Search: “models”

Convex--Concave Quadratic Spectral Filtering for Graph Neural Networks

arXiv:2606.24956v1 Announce Type: new Abstract: Spectral graph neural networks (GNNs) interpret message passing as frequency-selective filtering. While low-order spectral filters are efficient, their

Research

Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

DGX agent

arXiv:2606.25579v1 Announce Type: cross Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using curr

researcharxiv-cs-cv

25 Jun 2026

Tutorials

Cross-Modality Structural Guidance in 3D Latent Diffusion for Robust FLAIR Super-Resolution

arXiv:2606.25255v1 Announce Type: new Abstract: High-resolution (HR) MRI acquisition is often hampered by scan time constraints, resulting in anisotropic or low-resolution scans (e.g., thick-slice FLA

tutorialsarxiv-cs-cv

DomainShuttle: Freeform Open Domain Subject-driven Text-to-video Generation

arXiv:2606.26058v1 Announce Type: new Abstract: Open domain subject-driven text-to-video (S2V) generation has drawn significant interest in academia and industry. Open domain S2V mainly involves two s

Local Ai

EmotionAI: A Privacy-Preserving Computational Intelligence Pipeline for Speech-Emotion-Grounded Conversational Analysis

arXiv:2606.24941v1 Announce Type: cross Abstract: Reviewing recorded interviews for affective cues such as composure, hesitation and agitation is slow and subjective, and cloud services that could aut

local-aiarxiv-cs-ai

Erased, but Not Gone: Output Forgetting Is Not True Forgetting

arXiv:2606.25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference. But if out

Industry

Getting used to being liked likely means you are overfit to RLHF. The problem with overfitting is that the pain overwhelms the limbic system…

Getting used to being liked likely means you are overfit to RLHF. The problem with overfitting is that the pain overwhelms the limbic system once you try to sample trajectories outside the known distr

industryelon-musk--x

Applications

HiFiVe: High-Fidelity Vehicle Generation Leveraging Auto-Regressive 2D Generative Priors

arXiv:2606.25300v1 Announce Type: new Abstract: Existing 3D vehicle generation methods often suffer from low geometric fidelity and blurry textures, hindering their downstream applications. While rece

applicationsarxiv-cs-cv

Local Ai

HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation

arXiv:2507.00028v2 Announce Type: replace-cross Abstract: The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns. Despite considerable pro

local-aiarxiv-cs-cv

ILV: Iterative Latent Volumes for Fast and Accurate Sparse-View CT Reconstruction

arXiv:2603.14915v2 Announce Type: replace Abstract: A long-term goal in CT imaging is to achieve fast and accurate 3D reconstruction from sparse-view projections, thereby reducing radiation exposure,

Introducing corpora Hlava Cor and Hlava AD: Human Label Variation in Coreference and Discourse Relations

arXiv:2606.25383v1 Announce Type: new Abstract: As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to

researcharxiv-cs-cl

Just had a great discussion on dynamic workflows. Rough notes: - applies to a very small set of use cases - think of it as a new paradigm of…

Just had a great discussion on dynamic workflows. Rough notes: - applies to a very small set of use cases - think of it as a new paradigm of (test-time compute) TTC - strong for hill-climbing research

agentsdair-ai--x

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

arXiv:2606.25589v1 Announce Type: new Abstract: As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require carefu

Learning with Monotone Adversarial Corruptions

arXiv:2601.02193v2 Announce Type: replace Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversa

Lightweight PCGAE-Net: Parallel CrossGate Attention and Bottleneck AutoEncoder for Efficient 5G Channel Prediction

arXiv:2606.25401v1 Announce Type: cross Abstract: Accurate channel state information (CSI) prediction is essential for proactive beamforming and resource management in 5G massive MIMO systems, yet the

MAPL: Multi-Objective Preference Learning for Robot Locomotion

arXiv:2606.25398v1 Announce Type: new Abstract: Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task

safetyarxiv-cs-ro

Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control

arXiv:2606.25136v1 Announce Type: new Abstract: General-purpose robots operating in partially observable environments, such as homes, require memory to support autonomy. They must recall diverse infor

safetyarxiv-cs-ro

Tutorials

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

arXiv:2606.25225v1 Announce Type: new Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual strea

tutorialsarxiv-cs-cv

OmegAMP: Targeted AMP Discovery via Biologically Informed Generation

arXiv:2504.17247v3 Announce Type: replace Abstract: Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that ef

Position Spaces and Graphs

arXiv:2606.25719v1 Announce Type: new Abstract: In this paper, we introduce position graphs, a graph-based reasoning framework based on the formalization of position spaces. This framework utilizes tw

Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz

arXiv:2606.25622v1 Announce Type: cross Abstract: The NIS-2 Directive mandates robust Risk Management from thousands of small and medium enterprises. To ensure compliance, companies rely on establishe

agentsarxiv-cs-ai

ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact

arXiv:2606.24892v1 Announce Type: cross Abstract: Peer review is central to scientific quality control, yet it can undervalue papers that later achieve substantial citation impact. While frontier larg

REViT: Roto-reflection Equivariant Convolutional Vision Transformer

arXiv:2606.25318v1 Announce Type: new Abstract: In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention. Roto-reflection equivariant netw

Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds

arXiv:2606.25234v1 Announce Type: new Abstract: What is the geometry of a visual percept? The most widely used protocols for decomposing neural network representations into interpretable parts treat c

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606.24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challe

The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing

arXiv:2606.25108v1 Announce Type: new Abstract: Autonomous AI systems are transitioning from advisory to autonomous roles for medication prescriptions. Recent United States bill H.R. 238 and Utah's pr

The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems

arXiv:2606.26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems. The dominant approach places co

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization

arXiv:2505.23866v2 Announce Type: replace Abstract: Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many stu

TrueFoundry acquires MLOps pioneer Seldon AI to accelerate enterprise agentic AI

TrueFoundry Inc., a startup providing management for artificial intelligence workloads, announced Wednesday that it acquired open-source cloud-agnostic enterprise machine learning platform Seldon Tech

agentssiliconangle

VADAOrchestra: Neurosymbolic Orchestration of Adaptive Reasoning Workflows

arXiv:2606.22485v2 Announce Type: replace-cross Abstract: Decision-making in real-world settings rarely follows a fixed script. Instead, it unfolds as a dynamic reasoning process in which the appropri

agentsarxiv-cs-cl

Variational Autoencoder Layer

arXiv:2606.25900v1 Announce Type: new Abstract: Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by produci

VENI: Variational Encoder for Natural Illumination

arXiv:2601.14079v2 Announce Type: replace Abstract: Inverse rendering is an ill-posed problem, but priors such as illumination priors can help simplify it. Existing work either disregards the spherica

VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction

arXiv:2509.19297v3 Announce Type: replace Abstract: Feed-forward 3D Gaussian Splatting (3DGS) has emerged as a highly effective solution for novel view synthesis. Existing methods predominantly rely o

safetyarxiv-cs-cv

Weight Space Representation Learning via Neural Field Adaptation

arXiv:2512.01759v3 Announce Type: replace Abstract: We investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the o

Hardware

Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation

arXiv:2606.24369v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregr

hardwarearxiv-cs-ai

Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach

arXiv:2606.24188v1 Announce Type: new Abstract: Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However,

researcharxiv-cs-cl

Local Ai

Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation

arXiv:2606.24633v1 Announce Type: new Abstract: Human demonstrations for robot imitation learning often contain mistakes and corrective behaviors, such as imprecise grasps, object misalignment, unstab

local-aiarxiv-cs-ro

Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators

arXiv:2602.23334v2 Announce Type: replace-cross Abstract: Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc. Previous

Configurable Holography: Towards Display and Scene Adaptation

arXiv:2405.01558v4 Announce Type: replace Abstract: Rendering holograms for holographic displays is often an iterative and computationally costly process. Emerging learned holography methods have alle

Context-Aware Prediction of Student Quiz Performance with Multimodal Textbook Features

arXiv:2606.24770v1 Announce Type: cross Abstract: Educational platforms often predict student performance from prior interactions, but the assessment content itself also varies in linguistic and visua

Cycle-Consistent Neural Explanation of Formal Verification Certificates

arXiv:2606.24414v1 Announce Type: new Abstract: Formal verification produces machine-checkable certificates that attest to the satisfaction or violation of temporal properties, yet these certificates

Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

arXiv:2606.23830v1 Announce Type: cross Abstract: Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. Howev

Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics

arXiv:2601.03673v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostic

Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training

arXiv:2602.21321v2 Announce Type: replace Abstract: Analog in-memory computing (AIMC) performs computation directly within resistive crossbar arrays, offering an energy-efficient platform to scale lar

EPEdit: Redefining Image Editing with Generative AI and User-Centric Design

arXiv:2606.24057v1 Announce Type: new Abstract: The demand for image manipulation has seen a significant increase recently. Traditional tools like Photoshop and Capture One, while powerful, require co

Excited to partner with @OpenRouter ⚡ Products like OpenRouter Fusion and Sakana Fugu have sparked a serious conversation about dependency a…

Excited to partner with @OpenRouter ⚡ Products like OpenRouter Fusion and Sakana Fugu have sparked a serious conversation about dependency and resilience in AI. I believe this is just the start of a g

researchdavid-ha--x

Applications

FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filters

arXiv:2606.24113v1 Announce Type: new Abstract: Federated unlearning (FU) is critical for complying with legal mandates like the right to be forgotten in decentralized systems, yet current methods fac

applicationsarxiv-cs-lg

FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning

arXiv:2606.24231v1 Announce Type: new Abstract: Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are con