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Rodrigues Network for Learning Robot Actions

arXiv:2506.02618v2 Announce Type: replace-cross Abstract: Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers l

safetyarxiv-cs-cv

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking

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DGX agent

arXiv:2604.20623v1 Announce Type: cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change cap

model-releasesarxiv-cs-ai

23 Apr 2026

Local Ai

SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition

arXiv:2604.20146v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to extract named entities and localize their visual regions within image-text pairs, serving

local-aiarxiv-cs-cl

Same Content, Different Answers: Cross-Modal Inconsistency in MLLMs

arXiv:2512.08923v2 Announce Type: replace Abstract: We introduce two new benchmarks REST and REST+ (Render-Equivalence Stress Tests) to enable systematic evaluation of cross-modal inconsistency in mul

SAMix: Calibrated and Accurate Continual Learning via Sphere-Adaptive Mixup and Neural Collapse

arXiv:2510.15751v2 Announce Type: replace Abstract: While most continual learning methods focus on mitigating forgetting and improving accuracy, they often overlook the critical aspect of network cali

safetyarxiv-cs-lg

Sampling-Aware Quantization for Diffusion Models

arXiv:2505.02242v2 Announce Type: replace Abstract: Diffusion models have recently emerged as the dominant approach in visual generation tasks. However, the lengthy denoising chains and the computatio

safetyarxiv-cs-cv

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models

arXiv:2604.19768v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit systematic miscalibration with rhetorical intensity not proportionate to epistemic grounding. This study tests th

Scalable AI Inference: Performance Analysis and Optimization of AI Model Serving

arXiv:2604.20420v1 Announce Type: cross Abstract: AI research often emphasizes model design and algorithmic performance, while deployment and inference remain comparatively underexplored despite being

applicationsarxiv-cs-ai

Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization

arXiv:2509.16002v2 Announce Type: replace-cross Abstract: A scalable and resource-efficient quantum reinforcement learning framework is presented that eliminates the linear qubit-scaling barrier in mu

agentsarxiv-cs-lg

Scaling Self-Play with Self-Guidance

model-releasesarxiv-cs-lg

arXiv:2604.20209v1 Announce Type: new Abstract: LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both i

SceneOrchestra: Efficient Agentic 3D Scene Synthesis via Full Tool-Call Trajectory Generation

arXiv:2604.19907v1 Announce Type: new Abstract: Recent agentic frameworks for 3D scene synthesis have advanced realism and diversity by integrating heterogeneous generation and editing tools. These to

SciCoQA: Quality Assurance for Scientific Paper--Code Alignment

arXiv:2601.12910v3 Announce Type: replace-cross Abstract: Discrepancies between scientific papers and their code undermine reproducibility, a concern that grows as automated research agents scale scie

scpFormer: A Foundation Model for Unified Representation and Integration of the Single-Cell Proteomics

arXiv:2604.20003v1 Announce Type: cross Abstract: The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we i

Secure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism

arXiv:2604.20245v1 Announce Type: cross Abstract: Fundamental rate-distortion-perception (RDP) trade-offs arise in applications requiring maintained perceptual quality of reconstructed data, such as n

Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction

arXiv:2604.20311v1 Announce Type: cross Abstract: Micro-video popularity prediction (MVPP) aims to forecast the future popularity of videos on online media, which is essential for applications such as

applicationsarxiv-cs-ai

SegEarth-OV3: Exploring SAM 3 for Open-Vocabulary Semantic Segmentation in Remote Sensing Images

arXiv:2512.08730v2 Announce Type: replace Abstract: Most existing methods for training-free open-vocabulary semantic segmentation are based on CLIP. While these approaches have made progress, they oft

Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge

model-releasesarxiv-cs-cl

arXiv:2604.20598v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems treat vector embeddings as static, context-free artifacts: an embedding has no notion of when it w

Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness

arXiv:2604.20413v1 Announce Type: new Abstract: Large language models perform well on many reasoning tasks, yet they often lack awareness of whether their current knowledge or reasoning state is compl

Self-Describing Structured Data with Dual-Layer Guidance: A Lightweight Alternative to RAG for Precision Retrieval in Large-Scale LLM Knowledge Navigation

arXiv:2604.19777v1 Announce Type: cross Abstract: Large Language Models (LLMs) exhibit a well-documented positional bias when processing long input contexts: information in the middle of a context win

Self-Guided Plan Extraction for Instruction-Following Tasks with Goal-Conditional Reinforcement Learning

arXiv:2604.20601v1 Announce Type: new Abstract: We introduce SuperIgor, a framework for instruction-following tasks. Unlike prior methods that rely on predefined subtasks, SuperIgor enables a language

Self-supervised pretraining for an iterative image size agnostic vision transformer

arXiv:2604.20392v1 Announce Type: new Abstract: Vision Transformers (ViTs) dominate self-supervised learning (SSL). While they have proven highly effective for large-scale pretraining, they are comput

Semantic-Fast-SAM: Efficient Semantic Segmenter

arXiv:2604.20169v1 Announce Type: new Abstract: We propose Semantic-Fast-SAM (SFS), a semantic segmentation framework that combines the Fast Segment Anything model with a semantic labeling pipeline to

Semantic-guided Gaussian Splatting for High-Fidelity Underwater Scene Reconstruction

arXiv:2509.00800v3 Announce Type: replace Abstract: Accurate 3D reconstruction in degraded imaging conditions remains a key challenge in photogrammetry and neural rendering. In underwater environments

applicationsarxiv-cs-cv

Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction

arXiv:2604.19971v1 Announce Type: cross Abstract: Interactive spatial layouts empower users to synthesize information and organize findings for sensemaking. While Large Language Models (LLMs) can auto

safetyarxiv-cs-ai

Semantic Recall for Vector Search

arXiv:2604.20417v1 Announce Type: cross Abstract: We introduce Semantic Recall, a novel metric to assess the quality of approximate nearest neighbor search algorithms by considering only semantically

Semi-Supervised Flow Matching for Mosaiced and Panchromatic Fusion Imaging

arXiv:2604.20128v1 Announce Type: new Abstract: Fusing a low resolution (LR) mosaiced hyperspectral image (HSI) with a high resolution (HR) panchromatic (PAN) image offers a promising avenue for video

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

SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze

arXiv:2604.19888v1 Announce Type: new Abstract: Driver gaze estimation is essential for understanding the driver's situational awareness of surrounding traffic. Existing gaze estimation models use dri

Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning

arXiv:2604.20308v1 Announce Type: new Abstract: Graph neural networks face two fundamental challenges rooted in the linear structure of Euclidean vector spaces: (1) Current architectures represent geo

researcharxiv-cs-lg

Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings

arXiv:2604.20436v1 Announce Type: cross Abstract: Generative AI (GenAI) is reshaping software engineering by shifting development from manual coding toward agent-driven implementation. While vibe codi

SignDATA: Data Pipeline for Sign Language Translation

arXiv:2604.20357v1 Announce Type: cross Abstract: Sign-language datasets are difficult to preprocess consistently because they vary in annotation schema, clip timing, signer framing, and privacy const

safetyarxiv-cs-cl

SkillGraph: Graph Foundation Priors for LLM Agent Tool Sequence Recommendation

arXiv:2604.19793v1 Announce Type: new Abstract: LLM agents must select tools from large API libraries and order them correctly. Existing methods use semantic similarity for both retrieval and ordering

SkillLearnBench: Benchmarking Continual Learning Methods for Agent Skill Generation on Real-World Tasks

model-releasesarxiv-cs-cl

arXiv:2604.20087v1 Announce Type: new Abstract: Skills have become the de facto way to enable LLM agents to perform complex real-world tasks with customized instructions, workflows, and tools, but how

Skyline-First Traversal as a Control Mechanism for Multi-Criteria Graph Search

arXiv:2604.19807v1 Announce Type: new Abstract: In multi-criteria graph traversal, paths are compared via Pareto dominance, an ordering that identifies which paths are non-dominated, but says nothing

SL(C)AMma: Simultaneous Localisation, (Calibration) and Mapping With a Magnetometer Array

arXiv:2604.19946v1 Announce Type: new Abstract: Indoor localisation techniques suffer from attenuated Global Navigation Satellite System (GNSS) signals and from the accumulation of unbounded drift by

researcharxiv-cs-ro

SMART: A Spectral Transfer Approach to Multi-Task Learning

arXiv:2604.20161v1 Announce Type: new Abstract: Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning c

researcharxiv-cs-lg

SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models

arXiv:2509.15174v3 Announce Type: replace-cross Abstract: WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMAR

Soft-Label Governance for Distributional Safety in Multi-Agent Systems

arXiv:2604.19752v1 Announce Type: cross Abstract: Multi-agent AI systems exhibit emergent risks that no single agent produces in isolation. Existing safety frameworks rely on binary classifications of

SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution

arXiv:2604.19825v1 Announce Type: cross Abstract: State-of-the-art code generation frameworks rely on mental simulation, where LLMs internally trace execution to verify correctness. We expose a fundam

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

Spatio-temporal modelling of electric vehicle charging demand

model-releasesarxiv-cs-lg

arXiv:2604.19841v1 Announce Type: cross Abstract: Accurate forecasting of electric vehicle (EV) charging demand is critical for grid management and infrastructure planning. Yet the field continues to

SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation

arXiv:2604.20842v1 Announce Type: cross Abstract: Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited

SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

arXiv:2603.01168v2 Announce Type: replace-cross Abstract: Reliable decision-making in complex multi-agent systems requires calibrated predictions and interpretable uncertainty. We introduce SphUnc, a

SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models

arXiv:2604.20705v1 Announce Type: new Abstract: Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large

Stabilising Generative Models of Attitude Change

arXiv:2604.19791v1 Announce Type: new Abstract: Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal the

Tutorials

Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation

arXiv:2604.20336v1 Announce Type: new Abstract: Co-manipulation requires multiple humans to synchronize their motions with a shared object while ensuring reasonable interactions, maintaining natural p

tutorialsarxiv-cs-cv

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

Statistics, Not Scale: Modular Medical Dialogue with Bayesian Belief Engine

arXiv:2604.20022v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous diagnostic agents, yet they conflate two fundamentally different capabilities: natural-l