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SHEFL: Sparse Heterogeneous Ensemble Federated Learning with Group-Balanced Aggregation

arXiv:2508.08552v3 Announce Type: replace Abstract: System heterogeneity in federated learning (FL) requires training workloads and communication demands to be adapted to client resource capacities. F

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Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach

DGX agent

arXiv:2609.33605v1 Announce Type: new Abstract: Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to

model-releasesarxiv-cs-lg

29 Sept 2026

SIFT: Enhancing Time Series Foundation Models via Semantic Invariance and Structural Fidelity Fine-Tuning

arXiv:2609.32676v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable zero-shot performance through extensive pre-training on massive time series datasets. Nev

applicationsarxiv-cs-lg

SignFLIP: A Unified Model for Sign Language Translation and Generation via Stage-wise Alignment at Scale

arXiv:2609.35225v1 Announce Type: cross Abstract: Sign language translation and generation share the goal of bidirectional alignment between text and sign representations. However, existing approaches

safetyarxiv-cs-cv

Silent Failures in Agentic Security Evaluation: A Validated Harness for Tool-Call Mediation Under Indirect Prompt Injection

arXiv:2609.32691v1 Announce Type: cross Abstract: LLM agents that invoke privileged tools are vulnerable to indirect prompt injection (IPI), in which adversarial instructions embedded in retrieved dat

SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flows

arXiv:2609.32279v1 Announce Type: new Abstract: Efficiently learning and sampling from high dimensional, multimodal unnormalized distributions without target samples remains a challenging problem. Alt

Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported

arXiv:2609.33947v1 Announce Type: cross Abstract: Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which dimi

Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization

arXiv:2609.31940v1 Announce Type: new Abstract: Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to expli

applicationsarxiv-cs-lg

Simplex Diffusion Models

arXiv:2609.35553v1 Announce Type: new Abstract: Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state. This iterative refinement

Simulating Robotic Locomotion in Sand: Resistive Force Theory in an Open-Source Physics Engine

arXiv:2606.19504v2 Announce Type: replace Abstract: Recent advancements in Resistive Force Theory (RFT) enable approximation of ground reaction forces for locomotion in sand without the computational

researcharxiv-cs-ro

Simulation-Based Inference for Plate Reverb System Identification

arXiv:2609.35295v1 Announce Type: cross Abstract: We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse resp

Simulation-Based Quantum System Inference with Neural Posterior Estimation

arXiv:2609.34995v1 Announce Type: cross Abstract: Models of quantum systems faithfully map system parameters to observations, but the inverse problem of parameter inference from measurement data prese

Simulation for Planetary Robotic Perception and Autonomy: A Concise Survey of Recent Capabilities and Gaps

arXiv:2609.34743v1 Announce Type: new Abstract: Planetary robotics is an important enabler of scientific exploration in environments where direct human-in-the-loop operation is costly, hazardous, or i

researcharxiv-cs-ro

Simulation-Free Learning of GP-SDEs from Irregular Observations

arXiv:2609.33112v1 Announce Type: new Abstract: Gaussian process stochastic differential equations (GP-SDEs) provide a flexible Bayesian model for unknown continuous-time state dynamics with uncertain

Simultaneous Translation between Sign Languages

arXiv:2609.35608v1 Announce Type: new Abstract: Deaf and hard-of-hearing (DHH) signers cannot converse in real time across different sign languages today: existing sign-to-sign translation systems run

applicationsarxiv-cs-cv

SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living

arXiv:2609.34907v1 Announce Type: cross Abstract: Bathroom acoustic-event recognition can support ambient assisted living in settings where continuous video monitoring is undesirable. However, practic

Hardware

Single-Layer MeMo as a Randomized Hamming-Kernel Classifier

arXiv:2609.34562v1 Announce Type: new Abstract: MeMo (Zanzotto et al., 2025) is a recent language-model architecture that stores associations between token contexts and next tokens in a correlation ma

hardwarearxiv-cs-lg

Singularities of Non-negative Matrix Factorization and their application to Bayesian inference

arXiv:2609.34043v1 Announce Type: cross Abstract: Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (R

Skeletons in Flow: Graph Structured Flow Matching for Human Motion Prediction

arXiv:2609.32231v1 Announce Type: new Abstract: Human motion prediction requires diverse future trajectories that remain consistent with observed motion and the articulated physical structure of the b

SketchSSM: Write to the Full State, Read from a Compact Sketch

arXiv:2609.33051v1 Announce Type: new Abstract: Hybrid-attention models replace most softmax attention layers with linear attention, reducing KV-cache growth and enabling larger decode batches where r

SkillPE: Creativity-Oriented Cinematic Skill Evolution for Text-to-Video Prompt Engineering

arXiv:2609.34335v1 Announce Type: new Abstract: Achieving high-quality, cinematic results in text-to-video generation remains challenging for non-experts, whose prompts often lack professional narrati

SleuthBench: Benchmarking Statistical LLM Evaluation Using Tabular Hidden Signals

arXiv:2609.34228v1 Announce Type: new Abstract: Evaluating statistical discovery by large language model (LLM) agents requires verifiable analytical ground truth. Establishing such ground truth for re

Sliced Orlicz-Wasserstein

arXiv:2609.32847v1 Announce Type: cross Abstract: We propose sliced Orlicz-Wasserstein (SOW) distance which is a generalization of sliced Wasserstein (SW) distance. SOW replaces the L^p norm in SW wit

SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving

arXiv:2609.34117v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire expert pool, making expert-weight traf

SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models

arXiv:2609.33575v1 Announce Type: new Abstract: Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without e

safetyarxiv-cs-ro

Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoders

arXiv:2607.11196v2 Announce Type: replace Abstract: Deploying object-centric models for real-world scene understanding typically requires complex pipelines to achieve both robust scene decomposition a

safetyarxiv-cs-cv

SLP-ProbHard: Probabilistic Hard-Constrained Learning via Structural Latent Parameterization

arXiv:2609.33515v1 Announce Type: new Abstract: Many probabilistic predictors must satisfy exact structure in every stochastic realization, yet common hard-constraint approaches form predictions in am

applicationsarxiv-cs-lg

Small transformers track Bayesian evidence for latent common causes via a context-invariant mechanism

arXiv:2609.35161v1 Announce Type: new Abstract: We present an in-depth investigation of how a form of Bayesian reasoning about common causes can emerge as a cross-contextual generalization in small, t

SMAT: Simple and Efficient Merge-Aware Training

arXiv:2609.33437v1 Announce Type: new Abstract: Model merging integrates the capabilities of multiple experts without joint retraining, but standard expert training optimizes task loss alone and does

Smoothing the Semantic Landscape: Generalizable AI-Generated Image Detection via Text-Induced Flatness

arXiv:2603.00717v3 Announce Type: replace Abstract: Recent advances in generative models have exposed a fundamental weakness of image fake detectors: they often fail to generalize to unseen synthesis

researcharxiv-cs-cv

SMORE: Stability-Promoting Mesh-Agnostic Model Reduction for Time-Dependent PDEs

arXiv:2609.33205v1 Announce Type: new Abstract: High-fidelity simulations of time-dependent partial differential equations (PDEs) are computationally expensive, motivating data-driven reduced-order su

SNaP: One-Step Posterior Sampling for Noisy Inverse Problems

arXiv:2609.34071v1 Announce Type: new Abstract: Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network e

researcharxiv-cs-cv

SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression

arXiv:2609.31965v1 Announce Type: cross Abstract: Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scie

safetyarxiv-cs-lg

SocialHumanoid: Towards Expressive Humanoid Behavior via One-Step Co-Speech Motion Generation

arXiv:2609.33311v1 Announce Type: cross Abstract: Humanoid robots are increasingly expected to serve as embodied social agents that communicate naturally with humans through face-to-face interaction.

researcharxiv-cs-cv

Soft Geometric Inductive Bias for Object Centric Dynamics

arXiv:2512.15493v2 Announce Type: replace Abstract: Physical systems are naturally parameterized in terms of geometric entities and their transformations. While exact equivariance can be a powerful in

safetyarxiv-cs-lg

Agents

SoFT: Soft Targets for Generalizable LLM Fine-Tuning

arXiv:2609.32493v1 Announce Type: new Abstract: Distillation enables student language models to acquire new capabilities from expert teachers. However, integrating knowledge from multi-teacher, multi-

agentsarxiv-cs-lg

Local Ai

Sol-H3: Recursive Self-Improvement for MiniMax-H3 Inference Acceleration on Sol-Engine across Cloud and Edge

arXiv:2609.35110v1 Announce Type: cross Abstract: Video diffusion models are rapidly scaling and exhibiting enhanced generation capabilities. Among these recent advancements, MiniMax-H3 stands out as

local-aiarxiv-cs-cv

SOLAR: A State-Driven Online Learning Rate Scheduler for LLM Pretraining

arXiv:2609.34681v1 Announce Type: new Abstract: Learning-rate (LR) scheduling plays a central role in large language model (LLM) pretraining, yet current practice still relies heavily on hand-crafted

SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning

arXiv:2609.35440v1 Announce Type: new Abstract: Large language models are trained with backpropagation, whose global gradient coordinates all layers but forces each to hold its activations and wait fo

SolveEdit: Benchmarking Visual Problem Solving in Generative Models

arXiv:2609.35504v1 Announce Type: new Abstract: Machine intelligence is often evaluated through abstract reasoning problems, yet many real-world problems are visual, such as arranging objects, repairi

Agents

SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation

arXiv:2609.34707v1 Announce Type: new Abstract: Object navigation requires an embodied agent to find an object in an unseen environment under partial observability and a limited motion budget. Existin

agentsarxiv-cs-ro

Source Anchoring for Physical Consistency in Flow Matching Models

arXiv:2609.33510v1 Announce Type: new Abstract: Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generat

Source-Lifted Flow Matching for Intervenable Multimodal Imitation

model-releasesarxiv-cs-ro

arXiv:2607.10206v2 Announce Type: replace Abstract: Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions. However, their stochasticity

SPACE-LoRA: Allocating Activation-Subspace Protection for Continual Learning

arXiv:2609.34453v1 Announce Type: new Abstract: This study addresses the catastrophic forgetting problem that occurs when sequentially learning successive tasks using Low-Rank Adaptation (LoRA) from a

SPACE: Sparse Predictive Attractor via Counterfactual Eviction for Streaming Video Memory

arXiv:2609.32592v1 Announce Type: new Abstract: Fixed-capacity streaming video memory requires repeated eviction decisions whose effects accumulate over time. Yet existing policies are evaluated prima

Local Ai

Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies

arXiv:2511.03095v2 Announce Type: replace Abstract: Modern artificial intelligence has revolutionized how we extract representations from scientific data, yet the statistical properties of these repre

local-aiarxiv-cs-lg

Sparsity by Default: The Theory and Practice of ARD in Gaussian Process Regression for Variable Selection

arXiv:2609.33550v1 Announce Type: cross Abstract: Automatic relevance determination (ARD) is the standard device for input selection in Gaussian process (GP) regression. By giving the covariance kerne

Hardware

SparSP: Exploiting Communication Sparsity for Sequence-Parallel Video DiTs

arXiv:2609.32197v1 Announce Type: cross Abstract: Diffusion Transformers have become the dominant architecture for video generation. Their substantial computational cost motivates scaling inference ac

hardwarearxiv-cs-lg