Search: “model-releases”

The Surrogate Is Not the Reward: Post-Surrogate Primary-Outcome Acquisition in Contextual Bandits

arXiv:2610.06610v1 Announce Type: cross Abstract: We study contextual bandits in which a surrogate is observed after the action but before the learner decides whether to acquire the primary outcome th

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Model Releases

The Universal Weight Subspace Hypothesis

DGX agent

arXiv:2512.05117v3 Announce Type: replace-cross Abstract: We show that deep neural networks trained across diverse tasks exhibit remarkably similar low-dimensional parametric subspaces. We provide the

model-releasesarxiv-cs-cv

6 Oct 2026

THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage

arXiv:2507.09200v2 Announce Type: replace Abstract: The rapid proliferation of video in applications such as autonomous driving, surveillance, and sports analytics necessitates robust methods for dyna

TIGER: Time-Series Classification with In-Context-Learning Gated Ensemble of Representations

arXiv:2610.06156v1 Announce Type: new Abstract: A representation family is a distinct way of extracting features from time series. Ensemble algorithms that combine several representation families rema

TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution

arXiv:2610.05182v1 Announce Type: cross Abstract: Infrared image super-resolution is currently led by Mamba-based networks with 26 to 37 million parameters, which are difficult to deploy on the airbor

Today, we are launching a preview of our new model, Mistral Large 4 (ML4), aka le Chonk 🐈. ML4 is a 1T-parameter model with 49B active para…

model-releasesarthur-mensch--x

Today, we are launching a preview of our new model, Mistral Large 4 (ML4), aka le Chonk 🐈. ML4 is a 1T-parameter model with 49B active parameters, trained natively with multimodal capabilities. It is

Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection

arXiv:2610.06423v1 Announce Type: new Abstract: Spontaneous movement analysis in preterm infants relies increasingly on markerless pose estimation (PE) to derive clinically relevant motion biomarkers

Training-Free Transformer Merging via Sequential Local Operator Alignment

arXiv:2610.06415v1 Announce Type: new Abstract: Training-free model merging aims to combine multiple fine-tuned models into a single model without further optimization on labeled data. Yet, in transfo

Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents

arXiv:2610.04156v1 Announce Type: cross Abstract: LLM agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own reasoning or outputs can

TreeWalker: Partial Evaluation for Grouped Tree-Ensemble Inference

arXiv:2610.03939v1 Announce Type: new Abstract: Many inference workloads evaluate a trained tree ensemble on row groups that share feature values: discrete-time survival models expand each patient int

Two-Sample Testing via Path-based Inference

arXiv:2610.05684v1 Announce Type: cross Abstract: Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also s

ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invariant Option Scoring

arXiv:2610.06744v1 Announce Type: cross Abstract: ufakzeka-karar is an open Turkish decision model with 182,494,466 parameters. Given a Turkish text and questions of a fixed answer type (a choice, a l

UltraDub: Towards Authentic Dubbing by Unifying Visually-Steered Flow Learning and Trajectory Guidance

arXiv:2610.05932v1 Announce Type: new Abstract: Visual voice cloning requires intelligible, speaker-consistent speech synchronized with visible articulation. However, sequential multimodal conditionin

UnAct: Gradient-Free Unlearning via Targeted Activation Intervention

arXiv:2610.04426v1 Announce Type: cross Abstract: Machine unlearning seeks to remove the influence of designated training data from a trained model without retraining from scratch. Retrain-free method

Understanding Clustering in Slot Attention via Particle Dynamics

arXiv:2610.04493v1 Announce Type: cross Abstract: Studying attention through the lens of interacting particle dynamics has shown how token clustering can emerge from the underlying dynamics. We extend

UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation

arXiv:2610.04092v1 Announce Type: new Abstract: Generating a boundary representation (B-rep) conditioned on a single image requires faithful reconstruction of geometry, valid topology, and support for

Uniform-in-time convergence bounds for Persistent Contrastive Divergence algorithms

arXiv:2510.01944v2 Announce Type: replace-cross Abstract: We propose a continuous-time formulation of a noisy persistent contrastive divergence (PCD)-like method for maximum likelihood estimation (MLE

UniSlider: Perceptually Uniform Sliders for Continuous Image Editing

arXiv:2610.06831v1 Announce Type: new Abstract: Sliders provide an intuitive interface for continuous image editing. In current generative approaches, however, the slider is simply a rescaling of the

unsloth/Qwen3.8-Flash-Next-GGUF is being updated

model-releasesr-localllama

Looks like Unsloth is updating https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF to work with llama.cpp, so hopefully this will resolve the issue of having two different GGUF versions of Qwen Fla

Valid Stopping in Adaptive Generator-Verifier Loops

arXiv:2610.06432v1 Announce Type: cross Abstract: Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow ter

VERA: Verdict-Conditioned Reliability for Adaptive LLM Judges

arXiv:2610.05452v1 Announce Type: new Abstract: Accurately estimating judgment reliability is a central challenge in adapting LLM judges to newly verified feedback while preserving previously learned

Video Encoders Built on Image Representations

arXiv:2610.06616v1 Announce Type: new Abstract: The design of a video encoder determines when frames begin to interact and which frame-specific visual evidence remains accessible to the language model

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

arXiv:2610.04432v1 Announce Type: new Abstract: Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manu

VideoResearchAgent: Grounded Task Synthesis and Sim-to-Real RL for Open-Web Video Research

arXiv:2610.04911v1 Announce Type: cross Abstract: Existing deep research agents are designed primarily for text- and image-based web sources, while video reasoning systems typically assume that releva

VideoTapestry: Query-Adaptive Memory Refinement for Multi-Agent Long-Video Understanding

arXiv:2610.06672v1 Announce Type: new Abstract: Long-video understanding places substantial demands on memory, as answering questions often requires retrieving information distributed across extended

vMF Sentence LDA: A Spherical Topic Model over Sentence Embeddings

arXiv:2610.05095v1 Announce Type: cross Abstract: Latent Dirichlet Allocation (LDA) and models derived from it remain widely used topic models. LDA observes each document as a bag-of-words and models

Voltic: Distinguishing Volatility from Stochasticity in Recurrent Memory

arXiv:2610.05700v1 Announce Type: new Abstract: Recurrent sequence models must decide how strongly to overwrite their memory at each token. Read as Bayesian filtering, this write is the gain of a Kalm

WasserMan: Benchmark for Underwater Manipulation Policy Learning

model-releasesarxiv-cs-ro

arXiv:2610.04536v1 Announce Type: new Abstract: Underwater manipulation couples visual decisions, contact forces and a thruster-controlled floating base. We introduce WasserMan, to our knowledge the f

Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video

arXiv:2610.04151v1 Announce Type: new Abstract: Content-provenance standards such as C2PA let a platform recover a stripped manifest through a soft binding: an invisible watermark read from the conten

We are releasing the only Western OSS LLM that is competitive with the best Chinese OSS models for coding tasks! Mistral Large 4 preview, AK…

model-releasesarthur-mensch--x

We are releasing the only Western OSS LLM that is competitive with the best Chinese OSS models for coding tasks! Mistral Large 4 preview, AKA le Chonk 🐈 is SOTA OSS on cyber and human evals. Check 🧵 b

Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection

arXiv:2610.05865v1 Announce Type: new Abstract: Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces

Weight Decay and Neuron Condensation: A Three-Stage Analysis of Two-Layer ReLU Networks

arXiv:2610.04533v1 Announce Type: new Abstract: Weight decay is widely used as a regularization technique in neural network training, yet its role in neuron condensation (parameter direction alignment

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent…

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence a

model-releasesopenai--x

We’re so back.

model-releaseslm-studio--x

We’re so back. Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US or Europe on aggregated benchmarks. • State-of-the-art on

What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study

arXiv:2610.03792v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make it app

What Does a Harness Buy? Tokens, Mostly

arXiv:2610.04433v1 Announce Type: cross Abstract: A coding agent is a language model wrapped in a harness: the system prompt, the tool set, and the context management that turn a chat model into somet

What Does a Harness Repair? A Preregistered Study of Visibility, Baseline Adequacy and Evaluation Defects

arXiv:2610.05533v1 Announce Type: new Abstract: Harness search keeps a change to the prompts, reasoning switches, token budgets or parsers around a frozen model if the change raises a score. Such a ga

What Does an Observability Foundation Model Know?

arXiv:2610.05577v1 Announce Type: new Abstract: A linear probe can show that a label is recoverable from a model's hidden states, but not whether that goes beyond what the input already reveals, or wh

What Does It Cost to Simulate a Quantum Sentence Classifier? An Energy and Compute Perspective on Near-Term QNLP

arXiv:2610.06176v1 Announce Type: cross Abstract: Near-term quantum natural language processing (QNLP) experiments often run on classical simulators, so simulator cost is part of the field's practical

What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document

arXiv:2610.04128v1 Announce Type: cross Abstract: Split learning lets a client train a language model on a server without sending its text. The client runs the first layers itself and sends the server

What to Preserve in Recursive Computation: A Local Predictive Sufficiency Principle

arXiv:2610.04303v1 Announce Type: new Abstract: Recursive computation repeatedly compresses or reuses intermediate states, creating a simple tension: information that must remain useful across longer

What Will Post-Training Fix? Per-Problem Gains Are Shared Across Independent RL Runs, and Existing Checkpoints Predict Them Better Than A Priori Signals

arXiv:2610.04978v1 Announce Type: new Abstract: Data selection, curricula and the evaluation of post-training recipes all assume that we can tell, before training, which problems a model will improve

When Does Longer Reasoning Help? Predicting Mathematical Reasoning Through Discovery and Execution

arXiv:2610.05322v1 Announce Type: new Abstract: Test-time compute can improve mathematical reasoning, but can short-budget runs predict how mathematical reasoning scales with additional compute? We in

When Does Retrieval Help? A Study of In-Context Adaptation in Vision-Language-Action Models

arXiv:2610.05492v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential as generalist robot policies, but adapting them to unseen tasks often requires costly pa

When Is Enough Enough in Self-Evolving LLM Systems?

arXiv:2610.04756v1 Announce Type: cross Abstract: Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifac

Where mission meets moonshot: Join us at the Google Public Sector Summit 2026

model-releasesgoogle-cloud-ai

Across the public sector, the question has shifted from “what’s possible?” to “how can we drive mission impact?” We’ll be answering this question at the Google Public Sector Summit on October 20th at

Why 38% of AI Agent container escapes didn't need kernel 0-days: Analysis of 109 empirical incidents (Open Dataset + Defense Harness)

model-releasesr-localllama

Over the past several months, we conducted an empirical post-mortem investigation into 109 autonomous AI agent security incidents (cataloged with 193 falsification criteria across tool-use and multi-a

Why Convolution Still Matters: Evaluating Inductive Biases in Cryospheric Image Classification

arXiv:2610.04073v1 Announce Type: new Abstract: Recent advances in attention-based deep learning have motivated their adoption for remote sensing image classification; however, their benefits for cryo