Search: “models”

Gradient Regularization Mitigates Reward Hacking in Reinforcement Learning from Human Feedback and Verifiable Rewards

arXiv:2602.18037v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) or Verifiable Rewards (RLVR) are two key steps in the post-training of modern Language Model

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Safety

High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching

DGX agent

arXiv:2607.03865v1 Announce Type: cross Abstract: Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their

safetyarxiv-cs-ai

7 Jul 2026

Holo-Captioning: Toward the Text Equivalent of 3D Scenes

arXiv:2607.02908v1 Announce Type: new Abstract: This work introduces holo-captioning, a novel task that strives to seek the text equivalent of 3D scenes. As the initial step, we formulate holo-caption

How many labels do you need? A decision framework for cross-habitat marine species recognition

arXiv:2607.02559v1 Announce Type: new Abstract: Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on

Tutorials

How Much is Left? LLMs Linearly Encode Their Remaining Output Length

arXiv:2607.05316v1 Announce Type: new Abstract: Large language models generate one token at a time, yet their responses show remarkably consistent length structure: step-by-step solutions converge in

tutorialsarxiv-cs-cl

How to Avoid Debate: Scalable AI Safety via Doubly-Efficient Interactive Proofs

arXiv:2607.03561v1 Announce Type: new Abstract: As AI models continue to develop powerful capabilities, it becomes critical that we are able to verify that their output is aligned with our intentions.

Agents

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

arXiv:2607.04884v1 Announce Type: new Abstract: We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, informati

agentsarxiv-cs-cv

imo this is the most impt part of anthropic's J-space paper today. it's a two-parter: 1) ant proved that they can do 'brain surgery' interve…

imo this is the most impt part of anthropic's J-space paper today. it's a two-parter: 1) ant proved that they can do 'brain surgery' interventions into reasoning to change topics midstream* 2) THE MOD

model-releasesswyx--x

Tutorials

Incremental Learning of Sparse Attention Patterns in Transformers

arXiv:2602.19143v2 Announce Type: replace Abstract: This paper studies simple transformers trained on a high-order Markov chain, where the model must incorporate information from multiple past positio

tutorialsarxiv-cs-lg

Industrial3D: A Water-Treatment TLS Point Cloud Dataset and Cross-Paradigm Benchmark for MEP Scene Understanding

arXiv:2603.28660v2 Announce Type: replace Abstract: Automated semantic understanding of dense terrestrial laser scanning (TLS) point clouds is a prerequisite for Scan-to-BIM, digital twin maintenance,

JavaVulBench: A Java Vulnerability Benchmark with Realistic Splits, a Unified Multi-Backend Harness, and a Leakage-Aware Evaluation Mode

arXiv:2607.02825v1 Announce Type: cross Abstract: We release extsc{JavaVulBench}, a benchmark dataset and evaluation harness for Java vulnerability detection. The dataset contains sim30{,}600 Java met

Learning to Generate Human-Human-Object Interactions from Textual Descriptions

arXiv:2511.20446v3 Announce Type: replace Abstract: The way humans interact with each other, including interpersonal distances, spatial configuration, and motion, varies significantly across different

researcharxiv-cs-cv

Learning to Suppress SPAD-based LiDAR Flare

arXiv:2607.03247v1 Announce Type: new Abstract: Single-Photon Avalanche Diode (SPAD)-based Light Detection and Ranging (LiDAR) is emerging for autonomous vehicles due to its high sensitivity and preci

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

arXiv:2607.04934v1 Announce Type: new Abstract: Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring. Portable

Agents

LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

arXiv:2607.03651v1 Announce Type: new Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context. We

agentsarxiv-cs-lg

Local Ai

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

arXiv:2607.04733v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain

local-aiarxiv-cs-cl

MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network

arXiv:2407.20893v2 Announce Type: replace-cross Abstract: Cardiac arrhythmia, a condition characterized by irregular heartbeats, often serves as an early indication of various heart ailments. With the

researcharxiv-cs-ai

MUSON: A Reasoning-oriented Multimodal Dataset for Socially Compliant Navigation in Urban Environments

arXiv:2512.22867v2 Announce Type: replace Abstract: Socially compliant navigation requires structured reasoning about dynamic pedestrians and physical constraints to ensure safe and interpretable deci

Hardware

On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

arXiv:2607.02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, howeve

hardwarearxiv-cs-lg

Post-Generation Curation of Synthetic Images via Homogeneous-Heterogeneous Splitting

arXiv:2607.02637v1 Announce Type: cross Abstract: Recent generative models can produce high-quality synthetic images, offering scalable training training data for data-hungry models. Existing approach

Privacy-Preserving Robustness Verification for Neural Networks

arXiv:2607.05251v1 Announce Type: cross Abstract: Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both

researcharxiv-cs-ai

ProLaViT: Learning Progressive Latent Visual Thoughts in Structured Latent Space

arXiv:2607.02907v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step

researcharxiv-cs-cl

Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

arXiv:2505.09484v2 Announce Type: replace Abstract: Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillanc

Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control

arXiv:2607.04978v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA

researcharxiv-cs-cv

QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification

arXiv:2511.05730v2 Announce Type: replace Abstract: In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-p

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

arXiv:2509.14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highli

R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables

arXiv:2607.02921v1 Announce Type: cross Abstract: Quantitative 3D spatial reasoning from egocentric RGB-D video is a critical capability for next-generation wearable assistants. Yet existing benchmark

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607.03075v1 Announce Type: cross Abstract: Safety-critical applications require classifiers that are both robust and reliable. Adversarial training is a widely adopted defense for improving rob

RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

arXiv:2607.04713v1 Announce Type: cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks. However, in long-

RustMizan: A Compilable, Contamination-Aware Benchmarking Framework for Rust Vulnerabilities

arXiv:2607.04729v1 Announce Type: cross Abstract: LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable

S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval

arXiv:2607.02689v1 Announce Type: cross Abstract: As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capabil

SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection

arXiv:2607.03069v1 Announce Type: new Abstract: As video generation paradigms evolve from localized manipulation to full-scene synthesis, AI-generated video detection becomes increasingly challenging,

Sangam: Efficiently Serving Diffusion LLMs with the AR Stack

arXiv:2607.04206v1 Announce Type: cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising a masked response and can commit multiple output positions per model invocati

researcharxiv-cs-lg

Seeing Once is Enough? Online Geometry-Aware Token Pruning for 3D Question Answering

arXiv:2607.04079v1 Announce Type: cross Abstract: Recent Multi-modal Large Language Models (MLLMs) have demonstrated remarkable performance on 2D question answering tasks. However, extending these mod

researcharxiv-cs-ai

Tutorials

Show Me Examples: Inferring Visual Concepts from Image Sets

arXiv:2607.02402v2 Announce Type: replace Abstract: Vision-language models (VLMs) can follow complex textual instructions, yet they struggle to reason from purely visual context. In particular, curren

tutorialsarxiv-cs-cv

SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits

arXiv:2607.05187v1 Announce Type: new Abstract: As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of B

Agents

Social Networks of LLM Agents

arXiv:2607.03695v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe co

agentsarxiv-cs-lg

SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints

arXiv:2607.05363v1 Announce Type: new Abstract: Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negot

Hardware

SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference

arXiv:2607.03333v1 Announce Type: cross Abstract: LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: th

hardwarearxiv-cs-ai

Target-Guided Selective Reweighting for Physics-Informed Neural Network Inverse Problems: A Transfer Learning Approach

arXiv:2607.05271v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) encounter ill-posed optimization, loss competition, and parameter compensation in partial differential equation

Taste-aware music retrieval from audio embeddings

arXiv:2607.03296v1 Announce Type: cross Abstract: Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimed

The Map Behind the Flow: Finite-Step Gradient Descent as a Dynamical System

arXiv:2607.04993v1 Announce Type: cross Abstract: Many phenomena of deep learning are dynamical: they concern not only which minima exist, but how gradient descent reaches, avoids, or selects among th

Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion

arXiv:2601.15829v2 Announce Type: replace Abstract: Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scal

Local Ai

Track the Noise, Move the World:3D-Grounded Motion-Consistent Noise for Controllable Video Generation

arXiv:2607.02798v1 Announce Type: new Abstract: Modern image-and-text-to-video diffusion models can synthesize highly realistic videos by iteratively denoising an initial Gaussian noise tensor conditi

local-aiarxiv-cs-cv

TREK: Distill to Explore, Reinforce to Refine

arXiv:2607.05339v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard pr

Triple-Phase Multimodal Knowledge Aggregation Framework for Microbial Keratitis Subtype Diagnosis on Slit-Lamp Photography

arXiv:2607.03740v1 Announce Type: cross Abstract: Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive.

Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder

arXiv:2607.05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays

UniICL: Systematizing Unified Multimodal In-context Learning through a Capability-Oriented Taxonomy

arXiv:2603.24690v2 Announce Type: replace Abstract: In-context learning (ICL) enables fast task adaptation from demonstrations without per-task parameter updates but remains highly sensitive to exampl