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Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models

arXiv:2512.21651v2 Announce Type: replace Abstract: Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-cons

safetyarxiv-cs-lg

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Rethinking the Good Enough Embedding for Easy Few-Shot Learning

DGX agent

arXiv:2605.14145v1 Announce Type: new Abstract: The field of deep visual recognition is undergoing a paradigm shift toward universal representations. The Platonic Representation Hypothesis suggests th

researcharxiv-cs-cv

15 May 2026

REVISOR: Beyond Textual Reflection, Towards Multimodal Introspective Reasoning in Long-Form Video Understanding

arXiv:2511.13026v3 Announce Type: replace Abstract: Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied

safetyarxiv-cs-cv

ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization

arXiv:2605.14497v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lie

safetyarxiv-cs-ai

Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse

arXiv:2605.14925v1 Announce Type: new Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.g., rain, snow, fog), against a galler

safetyarxiv-cs-cv

Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons

arXiv:2603.02115v2 Announce Type: replace-cross Abstract: General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local,

local-aiarxiv-cs-ai

Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization

arXiv:2602.05285v2 Announce Type: replace Abstract: A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experiment

ROK-FORTRESS: Measuring the Effect of Geopolitical Transcreation for National Security and Public Safety

arXiv:2605.14152v1 Announce Type: cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual

RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution

arXiv:2605.15154v1 Announce Type: cross Abstract: Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attr

RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression

arXiv:2605.14359v1 Announce Type: cross Abstract: Vector quantization is a fundamental tool for compressing high-dimensional embeddings, yet existing multi-codebook methods rely on static codebooks th

researcharxiv-cs-ai

RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation

arXiv:2605.14543v1 Announce Type: cross Abstract: Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves.

S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning

arXiv:2605.13872v1 Announce Type: cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is operationalized as a hormonal

agentsarxiv-cs-ai

Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes

arXiv:2408.16307v3 Announce Type: replace-cross Abstract: Automatic controller tuning is attractive for robotics and mechatronic systems whose dynamics are difficult to model accurately, but direct bl

Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation

arXiv:2605.14174v1 Announce Type: new Abstract: Safe navigation for mobile robots demands policies that remain reliable under the high-consequence perception uncertainty of cluttered environments. Yet

safetyarxiv-cs-ro

SAGE3D: Soft-guided attention and graph excitation for 3D point cloud corner detection

arXiv:2605.15088v1 Announce Type: new Abstract: We present SAGE3D, a hybrid Transformer-based model for corner detection in airborne LiDAR point clouds. We propose a multi-stage solution built on a hi

researcharxiv-cs-cv

SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer

model-releasesarxiv-cs-cv

arXiv:2605.15178v1 Announce Type: new Abstract: We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p,

Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image

arXiv:2605.14984v1 Announce Type: cross Abstract: Generating a street-level 3D scene from a single satellite image is a crucial yet challenging task. Current methods present a stark trade-off: geometr

Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects

arXiv:2505.09552v3 Announce Type: replace-cross Abstract: Mixed-effects models are widely used to model data with hierarchical grouping structures and high-cardinality categorical predictor variables.

applicationsarxiv-cs-lg

Scalable Solution of the Stochastic Multi-path Traveling Salesman Problem via Neural Networks

arXiv:2605.14662v1 Announce Type: cross Abstract: The multi-path Traveling Salesman Problem with stochastic travel costs arises in hybrid vehicle routing applications designed for Smart City and City

Scalable Subset Selection in Linear Mixed Models

arXiv:2506.20425v3 Announce Type: replace-cross Abstract: Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalize

Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model

arXiv:2605.14567v1 Announce Type: cross Abstract: We propose a simple mechanism by which scaling laws emerge from feature learning in multi-layer networks. We study a high-dimensional hierarchical tar

ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning

arXiv:2510.23818v2 Announce Type: replace Abstract: As large language models (LLMs) continue to scale in size, the computational overhead has become a major bottleneck for task-specific fine-tuning. W

SceneForge: Structured World Supervision from 3D Interventions

arXiv:2605.14399v1 Announce Type: new Abstract: Many multimodal learning tasks require supervision that remains consistent across edits, viewpoints, and scene-level interventions. However, such superv

researcharxiv-cs-cv

SceneFunRI: Reasoning the Invisible for Task-Driven Functional Object Localization

arXiv:2605.14704v1 Announce Type: cross Abstract: In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from c

SceneParser: Hierarchical Scene Parsing for Visual Semantics Understanding

model-releasesarxiv-cs-cv

arXiv:2605.14923v1 Announce Type: new Abstract: General scene perception has progressed from object recognition toward open-vocabulary grounding, part localization, and affordance prediction. Yet thes

SciPaths: Forecasting Pathways to Scientific Discovery

model-releasesarxiv-cs-cl

arXiv:2605.14600v1 Announce Type: new Abstract: Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples

model-releasesarxiv-cs-cv

arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans

SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation

arXiv:2605.14926v1 Announce Type: new Abstract: Achieving pixel-level accurate segmentation of structural cracks across diverse scenarios remains a formidable challenge. Existing methods face signific

applicationsarxiv-cs-cv

SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification

arXiv:2504.09549v3 Announce Type: replace Abstract: Aerial-Ground Person Re-IDentification (AG-ReID) aims to retrieve specific persons across cameras with different viewpoints. Previous works focus on

researcharxiv-cs-cv

SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping

arXiv:2605.14683v1 Announce Type: new Abstract: High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for

researcharxiv-cs-ro

Second-Order Actor-Critic Methods for Discounted MDPs via Policy Hessian Decomposition

arXiv:2605.14982v1 Announce Type: cross Abstract: We address the discounted reward setting in reinforcement learning (RL). To mitigate the value approximation challenges in policy gradient methods, ac

safetyarxiv-cs-ai

SEDGE: Structural Extrapolated Data Generation

arXiv:2604.02482v2 Announce Type: replace Abstract: This paper aims to address the challenge of data generation beyond the training data and proposes a framework for Structural Extrapolated Data GEner

applicationsarxiv-cs-lg

SEDiT: Mask-Free Video Subtitle Erasure via One-step Diffusion Transformer

arXiv:2605.14894v1 Announce Type: new Abstract: Recent breakthroughs in video diffusion models have significantly accelerated the development of video editing techniques. However, existing methods oft

local-aiarxiv-cs-cv

Seed3D 2.0: Advancing High-Fidelity Simulation-Ready 3D Content Generation

arXiv:2605.13862v1 Announce Type: cross Abstract: We present Seed3D 2.0, an advanced 3D content generation system built on Seed3D 1.0, with substantial improvements across generation fidelity, simulat

local-aiarxiv-cs-cv

Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI

arXiv:2510.16196v2 Announce Type: replace-cross Abstract: Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to

applicationsarxiv-cs-ai

Tutorials

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies

arXiv:2605.14551v1 Announce Type: new Abstract: Instance normalization (IN) is widely used in non-stationary multivariate time series forecasting to reduce distribution shifts and highlight common pat

tutorialsarxiv-cs-lg

Selective Safety Steering via Value-Filtered Decoding

arXiv:2605.14746v1 Announce Type: new Abstract: While large language models (LLMs) are trained to align with human values, their generations may still violate safety constraints. A growing line of wor

safetyarxiv-cs-lg

Self-Distilled Agentic Reinforcement Learning

arXiv:2605.15155v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a central paradigm for post-training LLM agents, yet its trajectory-level reward signal provides only coars

safetyarxiv-cs-ai

Self-Pruned Key-Value Attention: Learning When to Write by Predicting Future Utility

arXiv:2605.14037v1 Announce Type: cross Abstract: Under modern test-time compute and agentic paradigms, language models process ever-longer sequences. Efficient text generation with transformer archit

local-aiarxiv-cs-cl

Self-Regulated Learning in Essay Writing: Consistency of Strategies and Impact on Outcomes

arXiv:2605.14228v1 Announce Type: cross Abstract: Background: Abilities for effective self-regulated learning (SRL) are critical for lifelong learning, particularly during adolescence when these skill

applicationsarxiv-cs-lg

Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems

arXiv:2605.14318v1 Announce Type: new Abstract: Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-releva

researcharxiv-cs-ai

SemaTune: Semantic-Aware Online OS Tuning with Large Language Models

arXiv:2605.15026v1 Announce Type: cross Abstract: Online OS tuning can improve long-running services, but existing controllers are poorly matched to live hosts. They treat scheduler, power, memory, an

Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems

arXiv:2605.15050v1 Announce Type: new Abstract: Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and s

Sequential Resource Trading Using Comparison-Based Gradient Estimation

arXiv:2408.11186v4 Announce Type: replace-cross Abstract: We study sequential multi-issue trading between two greedily rational agents who exchange resources from a finite set of categories. Each agen

agentsarxiv-cs-ai

Sharing the Load: Autonomous Multi-Rover Cargo Transport

arXiv:2510.18766v2 Announce Type: replace Abstract: A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to

agentsarxiv-cs-ro

Sheaf-Theoretic Transport and Obstruction for Detecting Scientific Theory Shift in AI Agents

arXiv:2605.14033v1 Announce Type: new Abstract: Scientific theory shift in AI agents requires more than fitting equations to data. An artificial scientific agent must detect whether an existing repres

Silent Collapse in Recursive Learning Systems

arXiv:2605.14588v1 Announce Type: new Abstract: Recursive learning -- where models are trained on data generated by previous versions of themselves -- is increasingly common in large language models,

agentsarxiv-cs-lg

Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming

arXiv:2505.01584v3 Announce Type: replace-cross Abstract: Adaptive video streaming optimizes Quality of Experience (QoE) metrics by selecting appropriate bitrates according to varying network bandwidt

applicationsarxiv-cs-ai