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Stability Enhanced Gaussian Process Variational Autoencoders

arXiv:2604.09331v1 Announce Type: new Abstract: A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invaria

applicationsarxiv-cs-lg

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StaRPO: Stability-Augmented Reinforcement Policy Optimization

DGX agent

arXiv:2604.08905v1 Announce Type: new Abstract: Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization

safetyarxiv-cs-ai

13 Apr 2026

State Space Models are Effective Sign Language Learners: Exploiting Phonological Compositionality for Vocabulary-Scale Recognition

arXiv:2604.08761v1 Announce Type: new Abstract: Sign language recognition suffers from catastrophic scaling failure: models achieving high accuracy on small vocabularies collapse at realistic sizes. E

Statistical Properties of the King Wen Sequence: An Anti-Habituation Structure That Does Not Improve Neural Network Training

arXiv:2604.09234v1 Announce Type: cross Abstract: The King Wen sequence of the I-Ching (c. 1000 BC) orders 64 hexagrams -- states of a six-dimensional binary space -- in a pattern that has puzzled sch

local-aiarxiv-cs-ai

STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting

arXiv:2509.25210v3 Announce Type: replace-cross Abstract: To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equa

STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

arXiv:2604.08597v1 Announce Type: cross Abstract: Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowle

Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains

arXiv:2604.09361v1 Announce Type: new Abstract: In this paper, we propose a stochastic-dimension frozen sampled neural network (SD-FSNN) for solving a class of high-dimensional Gross-Pitaevskii equati

researcharxiv-cs-lg

Strategic Algorithmic Monoculture:Experimental Evidence from Coordination Games

arXiv:2604.09502v1 Announce Type: new Abstract: AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- base

agentsarxiv-cs-ai

Streaming Video Instruction Tuning

arXiv:2512.21334v2 Announce Type: replace Abstract: We present Streamo, a real-time streaming video LLM that serves as a general-purpose interactive assistant. Unlike existing online video models that

StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding

model-releasesarxiv-cs-cv

arXiv:2604.09000v1 Announce Type: new Abstract: Vision agent memory has shown remarkable effectiveness in streaming video understanding. However, storing such memory for videos incurs substantial memo

Strips as Tokens: Artist Mesh Generation with Native UV Segmentation

arXiv:2604.09132v1 Announce Type: new Abstract: Recent advancements in autoregressive transformers have demonstrated remarkable potential for generating artist-quality meshes. However, the token order

StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning

arXiv:2604.08620v1 Announce Type: cross Abstract: Reinforcement learning is typically treated as a uniform, data-driven optimization process, where updates are guided by rewards and temporal-differenc

Structural Evaluation Metrics for SVG Generation via Leave-One-Out Analysis

arXiv:2604.08809v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) represent visual content as structured, editable code. Each element (path, shape, or text node) can be individually inspe

researcharxiv-cs-lg

Structure-Aware Fine-Grained Gaussian Splatting for Expressive Avatar Reconstruction

arXiv:2604.09324v1 Announce Type: new Abstract: Reconstructing photorealistic and topology-aware human avatars from monocular videos remains a significant challenge in the fields of computer vision an

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation

arXiv:2604.08578v1 Announce Type: cross Abstract: High-quality labeled data is critical for training reliable machine learning and deep learning models, yet manual annotation remains costly and error-

researcharxiv-cs-ai

Structured Uncertainty guided Clarification for LLM Agents

arXiv:2511.08798v2 Announce Type: replace-cross Abstract: LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and

Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning

arXiv:2604.09158v1 Announce Type: cross Abstract: Supporting students in developing diagnostic reasoning is a key challenge across educational domains. Novices often face cognitive biases such as prem

agentsarxiv-cs-ai

SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

arXiv:2602.17330v3 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of p

SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

arXiv:2604.08477v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved large language model (LLM) reasoning in formal domains such as mathem

Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study

arXiv:2604.08621v1 Announce Type: new Abstract: In consumer applications, Customer Relationship Management (CRM) has traditionally relied on the manual optimisation of static, rule-based messaging str

agentsarxiv-cs-ai

SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve

applicationsarxiv-cs-cl

SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data

arXiv:2604.09411v1 Announce Type: new Abstract: Reliable 3D dynamic perception requires models that can anticipate motion beyond predefined categories, yet progress is hindered by the scarcity of dens

applicationsarxiv-cs-cv

Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network

arXiv:2604.09091v1 Announce Type: new Abstract: The use of synthetic data in machine learning applications and research offers many benefits, including performance improvements through data augmentati

applicationsarxiv-cs-lg

TAIHRI: Task-Aware 3D Human Keypoints Localization for Close-Range Human-Robot Interaction

arXiv:2604.08921v1 Announce Type: new Abstract: Accurate 3D human keypoints localization is a critical technology enabling robots to achieve natural and safe physical interaction with users. Conventio

local-aiarxiv-cs-cv

'Take Me Home, Wi-Fi Drone': A Drone-based Wireless System for Wilderness Search and Rescue

arXiv:2604.09115v1 Announce Type: cross Abstract: Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solut

agentsarxiv-cs-ro

Tango: Taming Visual Signals for Efficient Video Large Language Models

arXiv:2604.09547v1 Announce Type: new Abstract: Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances th

local-aiarxiv-cs-cv

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2505.12318v2 Announce Type: replace Abstract: Federated Parameter-Efficient Fine-Tuning (Fed-PEFT) enables lightweight adaptation of large pre-trained models in federated learning settings by up

Task-Aware Bimanual Affordance Prediction via VLM-Guided Semantic-Geometric Reasoning

arXiv:2604.08726v1 Announce Type: new Abstract: Bimanual manipulation requires reasoning about where to interact with an object and which arm should perform each action, a joint affordance localizatio

local-aiarxiv-cs-ro

Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios

arXiv:2604.09377v1 Announce Type: new Abstract: Large language models (LLMs) exhibit substantial variability in performance and computational cost across tasks and queries, motivating routing systems

Task-Distributionally Robust Data-Free Meta-Learning

arXiv:2311.14756v2 Announce Type: replace-cross Abstract: Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models w

applicationsarxiv-cs-ai

Task Vectors, Learned Not Extracted: Performance Gains and Mechanistic Insight

arXiv:2509.24169v2 Announce Type: replace Abstract: Large Language Models (LLMs) can perform new tasks from in-context demonstrations, a phenomenon known as in-context learning (ICL). Recent work sugg

TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice

model-releasesarxiv-cs-cl

arXiv:2604.08948v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel in various general domains, they exhibit notable gaps in the highly specialized, knowledge-intensive, and legal

Temperature-Dependent Performance of Prompting Strategies in Extended Reasoning Large Language Models

arXiv:2604.08563v1 Announce Type: cross Abstract: Extended reasoning models represent a transformative shift in Large Language Model (LLM) capabilities by enabling explicit test-time computation for c

Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations

arXiv:2604.08870v1 Announce Type: cross Abstract: Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous proto

Temporal Patch Shuffle (TPS): Leveraging Patch-Level Shuffling to Boost Generalization and Robustness in Time Series Forecasting

arXiv:2604.09067v1 Announce Type: new Abstract: Data augmentation is a crucial technique for improving model generalization and robustness, particularly in deep learning models where training data is

local-aiarxiv-cs-lg

Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

arXiv:2312.09436v3 Announce Type: replace-cross Abstract: The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to max

researcharxiv-cs-ai

Hardware

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v1 Announce Type: cross Abstract: Modern LLM reinforcement learning (RL) workloads require a highly efficient weight transfer system to scale training across heterogeneous computationa

hardwarearxiv-cs-ai

Testing the Assumptions of Active Learning for Translation Tasks with Few Samples

arXiv:2604.08977v1 Announce Type: new Abstract: Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful w

Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design

model-releasesarxiv-cs-cv

arXiv:2604.09047v1 Announce Type: new Abstract: Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relie

The AI Codebase Maturity Model: From Assisted Coding to Self-Sustaining Systems

arXiv:2604.09388v1 Announce Type: cross Abstract: AI coding tools are widely adopted, but most teams plateau at prompt-and-review without a framework for systematic progression. This paper presents th

The causal relation between off-street parking and electric vehicle adoption in Scotland

arXiv:2604.09271v1 Announce Type: new Abstract: The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'c

safetyarxiv-cs-lg

The Fast Lane Hypothesis: Von Economo Neurons Implement a Biological Speed-Accuracy Tradeoff

arXiv:2604.09229v1 Announce Type: cross Abstract: Von Economo neurons (VENs) are large bipolar projection neurons found exclusively in the anterior cingulate cortex (ACC) and frontal insula of species

researcharxiv-cs-ai

The Hot Mess of AI: How Does Misalignment Scale With Model Intelligence and Task Complexity?

arXiv:2601.23045v2 Announce Type: replace Abstract: As AI becomes more capable, we entrust it with more general and consequential tasks. The risks from failure grow more severe with increasing task sc

The Impact of Gait Pattern Personalization on the Perception of Rigid Robotic Guidance: A Pilot User Experience Evaluation

arXiv:2512.17425v2 Announce Type: replace Abstract: Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforc

researcharxiv-cs-ro

The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge

model-releasesarxiv-cs-lg

arXiv:2604.09034v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has significantly impacted the field of natural language processing, but their growing complexity ra

The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?

model-releasesarxiv-cs-cl

arXiv:2601.07220v3 Announce Type: replace Abstract: Multilingual language models (LMs) promise broader NLP access, yet current systems deliver uneven performance across the world's languages. This sur

The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs

arXiv:2601.01580v2 Announce Type: replace-cross Abstract: Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT c

Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems

arXiv:2511.09829v3 Announce Type: replace Abstract: Adversarial patches have emerged as a popular privacy-preserving approach for resisting AI-driven surveillance systems. However, their conspicuous a

applicationsarxiv-cs-ai