Search: “model-releases”

GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

arXiv:2609.09082v1 Announce Type: new Abstract: Open vocabulary 3D semantic segmentation methods typically lift CLIP features into 3D. This embeds points in a joint vision-language space known to beha

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GradeTrap: Authority Cues in Images Shift VLM Judgments Despite Explicit Instructions to Ignore Them

DGX agent

arXiv:2609.06058v1 Announce Type: new Abstract: As vision-language models (VLMs) become increasingly capable and are deployed in consequential real-world settings, they must evaluate evidence independ

model-releasesarxiv-cs-cv

9 Sept 2026

GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models

arXiv:2512.15560v3 Announce Type: replace Abstract: The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the

Grounded and Faithful P&ID Reasoning: Constraining Vision-Language Models with Recovered Evidence Graphs

arXiv:2609.05880v1 Announce Type: new Abstract: Piping and Instrumentation Diagrams (P&IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what con

Harnessing CLIP and DINO: An Uncertainty-Aware Cascaded Fusion Network for Generalizable Deepfake Image Detection

arXiv:2609.07670v1 Announce Type: new Abstract: The growing realism and accessibility of manipulated and generated faces threaten the trustworthiness of digital media. To detect such forgeries, deepfa

HealthLoopQA: A Context-Aware Question Answering Benchmark for Interpreting Wearable Monitoring Data in Diabetes Care

arXiv:2609.06976v1 Announce Type: new Abstract: As medical wearables become integrated into daily chronic disease care, effectively interpreting longitudinal monitoring data is essential for patients

Hidden in Plain Sight: The Overlooked Significance of Canonical Elements for Extreme LLM Sparsity

arXiv:2609.06557v1 Announce Type: new Abstract: Large language models (LLMs) are often considered fragile under aggressive sparsification, and maintaining reliable performance typically requires stick

Hierarchical Prompt Injector for Domain Generalization Segmentation

arXiv:2609.05864v1 Announce Type: new Abstract: Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domai

How Does Parameter Pruning Reshape DNN Representations? An Interaction-Driven Exploration

arXiv:2609.06483v1 Announce Type: new Abstract: This study focuses on the scientific problem of understanding internal factors that govern the diverse performance degradation of deep neural networks (

How Long Until Your Robot Ignores You? A Safety Benchmark for LLM Orchestrators in Human-Humanoid Collaboration

arXiv:2609.07288v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly employed to orchestrate robot behavior through natural-language interfaces, yet no benchmark exists to eva

How Well Can Modern LLMs Act as Agent Cores in Radiology Environments?

arXiv:2412.09529v4 Announce Type: replace Abstract: Radiology, with its heterogeneous modalities, anatomies, and evolving protocols, is a natural yet high-stakes testbed for agentic AI. As LLMs grow m

Human-Centric Image Captioning with Subject-Centered Spatial Understanding

arXiv:2609.08300v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural halluc

Hyperparameter Scaling Laws Across MoE Sparsity

arXiv:2609.08690v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models expand model capacity without a proportional increase in training compute, but increasing sparsity makes reliable hyperp

HyperTransfer: Understanding the Equivalence between Base Optimizer and Hyperball

arXiv:2609.07017v1 Announce Type: new Abstract: Hyperball optimizers constrain parameter norms and update only their directions, establishing a distinct paradigm for neural network optimization. Altho

Image-Scale Robustness and Visual Recognition Performance: A Cross-Architecture Analysis

arXiv:2609.06051v1 Announce Type: new Abstract: The sensitivity of visual recognition models to changes in image scale is well established, yet the factors governing this sensitivity across heterogene

Improving Federated Graph Recommendation with Semantic Guidance

arXiv:2606.15277v2 Announce Type: replace-cross Abstract: Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning

Inducing Emergent Misalignment from Reward Hacks with Iterative DPO

arXiv:2609.06649v1 Announce Type: new Abstract: Reward hacking during reinforcement learning from verifiable rewards (RLVR) can induce reward seeking and broad misalignment in language models. Studyin

IXPLORE: Bounded Ideal Point Estimation with Grid-Based Uncertainty Quantification

arXiv:2609.06018v1 Announce Type: new Abstract: Ideal point estimation is widely used to analyze and visualize political data. However, selecting the corresponding spatial model involves various trade

JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery

arXiv:2609.07915v1 Announce Type: new Abstract: Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field

L2G-Map: Local-to-Global Mapping via Hierarchical Diffusion Refinement and Elliptical Bayesian Fusion

arXiv:2503.02578v2 Announce Type: replace Abstract: Offline high-definition maps provide essential geometric and topological priors for autonomous driving systems. Pure-vision solutions have become th

LANTERN: A Closed-Loop Benchmark for VLM-Based Cooperative Driving with Temporally Grounded Warnings

arXiv:2609.06368v1 Announce Type: new Abstract: We present LANTERN, a closed-loop benchmark for temporally grounded cooperative warnings. LANTERN separates warning onset, hazard onset, warning termina

Large Classification-Risk-Optional Label Acquisition

arXiv:2609.06873v1 Announce Type: cross Abstract: We study how a limited labeling budget should be allocated to minimize multiclass zero-one classification risk. We consider parametric classification

Latent class analysis by regularized spectral clustering

arXiv:2310.18727v2 Announce Type: replace Abstract: The latent class model is a highly effective tool in the analysis of categorical data from social, psychological, and behavioral sciences, where pop

LatentMD: Benchmarking Markdown Boundary Failures in LLM-Generated Text

arXiv:2609.06993v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly generate Markdown that is consumed by renderers, agents, code extractors, and structured downstream pipeline

Learning Acrobatic Flight from Preferences

arXiv:2508.18817v3 Announce Type: replace-cross Abstract: Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions,

Learning functional components of PDEs from data using neural networks

arXiv:2602.13174v2 Announce Type: replace Abstract: Partial differential equation (PDE) models frequently contain unknown functional terms that cannot be measured directly, limiting their predictive u

Learning Length-Extrapolatable Recurrent Models

arXiv:2609.09157v1 Announce Type: new Abstract: Recurrent models provide a natural path to long-context modeling, yet models trained with backpropagation through time (BPTT) often fail beyond their tr

LLM-Aided Design for Manufacturing: A Multi-Agent System for Intent-Preserving Redesign of CAD for Improved Manufacturability

arXiv:2609.05559v1 Announce Type: cross Abstract: We introduce autonomous, intent-preserving Design for Manufacturing (DFM) redesign of CAD parts: given an engineer's CAD model, the method returns a v

Local gradient neural operator

arXiv:2609.07752v1 Announce Type: new Abstract: Field temporal prediction and source identification constitute canonical problems in dynamical systems. Conventional approaches to these problems depend

Low-Rank Plus Sparse Matrix Transfer Learning under Growing Representations and Ambient Dimensions

arXiv:2601.21873v2 Announce Type: replace Abstract: Learning systems often expand their ambient features or latent representations over time, embedding earlier representations into larger spaces with

MARR: Decoupling Policy, Execution, and Calibration for All-in-One Medical Image Restoration

arXiv:2609.06645v1 Announce Type: new Abstract: All-in-one medical image restoration seeks to recover heterogeneous clinical images with a single model, but PET, CT, and MRI differ substantially in de

Masking Radar Cognition under Adversarial Surveillance: A Distributional Privacy Framework

arXiv:2609.07428v1 Announce Type: cross Abstract: In this article, we propose an online electronic counter-countermeasure (ECCM) framework designed to conceal the strategic decision-making processes o

Measuring Language Transfer in Robot Policies: Adding Greek to a Cosmos3 Vision-Language-Action Policy

arXiv:2609.07470v1 Announce Type: new Abstract: Robot foundation models are trained and evaluated predominantly in English, and robot demonstration corpora do not exist for most languages. We study th

MEMOBench: A Process Level Memory Benchmark for Robotic Manipulation

arXiv:2609.07047v1 Announce Type: new Abstract: Robotic manipulation often requires acting on information that is no longer visible, yet Vision-Language-Action policies are usually evaluated when the

MI-PEFT: Mixture-of-Experts Integrated Parameter-Efficient Fine-Tuning Protein Language Models Improves Acidophilic Proteins Classification

arXiv:2609.08059v1 Announce Type: cross Abstract: Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioproc

Miles v0.1: Production-Level Post-Training

arXiv:2609.08368v1 Announce Type: new Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each sta

MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials

arXiv:2609.08399v1 Announce Type: new Abstract: Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations. Benchmarks enable reproducible evaluation b

MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search

arXiv:2609.05992v1 Announce Type: cross Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task comple

MOLE: Detecting Insider Threats in AI Agents

arXiv:2609.06966v1 Announce Type: new Abstract: Model misalignment, prompt injection, or operator misuse could lead AI agents operating frontier-lab accounts to exfiltrate model weights, poison traini

Monkey See, Can Monkey Do? A Benchmark for Evaluating Robot Skill Learning by Observation

arXiv:2609.08209v1 Announce Type: new Abstract: Learning from Observation (LfO) is a fundamental robotic capability that replicates how humans and animals socially learn from each other. Beyond its bi

Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias

arXiv:2606.17886v3 Announce Type: replace Abstract: Monotonicity has been a long-running architectural inductive bias for neural networks, motivated by tabular, scientific, and economic settings where

MorphoOrgaAgent: A Foundation-Model-Based Multi-Agent System for Autonomous Organoid Analysis

arXiv:2609.08696v1 Announce Type: cross Abstract: Organoids are three-dimensional tissue models whose morphology provides important insights into tumor development, disease progression, and drug testi

MpSub: A Momentum p-Dimensional Subspace Trust-Region Method for Derivative-Free Fine-Tuning of Large Language Models

arXiv:2609.07666v1 Announce Type: new Abstract: Full-parameter fine-tuning of large language models has substantial memory costs because backpropagation stores activations and gradients. Zeroth-order

MSCA-UNet: Multi-Scale Context and Attention U-Net for Image Segmentation

arXiv:2609.06356v1 Announce Type: new Abstract: U-Net remains a practical baseline for image segmentation because of its simple encoder-decoder structure and skip connections. However, the bottleneck

Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equations

arXiv:2602.01176v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs) by embedding physical

Multi-granularity Adaptive Hypergraph Representation Learning via Granular-ball

arXiv:2609.05574v1 Announce Type: new Abstract: Hypergraph representation learning aims to capture high-order information in graphs by constructing hyperedges that simultaneously connect multiple node

Multi-Grid Post-Training for Long-Form Multi-Shot Video Generation

arXiv:2609.06373v1 Announce Type: new Abstract: Generating long-form multi-shot videos requires coherent within-shot motion and visually consistent narratives across shots. Existing video generators f

NEO-BENCH: A New Multi-Source Benchmark for Generalizable Astronomical Streak Detection

arXiv:2609.06774v1 Announce Type: cross Abstract: Near-Earth Objects (NEOs) can appear as faint streaks in long-exposure astronomical images. Detecting these streaks across diverse observatories requi