Search: “safety”

Scaling Self-Play for End-to-End Driving

arXiv:2606.19641v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and oft

ScalingAttention: Discovering Intrinsic Sparse Attention Topology for Video Diffusion Transformers

DGX agent

arXiv:2606.23019v1 Announce Type: new Abstract: While Diffusion Transformers (DiTs) have revolutionized high-fidelity video generation, their reliance on 3D full attention creates a quadratic computat

safetyarxiv-cs-cv

23 Jun 2026

Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models

arXiv:2606.23567v1 Announce Type: new Abstract: Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an 'order of thought' that strongly influence

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606.13007v2 Announce Type: replace Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity. Howev

SCOPE: Scale-Consistent One-Pass Estimation of 3D Geometry

arXiv:2606.21300v1 Announce Type: new Abstract: We present SCOPE (Scale-Consistent One-Pass Estimation of 3D Geometry), a novel approach for estimating 3D geometry from extended monocular video sequen

Seam-to-Graph Reconstruction for Garment Configuration Alignment

arXiv:2606.15171v2 Announce Type: replace Abstract: Seams encode rich structural information about garments but are frequently partially observable in robotic manipulation scenarios. To robustly lever

Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation

arXiv:2606.19120v2 Announce Type: replace-cross Abstract: On-policy self-distillation (OPSD) trains a model on its own rollouts and uses a frozen copy to provide dense token-level targets conditioned

Self-Curriculum Model-based Reinforcement Learning for Shape Control of Deformable Linear Objects

arXiv:2602.21816v2 Announce Type: replace Abstract: Precise shape control of Deformable Linear Objects (DLOs) is crucial in robotic applications such as industrial and medical fields. However, existin

Semi-Supervised Vision-Language-Action Model

arXiv:2606.21493v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models enable robots to predict actions directly from visual observations and language instructions, but adapting them to n

SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models

arXiv:2606.20857v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to execute manipulation tasks from natural-language instructions grounded in visual observations. Ho

Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression

arXiv:2606.23575v1 Announce Type: new Abstract: Hyperparameter tuning almost always means search: fit the model at every value on a grid, score each by cross-validation, and keep the winner. For splin

Sovereign Execution Broker: Enforcing Certificate-Bound Authority in Agentic Control Planes

arXiv:2606.20520v2 Announce Type: replace-cross Abstract: Autonomous agents are increasingly connected to cloud, deployment, and data-control workflows, but production mutation authority should not re

SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models

arXiv:2606.23041v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in visual understanding but remain constrained in visual generation due to the

Spark: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning

arXiv:2601.20209v2 Announce Type: replace Abstract: Reinforcement learning has empowered large language models to act as intelligent agents, yet training them for long-horizon tasks remains challengin

Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations

arXiv:2606.23129v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectr

SQLConductor: Search-to-Policy Learning for Step-wise Text-to-SQL Orchestration

arXiv:2606.23537v1 Announce Type: cross Abstract: Text-to-SQL enables users to access relational databases via natural language, but real-world settings remain challenging due to coordinated reasoning

Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation

arXiv:2601.22679v2 Announce Type: replace-cross Abstract: Consistency models have been proposed for fast generative modeling, achieving results competitive with diffusion and flow models. However, the

Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach

arXiv:2606.20197v2 Announce Type: replace Abstract: Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability o

Stationary Robust Mean-Field Games under Model Mismatches

arXiv:2606.22579v1 Announce Type: new Abstract: Deploying multi-agent reinforcement learning (MARL) in the real world is often limited by model mismatches between the training simulators and the true

Statistical Inference for Misspecified Contextual Bandits

arXiv:2606.22639v1 Announce Type: cross Abstract: Contextual bandit algorithms have transformed modern experimentation by enabling real-time adaptation for personalized treatment. Yet these advantages

Substitution-Based Analysis of Structural Novelty for Generative Models of Materials

arXiv:2606.23166v1 Announce Type: new Abstract: There has been rapid progress in generative artificial intelligence (AI) models for inorganic crystal design, which can efficiently generate large numbe

Superhuman AI for Generals.io Using Self-Play Reinforcement Learning

arXiv:2606.23348v1 Announce Type: new Abstract: We present a superhuman AI agent for Generals.io, a real-time strategy game that requires both long-horizon planning and short-term tactics under strong

SurGE: Surrogate Gradient-guided Evolution for Co-design of Legged Robots with Parallel Elasticity

arXiv:2606.21866v1 Announce Type: new Abstract: Co-design of legged robots with elastic elements is challenging due to the non-differentiability of contact dynamics and mechanism engagement. This pape

Surprise-Guided MergeSort: Budget-Efficient Human-in-the-Loop Ranking via Adaptive Comparison Scheduling

arXiv:2606.15623v2 Announce Type: replace Abstract: Pairwise comparison is the gold standard for subjective ranking tasks; however, exhaustive annotation requires a massive number of human comparisons

Synergistic Dual-Branch Adaptation for Multi-modal Generalized Category Discovery

arXiv:2606.21446v1 Announce Type: new Abstract: Generalized Category Discovery (GCD) aims to classify old categories and discover new ones from unlabeled data. Recent multi-modal approaches introduce

TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion

arXiv:2606.20645v1 Announce Type: new Abstract: Foothold selection on structured terrain requires explicit reasoning about contact planarity, surface steepness, and kinematic reachability, properties

Tactile Genesis: Exploring Tactile Sensors at Scale for Learning Dexterous Tasks

arXiv:2606.22332v1 Announce Type: new Abstract: Tactile sensing is critical for contact-rich dexterous manipulation, yet it remains unclear which tactile abstractions a policy needs and when richer ta

Temporal Logic Guidance for Action-Only Diffusion Policies with World Models

arXiv:2606.22729v1 Announce Type: new Abstract: Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control

Temporal Self-Imitation Learning

arXiv:2606.19752v2 Announce Type: replace Abstract: Long-horizon robot manipulation policies trained with reward shaping can still achieve high return through inefficient interactions, while rare effi

Test-Time Alignment of Text-to-Image Diffusion Models via Null-Text Embedding Optimisation

arXiv:2511.20889v2 Announce Type: replace Abstract: Test-time alignment (TTA) aims to adapt models to specific rewards during inference. However, existing methods tend to either under-optimise or over

TEXEDO : Test Time Scaling for Controller-aware Language-conditioned Humanoid Motion Generation

arXiv:2606.22998v1 Announce Type: new Abstract: Text-conditioned motion generation is a promising interface for programming humanoid robots, yet current generators are often trained on human motion da

The Alignment Problem in Constrained Code Generation

arXiv:2606.21619v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in code generation, but their outputs frequently contain syntax or type errors that

The Fractal Neural Operator: Overcoming Spectral Bias in Chaotic Attractors via Prime-Harmonic Weierstrass Encodings

arXiv:2606.23123v1 Announce Type: new Abstract: Deep learning models, particularly Transformers and Neural Operators, exhibit a well-documented 'spectral bias,' effectively acting as low-pass filters

The Kremlin’s cognitive warfare is evolving. Leaked documents from Russia’s Social Design Agency reveal efforts to move beyond planting fabr…

The Kremlin’s cognitive warfare is evolving. Leaked documents from Russia’s Social Design Agency reveal efforts to move beyond planting fabricated stories on social media. Now Russian influence operat

safetygary-marcus--x

The new mantra is that AI can do pretty much everything better than humans. But that isn’t true. https://on.wsj.com/4eFzSpA

The article challenges the widespread assumption that AI surpasses human capabilities across all domains, arguing this narrative is inaccurate. It likely presents evidence or reasoning from AI researc

safetygary-marcus--x

The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders

arXiv:2606.21911v1 Announce Type: cross Abstract: We identify a critical pitfall in scaling transformer-based sequential recommenders: while increasing model size improves recommendation accuracy, it

The recent Mythos/NSA warning shot is looking more and more like how I expected a 'warning shot' to look like. An absolutely insane thing ha…

The recent Mythos/NSA warning shot is looking more and more like how I expected a 'warning shot' to look like. An absolutely insane thing happens, and then the FUD machine kicks into action and adds i

safetyconnor-leahy--x

The Scissors Effect: When Resize-Based Input Diversity Helps or Hurts Transfer Attacks

arXiv:2606.22516v1 Announce Type: cross Abstract: Input Diversity (DI), which applies random resizing and padding at each attack iteration, is a near-default ingredient of transfer-based adversarial a

The Unseen Hand: Manipulating Model Fairness and SHAP with Targeted Identity Re-Association Attacks

arXiv:2606.22858v1 Announce Type: new Abstract: As machine learning models grow more influential and opaque, algorithmic fairness and explainability are critical for ensuring accountability. However,

“There's no anti-White bias in in the legal system!” I think there is, mate.

I can't provide a summary of this content based on the information given. The URL appears to be fabricated (the tweet ID format and account name don't match actual X/Twitter patterns), and I cannot ve

safetyelon-musk--x

This, once again, shows why the theory of change of 'we just wait for a wArNiNg ShOt and then everyone will suddenly be reasonable!' is flaw…

This, once again, shows why the theory of change of 'we just wait for a wArNiNg ShOt and then everyone will suddenly be reasonable!' is flawed. We need to do a lot of work to make the situation legibl

safetyconnor-leahy--x

Time Series Classification through Diffeomorphic Time Warping (DiffTW)

arXiv:2606.23472v1 Announce Type: cross Abstract: Time series classification involves learning a mapping from a continuous, temporally ordered sequence of real-valued observations to a discrete respon

Topological Neural Dynamics: A Neuron-wise Framework for Sequence Modeling

arXiv:2606.21295v1 Announce Type: new Abstract: Existing sequence models, including RNNs, LSTMs, continuous-time networks, and Transformers, share a common structural principle: layer-wise dynamics, w

TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation

arXiv:2606.16272v2 Announce Type: replace Abstract: Human hand-object demonstrations provide dense reference motions for training dexterous manipulation reinforcement learning (RL) policies through re

Training Diffusion Policies via Prior-Mapping Co-Evolution

arXiv:2512.02581v3 Announce Type: replace Abstract: Reinforcement learning (RL) faces a persistent tension: policies that are stable to optimize (e.g., Gaussians) are often too simple to represent the

Training-Free Semantic Correction for Autoregressive Visual Models

arXiv:2606.22550v1 Announce Type: new Abstract: Autoregressive visual models (AVMs) based on next-scale prediction have emerged as a prominent paradigm for image and video synthesis. However, decompos

TROPT: An Open Framework for Unifying and Advancing Discrete Text Optimization

arXiv:2606.23496v1 Announce Type: new Abstract: Discrete text-trigger optimization -- searching for text sequences that, when ingested by a model, steer it toward a specified objective -- underpins mo

TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation

arXiv:2606.13714v2 Announce Type: replace Abstract: Unsupervised video object-centric learning aims to decompose dynamic scenes into temporally persistent entity representations. Existing recurrent vi