Search: “context”

RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery

model-releasesarxiv-cs-cv

arXiv:2604.20000v1 Announce Type: new Abstract: Automated wildlife monitoring from aerial imagery is vital for conservation but remains limited by two persistent challenges: the difficulty of detectin

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Tutorials

Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback

DGX agent

arXiv:2604.20730v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. Howev

tutorialsarxiv-cs-cv

23 Apr 2026

Separable Pathways for Causal Reasoning: How Architectural Scaffolding Enables Hypothesis-Space Restructuring in LLM Agents

arXiv:2604.20039v1 Announce Type: new Abstract: Causal discovery through experimentation and intervention is fundamental to robust problem solving. It requires not just updating beliefs within a fixed

SpaCeFormer: Fast Proposal-Free Open-Vocabulary 3D Instance Segmentation

arXiv:2604.20395v1 Announce Type: new Abstract: Open-vocabulary 3D instance segmentation is a core capability for robotics and AR/VR, but prior methods trade one bottleneck for another: multi-stage 2D

Stateless Decision Memory for Enterprise AI Agents

arXiv:2604.20158v1 Announce Type: new Abstract: Enterprise deployment of long-horizon decision agents in regulated domains (underwriting, claims adjudication, tax examination) is dominated by retrieva

applicationsarxiv-cs-ai

Task-Dependent Evaluation of LLM Output Homogenization: A Taxonomy-Guided Framework

arXiv:2509.21267v3 Announce Type: replace Abstract: Large language models often generate homogeneous outputs, but whether this is problematic depends on the specific task. For objective math tasks, re

Transparent Screening for LLM Inference and Training Impacts

arXiv:2604.19757v1 Announce Type: cross Abstract: This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited obs

Utterance-Level Methods for Identifying Reliable ASR-Output for Child Speech

arXiv:2604.19801v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) is increasingly used in applications involving child speech, such as language learning and literacy acquisition. Ho

What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization

arXiv:2601.17609v2 Announce Type: replace Abstract: In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that str

applicationsarxiv-cs-cl

WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling

arXiv:2508.16676v2 Announce Type: replace-cross Abstract: Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language mode

Article and Comment Frames Shape the Quality of Online Comments

arXiv:2603.27889v2 Announce Type: replace Abstract: Framing theory posits that how information is presented shapes audience responses, but computational work has largely ignored audience reactions. Wh

Agents

Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing

arXiv:2604.19618v1 Announce Type: new Abstract: Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspec

agentsarxiv-cs-ro

Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Optical Remote Sensing

arXiv:2512.19302v2 Announce Type: replace Abstract: Large Vision--Language Models (LVLMs) hold great promise for advancing optical remote sensing (RS) analysis, yet existing reasoning segmentation fra

CoDA: Towards Effective Cross-domain Knowledge Transfer via CoT-guided Domain Adaptation

arXiv:2604.19488v1 Announce Type: new Abstract: Large language models (LLMs) have achieved substantial advances in logical reasoning, yet they continue to lag behind human-level performance. In-contex

applicationsarxiv-cs-ai

Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation

arXiv:2604.19141v1 Announce Type: new Abstract: Diffusion- and flow-based models usually allocate compute uniformly across space, updating all patches with the same timestep and number of function eva

Hardware

Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs

arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen

hardwarearxiv-cs-lg

Tutorials

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

arXiv:2507.01918v3 Announce Type: replace-cross Abstract: We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform hi

tutorialsarxiv-cs-ai

Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract

arXiv:2604.19505v1 Announce Type: cross Abstract: Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previou

Epistemic orientation in parliamentary discourse is associated with deliberative democracy

arXiv:2604.19699v1 Announce Type: new Abstract: The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref

Tutorials

FoNE: Precise Single-Token Number Embeddings via Fourier Features

arXiv:2502.09741v2 Announce Type: replace Abstract: Large Language Models (LLMs) typically represent numbers using multiple tokens, which requires the model to aggregate these tokens to interpret nume

tutorialsarxiv-cs-cl

Mechanistic Anomaly Detection via Functional Attribution

model-releasesarxiv-cs-lg

arXiv:2604.18970v1 Announce Type: new Abstract: We can often verify the correctness of neural network outputs using ground truth labels, but we cannot reliably determine whether the output was produce

Local Ai

Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis

arXiv:2604.18765v1 Announce Type: cross Abstract: Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display no

local-aiarxiv-cs-ai

Agents

Multi-Step Gaussian Process Propagation for Adaptive Path Planning

arXiv:2604.19148v1 Announce Type: new Abstract: Efficient and robust path planning hinges on combining all accessible information sources. In particular, the task of path planning for robotic environm

agentsarxiv-cs-ro

Owner-Harm: A Missing Threat Model for AI Agent Safety

model-releasesarxiv-cs-ai

arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a d

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials

arXiv:2604.18864v1 Announce Type: new Abstract: Generalized Additive Models (GAMs) can be used to create non-linear glass-box (i.e. explicitly interpretable) models, where the predictive function is f

Quantum Non-Linear Bandit Optimization

arXiv:2503.03023v2 Announce Type: replace Abstract: We study non-linear bandit optimization where the learner maximizes a black-box function with zeroth order function oracle, which has been successfu

Agents

Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery

arXiv:2604.19049v1 Announce Type: cross Abstract: LLM-assisted defect discovery has a precision crisis: plausible-but-wrong reports overwhelm maintainers and degrade credibility for real findings. We

agentsarxiv-cs-ai

ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis

arXiv:2604.19720v1 Announce Type: new Abstract: Human video generation remains challenging due to the difficulty of jointly modeling human appearance, motion, and camera viewpoint under limited multi-

Remask, Don't Replace: Token-to-Mask Refinement in Masked Diffusion Language Models

arXiv:2604.18738v1 Announce Type: new Abstract: Masked diffusion language models such as LLaDA2.1 rely on Token-to-Token (T2T) editing to correct their own generation errors: whenever a different toke

RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation

arXiv:2604.19570v1 Announce Type: new Abstract: Accurate medical image segmentation requires both long-range contextual reasoning and precise boundary delineation, a task where existing transformer- a

SimDiff: Depth Pruning via Similarity and Difference

arXiv:2604.19520v1 Announce Type: new Abstract: Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standar

Local Ai

'The Order in the Horse's Heart': A Case Study in LLM-Assisted Stylometry for the Discovery of Biblical Allusion in Modern Literary Fiction

arXiv:2604.19447v1 Announce Type: new Abstract: We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embeddi

local-aiarxiv-cs-cl

The PROPER Approach to Proactivity: Benchmarking and Advancing Knowledge Gap Navigation

arXiv:2601.09926v3 Announce Type: replace Abstract: Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden user

safetyarxiv-cs-lg

Toward Clinically Acceptable Chest X-ray Report Generation: A Qualitative Retrospective Pilot Study of CXRMate-2

arXiv:2604.18967v1 Announce Type: new Abstract: Chest X-ray (CXR) radiology report generation (RRG) models have shown rapid progress, yet their clinical utility remains uncertain due to limited evalua

TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation

arXiv:2604.19473v1 Announce Type: new Abstract: Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is a key unsolved problem. Existing methods a

VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation

arXiv:2507.21563v4 Announce Type: replace-cross Abstract: Recommendation systems often suffer from data sparsity caused by limited user-item interactions, which degrade their performance and amplify p

safetyarxiv-cs-lg

When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide

arXiv:2604.19206v1 Announce Type: new Abstract: The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely ha

AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

arXiv:2604.17846v1 Announce Type: new Abstract: MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by the

An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs

arXiv:2603.04545v2 Announce Type: replace Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference qu

AnyLift: Scaling Motion Reconstruction from Internet Videos via 2D Diffusion

arXiv:2604.17818v1 Announce Type: new Abstract: Reconstructing 3D human motion and human-object interactions (HOI) from Internet videos is a fundamental step toward building large-scale datasets of hu

Appearance-free Action Recognition: Zero-shot Generalization in Humans and a Two-Pathway Model

arXiv:2604.16675v1 Announce Type: new Abstract: Action recognition is a fundamental ability for social species. Yet, its underlying computations are not well understood. Classical psychophysical studi

applicationsarxiv-cs-cv

Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods

model-releasesarxiv-cs-cv

arXiv:2510.07143v3 Announce Type: replace Abstract: Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectivene

Auditing Support Strategies in LLMs through Grounded Multi-Turn Social Simulation

model-releasesarxiv-cs-cl

arXiv:2604.17079v1 Announce Type: new Abstract: When users seek social support from chatbots, they disclose their situation gradually, yet most evaluations of supportive LLMs rely on single-turn, full

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

arXiv:2604.16804v1 Announce Type: new Abstract: Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating complicated des

Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion

model-releasesarxiv-cs-lg

arXiv:2604.18566v1 Announce Type: cross Abstract: We present a systematic evaluation of large language model families -- spanning both proprietary cloud APIs and locally-hosted open-source models -- o

Bias-constrained multimodal intelligence for equitable and reliable clinical AI

arXiv:2604.16884v1 Announce Type: new Abstract: The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. Howeve

Hardware

Bit-Flip Vulnerability of Shared KV-Cache Blocks in LLM Serving Systems

arXiv:2604.17249v1 Announce Type: cross Abstract: Rowhammer on GPU DRAM has enabled adversarial bit flips in model weights; shared KV-cache blocks in LLM serving systems present an analogous but previ

hardwarearxiv-cs-lg