Search: “models”

VLMs are Native Action Models: Unleashing the Manipulation Capabilities of VLMs with Explicit Action-Space Grounding

arXiv:2610.12832v1 Announce Type: new Abstract: Pretrained vision-language models (VLMs) already exhibit strong visual, spatial and reasoning capabilities relevant to robotic manipulation. The key cha

safetyarxiv-cs-ro

12 Oct 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Industry

Introducing Decision 3.0: towards open multimodal foundation decision models ⚡ Open and state-of-the-art from 0.6B to 27B: #1 on both Jev De…

DGX agent

Introducing Decision 3.0: towards open multimodal foundation decision models ⚡ Open and state-of-the-art from 0.6B to 27B: #1 on both Jev Decision Index boards in our evaluation, on a new Pareto front

industryclem-delangue--x

11 Oct 2026

Agents

One of the biggest historical bottlenecks for training specialized models has been curating ground-truth. This is typically a very labor int…

One of the biggest historical bottlenecks for training specialized models has been curating ground-truth. This is typically a very labor intensive process done with human supervision. Now frontier mod

agentsjerry-liu--x

11 Oct 2026

C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models

arXiv:2502.17920v2 Announce Type: replace Abstract: Pre-trained visual models have become fundamental in computer vision, but they face challenges in continual learning scenarios where data and tasks

Can Decision Models Understand Stance? Evaluating Jev Against General-Purpose LLMs

model-releasesarxiv-cs-cl

arXiv:2610.11901v1 Announce Type: new Abstract: Stance detection requires identifying an author's attitude toward a given target, sometimes based on conversational context. Jev, a specialized decision

Equal Path Cost, Unequal Output Effects: Understanding Perturbation Propagation in Diffusion Models

arXiv:2610.11380v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in generative modeling, with their sampling procedures routinely modified to control generation and im

researcharxiv-cs-ai

Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models

model-releasesarxiv-cs-ai

arXiv:2610.08966v2 Announce Type: replace Abstract: Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek det

LMSpell: Spell Correction with Pre-Trained Language Models

model-releasesarxiv-cs-cl

arXiv:2512.05414v4 Announce Type: replace Abstract: Spell correction is still a challenging problem for many languages, especially low-resource languages (LRLs). While pre-trained language models (PLM

Agents

MemoWM: How World Models Change What Agents Need to Remember

arXiv:2610.10778v1 Announce Type: cross Abstract: Long-term agents face growing storage demands as they accumulate experience. World models capture reusable regularities that can reduce the informatio

agentsarxiv-cs-ai

REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models

model-releasesarxiv-cs-ai

arXiv:2610.12007v1 Announce Type: cross Abstract: Flow-based vision-language-action (VLA) models generate action chunks for temporally coherent robot motion, but chunked control creates a fundamental

RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models

model-releasesarxiv-cs-cv

arXiv:2510.25257v2 Announce Type: replace Abstract: Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the p

WARP-VLA: Wrist-Camera Adaptation for View-Robust Policy Execution in Vision-Language-Action Models

model-releasesarxiv-cs-cv

arXiv:2610.11508v1 Announce Type: cross Abstract: Despite recent advances in Vision-Language-Action models (VLAs) for robotic manipulation, their performance remains sensitive to changes in camera con

Backdooring Acoustic Foundation Models for Physically Realizable Triggers

arXiv:2610.09819v1 Announce Type: cross Abstract: Acoustic foundation models (AFMs) have democratized acoustic applications, enabling powerful models for tasks ranging from speech recognition to speak

Beyond Explanation: Debugging Medical Imaging Models via Concept Intervention

arXiv:2610.09031v1 Announce Type: new Abstract: Medical imaging models often operate as black boxes, limiting interpretability and systematic debugging. We introduce an easy-to-use, plug-and-play fram

researcharxiv-cs-cv

Do Vision Models Learn Physical Constraints or Rendering Shortcuts? A Counterfactual Benchmark for Grounded Physical Consistency

model-releasesarxiv-cs-cv

arXiv:2610.09205v1 Announce Type: new Abstract: Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene. A new object may cast no shadow, a mirror may

ELROND: Exploring and decomposing intrinsic capabilities of diffusion models

arXiv:2602.10216v2 Announce Type: replace Abstract: A single text prompt passed to a diffusion model yields a wide range of visual outputs determined solely by a stochastic process, leaving users with

Google Cloud unveils a universal Gemini agent to handle multi-day enterprise workflows in Workspace, Microsoft 365, and Slack that supports several AI models (Carl Franzen/VentureBeat)

Carl Franzen / VentureBeat: Google Cloud unveils a universal Gemini agent to handle multi-day enterprise workflows in Workspace, Microsoft 365, and Slack that supports several AI models — Google is un

model-releasestechmeme

Have URMs and UTs been integrated into frontier models? Or did they disappear into the dustbin of forgotten papers? [D]

researchr-machinelearning

UT-based small models, despite being trained from scratch on these tasks without internet-scale pre-training, consistently outperform most standard Transformer-based Large Language models (LLMs) by a

Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models

arXiv:2610.09045v1 Announce Type: cross Abstract: We study the problem of learning transition kernels for time-homogeneous jump-diffusion processes using conditional diffusion models, with the goal of

Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions

arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin

Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models

arXiv:2610.09004v1 Announce Type: new Abstract: Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot univ

Think Before You Paint: Recursive Latent Reasoning for Diffusion Models

arXiv:2610.09876v1 Announce Type: cross Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze. W

UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map

arXiv:2610.09785v1 Announce Type: new Abstract: World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--observ

local-aiarxiv-cs-cv

Inference-Time Projection for Physically Valid Biomolecular Diffusion Models

arXiv:2610.07037v1 Announce Type: new Abstract: AlphaFold 3-style cofolding models predict biomolecular complexes with high structural accuracy, yet a large fraction of their outputs are physically in

local-aiarxiv-cs-ai

Applications

Large Language Models for Cryptocurrency Transaction Analysis: A Bitcoin Case Study

arXiv:2501.18158v4 Announce Type: replace-cross Abstract: Cryptocurrencies are widely used, yet current methods for analyzing transactions often rely on opaque, black-box models. While these models ma

applicationsarxiv-cs-lg

Learning What to Distill: Bilevel Top-K Token Selection for Self-Distillation in Large Language Models

arXiv:2610.07247v1 Announce Type: new Abstract: Large language models have shown strong reasoning capabilities, but their high inference costs make knowledge distillation an important approach for tra

safetyarxiv-cs-lg

Nested Power Models for Multirotor Propulsion: From Aerodynamic Drag to Electrical Losses

arXiv:2610.08177v1 Announce Type: new Abstract: Speed-only aerodynamic power models for multirotor propulsion cannot represent acceleration-dependent effects. This work develops a nested sequence of p

researcharxiv-cs-ro

RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation

arXiv:2610.08640v1 Announce Type: new Abstract: Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failur

local-aiarxiv-cs-ro

SAFESHIELD: A Decision-Organization Framework for Deployment-Time Safety of Small Language Models

arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification

safetyarxiv-cs-ai

Steering Diffusion Models to Rare Events with Sequential Monte Carlo

arXiv:2610.08652v1 Announce Type: cross Abstract: Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design. In thes

VLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models

arXiv:2610.08133v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token sequ

When Can World Models Recover Physical Laws?

arXiv:2610.06877v1 Announce Type: cross Abstract: Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the sam

researcharxiv-cs-ai

BRANCH-MoE: Balance-Aware Tree Routing for Large Embedding Models

arXiv:2610.06725v1 Announce Type: new Abstract: Mixture-of-experts (MoE) layers increase model capacity without a proportional increase in per-example computation. However, conventional flat routers c

EfficientXpert: Efficient Domain Adaptation for Large Language Models via Propagation-Aware Pruning

arXiv:2511.19935v3 Announce Type: replace Abstract: Deploying domain-specialized large language models on resource-constrained hardware motivates reducing both adaptation cost and the number of retain

ExStereo: Lifting 2D Vision-Language-Action Models to 3D with Explicit Stereo Representations

arXiv:2610.04805v1 Announce Type: cross Abstract: Three-dimensional perception is critical for robotic manipulation, particularly for high-precision tasks, as recovering metric depth and precise 3D ob

Industry

GLM-5.3's open release has yet to produce major attacks despite Anthropic's warnings about its Mythos-level cyber risk, undercutting calls to ban open models (Nathan Lambert/Interconnects AI)

Nathan Lambert / Interconnects AI: GLM-5.3's open release has yet to produce major attacks despite Anthropic's warnings about its Mythos-level cyber risk, undercutting calls to ban open models — Open-

industrytechmeme

Kandinsky 6.0 Video: Foundation Models for Synchronized Video and Audio Generation

arXiv:2610.05608v1 Announce Type: new Abstract: We present Kandinsky 6.0 Video, a family of foundation diffusion models for synchronized text-to-audio-video generation, comprising Kandinsky 6.0 Video

Repeated-Measure Leakage, Distribution Shift, and Reliability under Partial Observation in Patient World Models

arXiv:2610.04778v1 Announce Type: new Abstract: Patient world models are increasingly proposed for longitudinal prediction, intervention-aware reasoning, and clinical-trial simulation. Causal or clini

Salvation Lies Within: Eliciting Inherent Style Transfer in Step-Distilled Diffusion Models

arXiv:2610.05066v1 Announce Type: new Abstract: Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation be

researcharxiv-cs-cv

Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models

arXiv:2610.03076v1 Announce Type: cross Abstract: Neural network-based approaches have emerged as efficient alternatives to traditional optimization-based procedures for the calibration of stochastic

Systematic Hazard Sampling: Minimal-Variance Inference for Discrete Diffusion and Flow Models

arXiv:2601.02799v3 Announce Type: replace Abstract: Uniform-noise discrete diffusion and flow models generate sequences non-autoregressively through iterative, context-dependent token replacements. Ho

Agents

The Optimization Landscape of Learning Compacted Context Models

arXiv:2610.05885v1 Announce Type: new Abstract: Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV c

agentsarxiv-cs-lg

Your Unlearning Gives You Away: Identifying Erased Concepts in Diffusion Models

arXiv:2610.05601v1 Announce Type: new Abstract: Existing attacks on unlearned diffusion models assume that the erased concepts are known in advance and focus on recovering them. In practice, however,

Corrupted but Correct: Why Vision-Language Models Lie to Themselves Internally

arXiv:2610.03445v1 Announce Type: cross Abstract: A targeted adversarial perturbation can drive a vision-language model's (VLM's) teacher-forced training loss for a fixed target caption to near zero,

local-aiarxiv-cs-ai

From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models

arXiv:2610.02513v1 Announce Type: cross Abstract: Large-scale vectorized HD maps provide structured road information that is essential for perception, localization, and planning in autonomous driving.

local-aiarxiv-cs-ai

From Patching to Pruning Visual Computation in Vision Language Models

arXiv:2610.03389v1 Announce Type: new Abstract: Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every dec

researcharxiv-cs-cv

Large Language Continuous Diffusion Models

arXiv:2610.02665v1 Announce Type: cross Abstract: Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajecto

researcharxiv-cs-ai

Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving

arXiv:2610.02765v1 Announce Type: cross Abstract: Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the ego