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Model Releases

Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation

DGX agent

arXiv:2610.14162v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as infe

model-releasesarxiv-cs-lg

14 Oct 2026

NVIDIA Nemotron Bolt 3.5 74B A9B releases Oct. 15th

model-releasesr-localllama

This model is a 74B parameter, 9B-active-parameter mixture-of-experts (MoE) model, packed into 24GB using 2-bit quantization with advanced quantization-aware training techniques to retain quality. It

Observation Quality in DSM-Assisted Aerial Localization: A Benchmark and Base-Anchored Uncertainty Calibration

arXiv:2610.18564v1 Announce Type: new Abstract: Aerial map matching provides position observations for navigation, but a position error alone does not determine how an observation should be weighted.

Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration

arXiv:2610.16895v1 Announce Type: cross Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating

Offensive security startup CodeWall launches with $4M for AI agents that attack enterprises

model-releasessiliconangle

London-based offensive security startup CodeWall Ltd. officially launched today with 4 million in new funding to give enterprises such as banks autonomous artificial intelligence agents that attack th

OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding

arXiv:2610.18829v1 Announce Type: new Abstract: With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly import

OmniRaindrop: A Comprehensive Benchmark for Dual-Focused Raindrop Removal under Day and Night Scenes

arXiv:2510.17719v3 Announce Type: replace Abstract: Adherent raindrops on camera lenses are a pervasive source of real-world image degradation, undermining outdoor visual perception for downstream app

On the Depth Transfer of Neural Optimizers via L-Smoothness

arXiv:2610.14344v1 Announce Type: new Abstract: Scaling deep learning models efficiently requires predictable performance transfer across compute regimes. Establishing reliable hyperparameter transfer

On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination

arXiv:2610.18727v1 Announce Type: new Abstract: Natural Language Autoencoders (NLAs) are proposed as oversight tools for language models: an Activation Verbaliser (AV) writes an English explanation of

On the Statistical Mechanics of Attention

arXiv:2610.17347v1 Announce Type: new Abstract: We revisit the statistical mechanics of Transformer attention via modern Hopfield models. We present a free-energy formalism to characterize parameter r

One Size Does Not Fit All: Distribution-Guided Tensor-Wise Adaptive Sparsification for Model Merging

arXiv:2508.06163v2 Announce Type: replace-cross Abstract: Merging multiple task-specific fine-tuned checkpoints into a single multi-task model provides an attractive alternative to joint multi-task tr

One-Way Coupling via Patchwise Adaptive Normalization in Transformer Surrogates

arXiv:2610.14687v1 Announce Type: new Abstract: Many physical systems exhibit one-way coupling: exogenous fields such as medium density or obstacle geometry influence the dynamics without being affect

Online LLM Personalization from Pairwise Preferences Across Users

arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

arXiv:2502.04356v2 Announce Type: replace-cross Abstract: In response to the success of proprietary Large Language Models (LLMs), there is a growing interest in developing open, non-proprietary LLMs a

Open-o3-Video-v2: Seek, Ground, and Reason with Verifiable Spatio-Temporal Evidence

arXiv:2610.17656v1 Announce Type: new Abstract: Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by

OpenAI Employee Uses 'Unnamed Chatbot' to Spy on OpenAI, Gets Arrested by FBI

OpenAI is in the news again. This time, it’s due to a recent incident involving corporate espionage. According to a statement the AI lab released on 13th October, the company discovered a “sophisticat

model-releasesr-chatgpt

OpenDGO: Benchmarking Multi-Agent Organizations Across Diverse Application Domains

arXiv:2610.17143v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms

Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness

arXiv:2610.18318v1 Announce Type: new Abstract: Over-parameterization is ubiquitous in modern machine learning, yet its effect on robustness remains poorly understood. We study this question in matrix

Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification

arXiv:2610.18404v1 Announce Type: cross Abstract: Automated radar waveform recognition classifies the waveform and modulation type of a received signal. As new waveform variants appear over time, a de

PACE: Temporal-Role Tactile Conditioning for Chunk-Based Insertion Policies

arXiv:2610.16308v1 Announce Type: new Abstract: Contact-rich insertion requires robots to combine visual alignment with tactile adjustment. However, existing visuotactile chunk-based policies collapse

Parameter-Efficient Multimodal Brain Tumor Segmentation With Stage-Wise Shared Adapters

arXiv:2610.18571v1 Announce Type: new Abstract: Different MRI modalities reveal different parts of a brain tumor. Segmentation models combine information from these modalities to outline tumor regions

PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

arXiv:2610.15020v1 Announce Type: new Abstract: PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nod

PC7 Model: Dynamics of Expanding and Contracting Soft Robots using Piece-wise Constant 7D Deformation Based on Similarity Transformation Group

arXiv:2610.14586v1 Announce Type: new Abstract: This paper proposes a novel soft robot model named piece-wise constant 7D deformation (PC7) model that can represent expanding and contracting deformati

Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes

arXiv:2610.15456v1 Announce Type: cross Abstract: Accurate, physiologically consistent glucose forecasts could help people with type 1 diabetes respond to impending excursions. We applied our physiolo

Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning

arXiv:2610.15428v1 Announce Type: new Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task identity

PGSB: Pretrained-Guided Shared Basis for LoRA Model Merging

arXiv:2610.14558v1 Announce Type: new Abstract: Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: ful

PhaseQuant: Mitigating Activation Quantization Collapse via Residual-Stream Dynamics

arXiv:2606.12487v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is essential for efficient large language model (LLM) inference, but reliably quantizing activations remains challe

PhD students, how ya feeling? [D]

model-releasesr-machinelearning

All seems a bit pointless now. Either pump out AI papers and neglect your own development, or take it slow and fall behind in the publishing race. I already have two papers made largely without LLMs (

Physical-Field-Bridged Material Parameter Inference from Multi-View Deformation Videos

arXiv:2610.17591v1 Announce Type: new Abstract: Estimating simulation-ready material parameters from visual observations is crucial for deformable-object manipulation but challenging because material

Physical SLAM: Physics-aware Simultaneous Localisation and Mapping

arXiv:2610.17260v1 Announce Type: new Abstract: To reconstruct a predictive 4D representation of the physical world from monocular video captured by a moving camera, we jointly reconstruct dynamic Gau

Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching

arXiv:2610.15947v1 Announce Type: cross Abstract: Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties. This st

Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification

arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf

PhysicsSliders: Learning Physical Controls for Audio Foley Generation

arXiv:2610.18597v1 Announce Type: cross Abstract: Physical properties provide intuitive controls for sounds made by interacting objects. The challenge is to make a generative model respond predictably

PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation

arXiv:2610.15722v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physic

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

arXiv:2610.16129v1 Announce Type: new Abstract: Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every repor

PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts

arXiv:2610.18857v1 Announce Type: new Abstract: Pixel-space diffusion models provide an appealing alternative to latent-space diffusion models by directly modeling raw images without relying on a lear

Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?

arXiv:2610.18836v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as

PLAYGROUND: Progressive Layout Generation with Render-Grounded Planning

arXiv:2610.18865v1 Announce Type: new Abstract: Generating editable graphic designs requires arranging visual and textual elements into a coherent composition while preserving their content and editab

PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration

arXiv:2610.17991v1 Announce Type: new Abstract: Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an

PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation

arXiv:2610.14409v1 Announce Type: new Abstract: Generating an image with a diffusion or flow-matching model typically involves integrating a velocity field for hundreds of sequential steps. That compu

Predictable AI spend in BigQuery: Introducing cost management for generative AI functions

model-releasesgoogle-cloud-ai

The generative AI functions in BigQuery (AI.GENERATE, AI.IF, AI.CLASSIFY, AI.SCORE, and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of supp

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev

Privacy-Preserving GNN Inference on Financial Transaction Graphs via Homomorphic Encryption

arXiv:2610.18054v1 Announce Type: cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks

ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty

arXiv:2610.17016v1 Announce Type: new Abstract: Evaluating model uncertainty on real-world data is challenging because each instance provides only a single observed label, leaving true conditional cla

Probing and Inducing Clinical Modality Grounding in Brain Tumor Segmentation

arXiv:2603.19844v3 Announce Type: replace Abstract: Multimodal MRI provides complementary information for brain tumor segmentation, but high accuracy alone does not reveal whether a model relies on cl

ProML: Progressive Multi-label Learning

arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari

ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization

arXiv:2610.18481v1 Announce Type: new Abstract: Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modal