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STHMoE: Hypergraph-Enhanced Heterogeneous Dependency Coordination for LLM-Based Urban Traffic Data Forecasting

arXiv:2609.15172v1 Announce Type: new Abstract: Spatio-temporal traffic forecasting is a fundamental big data analytics task for intelligent transportation systems, where massive urban sensor streams

applicationsarxiv-cs-ai

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Stochastic Gradient Descent for Operator Learning in Hilbert Spaces: Convergence Rates and Minimax Lower Bounds

DGX agent

arXiv:2402.04691v5 Announce Type: replace-cross Abstract: This study investigates the use of stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We study weak and stro

tutorialsarxiv-cs-lg

15 Sept 2026

Stochastic Gradient Descent over P2

arXiv:2609.13343v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) admits diffusion approximations that replace the complicated randomness of stochastic gradients by Gaussian noise, p

Agents

Strong and Compact Policies for Submodular Markov Decision Processes via LP-Based Submodular Orienteering

arXiv:2609.15539v1 Announce Type: cross Abstract: Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we h

agentsarxiv-cs-lg

Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding

model-releasesarxiv-cs-cv

arXiv:2609.13267v1 Announce Type: new Abstract: Scientific charts encode quantities in axes, legends, and geometric marks, yet large vision-language models still treat them as natural photographs. Vis

Structured Features Overfit Where Random Features Grok

arXiv:2609.15047v1 Announce Type: new Abstract: Xu, Vardi and Safran (ICML 2026) prove that over-parameterized ridge regression over an unstructured random Gaussian feature map groks, with the delay b

Sublinear Sketches for Approximate Nearest Neighbor and Kernel Density Estimation

arXiv:2510.23039v2 Announce Type: replace Abstract: Approximate Nearest Neighbor (ANN) search and Approximate Kernel Density Estimation (A-KDE) are fundamental problems at the core of modern machine l

Local Ai

SURE-Map: Self-Correcting Streaming Geometric Foundation Model

arXiv:2609.15795v1 Announce Type: new Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issue

local-aiarxiv-cs-cv

SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM

arXiv:2601.10814v3 Announce Type: replace Abstract: Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metr

applicationsarxiv-cs-ro

Surprising Effectiveness of Self-Demonstrations in Enhancing Schema-Ontology Mapping with LLMs

arXiv:2609.13776v1 Announce Type: new Abstract: Integrating heterogeneous relational databases into a centralized ontology remains a persistent challenge in enterprise knowledge representation, primar

Surrogate-Assisted Genetic Programming with Phenotypic Characterisation in Dynamic Multi-Mode Project Scheduling

arXiv:2609.14418v1 Announce Type: cross Abstract: Dynamic multi-mode resource-constrained project scheduling requires decisions to be made under precedence constraints, limited resources, multiple exe

Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals

arXiv:2609.13272v1 Announce Type: cross Abstract: Gravitational-wave denoising must handle the full diversity of spinning, precessing binaries, since the recovered waveform underpins parameter estimat

Sweet Talkers: How Query Formulation Shapes Sycophancy in Romantic Relationship Advice

arXiv:2609.13841v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support and relationship advice, where a model's tendency to preserve a user's face can

Tutorials

Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

arXiv:2609.15763v1 Announce Type: cross Abstract: Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it import

tutorialsarxiv-cs-ai

Symmetries and Singularities

arXiv:2609.14663v1 Announce Type: new Abstract: Deep neural networks are highly over-parameterized, and different parameter values represent the same predictive function. This makes their effective co

Symmetry- and Property-Aware Crystal Generation with Reinforcement Learning for Inverse Materials Design

arXiv:2609.13468v1 Announce Type: cross Abstract: The inverse design of crystalline materials ultimately seeks structures with desired physical properties. However, for many functional responses, a fa

Syn2Logic: End-to-End Neuromorphic Design Automation

arXiv:2608.25536v2 Announce Type: replace-cross Abstract: In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges com

SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer

arXiv:2402.18945v5 Announce Type: replace-cross Abstract: Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and train

Tutorials

Synthetic Data in Marketing Research: How to Evaluate and When to Trust

arXiv:2609.13995v1 Announce Type: new Abstract: Debate over synthetic data in marketing research has polarized between claims that large language models (LLMs) make human respondents obsolete and call

tutorialsarxiv-cs-ai

Synthetic Leprosy Image Generation Using Mask-Conditioned Latent Diffusion and Transfer Learning from Large Chronic Wound Datasets

arXiv:2609.13226v1 Announce Type: new Abstract: Machine learning for neglected tropical diseases is limited by data, not algorithms: public annotated image sets for leprosy (Hansen's disease) number i

researcharxiv-cs-cv

SyntheticDoc: A Large Synthetic Dataset for Document Unwarping and Illumination Correction

arXiv:2609.15503v1 Announce Type: new Abstract: Deep learning models have become the standard tool for document rectification and illumination correction, yet their performance is fundamentally bound

researcharxiv-cs-cv

SyRHM: Symbolic-Language-Enhanced Reasoning with Associative Retrieval for Zero-shot Harmful Meme Detection

arXiv:2609.13794v1 Announce Type: new Abstract: Detecting harmful memes is critical for maintaining safe online communities. However, harmful intent is often implicit, arising from visual-textual inco

researcharxiv-cs-cl

T-LoopFormer: Token-Level Elastic-Depth Looped Transformers for Latent Reasoning With Dynamic Routing

arXiv:2609.15160v1 Announce Type: new Abstract: Looped Transformers have recently demonstrated strong performance in both reasoning and language tasks by reusing a shared set of parameters across mult

T-SMART: Mechanism-Level Attribution for Tool-Augmented Time-Series Question Answering

arXiv:2609.14142v1 Announce Type: new Abstract: Large language models (LLMs) can struggle with time-series question answering (TS-QA), especially when numerical signals are serialized as text and requ

Tabby: An Open Pretraining Recipe for Time Series Foundation Models

arXiv:2609.13956v1 Announce Type: new Abstract: In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was buil

Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients

arXiv:2609.14841v1 Announce Type: new Abstract: Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability wh

Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning

arXiv:2509.18930v4 Announce Type: replace-cross Abstract: Neural algorithmic reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning. Despite its

Talking to Me or Someone Else? Rethinking Talk-to-Me Detection in Egocentric Videos

arXiv:2609.14118v1 Announce Type: cross Abstract: Online understanding of who is talking to the camera wearer is a key capability for egocentric social interaction. However, existing talk-to-me (TTM)

Target-Checked Reliability Score Refinement for Video Question Answering

arXiv:2609.13288v1 Announce Type: new Abstract: Video-language models can answer multiple-choice questions with high confidence yet be wrong. We study whether answer-level reliability scores can be im

researcharxiv-cs-cv

Task-Aware Federated Fine-Tuning for MoE-based Large Language Models

arXiv:2609.13395v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become a widely adopted architecture for Large Language Models (LLMs), as it improves model capacity while limiting compu

Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials

arXiv:2609.13309v1 Announce Type: cross Abstract: Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of a

safetyarxiv-cs-ai

Task-Directed Residual AddUNet:Perfect-Reconstruction Routing for Full-Rate Representations

arXiv:2609.15857v1 Announce Type: new Abstract: This paper establishes a perfect-reconstruction (PR) interpretation of AddUNet and its full-rate realization, and introduces a Residual Full-Rate PR arc

Task-Distribution-Aware Counterweight Synthesis and Constrained Co-Design for Serial Manipulators

arXiv:2609.15082v1 Announce Type: cross Abstract: Passive counterweights are simple gravity compensators, but a counterweight selected from a single pose is not generally optimal for the configuration

applicationsarxiv-cs-lg

Task-Specified Active Metrological Inspection with Measurement-Steered VLA Manipulation and Deterministic Evidence Gating

arXiv:2609.14219v1 Announce Type: cross Abstract: High-mix low-volume (HMLV) manufacturing requires inspection systems to adapt to changing parts, specifications, and work orders without repeated task

applicationsarxiv-cs-ai

TATK: Triple-Aware Top-K Learning with Knowledge-Grounded Verification for LLM-based Sequential Recommendation

arXiv:2609.14565v1 Announce Type: new Abstract: LLM-based sequential recommenders usually cast next-item prediction as text generation, but this interface is poorly matched to full-catalog top-K ranki

TEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models

arXiv:2609.15205v1 Announce Type: cross Abstract: Table extraction from texts is an important task for information systems, and recent approaches that prompt large language models (LLMs) with instruct

Tele360: Real-Time Feed-Forward Human Reconstruction from Sparse Unposed Cameras

arXiv:2609.15032v1 Announce Type: new Abstract: Live free-viewpoint visualization of real humans is critical for immersive communication and interactive digital experiences. Existing methods either re

safetyarxiv-cs-cv

Temperature Fragility and the Conditional Benefits of Truncation Sampling

arXiv:2609.15476v1 Announce Type: new Abstract: Large language models generate text by sampling each token from a predicted distribution, and a temperature parameter sets how far the draw strays from

Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models

arXiv:2609.15177v1 Announce Type: new Abstract: Diffusion language models (dLLMs) promise fast inference by generating multiple tokens in parallel, but suffer severe performance degradation when paral

safetyarxiv-cs-lg

Tensor Decomposition Structure Search Framework from an Interaction Perspective

arXiv:2603.02720v2 Announce Type: replace Abstract: Recently, tensor decompositions have attracted increasing attention. Fundamentally, different interactions among factors induce distinct tensor deco

applicationsarxiv-cs-cv

Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks

arXiv:2505.20132v2 Announce Type: replace-cross Abstract: Tensorizing a neural network involves reshaping some or all of its dense weight matrices into higher-order tensors and approximating them usin

TestHallVQA: Exploring LVLMs' Document-Level Reasoning under Redundant Contexts from Scientific Exams

arXiv:2609.13158v1 Announce Type: new Abstract: Large Vision--Language Models (LVLMs) are increasingly expected to perform visual question answering (VQA) over planar media. However, existing planar V

Testing the Limits of Truth Directions in LLMs

arXiv:2604.03754v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth direction. Previous

TF-IDF and BM25 Are Exact KL Divergences

arXiv:2609.14016v1 Announce Type: cross Abstract: TF-IDF and BM25 are two of the most widely used methods for scoring query-document relevance, yet neither has a standard probabilistic derivation that

researcharxiv-cs-cl

The Agentic Company OS: Substrate Inversion for Sustained Enterprise Agent Deployment

arXiv:2609.13334v1 Announce Type: cross Abstract: Enterprise AI agents often succeed in a demonstration and then stall once they must operate day after day. An industry report estimates that most pilo

safetyarxiv-cs-ai

The Anthropic grift machine! FED BY FEAR THEATER. Take a moment and sit with @kevinnbass chart here. You do not need to be a forensic accoun…

The Anthropic grift machine! FED BY FEAR THEATER. Take a moment and sit with @kevinnbass chart here. You do not need to be a forensic accountant to understand this money laundering. If this is not pro

researchyann-lecun--x

The Attribution-Compression Frontier in Retrieval-Augmented Generation

arXiv:2609.14245v1 Announce Type: cross Abstract: Context compression reduces generator input in retrieval-augmented generation, but answer quality alone does not characterize citation attribution. We

The average-farmer illusion in language-model simulations of agricultural decisions

arXiv:2609.15038v1 Announce Type: new Abstract: Language-model agents are increasingly used as synthetic people in surveys and social simulations, yet their apparent realism is often judged from popul