Search: “models”

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

model-releasesarxiv-cs-ro

arXiv:2607.15330v2 Announce Type: replace Abstract: We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a

23 Jul 2026

Model Releases

Reproducing OpenAI’s “persistently beneficial models” - GRPO trait install barely moves. Ideas? [P] [R]

DGX agent

TL;DR: I’m reproducing the trait-persistence result from arXiv:2606.24014 on one RTX 3090. Before I can test persistence I need to install a trait via RL — and my GRPO run moves the trait only +2.4 po

model-releasesr-machinelearning

21 Jul 2026

Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation

arXiv:2603.00546v2 Announce Type: replace Abstract: Using Multimodal Large Language Models (MLLMs) as judges to achieve precise and consistent evaluations has gradually become an emerging paradigm acr

Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steering

arXiv:2607.13568v1 Announce Type: cross Abstract: Can a language model estimate its familiarity with an entity before generating an answer? We study activations at the final prompt token in twelve ins

MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model

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-

S-squared-VLA: Decoupling Semantic and Spatial Streams in Vision-Language-Action Models for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2607.13926v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable potential for high-level reasoning in autonomous driving, yet they fundamentally struggle to

Research

Self-Improving is Often Sudden: Enlightenment-style Finetuning for Large-Scale Models

arXiv:2607.13395v1 Announce Type: new Abstract: The pursuit of autonomously self-improving models has attracted growing interest in the era of large-scale foundation models. Drawing inspiration from t

researcharxiv-cs-lg

Agents

Amplitude-Only FFN Intervention for Tool-Structured LLM Inference Method: Gated Evaluation Protocol, and Cross-Model Empirical Results

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

agentsarxiv-cs-cl

Belief-reality separation lives in routing over a shared value slot in language models

model-releasesarxiv-cs-cl

arXiv:2607.11945v1 Announce Type: new Abstract: Capable language models hold what a character believes apart from what is true: told 'Anna believes the cup is blue; in reality it is red,' they answer

Tutorials

Can a Language Model Learn Facts Continually in Its Weights?

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

tutorialsarxiv-cs-cl

Research

Saturation Makes Quantization Error Additive: A Coverage Model with a Certificate

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

researcharxiv-cs-lg

So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis

arXiv:2607.11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditiona

Industry

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 (Thinking Machines Lab)

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

industrytechmeme

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

arXiv:2607.12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions. Thermal energy storage (TES) enables this shift, but scheduling it well re

VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression

arXiv:2607.12756v1 Announce Type: new Abstract: Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference latency and memory overhead. This has motivated

Nemotron Labs: How Open Models Give Enterprises and Nations AI They Can Trust, Control and Customize

model-releasesnvidia-blog

Enterprises have plenty of powerful models to choose from. The real test is whether the AI an enterprise builds uniquely addresses the needs of the business: improving workflows, tapping into domain k

14 Jul 2026

Joint Bayesian Parameter and Model Order Estimation for Low-Rank Probability Mass Tensors

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

10 Jul 2026

Checkout the latest version of Zed, adding proper support for llama.cpp as a model provider

model-releasesgeorgi-gerganov--x

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

InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models

arXiv:2607.07288v1 Announce Type: new Abstract: Infrared vision-language models are increasingly used for perception under low-light and adverse visual conditions, yet their robustness to localized st

Meta says its new AI model is ready to compete on coding

model-releasesthe-verge-ai

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

Multi-Agent Robotic Control with Onboard Vision-Language Models

model-releasesarxiv-cs-ro

arXiv:2607.07403v1 Announce Type: cross Abstract: Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges reg

Thinking Ahead: Foresight Intelligence in MLLMs and World Model

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

Agents

Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World Models [1] 'was never accepted through peer review.' …

Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World Models [1] 'was never accepted through peer review.' This is simply false. The core concepts from the tech report

agentsdavid-ha--x

Applications

FedDAF: Federated Domain Adaptation Using Model Functional Distance

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

applicationsarxiv-cs-cv

8 Jul 2026

Another day another big public tech co saying they’re using open source AI models extensively

model-releasesclem-delangue--x

Another day another big public tech co saying they’re using open source AI models extensively With our internal coding benchmark, we're able to confidently introduce open-weight models into our AI cod

Safety

Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

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

safetyarxiv-cs-lg

Don't Blame the Large Language Model: How Scaffolding Evolution Shapes Coding Agent Quality

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

DynaWM: A Base-VLA-Guided World Foundation Model for Moving-Object Manipulation

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

Safety

ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models

arXiv:2506.09740v2 Announce Type: replace-cross Abstract: Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode

safetyarxiv-cs-ai

Agents

evalci: A Python Library for Statistically Rigorous Comparison of Language Model Evaluations

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

agentsarxiv-cs-ai

Prior Bias in Vision Language Models on UML Diagram Interpretation

arXiv:2607.02853v1 Announce Type: new Abstract: Vision Language Models (VLMs) are increasingly applied to software engineering artifacts, especially UML class diagrams whose meaning depends on visual

Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks

arXiv:2607.03801v1 Announce Type: cross Abstract: We define Tiny Language Models (TLMs) as models below roughly 3B parameters that fit on mainstream consumer devices. We study how to adapt them for an

Retroactive Chain-of-Thought (RetroCoT): Forensic Reconstruction Prompts as a Safety Diagnostic Across Model Generations

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

SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models

arXiv:2607.05365v1 Announce Type: cross Abstract: Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks d

SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments

arXiv:2607.05264v1 Announce Type: new Abstract: Existing video benchmarks evaluate action recognition on consumer videos, egocentric recordings, or simulated industrial environments. They do not test

Streaming Model Cascades for Semantic SQL

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

The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models

arXiv:2607.04401v1 Announce Type: new Abstract: How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation mod

Research

They Infer What You Meant: Models Represent Communicative Intent More Reliably Than They Act On It

arXiv:2607.03598v1 Announce Type: cross Abstract: When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by send

researcharxiv-cs-ai

TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior

model-releasesarxiv-cs-cl

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

Safety

VISOR++: Universal Visual Inputs based Steering for Large Vision Language Models

arXiv:2509.25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has

safetyarxiv-cs-ai

VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

arXiv:2407.11691v5 Announce Type: replace Abstract: We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friend

When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters

arXiv:2607.04919v1 Announce Type: new Abstract: Deploying a time series foundation model requires GPU infrastructure, engineering overhead, and carries no guarantee of improvement over XGBoost. We pro

The Nemotron family just passed 100M downloads! Huge thank you to the community building with us and showing what’s possible with open model…

model-releasesclem-delangue--x

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

6 Jul 2026

With our internal coding benchmark, we're able to confidently introduce open-weight models into our AI code reviewer w/o degrading code qual…

model-releasesclem-delangue--x

With our internal coding benchmark, we're able to confidently introduce open-weight models into our AI code reviewer w/o degrading code quality. Have the frontier model (Fable) to the hardest work, de

6 Jul 2026

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection

arXiv:2512.10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiv

Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space

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

mupscaling small models: Principled warm starts and hyperparameter transfer

arXiv:2602.10545v2 Announce Type: replace-cross Abstract: Modern large-scale neural networks are often trained and released in multiple sizes to accommodate diverse inference budgets. To improve effic

OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration

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