Search: “models”

CodePercept: Code-Grounded Visual STEM Perception for MLLMs

arXiv:2603.10757v2 Announce Type: replace Abstract: When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual

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CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation

DGX agent

arXiv:2606.21982v1 Announce Type: new Abstract: Few-step distillation for video diffusion models has attracted significant attention, driven by the urgent demand for efficient deployment in real-world

local-aiarxiv-cs-cv

23 Jun 2026

Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning

arXiv:2606.21083v1 Announce Type: cross Abstract: Large language models (LLMs) deployed for logical reasoning in knowledge-intensive domains exhibit a subtle but critical failure: coherence can be vac

Data Selection Through Iterative Self-Filtering for Vision-Language Settings

arXiv:2606.23611v1 Announce Type: new Abstract: The availability of large amounts of clean data is paramount to training neural networks. However, at large scales, manual oversight is impractical, res

researcharxiv-cs-cv

Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting

arXiv:2606.23391v1 Announce Type: new Abstract: Time series forecasting is a fundamental machine learning task. Recent work has explored Large Language Models (LLMs) for this purpose due to their stro

safetyarxiv-cs-lg

Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales

arXiv:2606.23453v1 Announce Type: new Abstract: Location encoders transform geographic coordinates into high dimensional embeddings for downstream machine learning, but it is unclear how well these re

DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression

arXiv:2606.22625v1 Announce Type: new Abstract: Degraded document image binarization is sensitive to domain shifts caused by paper aging, bleed-through, stains, shadows, and uneven illumination, and t

DrivingVoxels: Compositional Sparse Voxel Rasterization for Dynamic Driving Scene Reconstruction

arXiv:2606.23031v1 Announce Type: new Abstract: Reconstructing dynamic urban scenes remains challenging due to the unbounded nature of driving environments and the presence of multiple dynamic objects

Local Ai

Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

arXiv:2606.22496v1 Announce Type: cross Abstract: Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however,

local-aiarxiv-cs-lg

Agents

Enhancing Creativity in 3D Generative Design via a TRIZ-Inspired Text-to-CAD Framework

arXiv:2606.21378v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated significant potential in supporting engineering design tasks, including computer-aided

agentsarxiv-cs-lg

ENVS: Environment-Native Verified Search for Long-Horizon GUI Agents

arXiv:2606.22948v1 Announce Type: cross Abstract: As multimodal agents move from interface understanding to real software control, successful trajectory discovery in live desktop environments becomes

Tools

Experimenting with the proposed Cross-Origin Storage API in Transformers.js

This article explores the Cross-Origin Storage API and its implementation within Transformers.js, a JavaScript library for machine learning models. It likely discusses how this API enables secure cros

toolshugging-face

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm

arXiv:2509.23946v3 Announce Type: replace Abstract: Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or

Factor-Aware Mixture-of-Experts with Pretrained Encoder for Combinatorial Generalization

model-releasesarxiv-cs-ro

arXiv:2606.21100v1 Announce Type: new Abstract: The integration of pretrained encoders with diffusion policies has become a dominant paradigm for visual robotic manipulation. However, it still struggl

Factored Gossip DiLoCo: Reducing Blocking Communication in DiLoCo

arXiv:2606.22768v1 Announce Type: new Abstract: To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoC

Faithful Grounded Visual Reasoning via Learned Proxy-Tokens

arXiv:2606.23354v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in Visual Question Answering (VQA), yet their 'black-box' nature hinders deplo

researcharxiv-cs-cv

FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes

arXiv:2606.20769v1 Announce Type: cross Abstract: AI systems for peer review fail on three fronts: they train on Computer Science and Machine Learning venues alone, ignore the iterative dialogue that

Applications

FLFL: Federated Latent Factor Learning for Private Recovery of Spatio-Temporal Signals

arXiv:2606.23091v1 Announce Type: new Abstract: Wireless sensor network (WSNs) stands out as a burgeoning and promising domain in intelligent sensing. Owing to various factors such as sudden sensor ma

applicationsarxiv-cs-lg

FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation

arXiv:2606.20867v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable general-purpose robotic control via large-scale multimodal pretraining, yet their effectiveness under few-sho

safetyarxiv-cs-cv

From Markov to Laplace: How Mamba In-Context Learns Markov Chains

arXiv:2502.10178v2 Announce Type: replace Abstract: While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viabl

Full Nonlinear Nonholonomic Dynamics and Motion Analysis of a 3-DoF Underactuated Spherical Rolling Robot

arXiv:2606.22169v1 Announce Type: new Abstract: This paper presents a full nonlinear constrained dynamic model of MonoRollBot, a novel 3-DoF spherical rolling robot driven by a single motor, a lead-sc

researcharxiv-cs-ro

GeoRouteNet: Geometry-Enhanced Non-Autoregressive Neural Solver for the Traveling Salesman Problem

arXiv:2606.22776v1 Announce Type: new Abstract: The traveling salesman problem (TSP) is a canonical NP-hard combinatorial optimization benchmark that tests the representational capacity and generaliza

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training

arXiv:2606.20189v2 Announce Type: replace Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data

safetyarxiv-cs-cv

How Omio is building the future of conversational travel

Omio, a travel platform, is leveraging OpenAI's technology to enhance its services through conversational AI capabilities. This implementation likely enables customers to search for and book travel op

model-releasesopenai

HUGE-Bench: A Benchmark for High-Level UAV Vision-Language-Action Tasks

arXiv:2603.19822v2 Announce Type: replace Abstract: Existing UAV vision-language navigation (VLN) benchmarks have enabled language-guided flight, but they largely focus on long, step-wise route descri

IMAGIN-4D: Image-Guided Controllable Interaction Generation

arXiv:2606.23675v1 Announce Type: new Abstract: Generating human-object interactions (HOI) is central to character animation, robotics, AR/VR, and embodied AI. Recent HOI generation methods synthesize

Integrating Facial Generation into Full-Duplex Spoken Dialogue Systems

arXiv:2606.21970v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models, such as Moshi, enable natural, low-latency voice conversations. However, they remain limited to the audio modality

safetyarxiv-cs-cv

Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting

arXiv:2606.23425v1 Announce Type: new Abstract: Accurate electricity load forecasting is a crucial prerequisite for stable power system operations. While prevalent deep learning models present competi

Jury Duty: Calibration and Orientation Failures in MLLM-as-a-Judge Under Cultural Ambiguity

arXiv:2606.20676v1 Announce Type: new Abstract: MLLM-as-a-Judge is conventionally validated by agreement with human annotations, but this metric is undefined when the human pool is culturally heteroge

Tutorials

Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose

tutorialsarxiv-cs-lg

LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation

arXiv:2606.21527v1 Announce Type: cross Abstract: Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the ro

Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

arXiv:2606.21830v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existi

Metric-Dependent Annotation Saturation for Learning from Label Distributions

researchapple-ml-research

When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distrib

Local Ai

MIRAGE: Stealthy Visual Prompt Injection for Vulnerability Detection in Web Agents

arXiv:2606.20717v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM)-based web agents provide practical, high-precision solutions for visual browser automation; however, they inheren

local-aiarxiv-cs-cv

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection

arXiv:2501.03432v3 Announce Type: replace Abstract: The Large Hadron Collider at CERN produces immense volumes of complex data from high-energy particle collisions, demanding sophisticated analytical

Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction

arXiv:2606.22077v1 Announce Type: new Abstract: Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches ha

MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learninga

arXiv:2606.22220v1 Announce Type: new Abstract: Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also

researcharxiv-cs-cv

Applications

Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings

arXiv:2507.07532v4 Announce Type: replace Abstract: While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to

applicationsarxiv-cs-lg

New Smooth Loss functions for Robust Regression that Closely Approximate Absolute Error and Provide Improved Performance on Datasets With Significant Outliers

arXiv:2606.22068v1 Announce Type: new Abstract: The performance of supervised machine learning models is directly related to the quality of the training dataset. In particular, the presence of signifi

Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense

arXiv:2602.09012v2 Announce Type: replace Abstract: The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a

OFMU: Optimization-Driven Framework for Machine Unlearning

arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy

safetyarxiv-cs-lg

Agents

OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs

arXiv:2606.22617v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance on 2D visual tasks, yet enhancing their spatial intelligence for real-worl

agentsarxiv-cs-cv

Tutorials

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606.17276v3 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been

tutorialsarxiv-cs-lg

One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation

arXiv:2606.21415v1 Announce Type: new Abstract: Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain

safetyarxiv-cs-lg

Orthogonal Discrepancy Kernels for Learning with Partial Physics

arXiv:2606.21199v1 Announce Type: cross Abstract: We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Ort

OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals

arXiv:2606.21045v1 Announce Type: cross Abstract: The rapid growth of AI has increased the demand for domain-specific post-training, while the cost and specialization of accelerator infrastructure pus

PHOEBI: An Open-World Benchmark for Bacterial Identification in Phase-Contrast Microscopy

arXiv:2606.22890v1 Announce Type: new Abstract: Optical microscopy enables rapid, label-free imaging of live bacteria and is the standard instrument for species identification across clinical, environ

Physics-Guided Fully Convolutional Spatiotemporal Learning Toward Digital-Twin-Enabled Microstructure Evolution Prediction

arXiv:2606.20983v1 Announce Type: new Abstract: Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer