Search: “benchmark”

arXiv:2607.19064v2 Announce Type: replace-cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale

hardwarearxiv-cs-ai

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Model Releases

Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions

DGX agent

arXiv:2607.19444v1 Announce Type: new Abstract: Open-access datasets for marine-engine predictive maintenance remain scarce, particularly those from controlled fault experiments with documented operat

model-releasesarxiv-cs-lg

23 Jul 2026

Meta-Learning Preferences for Multilingual LLM Alignment

arXiv:2607.13315v2 Announce Type: replace Abstract: Unequal availability of human preference data across languages poses a significant challenge for aligning large language models in multilingual sett

safetyarxiv-cs-cl

MissingBench-Verified: Probing Vision-Language Models' Inability to Detect Missing Object Parts

arXiv:2607.18673v2 Announce Type: new Abstract: Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for

Mitigating Modality and Language-Style Gaps for Zero-Shot Video Moment Retrieval

arXiv:2607.19027v1 Announce Type: new Abstract: Zero-shot video moment retrieval aims to overcome the limitations of traditional approaches that require large-scale datasets annotated with text and it

safetyarxiv-cs-cv

Applications

MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts

arXiv:2607.19826v1 Announce Type: new Abstract: Action Quality Assessment (AQA) aims to objectively evaluate performance quality from action videos. Most existing methods follow a ``one-by-one'' parad

applicationsarxiv-cs-cv

MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing

arXiv:2607.19935v1 Announce Type: new Abstract: Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Sub

Multi-modal transformer for signal classification in nanopore blockade experiments

arXiv:2607.20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ioni

Multi-stage Dynamic Selection for Cross-Project Defect Prediction

arXiv:2607.20151v1 Announce Type: cross Abstract: Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from t

Research

Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?

arXiv:2607.20284v1 Announce Type: new Abstract: The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU

researcharxiv-cs-cv

Applications

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

arXiv:2508.00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learn

applicationsarxiv-cs-lg

Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training

arXiv:2607.20238v1 Announce Type: new Abstract: Visible-infrared (VIS-IR) alignment is a key pre-training task for robust multi-sensor perception. Most existing methods use uniform patch-wise contrast

Research

Notes to Self: Can LLMs Benefit from Experiential Abstractions?

arXiv:2607.20372v1 Announce Type: new Abstract: Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effecti

researcharxiv-cs-cl

OmniReasoner: Thinking with Long Audio-Video via Native Tool Use

arXiv:2607.19339v1 Announce Type: new Abstract: Long audio-video reasoning is difficult for omnimodal LLMs because the decisive evidence is often sparse, cross-modal, and too expensive to preserve wit

agentsarxiv-cs-cv

OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks

arXiv:2607.19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions throu

Opto-ViT-v2: Noise-Resilient On-Chip Fine-Tuning for Photonic Near-Sensor Vision Transformer Accelerators

arXiv:2607.19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications

PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration

arXiv:2607.16602v2 Announce Type: replace Abstract: Action-conditioned world models are a key component of embodied AI, serving as scalable policy evaluators that reduce reliance on expensive real-wor

safetyarxiv-cs-cv

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

arXiv:2607.19653v1 Announce Type: cross Abstract: Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature im

PG-KINN: A Physics-Informed Petrov-Galerkin Kolmogorov-Arnold Network for Solving Forward and Inverse PDEs

arXiv:2607.20378v1 Announce Type: new Abstract: Physics-informed learning of partial differential equations (PDEs) has been dominated by multilayer perceptrons (MLPs), whose spectral bias and dense pa

Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

arXiv:2607.19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are c

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

arXiv:2607.20268v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterativ

agentsarxiv-cs-ai

Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning

arXiv:2607.19400v1 Announce Type: new Abstract: Pre-trained foundation models (FMs) have begun transforming single-cell genomics, but scaling them raises privacy concerns. Moreover, unlike text data,

agentsarxiv-cs-lg

PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning

arXiv:2607.20064v1 Announce Type: new Abstract: Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This

agentsarxiv-cs-ai

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

arXiv:2607.20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and

PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference

arXiv:2607.20327v1 Announce Type: new Abstract: Large language models (LLMs) provide strong reasoning capabilities but are expensive to serve at scale, whereas small language models (SLMs) are cheaper

safetyarxiv-cs-cl

Quantile Transfer for Reliable Operating Point Selection in Visual Place Recognition

model-releasesarxiv-cs-ro

arXiv:2602.04401v4 Announce Type: replace Abstract: Visual Place Recognition (VPR) is a key component for localization in Global Navigation Satellite System (GNSS)-denied environments, but its perform

Rarity-Aware Discrete Diffusion with Spatially Consistent Decoding for Photo-Realistic Image Super-Resolution

arXiv:2607.17612v2 Announce Type: replace Abstract: Continuous diffusion models have become the dominant paradigm for photo-realistic image Super-Resolution (SR), but they typically formulate reconstr

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

arXiv:2607.19403v1 Announce Type: cross Abstract: Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic en

Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics

arXiv:2607.18540v1 Announce Type: new Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization

Reducing Learner Redundancy in Boosting via Residual Orthogonalization

arXiv:2606.17567v2 Announce Type: replace Abstract: While sequential residual fitting is the bedrock of standard boosting frameworks, it inherently breeds learner redundancy by repeatedly revisiting c

ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment

arXiv:2607.19722v1 Announce Type: new Abstract: Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace

Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

arXiv:2607.19678v1 Announce Type: cross Abstract: AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rather than a

Reinforcement Learning for Large Language Model Selective Evidence Adoption from Contaminated Retrieval Results

arXiv:2607.20090v1 Announce Type: cross Abstract: Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instruction-like cont

Rewarding Better Thinking for LLM Preference Alignment

arXiv:2607.19824v1 Announce Type: new Abstract: LLM preference alignment aims to optimize models toward human preferences across diverse user instructions. Reinforcement learning has become a major po

Local Ai

Robust Activation Map Rectification for Weakly Supervised Volumetric Segmentation: Temporal Coherence as a Free Lunch

arXiv:2607.19877v1 Announce Type: new Abstract: Weakly supervised segmentation relies heavily on class activation maps (CAMs) to initially localize target regions. However, CAMs are often noisy and pr

local-aiarxiv-cs-cv

Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems

arXiv:2607.20005v1 Announce Type: new Abstract: In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all. Yet existing automated remediation systems

Hardware

Scaling Time Series Classification via XAI-Driven Data Reduction

arXiv:2607.15774v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) for time series has seen significant algorithmic growth, but its utility in providing measurable performance gains for do

hardwarearxiv-cs-ai

Seeing Before Generating: Object Perception Enhances Single-View 3D Reconstruction

arXiv:2607.18630v1 Announce Type: new Abstract: The relationship between object perception and reconstruction is well established in human vision, yet remains underexplored in computer vision. In this

Local Ai

Self-organizing Architecture of Receptron Units: a Hardware-Aware Framework for Edge Intelligence

arXiv:2607.20162v1 Announce Type: new Abstract: The growing demand for intelligent processing at the edge of IoT networks is constrained by the severe computational and memory limitations of microcont

local-aiarxiv-cs-lg

SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data

arXiv:2607.19949v1 Announce Type: new Abstract: Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers a

Simultaneous Speech-to-Speech Translation Without Aligned Data

model-releasesarxiv-cs-cl

arXiv:2602.11072v2 Announce Type: replace Abstract: Simultaneous speech translation requires translating source speech into a target language in real-time while handling non-monotonic word dependencie

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

arXiv:2607.20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language

SOPD-SocialNav: Selective On-Policy Distillation for Vision-Language Social Navigation

arXiv:2607.19850v1 Announce Type: new Abstract: Vision-language models have shown strong potential for social robot navigation by leveraging rich semantic understanding of complex environments and hum

safetyarxiv-cs-ro

Sophisticated Policies from Epistemic Priors

arXiv:2607.19518v1 Announce Type: new Abstract: Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search. We argue that its central comp

Spatiotemporal Facial Action Unit Detection using Twin Cycle Autoencoders for Driver Monitoring

arXiv:2607.16760v2 Announce Type: replace-cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time. Facial Act

Research

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606.19411v2 Announce Type: replace Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curati

researcharxiv-cs-lg

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

arXiv:2607.19361v1 Announce Type: cross Abstract: Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise only over a

STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching

arXiv:2607.19986v1 Announce Type: new Abstract: Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a determi

safetyarxiv-cs-cv