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Scaling Versatile 3D Assets Editing with a Million-Scale Dataset

arXiv:2609.34271v1 Announce Type: new Abstract: Although recent 3D generative models produce increasingly realistic assets, controllable 3D asset editing remains challenging. Existing methods are limi

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ScAn-Bench: Evaluating Scaling Analysis Methodology

DGX agent

arXiv:2609.35707v1 Announce Type: new Abstract: Recent progress in machine learning is driven by large-scale foundation models, where scaling laws and finding optimal scaling prescriptions for archite

researcharxiv-cs-lg

29 Sept 2026

Scanning While Imagining: A Scene-Graph World Model for Robotic Ultrasound Navigation

arXiv:2609.32837v1 Announce Type: new Abstract: Ultrasound (US) acquisition depends on the operator's ability to interpret anatomy and anticipate how the view will change with probe motion. Many robot

researcharxiv-cs-ro

SceneScaffold: Active Scene-State Construction for Unified 3D Scene Understanding

arXiv:2609.33518v1 Announce Type: new Abstract: Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens com

SchemaMem: Schema-Indexed Recurrent Memory for Delayed State Retrieval

arXiv:2609.33436v1 Announce Type: new Abstract: Attention provides direct access to past representations, but retaining an ever-growing history is costly. Recurrent models bound persistent state, yet

Schrodinger--Follmer Actor--Critic: Diffusion Policy Improvement with Finite-Sample Analysis

arXiv:2609.33239v1 Announce Type: cross Abstract: Diffusion policies represent multimodal action distributions, but an advantage-weighted update does not specify how to sample from the resulting targe

safetyarxiv-cs-lg

Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter

arXiv:2609.32640v1 Announce Type: cross Abstract: We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning inte

SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation

arXiv:2609.33399v1 Announce Type: new Abstract: In realistic education, a solution is often expressed not only in words but in a drawing--a circuit, a geometric construction, a function plot--and a te

SCLATE: a Substrate for Continual-Learning Agent Training and Evaluation

arXiv:2609.32391v1 Announce Type: cross Abstract: Continual-learning agents are systems of models, harnesses, and memory operating over long multi-session horizons. Evaluating and training them requir

SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models

arXiv:2609.34044v1 Announce Type: new Abstract: Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pr

Hardware

Scope-WM: Scoped Computation for Efficient Visual World Models

arXiv:2609.33218v1 Announce Type: new Abstract: Visual world models enable robotic planning by predicting future observations, but dense latent-state propagation and sample-intensive trajectory optimi

hardwarearxiv-cs-cv

SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions

arXiv:2510.21330v2 Announce Type: replace Abstract: Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling meth

Scoring the Wrong Question: Readout Failures in Constrained-Option Evaluation

arXiv:2609.33179v1 Announce Type: cross Abstract: Constrained-option scoring reads a model's probabilities for a fixed set of permitted answers, so it returns a score even when the model is about to w

ScreenHaystack: Finding Blind Zones in GUI Grounding

arXiv:2609.32036v1 Announce Type: new Abstract: We introduce ScreenHaystack, a dynamic needle-in-a-haystack benchmark for evaluating spatial reliability in GUI grounding. Instead of testing each targe

Scriboora: Rethinking Human Pose Forecasting

arXiv:2511.15565v3 Announce Type: replace Abstract: Human pose forecasting predicts future human poses from past observations for applications including action recognition, sports analysis, and human-

See it, Say it, Sorted: Mechanistic Diagnosis and Parameter-Space Mitigation of Emergent Misalignment in LLMs

arXiv:2609.34970v1 Announce Type: new Abstract: Safety-aligned LLMs can exhibit emergent misalignment (EM): narrow domain adaptation unexpectedly triggers catastrophic safety failures across unrelated

See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology

arXiv:2609.34277v1 Announce Type: new Abstract: Pathological assessment relies on recognizing fine-grained visual details in histological images. Vision-language models (VLMs) increasingly support pat

Seeing and Solving Are Not Enough for Vision-Language Models

arXiv:2609.33694v1 Announce Type: new Abstract: Vision-language models (VLMs) answer visual questions by combining visual information extraction with downstream problem solving. We investigate a funda

Seeing Parts, Reasoning about Worlds: Visual Inference under Partial Observation

arXiv:2609.32456v1 Announce Type: new Abstract: World modeling under partial observation requires reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded obje

Local Ai

Seeing the Heat: Synthesizing High-Resolution Wood Thermal Responses from Optical Imagery

arXiv:2609.31737v1 Announce Type: new Abstract: The thermal behavior of wood is a critical factor in advanced material assembly. However, pixel-level thermal analysis remains fundamentally constrained

local-aiarxiv-cs-cv

SEES: A Self-Evolving Embodied System via Failure-Guided VLA Policy Adaptation

arXiv:2609.32698v1 Announce Type: new Abstract: Recent vision-language-action (VLA) policies demonstrate promising generalization across diverse short-horizon tasks. However, they remain unreliable on

safetyarxiv-cs-ro

Agents

SegBanana: Steering Unified Multimodal Models into Medical Segmenters

arXiv:2609.34235v1 Announce Type: new Abstract: Medical image segmentation remains challenging in practical deployment, as models often struggle to generalize beyond the distributions covered by their

agentsarxiv-cs-cv

Selecting Diverse SFT Traces Improves Post-RL Generalization

arXiv:2609.33780v1 Announce Type: new Abstract: Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route dive

Selective Fine-Tuning for Targeted and Robust Concept Unlearning

arXiv:2602.07919v2 Announce Type: replace-cross Abstract: Text guided diffusion models are used by millions of users, but can be easily exploited to produce harmful content. Concept unlearning methods

Self-Confirming Superposition Traps in Reinforcement Learning

arXiv:2609.32966v1 Announce Type: new Abstract: Reinforcement learning (RL) trains representations on data selected by the agent's policy, which then uses the resulting returns to guide its next choic

safetyarxiv-cs-lg

Self-Evolutionary Replanning for Failure-Aware Motion Planning

model-releasesarxiv-cs-ro

arXiv:2603.02772v2 Announce Type: replace Abstract: Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficie

Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence

arXiv:2609.35432v1 Announce Type: new Abstract: Vision-language-action (VLA) and world-action (WAM) models map observations and instructions directly to robot actions. This directness ties a policy to

safetyarxiv-cs-ro

Agents

Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis

arXiv:2609.32746v1 Announce Type: cross Abstract: While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several

agentsarxiv-cs-lg

Self-Evolving Time-Series Forecasting Agents with Episodic Memory and Online Policy Learning

arXiv:2609.32689v1 Announce Type: new Abstract: LLM-based agents are increasingly used for time-series forecasting because they can organise contextual information, perform multi-step analysis, and gu

Applications

Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

arXiv:2410.02331v3 Announce Type: replace Abstract: The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led

applicationsarxiv-cs-cv

Self-Reconstruction Dynamics for Autoencoder Reconstruction Refinement

arXiv:2609.32268v1 Announce Type: new Abstract: Standard autoencoder (AE) inference uses a single encoder-decoder pass, though the latent may not be optimal for each sample under a fixed decoder. We a

SelfCue: Making a 3D CT Report Generator Say What It Already Knows

arXiv:2609.31788v1 Announce Type: new Abstract: Progress in 3D CT report generation is usually sought in increasingly sophisticated architectures and larger pools of training data. We find instead tha

Tutorials

SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing

arXiv:2609.34736v1 Announce Type: cross Abstract: Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly

tutorialsarxiv-cs-lg

Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings

arXiv:2609.34294v1 Announce Type: new Abstract: Unsupervised visible-infrared person re-identification (USL-VI-ReID) learns person representations that can be compared across modalities without identi

Semantic Purification for Conditional Representation Learning

arXiv:2602.05464v2 Announce Type: replace-cross Abstract: Conditional representation learning aims to extract criterion-specific features for customized tasks. Recent methods construct conditional sub

SemReward-VL: Semantic Reward-Guided Video-Language Adaptation for Developmental Behavior Assessment

arXiv:2609.33082v1 Announce Type: new Abstract: Developmental screening videos show how children perform specific behaviors, but clinical records usually contain outcomes rather than descriptions of w

SentZero: An Enhanced Sentence-Centric Vision-Language Pretraining for Multi-Task Zero-Shot Chest X-Ray Analysis

arXiv:2609.34479v1 Announce Type: new Abstract: Vision-language (VL) pretraining using paired chest X-ray (CXR) images and radiology reports has shown strong potential for medical image understanding.

Separating personal from population gains when calibrating EEG foundation models for new users

arXiv:2609.34801v1 Announce Type: new Abstract: Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. Thi

Applications

seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequences

arXiv:2609.31801v1 Announce Type: new Abstract: Complex systems such as vehicles, patients, or genomes emit discrete event sequences whose operative question is causal, not predictive: which events ca

applicationsarxiv-cs-lg

Local Ai

Server-Enforced Watermarking in U-Shaped Split Federated Learning

arXiv:2511.14422v2 Announce Type: replace-cross Abstract: U-shaped split federated learning (U-SFL) enables resource-constrained Internet of Things devices to collaboratively train models with an edge

local-aiarxiv-cs-lg

SetOPD: From Few Visual Exemplars to Multimodal Candidate Sets for Remote-Sensing Open-Prompt Detection

arXiv:2609.32529v1 Announce Type: new Abstract: Open-prompt detectors allow users to specify targets with text, visual exemplars, or both. We argue that existing designs underuse the visual modality i

SGA-Flow-GRPO: Spatial Gradient-Guided Credit Assignment for Flow-GRPO

arXiv:2609.32340v1 Announce Type: new Abstract: Reinforcement Learning (RL) has proven effective in aligning flow-based generative models with human preferences. Recently, Flow-GRPO has emerged as an

Shallow Queries, Mature Values: Depth-Asynchronous Self-Speculation for Looped Transformers

arXiv:2609.34538v1 Announce Type: new Abstract: Looped Transformers reuse a shared block across recurrent depths, making autoregressive decoding expensive because every generated token requires many s

Local Ai

Shaping Persistent Representations from Independent Interactions

arXiv:2609.34604v1 Announce Type: new Abstract: World models learn environment dynamics from interaction experience. These dynamics depend on the current state and actions, as well as on properties th

local-aiarxiv-cs-lg

Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents

arXiv:2609.35576v1 Announce Type: cross Abstract: Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and

Shared Autoregressive Context Can Distort Relationships in Synthetic Data

arXiv:2609.32546v1 Announce Type: new Abstract: Large language models can generate several records within one autoregressive completion, making earlier answers available as context for later records.

Sharp Critical Minimax Laws and No-Learning Thresholds in Continuous-Time Adaptive Control

arXiv:2609.33154v1 Announce Type: cross Abstract: We study episodic continuous-time control with an unknown vector control gain, scalar state, quadratic action cost, and smooth convex terminal cost. I

Tutorials

Sharp training-conditional coverage for conformal prediction under covariate shift

arXiv:2609.33456v1 Announce Type: cross Abstract: Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and gua

tutorialsarxiv-cs-lg