Search: “benchmark”

CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention

arXiv:2607.25291v1 Announce Type: new Abstract: The quadratic cost of self-attention makes long-context inference prohibitively expensive, and proxy-based block-sparse attention has become a practical

researcharxiv-cs-cl

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Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation

DGX agent

arXiv:2607.24766v1 Announce Type: new Abstract: Large language models (LLMs) can generate individual charts, but coordinated multi-view visualizations (CMVs), where views share data flows and cross-vi

model-releasesarxiv-cs-ai

29 Jul 2026

CycleVLA: Proactive Self-Correcting Vision-Language-Action Models via Subtask Backtracking and Minimum Bayes Risk Decoding

arXiv:2601.02295v2 Announce Type: replace Abstract: Current work on robot failure detection and correction typically operates in a post hoc manner, analyzing errors and applying corrections only after

researcharxiv-cs-ro

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations

arXiv:2607.25397v1 Announce Type: new Abstract: Successfully automating dexterous, long-horizon robotic manipulation requires frameworks capable of both high-level reasoning and fine-grained execution

applicationsarxiv-cs-ro

DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome

arXiv:2511.09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood. Despite recen

Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs

arXiv:2607.25959v1 Announce Type: cross Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connect

Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting

arXiv:2602.18314v2 Announce Type: replace Abstract: Real-time reconstruction of deformable surgical scenes is vital for advancing robotic surgery, improving intraoperative guidance, and enabling autom

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization

arXiv:2607.24856v1 Announce Type: cross Abstract: Social media imagery (SMI) provides timely and fine-grained ground perspectives that are valuable for situational awareness and emergency response. Un

DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation

arXiv:2607.24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual proc

DSevolve: Enabling Real-Time Adaptive Scheduling on Dynamic Flexible Job Shop with LLM-Evolved Heuristic Portfolios

arXiv:2603.27628v2 Announce Type: replace Abstract: In dynamic flexible job shops, order arrivals, machine breakdowns, and processing-time deviations continually reshape the scheduling state and the p

applicationsarxiv-cs-ai

Dual-Domain Manifold Modeling for Hyperspectral Image Fusion

arXiv:2607.25338v1 Announce Type: new Abstract: Achieving a coherent integration of spectral richness and spatial fidelity remains a central objective in hyperspectral image fusion. However, existing

Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607.24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN

Empirical Evaluation of Out-Of-Distribution Performance of Tabular Foundation Models

arXiv:2607.26000v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as novel approaches for tabular predictive tasks, demonstrating competitive predictive performance to en

applicationsarxiv-cs-ai

Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2607.25123v1 Announce Type: new Abstract: Experience replay remains one of the most practical and useful algorithmic tools in the deep reinforcement learning (DRL) toolbox. Aside from the limite

Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases

arXiv:2607.25933v1 Announce Type: cross Abstract: Clinical diagnostic evaluation should not only assess whether models can provide correct diagnoses, but also reflect the realities of clinical practic

Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA

arXiv:2607.25921v1 Announce Type: cross Abstract: In this work, we study the use of Vision-Language Models (VLMs) for anomaly detection in an agent-driven game Quality Assurance (QA) pipeline focusing

EviDAG: Auditable Causal DAG Authoring with Biomedical Literature

arXiv:2607.21859v2 Announce Type: replace Abstract: Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process. Analysts m

Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Methods

arXiv:2502.11150v5 Announce Type: replace Abstract: Automatic methods for scoring text readability have been studied for over a century, and are widely used in research and in user-facing applications

applicationsarxiv-cs-cl

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Face De-Identification: A Domain-Centric Survey from Capture to Processing

arXiv:2607.25926v1 Announce Type: cross Abstract: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition w

safetyarxiv-cs-ai

Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion

arXiv:2607.25563v1 Announce Type: new Abstract: Open-vocabulary segmentation labels arbitrary categories from a text query without per-class training, yet on remote sensing imagery it underperforms on

FIDAC: An Easy-to-use Pipeline to Extract and Interpret Interpersonal Distance From Video

arXiv:2607.25146v1 Announce Type: new Abstract: The distance between persons reveals significant information about their perception of each other. However, such information is not easily extractable a

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion

arXiv:2607.25820v1 Announce Type: new Abstract: Food image segmentation plays a vital role in health-related applications such as nutrition tracking and personalized health monitoring. However, existi

From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations

arXiv:2607.25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making. However, the c

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models

arXiv:2607.24764v1 Announce Type: new Abstract: The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditio

HeAD-CP: Heterophily-Aware Diffused Conformal Prediction Sets for Graph Neural Networks

arXiv:2607.25273v1 Announce Type: new Abstract: Conformal prediction (CP) provides distribution-free uncertainty quantification, and its extension to graphs is an active research direction. Diffused A

researcharxiv-cs-lg

HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising

arXiv:2607.24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.g., PID controllers, model predictive control, offline RL

How Small Can You Go? A Controlled Study of LoRA Rank, Target Modules, and Quantization Trade-offs for Text-to-SQL on a 60M-Parameter Model

arXiv:2607.25583v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) and low-bit quantization are now standard tools for adapting language models under tight compute budgets, yet the

IMPRINT: Image-Conditioned Query Enrichment for Long-Tail Object Goal Navigation

arXiv:2607.25106v1 Announce Type: new Abstract: Embodied AI increasingly relies on queryable semantic maps built from pre-trained vision-language models to enable zero-shot Object Goal Navigation (Obj

Instruction-Tuned Language Models Cannot Sample from Distributions They Can Describe

arXiv:2607.25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's respon

Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification

arXiv:2607.25904v1 Announce Type: new Abstract: Graphical user interface task evaluation aims to determine whether a GUI agent has successfully completed a user instruction. Automated GUI task evaluat

Intrinsic and Triangulation-Agnostic Attention: A Simple and Powerful Approach for Learning on Meshes

arXiv:2607.24954v1 Announce Type: cross Abstract: This work proposes an adaptation of the attention mechanism for triangle meshes. The core observation is that endowing the attention mechanism with cr

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IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment

arXiv:2607.25579v1 Announce Type: cross Abstract: Entity alignment (EA) identifies entities across knowledge graphs (KGs) that refer to the same real-world object. Conventional EA methods mainly explo

safetyarxiv-cs-ai

JKO-RAG: Distributional Retrieval as Wasserstein Free-Energy Gradient Flow

arXiv:2607.24776v1 Announce Type: cross Abstract: RAG pipelines return a ranked list of passages. We argue this is a mismatch: the downstream language model conditions on a set, and the selection prob

researcharxiv-cs-lg

JobMatchAI-An Intelligent Job Matching Platform Using Knowledge Graphs, Semantic Search and Explainable AI

arXiv:2603.14558v3 Announce Type: replace Abstract: Recruiters and job seekers rely on search systems to navigate labor markets, making candidate matching engines critical for hiring outcomes. Most sy

LaP-Forensics: Latent-Pixel Consistency Guided Multimodal Reasoning for Deepfake Detection

arXiv:2607.25962v1 Announce Type: new Abstract: Recent generative models can produce images with few obvious visual artifacts, weakening detectors and explanations that rely only on surface appearance

Learned, Relied Upon, or Necessary? Separating Checkpoint Dependence from Task-Level Value in Sheaf GNNs

model-releasesarxiv-cs-lg

arXiv:2607.25387v1 Announce Type: new Abstract: Learned restriction maps in sheaf graph neural networks are often treated as proof that the model has discovered useful edge geometry. That conclusion d

Learning from 53.6K Real-World Developer Edits of AI-Generated Code

arXiv:2607.25130v1 Announce Type: cross Abstract: Imperfections in AI-generated code require that software developers modify the generated code manually, or by re-prompting an AI programming assistant

LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos

arXiv:2607.25125v1 Announce Type: new Abstract: Despite rapid progress in Multi-modal Large Language Models (MLLMs), understanding long-form videos is still bottlenecked by limited context windows. Wh

Safety

LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging

arXiv:2607.25197v1 Announce Type: new Abstract: Sleep staging remains challenging due to long-range temporal dependencies, ambiguous stage transitions-particularly in N1-and substantial distribution s

safetyarxiv-cs-cv

Localized Adaptation Reveals Distinct Learning Signatures in Transformers

arXiv:2607.25663v1 Announce Type: new Abstract: Transformer adaptation is typically distributed across model depth, even when the intended change is narrow. We investigate how adaptation site shapes w

Localized Anomaly Detection via Differentiable D-vine Copulas

arXiv:2607.25020v1 Announce Type: new Abstract: Vine copulas provide a flexible framework for modeling complex multivariate distributions through a hierarchical decomposition into bivariate pair-copul

Local Ai

MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation

arXiv:2607.25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pr

local-aiarxiv-cs-ai

Matrix-Free Photoacoustic Image Reconstruction via Sensor-Token Self-Attention

arXiv:2607.25576v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) combines the optical absorption contrast of biological tissue with the spatial resolution of ultrasound, yet recovering t

MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

arXiv:2607.24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs

MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities

arXiv:2607.25948v1 Announce Type: cross Abstract: Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-lang

Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications

model-releasesarxiv-cs-lg

arXiv:2606.05202v2 Announce Type: replace-cross Abstract: In reactor physics, neutronics and multi-physics phenomena can be modelled at different fidelity levels. High-fidelity models based on the Bol

Multi-Scale Structural Features for Continual, Comprehensible Visual Recognition in a Developmental Learning Framework

arXiv:2607.25531v1 Announce Type: cross Abstract: Contemporary machine learning struggles to learn continually, reuse prior knowledge, and expose a comprehensible internal structure. A recently propos

Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

arXiv:2607.24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning. Currently, me