Search: “safety”

Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer

arXiv:2605.03769v1 Announce Type: new Abstract: Matrix-based optimizers have demonstrated immense potential in training Large Language Models (LLMs), however, designing an ideal optimizer remains a fo

Normalized Matching Transformer

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arXiv:2503.17715v3 Announce Type: replace Abstract: We introduce the Normalized Matching Transformer (NMT), a deep learning approach for efficient and accurate sparse semantic keypoint matching betwee

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6 May 2026

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605.03065v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control

arXiv:2605.03290v1 Announce Type: new Abstract: Domain randomization (DR) is widely used in policy learning to improve robustness to modeling error, but remains underexplored in contact-rich sampling-

OpenAI violated Canadian privacy laws in developing first ChatGPT model, probe finds https://www.theglobeandmail.com/business/article-openai…

OpenAI violated Canadian privacy laws in developing first ChatGPT model, probe finds https://www.theglobeandmail.com/business/article-openai-chatgpt-violated-canadian-privacy-laws-watchdogs-report/?ut

Optimal Posterior Sampling for Policy Identification in Tabular Markov Decision Processes

arXiv:2605.03921v1 Announce Type: new Abstract: We study the (arepsilon, elta)-PAC policy identification problem in finite-horizon episodic Markov Decision Processes. Existing approaches provide finit

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

arXiv:2605.03490v1 Announce Type: new Abstract: The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI dat

Poly-EPO: Training Exploratory Reasoning Models

arXiv:2604.17654v3 Announce Type: replace Abstract: Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and sca

Population-Aware Imitation Learning in Mean-field Games with Common Noise

arXiv:2605.03357v1 Announce Type: new Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents. In this paper, we

Power-Softmax: Towards Secure LLM Inference over Encrypted Data

arXiv:2410.09457v2 Announce Type: replace Abstract: Modern cryptographic methods for implementing privacy-preserving LLMs such as gls{HE} require the LLMs to have a polynomial form. Forming such a rep

Predicting missing values: A good idea?

arXiv:2605.03733v1 Announce Type: cross Abstract: Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach p

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

arXiv:2605.02372v1 Announce Type: cross Abstract: The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy pr

Pseudo-differential-enhanced physics-informed neural networks

arXiv:2602.14663v2 Announce Type: replace Abstract: We present pseudo-differential enhanced physics-informed neural networks (PINNs), an extension of gradient enhancement but in Fourier space. Gradien

Reinforcement Learning Trained Observer Control for Bearings-Only Tracking

arXiv:2605.02120v1 Announce Type: new Abstract: This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer m

Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

arXiv:2602.00815v2 Announce Type: replace Abstract: Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards

Rethinking the Rank Threshold for LoRA Fine-Tuning

arXiv:2605.03724v1 Announce Type: new Abstract: A recent landscape analysis of LoRA fine-tuning in the neural tangent kernel regime establishes a sufficient condition r(r+1)/2 > KN on the LoRA rank r

RLDX-1 Technical Report

arXiv:2605.03269v1 Announce Type: cross Abstract: While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intellig

Sample-Efficient Optimization over Generative Priors via Coarse Learnability

arXiv:2503.06917v5 Announce Type: replace Abstract: We study zeroth-order optimization where solutions must minimize a cost d(s) while maintaining high probability under a complex generative prior L(s

SCION: Size-aware Policy Orchestration for Nonstationary Object Caches (Long Paper Version)

arXiv:2605.01055v1 Announce Type: cross Abstract: Object caches underpin cloud and edge services, but production workloads are heterogeneous, nonstationary, and throughput-constrained. Recent simple n

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision

arXiv:2605.03846v1 Announce Type: new Abstract: Designing an open-world quadrupedal loco-manipulation system is highly challenging. Traditional reinforcement learning frameworks utilizing exteroceptio

SMoE: An Algorithm-System Co-Design for Pushing MoE to the Edge via Expert Substitution

arXiv:2508.18983v3 Announce Type: replace Abstract: The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts pe

SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification

arXiv:2605.03371v1 Announce Type: new Abstract: Cross-scene hyperspectral image (HSI) classification stands as a fundamental research topic in remote sensing, with extensive applications spanning vari

Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

arXiv:2605.03849v1 Announce Type: new Abstract: Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching

Structured Diffusion Bridges: Inductive Bias for Denoising Diffusion Bridges

arXiv:2605.02973v1 Announce Type: new Abstract: Modality translation is inherently under-constrained, as multiple cross-modal mappings may yield the same marginals. Recent work has shown that diffusio

T^2PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning

arXiv:2605.02178v1 Announce Type: new Abstract: Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite

Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation

arXiv:2605.01750v1 Announce Type: cross Abstract: Grounding is the collaborative process of establishing mutual belief sufficient for the current communicative purpose. While static grounding maps lan

TeamUp: Semantic Project Matching and Team Formation for Learning at Scale

arXiv:2605.03237v1 Announce Type: cross Abstract: Project-based learning improves student engagement and learning outcomes, yet allocating students to appropriately challenging projects while forming

The Design and Composition of Structural Causal Decision Processes

arXiv:2605.02681v1 Announce Type: cross Abstract: We present two new classes of causal models of decision-making agents. Our approach is motivated by the needs of modeling the economics of computing s

The Garden of Forking Paths: Narrative Arc-Conditioned Gameplay Planning

arXiv:2605.01245v1 Announce Type: cross Abstract: Narrative archetypes (e.g., Hero's Journey, Three-act structure) provide universal story structures that resonate across cultures and media and are im

TRACE: A Metrologically-Grounded Engineering Framework for Trustworthy Agentic AI Systems in Operationally Critical Domains

arXiv:2605.03838v1 Announce Type: new Abstract: We introduce TRACE, a cross-domain engineering framework for trustworthy agentic AI in operationally critical domains. TRACE combines a four-layer refer

Tracing Like a Clinician: Anatomy-Guided Spatial Priors for Cephalometric Landmark Detection

arXiv:2605.03358v1 Announce Type: new Abstract: When orthodontists trace cephalometric radiographs, they follow a structured workflow: identify the soft tissue profile, partition the skull into anatom

truly scary: anybody, no matter how noble, can be destroyed by misused AI.

truly scary: anybody, no matter how noble, can be destroyed by misused AI. Artificial intelligence can create deceptive videos that rapidly damage a politician's public reputation. A new study reveals

Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

arXiv:2605.02640v1 Announce Type: new Abstract: As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, en

Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration

arXiv:2605.01257v1 Announce Type: new Abstract: Large-scale GPS trajectory data offer rich observations of human mobility, yet assigning trip purposes to detected stops remains challenging due to the

Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks

arXiv:2502.04419v3 Announce Type: replace-cross Abstract: Generating synthetic datasets via large language models (LLMs) has emerged as a promising approach to improve LLM performance. However, LLMs i

Understanding Self-Supervised Learning via Latent Distribution Matching

arXiv:2605.03517v1 Announce Type: new Abstract: Self-supervised learning (SSL) excels at finding general-purpose latent representations from complex data, yet lacks a unifying theoretical framework th

Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe

arXiv:2605.03677v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models

What’s going on in semis is not sustainable per GS Everyone spending on AI is losing money except for semiconductor companies and the dynami…

What’s going on in semis is not sustainable per GS Everyone spending on AI is losing money except for semiconductor companies and the dynamic is “unprecedented and unsustainable” “Something has to cha

When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning

arXiv:2605.03314v1 Announce Type: new Abstract: In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling

Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis

arXiv:2605.03304v1 Announce Type: new Abstract: The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Tradition

yep, we are about to live in a world where nobody trusts anything. just like Putin always wanted. congratulations! ps it will suck

yep, we are about to live in a world where nobody trusts anything. just like Putin always wanted. congratulations! ps it will suck @GaryMarcus Or very soon no one will take any photo or video seriousl

A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis

arXiv:2605.01336v1 Announce Type: new Abstract: News outlets shape public opinion at a scale that makes automated detection of political bias and factuality essential. However, the field still lacks u

A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning

arXiv:2605.01232v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have enabled visually realistic demonstration generation from a single expert trajectory and a short mul

A Theoretical Game of Attacks via Compositional Skills

arXiv:2605.01034v1 Announce Type: new Abstract: As large language models grow increasingly capable, concerns about their safe deployment have intensified. While numerous alignment strategies aim to re

A Theory of Generalization in Deep Learning

arXiv:2605.01172v1 Announce Type: new Abstract: We present a non-asymptotic theory of generalization in deep learning where the empirical neural tangent kernel partitions the output space. In directio

A Unified Multi-Dynamics Framework for Perception-Oriented Modeling in Tendon-Driven Continuum Robots

arXiv:2511.18088v2 Announce Type: replace Abstract: Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance.

Adaptive Alarm Threshold Prediction in 4G Mobile Networks: A Percentile-Guided Deep Learning Framework with Interpretable Outputs

arXiv:2605.00838v1 Announce Type: cross Abstract: In mobile telecommunications, alarms act as early warning signals. They are triggered when a cell, the basic unit of radio coverage, shuts down or beh