Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112).”

“PGMP framework outperforms state-of-the-art methods on unseen anatomy, setting new benchmarks in efficiency and diagnostic reliability.”

“MotivNet achieves competitive performance across datasets without cross-domain training.”

“The model achieves comparable performance with less than 5% of the task-specific data required by dedicated expert models.”

“Performance is consistent with a data limited regime rather than a model parameter-limited one.”

“The WMFM achieves a 17% improvement in balanced accuracy for LoS/nLoS classification and a 48.5% reduction in localization error compared to the end-to-end (E2E) benchmark, while reducing training time by up to 90-fold.”

“GZ Evo includes 104M crowdsourced labels for 823k images from four telescopes.”

“PathFound integrates pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement.”