Search: “multimodal”

MacJEPA: Missingness-Robust Audio-Visual Recognition from Untrimmed Egocentric Videos

arXiv:2610.08192v1 Announce Type: new Abstract: Audio-visual models improve egocentric action recognition by exploiting complementary cues, yet typically assume that both streams remain available at i

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

managed deep agents 0.9 is here! things we shipped: ⏰ agents can schedule tasks OBO themselves or the user 🏭 dynamically configure your mda…

DGX agent

managed deep agents 0.9 is here! things we shipped: ⏰ agents can schedule tasks OBO themselves or the user 🏭 dynamically configure your mda per run (models, tools, skills, etc.) 👀 Slack reactions show

model-releasesharrison-chase--x

7 Oct 2026

Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction

arXiv:2610.07712v1 Announce Type: cross Abstract: Existing molecular and materials learning approaches often rely on a limited set of structural representations, which may capture only selected aspect

Navigation with RF Cues: Embodied Perception Action under Multipath Uncertainty

model-releasesarxiv-cs-ro

arXiv:2610.08105v1 Announce Type: new Abstract: Smart factory inspection requires robots to reach connected equipment without a prior map or known target coordinates. Radio frequency (RF) signals from

Tutorials

OxiGen: Oxidation-State-Aware Crystal Generation

arXiv:2610.08296v1 Announce Type: new Abstract: Generative models have the potential to accelerate inorganic materials discovery by enabling inverse design, but generating experimentally realisable cr

tutorialsarxiv-cs-lg

PACE: Stage-Consistent Long-Horizon Robot Manipulation via Progress-Aligned Context for Execution

arXiv:2610.07917v1 Announce Type: new Abstract: Demonstration-conditioned policies provide a natural interface for specifying robot behavior, yet long-horizon manipulation remains difficult when visua

safetyarxiv-cs-ro

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

arXiv:2610.07342v1 Announce Type: new Abstract: On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectivenes

Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures

arXiv:2610.07485v1 Announce Type: new Abstract: We study estimating rare-event probabilities I = P(g(mathbf{X}) > gamma) with mathbf{X} sim N(oldsymbol{mu}, oldsymbol{Sigma}) and general g : R^d o R.

Seeing the Invisible: Physics-Guided Visual Prompting for Temperature- and Radiation-Aware VLA Navigation

arXiv:2610.07558v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have become a major paradigm for Vision-and-Language Navigation (VLN). However, in safety-critical facilities, inv

Sensor-Language-Action Models

model-releasesarxiv-cs-ai

arXiv:2610.08244v1 Announce Type: new Abstract: Sensors are useful not only for understanding the world but also for deciding what to do next. Existing sensor models however largely stop at perception

TEMPEST: Temporal Embeddings for Scalable Driver Identification via Angular Margin Learning

arXiv:2610.06855v1 Announce Type: cross Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formu

researcharxiv-cs-cv

Tutorials

The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation

arXiv:2610.07038v1 Announce Type: new Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pra

tutorialsarxiv-cs-lg

Trustworthy Method Comparison with AI Judges: Estimation and Design under Order, Batch, and Aggregation Effects

arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera

Unlocking Fine-Grained Perception in CLIP via Structurally-Aware Latent Masked Modeling

arXiv:2610.07689v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense pr

VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models

arXiv:2610.07606v1 Announce Type: new Abstract: This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (

researcharxiv-cs-ai

VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning

arXiv:2610.08761v1 Announce Type: new Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constr

What Words Keep of a Place: Zero-Shot Language Reasoning for Cross-View Geo-Localization

arXiv:2610.07269v1 Announce Type: new Abstract: Cross-view geo-localization is commonly solved as an image retrieval problem, matching a ground-level image against a database of satellite tiles throug

Which Image Property Carries the Jailbreak? A Controlled Dissection of Image-to-Text Jailbreaks

arXiv:2610.07009v1 Announce Type: cross Abstract: Image-to-text jailbreaks place harmful intent in text, image content, or the relationship between them. We examine image-side factors across four publ

Why is ik_llama.cpp said to be faster than Mainline? On my hybrid multi-GPU rig, Mainline easily beats it

model-releasesr-localllama

I constantly see recommendations saying that ik_llama.cpp (ikawrakow's fork) is the undisputed king of hybrid CPU/GPU offloading and MoE performance. However, every time I benchmark it against mainlin

Adaptive Mean Flow for Responsive Closed-Loop Robot Control

model-releasesarxiv-cs-ro

arXiv:2610.06089v1 Announce Type: new Abstract: Diffusion- and flow-based robot policies have recently become widespread in robotic Imitation Learning (IL) due to their high performance and ability to

ALoDLM: Adaptively Looped Diffusion Language Models

model-releasesarxiv-cs-lg

arXiv:2610.04198v1 Announce Type: cross Abstract: Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a p

Tutorials

Anomaly Detection and Generation with Diffusion Models: A Survey

arXiv:2506.09368v2 Announce Type: replace Abstract: Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by i

tutorialsarxiv-cs-lg

ARISE: Adaptive Agentic Reasoning with Image-grounded Self-Evaluation for Interpretable IBD Assessment

arXiv:2610.04777v1 Announce Type: new Abstract: Inflammatory bowel disease (IBD) requires frequent imaging-based assessment, yet interpretation of modalities such as wireless capsule endoscopy (WCE) a

agentsarxiv-cs-cv

Applications

ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image

arXiv:2610.05249v1 Announce Type: new Abstract: Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image

applicationsarxiv-cs-cv

Applications

AstraSR: Real-World Thermal Super-Resolution with GPT-6 Astra

arXiv:2610.05910v1 Announce Type: new Abstract: Real-world thermal super-resolution (SR) is constrained by limited sensor resolution and the difficulty of obtaining corresponding high-resolution (HR)

applicationsarxiv-cs-cv

Calibrated Weak Supervision for Post-Harvest Burned-Cropland Mapping Under Label Scarcity

arXiv:2610.05040v1 Announce Type: new Abstract: Mapping post-harvest burned cropland is difficult when fires are small and fragmented and reliable labels are scarce. We developed a calibrated weak-sup

CHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions

model-releasesarxiv-cs-lg

arXiv:2406.12150v2 Announce Type: replace Abstract: Post-hoc attribution is widely used to identify important model inputs, but evaluating whether these attributions identify truly informative feature

Code2Games: Enabling Coding Agents for Gaming World Generation

model-releasesarxiv-cs-cv

arXiv:2610.05033v1 Announce Type: new Abstract: Generating a high-quality gaming world from a natural-language game intent requires joint reasoning about scene structure, spatial layout, gameplay obje

Computational Onboard Data Management for Heterogeneous Autonomous Vehicle Streams

arXiv:2511.19453v2 Announce Type: replace-cross Abstract: Autonomous vehicles continuously generate large volumes of heterogeneous sensor and state data streams. While this data supports real-time aut

agentsarxiv-cs-ro

Construction and Evaluation of Machine Learning Models for Near-Real-Time Fire Detection from MTG FCI Imagery

arXiv:2610.05154v1 Announce Type: new Abstract: Geostationary satellite observations are important for wildfire detection and monitoring. The current study evaluates machine learning models for MTG FC

researcharxiv-cs-cv

Dependable AI-Assisted Engineering: A Formal Framework for AI Participation and Assurance in Safety-Critical Workflows

arXiv:2610.04084v1 Announce Type: new Abstract: Generative AI can produce engineering artefacts, but generation alone does not determine whether or how those artefacts should enter safety-critical wor

safetyarxiv-cs-cv

Applications

Execution-Aligned Progressive Noise for Consistent Asynchronous Replanning in Generative Robot Policies

arXiv:2610.06090v1 Announce Type: new Abstract: Continuous asynchronous replanning is essential for real-time generative robot policies, but independent stochastic initialization can cause mode switch

applicationsarxiv-cs-ro

For their Mistral Large 4 release, @MistralAI ran a blind Surge human eval, with our expert software engineers evaluating five frontier mode…

model-releasesmistral-ai--x

For their Mistral Large 4 release, @MistralAI ran a blind Surge human eval, with our expert software engineers evaluating five frontier models on coding quality. Le Chonk ranked #1 among open-weight m

From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2610.05993v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task e

G-CARB: Graph-Localized Conformal Agent Risk Budget for Compositional Harm

arXiv:2610.05563v1 Announce Type: cross Abstract: Small language model (SLM) agents need safety controls that track consequences across tool calls with little monitoring overhead. A private read, for

safetyarxiv-cs-lg

Integrated Imputation-Classification for Supervised Learning with Missing Data

arXiv:2610.04273v1 Announce Type: new Abstract: We study supervised classification problems with missing feature values. Existing approaches often decouple imputation from classification, producing im

Introducing Mistral Large 4: Le chonk

model-releasessimon-willison

Introducing Mistral Large 4: Le chonk Mistral are back in the game. Today they're releasing a preview of Mistral Large 4, a 1 trillion parameter, 49 billion active parameter model trained on their own

Managed Apache Iceberg at scale: How Spanner powers Lakehouse runtime catalog

As customers modernize to lakehouse architectures, they are standardizing on open formats such as Apache Iceberg to create a shared data estate across compatible engines. This enables you to build AI-

agentsgoogle-cloud-ai

Mistral can pelican now! https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F0db8b8196e57711b…

model-releasessimon-willison--x

Mistral can pelican now! https://tools.simonwillison.net/markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F0db8b8196e57711b352f6c5857cf5b35#response-1 Meet Mistral Large 4, aka Le Cho

MoCAR: Motion-code Coordinate-aware AutoRegression for Continuous Trajectory Forecasting

arXiv:2610.06210v1 Announce Type: new Abstract: Autoregressive generation is natural for language, where predicted tokens can be directly reused as the next prediction state, but trajectory forecastin

safetyarxiv-cs-cv

Outcome-Guided On-Policy Self-Distillation

arXiv:2610.05070v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiab

safetyarxiv-cs-lg

Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health

arXiv:2610.05699v1 Announce Type: new Abstract: The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments

PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data

model-releasesarxiv-cs-cv

arXiv:2610.06825v1 Announce Type: cross Abstract: Scientific figures often encode quantitative results that are not readily available in machine-readable form, making accurate plot digitization import

Recursive Self-Improvement of Visuomotor Policies through Local Recovery Supervision

arXiv:2610.05151v1 Announce Type: new Abstract: Visuomotor policies can execute familiar tasks yet lack the corrective behavior needed after their own mistakes. We present a framework for recursive se

safetyarxiv-cs-ro

ReDiffNet: Differential RGB-Infrared Learning for Low-Light UAV Oriented Vehicle Detection

arXiv:2610.05074v1 Announce Type: new Abstract: Low-light UAV-based RGB-infrared oriented small-vehicle detection is important for nighttime traffic monitoring, emergency response, and urban inspectio

local-aiarxiv-cs-lg

ROT: Rotating Hidden States towards Contextual Vectors for Hallucination Mitigation in LVLMs

arXiv:2610.06056v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) frequently suffer from object hallucination. Existing training-free interventions primarily manipulate attention we

Sharp dimensional analysis of midpoint methods for Langevin sampling

arXiv:2610.06308v1 Announce Type: cross Abstract: We study deterministic and randomized midpoint discretizations of Langevin dynamics for a target pi propto e^{-V}, where 0 prec alpha Ipreceqnabla^2Vp

SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation

arXiv:2601.17657v4 Announce Type: replace Abstract: Robotic and autonomous systems need dense spatial cues, yet adding a dedicated depth estimator can duplicate visual processing already performed by

agentsarxiv-cs-cv