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arXiv:2610.00936v1 Announce Type: new Abstract: With the global population rapidly aging, maintaining independence in Activities of Daily Living (ADLs), particularly bathing or showering, has become a

applicationsarxiv-cs-ro

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SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning

DGX agent

arXiv:2610.01962v1 Announce Type: cross Abstract: The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of p

researcharxiv-cs-cv

2 Oct 2026

Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces

arXiv:2610.00751v1 Announce Type: cross Abstract: Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These inclu

Safety

Signed Lexical Confidence for Risk-Calibrated Intent Routing

arXiv:2610.00262v1 Announce Type: cross Abstract: Selective intent routing allows an assistant to act on reliable predictions while deferring uncertain requests. Standard confidence scores primarily r

safetyarxiv-cs-ai

SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation

arXiv:2610.02201v1 Announce Type: cross Abstract: High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize l

local-aiarxiv-cs-ai

SimplexUQ: An Evaluation Framework and Benchmark for Conformal Uncertainty on Simplex-Valued Predictions

arXiv:2610.00523v1 Announce Type: new Abstract: Conformal prediction guarantees marginal coverage, but a single calibration threshold can still spread that coverage unevenly, over-covering easy region

Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems

arXiv:2610.01974v1 Announce Type: new Abstract: Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently stru

SkeleWAM: Skeleton World-Action Modeling for Efficient Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.02120v1 Announce Type: new Abstract: World action models (WAMs) combine robot action generation with future state prediction. Existing WAMs typically predict videos or learned visual latent

SkillEvoLean: Mutation-enhanced skill evolution for Lean provers

arXiv:2610.01799v1 Announce Type: new Abstract: Skill evolution offers a promising way to improve large language model agents without updating their parameters, but its use in formal theorem proving r

SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization

arXiv:2610.00704v1 Announce Type: new Abstract: Natural-language skills are textual procedural memories through which large language model (LLM) agents retain reusable task knowledge without updating

agentsarxiv-cs-lg

Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations

arXiv:2606.22676v2 Announce Type: replace Abstract: Refusal on a safety benchmark does not reveal how stable that behavior will remain after model updates. Benign downstream fine-tuning can weaken ref

SLIM: Simplex-Lattice Interpolation Merging

arXiv:2610.01037v1 Announce Type: new Abstract: Optimizing merging coefficients for large language models can require many costly benchmark evaluations. We propose extbf{Simplex-Lattice Interpolation

Smoother Flow Matching via Contrastive Trajectory Repulsion

arXiv:2610.01408v1 Announce Type: new Abstract: Trajectory crossing remains a critical bottleneck in Flow Matching (FM), and previous works typically view these crossings from a theoretical optimizati

local-aiarxiv-cs-cv

SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing

model-releasesarxiv-cs-cv

arXiv:2610.00851v1 Announce Type: new Abstract: Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes. A key challenge in OS

SoftServe: A Scalable Quasi-Newton Method for Deep Learning

arXiv:2610.02182v1 Announce Type: cross Abstract: Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limite

SoK: Decentralized Agent Economic Infrastructure

arXiv:2610.01756v1 Announce Type: cross Abstract: Decentralized agent economies increasingly build a single task from protocols that were designed and secured separately. This creates a simple problem

agentsarxiv-cs-ai

SonarVoxNet: Diver Detection in 3D Bounding Box using 3D Sonar

arXiv:2610.01644v1 Announce Type: new Abstract: Autonomous underwater vehicles (AUVs) assisting human divers must continuously track not only the diver's 3D position but also their full-body orientati

agentsarxiv-cs-ro

Soundwich: Video Generation with Layered and Controllable Audio

arXiv:2610.00691v1 Announce Type: new Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed track

Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution

arXiv:2610.00417v1 Announce Type: new Abstract: Repeated training on model-generated data can degrade later models. One possible response is to use provenance when deciding which generated examples to

Industry

Sources: Broadcom's Wall Street syndicate is amassing $60B in AI chip financing to help Anthropic and others access chips and other key AI infrastructure (Bloomberg)

Bloomberg: Sources: Broadcom's Wall Street syndicate is amassing 60B in AI chip financing to help Anthropic and others access chips and other key AI infrastructure — Broadcom Inc.'s Wall Street syndic

industrytechmeme

Spatial Lifting for Dense Prediction

arXiv:2610.00017v1 Announce Type: cross Abstract: We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a hig

Spatial Strategies, Not Actions: Vector-Quantized Geodesics as Tools for LLM-Driven Agents

arXiv:2610.00613v1 Announce Type: new Abstract: Large language model (LLM) based agents are often criticized for lacking spatial understanding and mainly exploiting statistical text patterns. We inves

agentsarxiv-cs-ai

Spatially Gated Diffusion for Localized Counterfactual Chest Radiograph Editing

arXiv:2610.00805v1 Announce Type: cross Abstract: Editing a chest radiograph requires completing the requested change while preserving unrelated content. We study a latent diffusion editor with an ins

Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

arXiv:2610.00395v1 Announce Type: new Abstract: Inference-time steering enables pretrained diffusion models to satisfy new constraints without full retraining. However, specificity-aware generation is

SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL

arXiv:2610.02023v1 Announce Type: new Abstract: While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making

local-aiarxiv-cs-ai

Sphere Encoder 2

model-releasesarxiv-cs-cv

arXiv:2610.02208v1 Announce Type: new Abstract: Sphere Encoder is an autoencoder that generates images by decoding random points from a high-dimensional latent sphere. We identify two limitations of t

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

arXiv:2610.01418v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (M

SPIRAL: Learning to Search and Aggregate

arXiv:2606.23595v2 Announce Type: replace Abstract: Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequen

StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

arXiv:2610.00742v1 Announce Type: cross Abstract: Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test w

Stable and Counterfactually Robust Physical World Models from Imposed Structure and Learned Physics

arXiv:2610.00280v1 Announce Type: new Abstract: A world model learns to forecast how a physical system evolves from recorded trajectories, yet the systems it imitates obey physical laws that are neith

STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models

arXiv:2610.01302v1 Announce Type: new Abstract: Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image diffu

STATERA: Hidden Mass Estimation via Zero-Shot Sim-to-Real Kinematics using Frozen Temporal Tubelets

arXiv:2610.00003v1 Announce Type: cross Abstract: Vision models pretrained for frame-level appearance often struggle to infer hidden physical properties from motion. We study center-of-mass (CoM) loca

Applications

STCFormer: Adaptive Spatio-Temporal Modeling with Dynamic Cluster Transformer for Station-based Weather Forecasting

arXiv:2610.00377v1 Announce Type: new Abstract: Station-based weather forecasting supports daily life and economic activity, yet accurate forecasts require modeling complex spatial dependencies among

applicationsarxiv-cs-lg

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

arXiv:2610.00907v1 Announce Type: cross Abstract: Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to pr

Applications

StoCFL: A Stochastically Clustered Federated Learning Framework for Non-IID Data with Dynamic Client Participation

arXiv:2303.00897v2 Announce Type: replace Abstract: Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federat

applicationsarxiv-cs-lg

Safety

Stochastic Optimal Control for Continuous-Time fMRI Representation Learning

arXiv:2502.04892v2 Announce Type: replace Abstract: Learning robust representations from functional magnetic resonance imaging (fMRI) is fundamentally challenged by the temporal irregularity and noise

safetyarxiv-cs-lg

Stochastic Rounding in Low-Precision Transformer Inference: A Variable-Precision Emulation Study of a Small GPT-2

arXiv:2610.01889v1 Announce Type: new Abstract: Should low-precision transformer inference use stochastic rounding (SR) or round-to-nearest (RN)? The answer depends on where in the network you look. W

Streaming algorithms for robust max-min diversification

arXiv:2610.01456v1 Announce Type: new Abstract: Given a set of n points X in a metric space and an integer k, max-min diversification aims to select k points of X maximizing their minimum pairwise dis

Structure-agnostic Causal Representation Learning

arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requir

Structured-Noise Masked Modeling for Video, Audio and Beyond

arXiv:2503.16311v2 Announce Type: replace-cross Abstract: Masked modeling has emerged as a robust self-supervised learning framework. However, most methods rely on random masking, which disregards the

Subtoken Vision Transformer for Fine-grained Recognition

arXiv:2607.09086v2 Announce Type: replace Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfor

local-aiarxiv-cs-cv

Super interesting paper from Meta Superintelligence Labs on controlling long agent runs. They use the same workers and same budget, and Prog…

Super interesting paper from Meta Superintelligence Labs on controlling long agent runs. They use the same workers and same budget, and ProgramBench goes from 63.7% to 71.5% with GPT-5.5 when a dedica

model-releasesdair-ai--x

Tutorials

SuperMotion: Source-Preserving Denoising for Text-Driven Human Motion Editing

arXiv:2610.01517v1 Announce Type: new Abstract: Text-driven human motion editing aims to realize a requested change while preserving compatible source content. Existing diffusion editors rely largely

tutorialsarxiv-cs-cv

Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning

arXiv:2610.01224v1 Announce Type: new Abstract: Latent world models plan by scoring candidate action sequences with distances in latent space. However, task success is judged by physical quantities, w

Supervising Sound Localization by In-the-wild Egomotion

arXiv:2610.01388v1 Announce Type: cross Abstract: We present a method for learning binaural sound localization using egomotion as a supervisory signal. Over the course of a video, the cameras directio

local-aiarxiv-cs-ai

SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systems

arXiv:2610.01381v1 Announce Type: cross Abstract: Peptide sequence governs both the structures formed through supramolecular assembly and the dynamics by which they emerge, but predicting either requi

Surface-volume self-supervised representation learning of brain MRI for genetic discovery

arXiv:2610.02114v1 Announce Type: new Abstract: Existing genome-wide association studies (GWAS) of brain imaging provide predefined or deep-learning-derived imaging phenotypes, yet these phenotypes co