Search: “models”

ICDAR 2026 HIPE-OCRepair Competition on LLM-Assisted OCR Post-Correction for Historical Documents

arXiv:2607.08143v1 Announce Type: cross Abstract: We present the results of HIPE-OCRepair-2026, an ICDAR competition on LLM-assisted OCR post-correction of historical documents. OCR post-correction re

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It Takes a MAESTRO To Prune Bad Experts

DGX agent

arXiv:2607.08601v1 Announce Type: new Abstract: Sparsely-activated Mixture-of-Experts (MoE) language models achieve remarkable inference efficiency by activating only a small fraction of parameters pe

safetyarxiv-cs-cl

10 Jul 2026

Jet-Long: Efficient Long-Context Extension with Dynamic Bifocal RoPE

arXiv:2607.07740v1 Announce Type: cross Abstract: Modern LLMs are increasingly deployed in long-context applications such as retrieval-augmented generation, repository-level coding, and agentic workfl

Joint Discrete-Continuous Flow Matching for Open-Vocabulary Inverse Design of Multilayer Optical Coatings

arXiv:2607.08392v1 Announce Type: cross Abstract: Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training

Agents

LEEVLA: Seeing What Matters in Latent Environment Evolution for Vision-Language-Action

arXiv:2607.08182v1 Announce Type: cross Abstract: Vision-language-action (VLA) models aim to map multimodal inputs to robot actions. However, most existing approaches struggle to cover complex dynamic

agentsarxiv-cs-ai

Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing

arXiv:2607.07953v1 Announce Type: cross Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at

Applications

Nigeria Machinery: A Low-Resource Industrial Dataset with a Domain-Grounded Reasoning Layer

arXiv:2607.07883v1 Announce Type: new Abstract: There is relatively little, public, and model-ready data on industrial machinery for African economies. This makes it hard to do quantitative analysis o

applicationsarxiv-cs-ai

OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing

arXiv:2606.15920v2 Announce Type: replace Abstract: Reinforcement learning for multimodal large language models (MLLMs) is often hindered by severe reward sparsity in complex reasoning tasks. This cha

local-aiarxiv-cs-cv

Open-Vocabulary Object-Goal Navigation by Generalizing Semantic Mapping with Dense CLIP

arXiv:2407.09016v2 Announce Type: replace Abstract: Object-oriented embodied navigation tasks require agents to locate specific objects, either defined by category or images, in unseen environments. W

researcharxiv-cs-ro

OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators

arXiv:2607.08766v1 Announce Type: new Abstract: We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR v

safetyarxiv-cs-cv

path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting

arXiv:2607.07935v1 Announce Type: cross Abstract: We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a grad

PhyMAGIC: Physical Motion-Aware Generative Inference with Confidence-guided LLM

arXiv:2505.16456v3 Announce Type: replace Abstract: Recent advances in 3D content generation have amplified demand for dynamic models that are both visually realistic and physically consistent. Howeve

safetyarxiv-cs-cv

Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction

arXiv:2607.08725v1 Announce Type: cross Abstract: Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose est

Post-Training in End-to-End Autonomous Driving

arXiv:2607.08072v1 Announce Type: new Abstract: End-to-end models that map multimodal inputs directly to future trajectories/maneuvers have emerged as an increasingly prominent research paradigm in au

safetyarxiv-cs-cv

RhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting

arXiv:2607.08234v1 Announce Type: cross Abstract: Real-world time series exhibit complex dynamics characterized by multiple simultaneous temporal patterns: short-term fluctuations, periodic seasonal c

local-aiarxiv-cs-ai

Tutorials

Switch-Reasoner: Learn When to Think in Multitask Mixtures via Reinforcement Learning

arXiv:2607.08572v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often follow a fixed Think-then-Answer paradigm, which is inefficient in heterogeneous multitask settings becau

tutorialsarxiv-cs-cv

TFP: Temporally Conditioned Memory-Fusion Policies for Visuomotor Learning

model-releasesarxiv-cs-ro

arXiv:2607.08283v1 Announce Type: new Abstract: Vision--Language--Action (VLA) policies such as pi_{0.5} and OpenVLA perform well on many manipulation tasks, but they are often reactive: the next acti

Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning

arXiv:2602.23440v4 Announce Type: replace Abstract: Reinforcement learning has emerged as an effective paradigm for training large language models to interleave reasoning with search engine calls. How

researcharxiv-cs-cl

Agents

TTHE: Test-Time Harness Evolution

arXiv:2607.08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context,

agentsarxiv-cs-lg

UAV-OVVIS: Unmanned Aerial Vehicles Also Need Open-Vocabulary Video Instance Segmentation

model-releasesarxiv-cs-cv

arXiv:2607.08075v1 Announce Type: new Abstract: Unmanned Aerial Vehicle (UAV) videos are widely used in traffic monitoring, urban management, and emergency rescue. However, existing UAV video percepti

Vision-Language Memory for Spatial Reasoning

arXiv:2511.20644v2 Announce Type: replace Abstract: Spatial reasoning is a critical capability for intelligent robots, yet current vision-language models (VLMs) still fall short of human-level perform

researcharxiv-cs-cv

Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows

arXiv:2607.08740v1 Announce Type: new Abstract: Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Exist

safetyarxiv-cs-ai

A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving

arXiv:2607.07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic

Tutorials

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602.06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments,

tutorialsarxiv-cs-ai

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

arXiv:2607.07033v1 Announce Type: cross Abstract: Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are

local-aiarxiv-cs-ai

Applications

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

arXiv:2603.11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, like

applicationsarxiv-cs-ai

ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection

model-releasesarxiv-cs-cv

arXiv:2607.07161v1 Announce Type: new Abstract: The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significan

Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts

arXiv:2607.06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and

researcharxiv-cs-ai

Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass

arXiv:2607.07696v1 Announce Type: cross Abstract: Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the datab

BUS: Brain-Inspired Unsupervised Self-Reflection for Advanced Multimodal Reasoning

arXiv:2607.07361v1 Announce Type: new Abstract: Current Vision-Language Models (VLMs) often struggle to handle complex visual tasks that require consistent and fine-grained reasoning. Recent methods a

researcharxiv-cs-cv

DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation

arXiv:2607.07669v1 Announce Type: cross Abstract: Large language models increasingly understand dialectal English, yet still produce only standard, US-leaning English, leaving dialectal generation, th

safetyarxiv-cs-ai

Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer

arXiv:2501.10870v2 Announce Type: replace-cross Abstract: The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rat

FMMVCC: Fuzzy Mamba-based Multi-View Contrastive Clustering for Univariate Time Series

arXiv:2607.07258v1 Announce Type: cross Abstract: In many realistic scenarios, large volumes of time series data are generated with limited or expensive annotations. This limitation makes supervised l

FPTQuant: Function-Preserving Transforms for LLM Quantization

arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective

researcharxiv-cs-lg

From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists

arXiv:2605.04724v2 Announce Type: replace-cross Abstract: The pervasive integration of AI has enabled Offensive AI: the exploitation of AI for malicious ends across the cyber-kill chain. A critical ma

From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings

model-releasesarxiv-cs-cl

arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating a cold start problem for item calibration

Health System Scale Semantic Search Across Unstructured Clinical Notes

arXiv:2604.25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval

How Learning Dynamics Drive Adversarially Robust Generalization?

arXiv:2410.07719v4 Announce Type: replace Abstract: Despite being widely adopted as a canonical framework for learning robust models, adversarial training suffers from robust overfitting. Existing emp

researcharxiv-cs-lg

Information Allocation Dynamics in Neural Network Optimization

arXiv:2607.07156v1 Announce Type: new Abstract: Different optimizers have different update biases, but these biases are usually implicit. Existing studies mainly analyze or control such biases from th

Latent graph encoding of multimodal neuroimaging features with generative AI architectures

arXiv:2607.07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frame

researcharxiv-cs-ai

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607.06839v1 Announce Type: cross Abstract: Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain

researcharxiv-cs-cv

Measuring the metacognition of AI

arXiv:2603.29693v3 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks. Because artificial intellig

Optimization Geometrodynamics: A Framework for Dynamic Geometric Optimization

arXiv:2607.06723v1 Announce Type: cross Abstract: Most gradient-based optimization methods move parameters through a fixed background geometry, even when their internal states implicitly define changi

PeTeR: Post-Training Robustification of Probabilistic Circuits

arXiv:2607.07671v1 Announce Type: new Abstract: Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However,

researcharxiv-cs-lg

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing use

local-aiarxiv-cs-ai

Agents

Reliable and Developer-Aligned Evaluation of Agents for Software Engineering

arXiv:2607.06713v1 Announce Type: cross Abstract: Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contribut

agentsarxiv-cs-ai

Restricted Dynamic Geometric Complexity: Certificates for Structured Preconditioning

arXiv:2607.07204v1 Announce Type: cross Abstract: Optimization geometrodynamics views optimizer state as evolving geometry. Its full positive-definite quadratic benchmark gives the least affine-invari

RL Post-Training Builds Compositional Reasoning Strategies

arXiv:2607.07646v1 Announce Type: new Abstract: Does RL post-training merely amplify primitive skills already latent in a base model, or can it compose primitive skills into new higher-level strategie

researcharxiv-cs-ai