Knowledge catalogue
arXiv:2607.13763v1 Announce Type: cross Abstract: Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-
arXiv:2607.11183v2 Announce Type: replace Abstract: Large language models increasingly operate as tool-using agents, where small format, argument, or function-call errors can invalidate otherwise plau
arXiv:2607.11020v2 Announce Type: replace Abstract: Continual learning promises a language model that keeps acquiring knowledge after training, with each new fact written into its weights. Whether wei
arXiv:2607.12266v1 Announce Type: new Abstract: Mixed-precision quantization must decide which parts of a model to keep at higher precision. A common premise, shared by sensitivity-based methods such
Thinking Machines Lab: Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area — Try on Tinker
arXiv:2410.06329v4 Announce Type: replace-cross Abstract: Obtaining a reliable estimate of the joint probability mass function (PMF) of a set of random variables from observed data is a significant ob
Zed editor has released a new version with integrated support for llama.cpp as a model provider, enabling users to run local language models within the editor. This update allows developers to use lla
After reentering the AI race with its first in-house Muse Spark model in April, Meta is now opening up the doors to developers with a new model that can plug into AI coding software with the new Meta
arXiv:2511.18735v3 Announce Type: replace-cross Abstract: In this work, we define Foresight Intelligence as the capability to anticipate and interpret future events-an ability essential for applicatio
arXiv:2509.11819v2 Announce Type: replace-cross Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating
arXiv:2303.08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theo
arXiv:2607.03691v1 Announce Type: cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agentic scaffolding: a middlewa
arXiv:2607.02604v1 Announce Type: new Abstract: Although vision-language-action (VLA) models have received widespread attention, many challenges remain in manipulating dynamic moving objects. In most
arXiv:2607.04429v1 Announce Type: cross Abstract: The dominant practice in language model evaluation is to report a single accuracy number per model and declare the higher one better, without testing
arXiv:2607.04645v1 Announce Type: cross Abstract: Safety alignment in large language models is typically evaluated against direct, imperative harmful requests. We show that this alignment is highly co
arXiv:2604.00660v2 Announce Type: replace-cross Abstract: Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference o
arXiv:2512.20757v2 Announce Type: replace Abstract: Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokeniz
The Nemotron model family from NVIDIA has reached 100 million downloads, marking a significant milestone for the open-source AI model community. The achievement reflects growing adoption and collabora
arXiv:2607.01689v1 Announce Type: cross Abstract: Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing appr
arXiv:2607.01531v1 Announce Type: new Abstract: Learning how an environment behaves from interaction is central to building agents that adapt to unfamiliar tasks. World models learned with deep networ
arXiv:2607.01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance. Recent methods optimize mixture weights via
arXiv:2511.05747v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, l
Dario just declared war on open-source. Anthropic's message is clear: open source could destroy the entire AI business model, and Chinese open-source models are the cause. I sat down with @jasonlk & @
arXiv:2607.01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety. Re
arXiv:2606.31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature,
arXiv:2603.14732v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is
arXiv:2601.00260v2 Announce Type: replace Abstract: While foundation models in radiology are expected to be applied to various clinical tasks, computational cost constraints remain a major challenge w
arXiv:2606.29501v1 Announce Type: new Abstract: We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict
arXiv:2606.29261v1 Announce Type: cross Abstract: We derive the linear union-of-subspaces (UoS) model for subspace clustering (SC) from the nonlinear mixture model (NMM) used in blind source separatio
arXiv:2501.07437v3 Announce Type: replace-cross Abstract: Most statistical models for pairwise comparisons, including the Bradley-Terry (BT) and Thurstone models and many extensions, make a relatively
arXiv:2606.29254v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate broad reasoning abilities but struggle with accuracy and reliability in specialized domains such as travel, whe
We’re shipping two major updates to streamline your creative workflow, allowing you to generate high-speed images with one model and then instantly animate them with the other—all at a fraction of the
arXiv:2606.28092v1 Announce Type: new Abstract: Attributing a generated image to its source diffusion model is a fundamental challenge in provenance verification and intellectual property protection.
arXiv:2511.20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods
arXiv:2606.27632v1 Announce Type: new Abstract: As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We arg
arXiv:2606.24051v1 Announce Type: new Abstract: Vision-Language-Action driving models convert a pretrained Vision-Language Model into a driving policy, allowing them to use world knowledge and follow
arXiv:2606.23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at
arXiv:2602.18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during train
arXiv:2606.24715v1 Announce Type: cross Abstract: We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule, we denote by the te
arXiv:2606.22075v1 Announce Type: new Abstract: Standard continuous-depth models, such as Neural Ordinary Differential Equations (NODEs), offer significant advantages in modeling physical systems by l
arXiv:2606.22841v1 Announce Type: cross Abstract: As Large Language Models (LLMs) achieve widespread integration across diverse linguistic landscapes, ensuring their safety and alignment with regional
Model council as a tool to push performance definitely works, I think the interesting next frontier is trying to scale this at the agent level have been thinking a bunch about model routing and relate
arXiv:2603.27048v3 Announce Type: replace Abstract: Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain l
arXiv:2606.22752v1 Announce Type: new Abstract: Physical simulations for intricate geometries with path-dependent constitutive models face difficulties due to the enormous computational cost they requ
arXiv:2606.22589v1 Announce Type: new Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific ex
arXiv:2606.11240v1 Announce Type: cross Abstract: Accurate modeling of quantum many-body systems often requires computationally expensive simulations such as Density Matrix Renormalization Group (DMRG
arXiv:2606.11201v1 Announce Type: cross Abstract: The wide deployment of LLMs has made model alignment necessary to make newly trained models safely and effectively respond to user instructions. Among
arXiv:2602.21331v2 Announce Type: replace Abstract: General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require
arXiv:2606.11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological
arXiv:2606.09878v1 Announce Type: new Abstract: Standard benchmarks report aggregate accuracy, but practitioners need to know which specific capabilities a model lacks. We introduce FailureScope, a be
arXiv:2606.09873v1 Announce Type: cross Abstract: Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer.
arXiv:2501.14717v2 Announce Type: replace Abstract: Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly t
Anthropic just announced Claude Fable 5, a new AI model it said is the most powerful model it has ever made widely available. According to the company, Fable 5 'shows exceptional performance in softwa
arXiv:2606.07681v1 Announce Type: cross Abstract: Differentiable programming offers transformative capabilities for scientific modeling, enabling gradient-based parameter estimation, sensitivity analy
arXiv:2606.06748v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models. Existing detection methods rely on flat si
arXiv:2606.06788v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) in scientific information seeking tasks have become increasingly use-centric, such as conducting live or mul
PSA: Just added a few thousand chips, including B200s and B300s to our Dedicated Model Inference (http://api.together.ai/endpoints). With Dedicated Model Inference, you can now on-click deploy our Bla
This is super big I think this is the first useful speculative decoding method deployed on a big quasi frontier model Massive unlock @fi56622380 🚀 1,000+ TOKENS/S ON A 1T MODEL! 🚀 We are thrilled to r
There was an inflection point recently where the tide shifted to model pickers and OSS Mix of tokenmaxxing/cost fatigue, nemotron coalition, harness step functions, brains/claws, etc Long live those w
arXiv:2602.13697v2 Announce Type: replace Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But si