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arXiv:2604.21931v1 Announce Type: cross Abstract: How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to

tutorialsarxiv-cs-ai

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Human DGX agent

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Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models

DGX agent

arXiv:2604.21523v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question a

model-releasesarxiv-cs-cl

24 Apr 2026

Seeing Without Eyes: 4D Human-Scene Understanding from Wearable IMUs

arXiv:2604.21926v1 Announce Type: new Abstract: Understanding human activities and their surrounding environments typically relies on visual perception, yet cameras pose persistent challenges in priva

safetyarxiv-cs-cv

Self-Predictive Representation for Autonomous UAV Object-Goal Navigation

arXiv:2604.21130v1 Announce Type: new Abstract: Autonomous Unmanned Aerial Vehicles (UAVs) have revolutionized industries through their versatility with applications including aerial surveillance, sea

safetyarxiv-cs-ro

SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis

arXiv:2604.21414v1 Announce Type: new Abstract: Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain

SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis

arXiv:2602.11569v2 Announce Type: replace Abstract: Population synthesis is essential for individual-level simulation in transport planning and socio-economic analysis, yet remains challenging due to

SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning

arXiv:2604.21782v1 Announce Type: new Abstract: We present the shared task on narrative similarity and narrative representation learning - NSNRL (pronounced 'nass-na-rel'). The task operationalizes na

Separable Expert Architecture: Toward Privacy-Preserving LLM Personalization via Composable Adapters and Deletable User Proxies

arXiv:2604.21571v1 Announce Type: new Abstract: Current model training approaches incorporate user information directly into shared weights, making individual data removal computationally infeasible w

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation

arXiv:2604.21076v1 Announce Type: cross Abstract: Medication reconciliation at clinical handoffs is a high-stakes, error-prone process. Large language models are increasingly proposed to assist with t

SGD at the Edge of Stability: The Stochastic Sharpness Gap

arXiv:2604.21016v1 Announce Type: cross Abstract: When training neural networks with full-batch gradient descent (GD) and step size eta, the largest eigenvalue of the Hessian -- the sharpness S(oldsym

SGG-R^{rm 3}: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation

arXiv:2603.07961v3 Announce Type: replace Abstract: Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have

safetyarxiv-cs-cv

Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs

arXiv:2604.20937v1 Announce Type: new Abstract: Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training

researcharxiv-cs-lg

Applications

Situationally-aware Path Planning Exploiting 3D Scene Graphs

arXiv:2508.06283v2 Announce Type: replace Abstract: 3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and

applicationsarxiv-cs-ro

Tutorials

SLAM as a Stochastic Control Problem with Partial Information: Optimal Solutions and Rigorous Approximations

arXiv:2604.21693v1 Announce Type: new Abstract: Simultaneous localization and mapping (SLAM) is a foundational state estimation problem in robotics in which a robot accurately constructs a map of its

tutorialsarxiv-cs-ro

SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding

model-releasesarxiv-cs-cl

arXiv:2510.26615v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but it must balance limited effective context, re

Slot Machines: How LLMs Keep Track of Multiple Entities

arXiv:2604.21139v1 Announce Type: new Abstract: Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multipl

SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction

model-releasesarxiv-cs-cl

arXiv:2601.10003v2 Announce Type: replace Abstract: Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current L

Local Ai

SparKV: Overhead-Aware KV Cache Loading for Efficient On-Device LLM Inference

arXiv:2604.21231v1 Announce Type: cross Abstract: Efficient inference for on-device Large Language Models (LLMs) remains challenging due to limited hardware resources and the high cost of the prefill

local-aiarxiv-cs-ai

Local Ai

Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation

arXiv:2604.21221v1 Announce Type: new Abstract: We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality w

local-aiarxiv-cs-cv

SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes

model-releasesarxiv-cs-cv

arXiv:2604.21356v1 Announce Type: new Abstract: High-quality digital terrain models derived from airborne laser scanning (ALS) data are essential for a wide range of geospatial analyses, and their gen

Spatial Metaphors for LLM Memory: A Critical Analysis of the MemPalace Architecture

arXiv:2604.21284v1 Announce Type: new Abstract: MemPalace is an open-source AI memory system that applies the ancient method of loci (memory palace) spatial metaphor to organize long-term memory for l

SpatiO: Adaptive Test-Time Orchestration of Vision-Language Agents for Spatial Reasoning

arXiv:2604.21190v1 Announce Type: new Abstract: Understanding visual scenes requires not only recognizing objects but also reasoning about their spatial relationships. Unlike general vision-language t

agentsarxiv-cs-cv

Spatio-temporal probabilistic forecast using MMAF-guided learning

arXiv:2603.15055v2 Announce Type: replace-cross Abstract: We present a theory-guided generalized Bayesian methodology for spatio-temporal raster data, which we use to train an ensemble of stochastic f

researcharxiv-cs-lg

Spectral Embeddings Leak Graph Topology: Theory, Benchmark, and Adaptive Reconstruction

model-releasesarxiv-cs-lg

arXiv:2604.21094v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on relational data, but standard benchmarks unrealistically assume the graph is centrally available. In practice, set

Spectral Kernel Dynamics for Planetary Surface Graphs: Distinction Dynamics and Topological Conservation

arXiv:2604.20887v1 Announce Type: cross Abstract: The spectral kernel field equation R[k] = T[k] lacks a conservation-law analog. We prove (i) the fixed-point flow is strictly volume-expanding (tr DF

researcharxiv-cs-lg

Speculative Actions: A Lossless Framework for Faster Agentic Systems

arXiv:2510.04371v2 Announce Type: replace Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and

agentsarxiv-cs-ai

SPIRE: Structure-Preserving Interpretable Retrieval of Evidence

arXiv:2604.20849v1 Announce Type: cross Abstract: Retrieval-augmented generation over semi-structured sources such as HTML is constrained by a mismatch between document structure and the flat, sequenc

SQLyzr: A Comprehensive Benchmark and Evaluation Platform for Text-to-SQL

arXiv:2604.21214v1 Announce Type: cross Abstract: Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applic

Stealthy Backdoor Attacks against LLMs Based on Natural Style Triggers

arXiv:2604.21700v1 Announce Type: cross Abstract: The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studie

StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching

arXiv:2601.09056v3 Announce Type: replace-cross Abstract: Stylometry--the identification of an author through analysis of a text's style (i.e., authorship attribution)--serves many constructive purpos

Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection

model-releasesarxiv-cs-lg

arXiv:2604.21282v1 Announce Type: cross Abstract: Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection accur

Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power

arXiv:2604.21043v1 Announce Type: cross Abstract: This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metap

Tutorials

Strategic Scaling of Test-Time Compute: A Bandit Learning Approach

arXiv:2506.12721v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typ

tutorialsarxiv-cs-ai

Stratified Topological Autonomy for Long-Range Coordination (STALC)

arXiv:2503.10475v4 Announce Type: replace Abstract: In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coor

agentsarxiv-cs-ro

Hardware

Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token (TTFT)

arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting fo

hardwarearxiv-cs-ai

StructMem: Structured Memory for Long-Horizon Behavior in LLMs

arXiv:2604.21748v1 Announce Type: cross Abstract: Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasonin

Structural Quality Gaps in Practitioner AI Governance Prompts: An Empirical Study Using a Five-Principle Evaluation Framework

arXiv:2604.21090v1 Announce Type: cross Abstract: AI governance programmes increasingly rely on natural language prompts to constrain and direct AI agent behaviour. These prompts function as executabl

agentsarxiv-cs-ai

Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models

arXiv:2603.21697v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visual

StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition

model-releasesarxiv-cs-cv

arXiv:2604.21689v1 Announce Type: cross Abstract: Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable ide

StyleVAR: Controllable Image Style Transfer via Visual Autoregressive Modeling

arXiv:2604.21052v1 Announce Type: cross Abstract: We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned lat

Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression

arXiv:2604.21335v1 Announce Type: cross Abstract: Sub-token routing offers a finer control axis for transformer efficiency than the coarse units used in most prior work, such as tokens, pages, heads,

Subject-level Inference for Realistic Text Anonymization Evaluation

model-releasesarxiv-cs-cl

arXiv:2604.21211v1 Announce Type: new Abstract: Current text anonymization evaluation relies on span-based metrics that fail to capture what an adversary could actually infer, and assumes a single dat

Supervised Learning Has a Necessary Geometric Blind Spot: Theory, Consequences, and Minimal Repair

arXiv:2604.21395v1 Announce Type: cross Abstract: We prove that empirical risk minimisation (ERM) imposes a necessary geometric constraint on learned representations: any encoder that minimises superv

SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding

model-releasesarxiv-cs-cv

arXiv:2511.03325v3 Announce Type: replace Abstract: Video Question Answering (VideoQA) in the surgical domain aims to enhance intraoperative understanding by enabling AI models to reason over temporal

Survey on Evaluation of LLM-based Agents

arXiv:2503.16416v2 Announce Type: replace Abstract: LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environ

Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning

arXiv:2604.21346v1 Announce Type: new Abstract: Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bot

SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery

arXiv:2604.21801v1 Announce Type: cross Abstract: Recent advances in deep learning for remote sensing rely heavily on large annotated datasets, yet acquiring high-quality ground truth for geometric, r

Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models

arXiv:2604.21031v1 Announce Type: cross Abstract: Synthetic data generation offers promise for addressing data scarcity and privacy concerns in educational technology, yet practitioners lack empirical