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Robust Bayesian Optimization with Q-Exponential Surrogates

arXiv:2609.32775v1 Announce Type: new Abstract: Bayesian optimization (BO) is a widely used framework for optimizing expensive black-box objectives, but standard BO methods often use Gaussian process

Robust Peak-cost Constrained Reinforcement Learning

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DGX agent

arXiv:2607.15457v2 Announce Type: replace Abstract: We study robust peak-cost constrained reinforcement learning (ours), where the objective is to maximize expected reward while controlling the maximu

safetyarxiv-cs-lg

29 Sept 2026

RoGSW4RLD: Feed-Forward 4D Gaussian Lifting for Robot World Model Rollouts

arXiv:2609.35311v1 Announce Type: new Abstract: Action-conditioned video world models predict future robot interactions from multiple cameras, yet their outputs remain disparate video collections rath

Role-Guided MOE for Encoder-Level Pathology Representation Learning in WSI Classification

arXiv:2609.34897v1 Announce Type: new Abstract: Whole slide image classification is a fundamental task in computational pathology, where patch representation quality directly affects downstream aggreg

Role Support in Knowledge-Graph Error Ranking: Predictor Regimes and Evaluation Policies

arXiv:2512.22318v3 Announce Type: replace Abstract: When do role-specific training counts improve knowledge-graph error ranking? We evaluate fifteen from-scratch predictors, nine development-selected

Applications

Rondo: Unsupervised Discovery of Recurring Temporal Structure

arXiv:2609.32500v1 Announce Type: new Abstract: Many real-world time series data exhibit structural properties at multiple scales, from short, recurring units to complex sequences composed of these un

applicationsarxiv-cs-lg

RoPE is Dead, Long Live RoPE: Towards Scalable Data-aware Positional Encodings

arXiv:2609.34556v1 Announce Type: cross Abstract: Transformers process tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural re

Rotated Manifold Optimization for Low-Rank Adaptation

arXiv:2609.34264v1 Announce Type: new Abstract: We propose a novel optimizer for low-rank adaptation (LoRA) that explicitly incorporates the gauge symmetry of low-rank factorization. Our optimizer ext

ROTE: Benchmarking Neural Memorization on Complexity-Controlled Symbolic Sequences

arXiv:2609.31918v1 Announce Type: new Abstract: We introduce ROTE (RollOut Testing of Exact memorization), a benchmarking protocol for evaluating symbolic memorization of neural architectures. We stud

Routing Drift Alone Does Not Diagnose Failure in Merged MoE LLMs

arXiv:2609.32821v1 Announce Type: cross Abstract: Model merging efficiently combines specialized large language models (LLMs) without joint retraining, but can substantially alter expert routing in Mi

Routing Without Embeddings: Fast And Interpretable Routing With Regular Expressions

arXiv:2609.34326v1 Announce Type: new Abstract: Large Language Model (LLM) routers commonly rely on neural query embeddings, with larger encoders expected to better capture query intent and difficulty

RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG

arXiv:2609.31698v1 Announce Type: new Abstract: Most existing MEG and EEG (M/EEG) visual decoding methods align brain signals with a single global embedding extracted from a pretrained visual encoder,

RRG-SLAM: Real-time Reflection-aware Gaussian SLAM for Indoor Scenes

arXiv:2609.34527v1 Announce Type: new Abstract: We introduce the first real-time reflection-aware Gaussian SLAM system for indoor scenes. The system features a reflection-aware TSDF-Gaussian hybrid re

RSD-Poker: Structure-Adaptive and Shift-Robust Risk-Utility Certification for Residual Policies in Imperfect-Information Games

arXiv:2609.33669v1 Announce Type: cross Abstract: Residual policy adaptation provides a lightweight way to modify a strong reference policy, but a shared scale and a fixed subgroup partition can hide

Tutorials

RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports

arXiv:2609.35637v1 Announce Type: new Abstract: Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, t

tutorialsarxiv-cs-cv

RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning

arXiv:2603.09160v2 Announce Type: replace Abstract: Dense image captioning is critical for vision-language pretraining and for text-to-image generation, but scaling expert-quality annotations is prohi

Tutorials

S^2COPE: Self-Supervised Concept Discovery via Preference Learning

arXiv:2606.14586v2 Announce Type: replace Abstract: Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficul

tutorialsarxiv-cs-cv

Safe Score Matching: Diffusion Policies with Hamilton-Jacobi Reachability for Online Safe Reinforcement Learning

arXiv:2609.33337v1 Announce Type: new Abstract: Online safe reinforcement learning (RL) seeks policies that maximize reward while satisfying safety constraints. A popular line of research in safe RL r

SafeMol: Dual-Modality Safety Alignment for Molecular Multimodal Models

arXiv:2609.33640v1 Announce Type: new Abstract: Molecular multimodal models support diverse understanding and generation tasks but may introduce safety vulnerabilities when handling hazardous molecule

Safety-Constrained Cascade Inference for Robust Malaria Cell Classification Under Field Corruptions

arXiv:2609.33005v1 Announce Type: new Abstract: Automated malaria diagnosis from thin blood-smear microscopy could meaningfully reduce the burden on under-resourced laboratories, but a model that maxi

SAGE: Semantic Audio Generative Encoder

arXiv:2609.32755v1 Announce Type: cross Abstract: Audio autoencoders compress waveforms into compact latent representations that serve as the interface between raw audio and downstream models. Current

SAGE: Subspace Alignment for Classifier-Free Guidance in Mixture-of-Experts Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2609.34525v1 Announce Type: new Abstract: Diffusion Transformers with Mixture-of-Experts (MoE) routing are a leading recipe for scaling generative models. Classifier-Free Guidance (CFG) is essen

SAGE: Symbolic Action-Gating and Editing for LLM Task Planners

model-releasesarxiv-cs-ro

arXiv:2609.34268v1 Announce Type: new Abstract: Large language models (LLMs) are now the default cognitive core of embodied household agents, yet the plans they emit are rarely checked against a groun

Sam Altman says OpenAI won’t go public until its models are safe

For months, people have wondered when OpenAI will go public. CEO Sam Altman says it won't happen until the company can make better promises about model safety, with no firm timeline in sight. 'We inte

safetythe-verge-ai

SAMBAR: Selective Anchoring via Method of Multipliers for Balanced Knowledge Acquisition and Retention in Vision-Language-Action Models

arXiv:2609.32108v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models leverage large-scale pretraining to ultimately achieve generalist manipulation. Deployed VLA policies must support c

Same Probe, Different Numbers: Are Activation Probes Robust to Inference-Time Numerical Non-Determinism?

arXiv:2609.31796v1 Announce Type: new Abstract: Activation probes are increasingly used to monitor LLMs in deployment. A probe is typically trained under one inference configuration, then used under w

Agents

Same Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures

arXiv:2609.34215v1 Announce Type: cross Abstract: Evaluating how LLM agents recover from mid-task failures is central to deploying reliable agentic systems. Existing checkpoint-based benchmarks measur

agentsarxiv-cs-lg

Sample What You Say: Aligning Language Models to Sample the Distributions They State

arXiv:2609.34929v1 Announce Type: new Abstract: Language models are increasingly used to sample from a specified distribution, for instance, to simulate survey respondents or generate synthetic data.

Hardware

SANTA++: Sampling Attention through Representative Keys

arXiv:2609.35629v1 Announce Type: new Abstract: Attention often concentrates on a small subset of tokens in the context, but which subset matters changes from one query to the next. To exploit this ch

hardwarearxiv-cs-lg

Saturation-Insensitive Dueling Bandits with General Function Approximation

arXiv:2609.33011v1 Announce Type: new Abstract: We study contextual dueling bandits with general function approximation under the Bradley-Terry-Luce (BTL) preference model. A key challenge in this set

Save Your Saturated Data: Learning Beyond Reward Saturation in Group-Based RL

arXiv:2609.33126v1 Announce Type: new Abstract: Group-relative reinforcement learning (RL) relies on reward variation among sampled responses to estimate informative relative advantages. As language m

Scaffold Then Internalize: Representation Injection for Diffusion Transformers

arXiv:2609.35292v1 Announce Type: new Abstract: Recent representation alignment (REPA) methods accelerate diffusion transformer training by aligning projections of the transformer's hidden states with

Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process

arXiv:2609.33641v1 Announce Type: cross Abstract: Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas tur

Scalable Attribution and Control of Model Behavior During Training

arXiv:2609.33667v1 Announce Type: new Abstract: Attributing and controlling model behavior during training requires identifying each example's contribution quickly enough to act before the next update

Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing

arXiv:2609.34499v1 Announce Type: new Abstract: Graph neural networks (GNNs) excel at representation learning on Knowledge Graphs (KGs), achieving stateof-the-art performance on tasks like link predic

Scalable In-Context Reinforcement Learning with Recurrent Algorithm Distillation

arXiv:2609.35333v1 Announce Type: new Abstract: Algorithm Distillation (AD) has demonstrated the remarkable ability of Transformers to perform in-context reinforcement learning without explicit weight

Scalable In-Domain Self-Supervised Foundation Model for Dense Representation Transfer in High-Resolution Plant Imaging

arXiv:2609.32183v1 Announce Type: new Abstract: High-resolution plant imaging enables detailed characterization of plant morphology, but dense scientific analysis remains limited by costly pixel-level

Scaling Properties of Same-Family On-Policy Distillation

arXiv:2609.32722v1 Announce Type: new Abstract: *Reinforcement learning (RL)* can induce substantial reasoning capabilities in large language models (LLMs), but how much of this capability transfers a

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

model-releasesarxiv-cs-cv

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

ScAn-Bench: Evaluating Scaling Analysis Methodology

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

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

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

model-releasesarxiv-cs-cv

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