Search: “benchmark”

PrISM-IQA: Image Quality Assessment Made Practical for Smartphone Photography

model-releasesarxiv-cs-cv

arXiv:2606.31626v1 Announce Type: new Abstract: Existing smartphone image quality assessment (IQA) methods commonly reduce perceptual quality to a single score. However, this scalar formulation is poo

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Research

PRISM: Latent Composition Consistency for Single-Image Reflection Removal

DGX agent

arXiv:2606.31513v1 Announce Type: new Abstract: Single-image reflection removal (SIRR) seeks to recover the transmission layer from a mixture corrupted by reflections -- a severely ill-posed problem.

researcharxiv-cs-cv

1 Jul 2026

Local Ai

PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding

arXiv:2606.31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform rea

local-aiarxiv-cs-ai

Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

arXiv:2507.18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as

researcharxiv-cs-lg

Agents

Real-Time Source-Free Object Detection

arXiv:2606.31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet exi

agentsarxiv-cs-ai

Local Ai

Reasoning in machine vision by learning fast and slow thinking

arXiv:2506.22075v2 Announce Type: replace Abstract: Reasoning is a hallmark of human intelligence, enabling adaptive decision-making in complex unfamiliar scenarios. In contrast, machine intelligence

local-aiarxiv-cs-cv

Tutorials

Region-Aware Multimodal Large Language Model via SlowFast Tokenization and Pseudo-Mask Guidance for 3D CT Report Generation

arXiv:2506.23102v2 Announce Type: replace-cross Abstract: Current CT report generation frameworks predominantly rely on global feature representations, often failing to capture region-specific details

tutorialsarxiv-cs-cv

Applications

Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments

arXiv:2508.02187v3 Announce Type: replace-cross Abstract: 4D millimeter wave radars (4D radars) are new emerging sensors that provide point clouds of objects with both position and radial velocity mea

applicationsarxiv-cs-cv

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models

arXiv:2606.31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neu

safetyarxiv-cs-lg

REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent

arXiv:2511.17442v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines. These models include unimodal vision encoders and mult

Rethinking On-policy Optimization for Query Augmentation

arXiv:2510.17139v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have led to a surge of interest in query augmentation for information retrieval (IR). Two main appro

safetyarxiv-cs-cl

Revisiting Parameter Redundancy in Vision-Language-Action Models: Insights from VLM-to-VLA Adaptation

model-releasesarxiv-cs-ro

arXiv:2606.31382v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have made significant strides in embodied intelligence by integrating the powerful representations of pre-trained Vi

Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings

arXiv:2606.31602v1 Announce Type: new Abstract: This work presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for large language models (LLMs) that leverages contextual and toke

safetyarxiv-cs-cl

RoPoLL: Robust Panel of LLM Judges

arXiv:2606.30931v1 Announce Type: new Abstract: The LLM Jury, a Panel of LLM Evaluators (PoLL) reporting consensus scores, has become a practical alternative to single-judge LLM evaluation, yet its st

Safe Online Learning via Smooth Safety-Structured Policy Composition

arXiv:2606.31320v1 Announce Type: new Abstract: Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches t

safetyarxiv-cs-lg

Agents

SAGE: A Search-AuGmented Evaluation of Large Language Models on Free-Form QA

arXiv:2504.07385v3 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) become increasingly used for question-answering (QA), relying on static, pre-annotated references for evaluati

agentsarxiv-cs-ai

SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition

arXiv:2606.31668v1 Announce Type: new Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) is critical for Earth observation and defense, but its practical deployment is constrain

researcharxiv-cs-cv

Same-Origin Policy for Agentic Browsers

arXiv:2606.14027v3 Announce Type: replace-cross Abstract: Agentic browsers integrate autonomous AI agents into web browsers, enabling users to accomplish web tasks through natural-language instruction

Security--Fidelity Tradeoffs: The Hidden Cost of Prompt Injection Defense

arXiv:2606.30783v1 Announce Type: cross Abstract: We identify a security-fidelity tradeoff in defending LLMs against indirect prompt injection: defenses resist injected instructions largely by suppres

Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation

arXiv:2606.31099v1 Announce Type: cross Abstract: Recent years have seen substantial advances in radiology report generation (RRG), yet existing approaches predominantly adopt direct feature fusion wh

Hardware

SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference

arXiv:2606.31145v1 Announce Type: new Abstract: Large language models increasingly operate over long contexts, where the KV cache becomes a dominant memory bottleneck: its size grows linearly with seq

hardwarearxiv-cs-cl

SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2606.31875v1 Announce Type: new Abstract: Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent re

Sequential sparse Gaussian process quantile regression

model-releasesarxiv-cs-lg

arXiv:2606.31284v1 Announce Type: new Abstract: Quantile regression aims to estimate the conditional quantiles of a response variable from observed data. In a Bayesian setting, Gaussian process quanti

ShardNet: Training Neural Controllers with Hard, Non-Convex Constraints

arXiv:2606.30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.

safetyarxiv-cs-lg

SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos

arXiv:2606.31127v1 Announce Type: cross Abstract: To enable personalized, real-time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a syst

Sparsity-Inducing Divergence Losses for Biometric Verification

arXiv:2606.31664v1 Announce Type: cross Abstract: Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace. Recently introduced alpha

SpikeLogBERT: Energy-Efficient Log Parsing Using Spiking Transformer Networks

model-releasesarxiv-cs-cl

arXiv:2606.31781v1 Announce Type: cross Abstract: Log parsing is a fundamental step in automated log analysis, transforming raw system logs into structured event templates for downstream tasks such as

Stealthy Multi-Task Adversarial Attacks

arXiv:2411.17936v2 Announce Type: replace-cross Abstract: Deep neural networks are highly vulnerable to adversarial perturbations, raising serious safety concerns in the real-world systems. While prio

safetyarxiv-cs-cv

Structure-Regularized Interpretable TCR-Epitope Prediction

model-releasesarxiv-cs-lg

arXiv:2606.30902v1 Announce Type: cross Abstract: T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies. Existing sequence- a

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

arXiv:2606.28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the

T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World

arXiv:2603.18481v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions. Whi

researcharxiv-cs-cv

TabPATE: Differentially Private Tabular In-Context Learning Without Public Data

arXiv:2606.31474v1 Announce Type: new Abstract: Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak thro

researcharxiv-cs-lg

Local Ai

TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning

arXiv:2606.31166v1 Announce Type: new Abstract: Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Ex

local-aiarxiv-cs-cl

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

arXiv:2601.18778v3 Announce Type: replace-cross Abstract: RL methods for scaling large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigat

researcharxiv-cs-cl

Test-Time Verification for Text-to-SQL via Outcome Reward Models

arXiv:2606.30851v1 Announce Type: cross Abstract: Improving the reliability of large language models (LLMs) at inference time is a central challenge in structured reasoning tasks such as Text-to-SQL.

safetyarxiv-cs-ai

The Bidirectional Process Reward Model

model-releasesarxiv-cs-cl

arXiv:2508.01682v3 Announce Type: replace Abstract: Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promi

The Calibration Turn in AI-Assisted Research: A Conceptual and Methodological Framework for Evidence-Licensed Claims

model-releasesarxiv-cs-lg

arXiv:2606.31273v1 Announce Type: new Abstract: AI-assisted research has entered a stage in which the central question is not only whether systems can generate hypotheses, run experiments, or produce

The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills

model-releasesarxiv-cs-cl

arXiv:2606.31272v1 Announce Type: cross Abstract: AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from mark

The HydroGym Reinforcement Learning Platform for Fluid Dynamics

arXiv:2512.17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mix

Think While You Map: Asynchronous Vision-Language Agents for Incremental 3D Scene Graphs

arXiv:2606.31471v1 Announce Type: new Abstract: Open-vocabulary 3D scene graph methods typically operate in two stages: first reconstruct, then enrich with vision-language models, leaving the graph un

researcharxiv-cs-cv

Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning

arXiv:2606.31599v1 Announce Type: cross Abstract: Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medica

safetyarxiv-cs-ai

TORA: Topological Representation Alignment for 3D Shape Assembly

arXiv:2604.04050v2 Announce Type: replace Abstract: Flow-matching methods for 3D shape assembly learn point-wise velocity fields that transport parts toward assembled configurations, yet they receive

safetyarxiv-cs-cv

TraCeS: Learning Per-Timestep Constraint-Violation Credit from Sparse Trajectory-Level Labels

arXiv:2504.12557v3 Announce Type: replace-cross Abstract: Ensuring safe behavior in reinforcement learning (RL) is challenging when safety constraints are implicit and cannot be densely measured. In m

safetyarxiv-cs-ai

Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks

arXiv:2606.31074v1 Announce Type: cross Abstract: Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect De

UniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization

model-releasesarxiv-cs-cv

arXiv:2606.31732v1 Announce Type: new Abstract: Visual-to-Code generation, which transforms scientific plots, vector graphics, and webpages into executable scripts, demands a level of pixel-precise al

Agents

Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddings

arXiv:2606.30777v1 Announce Type: new Abstract: The growing availability of trajectory datasets has fueled major advances in data-driven motion prediction. Yet, models trained on one dataset often fai

agentsarxiv-cs-cv

Verification-Gated Agentic Mission-State Governance for Intelligent Industrial Multi-Robot Systems

arXiv:2606.31339v1 Announce Type: new Abstract: Agentic artificial intelligence is increasingly used to decompose industrial tasks, propose robot actions, and adapt execution plans in dynamic cyber-ph

safetyarxiv-cs-ro

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection

arXiv:2502.15845v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-cons