Search: “safety”

The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech

arXiv:2609.13150v1 Announce Type: cross Abstract: Reference-free quality predictors such as UTMOS, DNSMOS and SCOREQ are the de facto automatic evaluators for text-to-speech (TTS) and are increasingly

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The Marked Edge Walk: A Novel MCMC Algorithm for Sampling of Graph Partitions

DGX agent

arXiv:2510.17714v3 Announce Type: replace-cross Abstract: Novel Markov Chain Monte Carlo (MCMC) methods have enabled the generation of large ensembles of redistricting plans modeled as a graph partiti

safetyarxiv-cs-lg

15 Sept 2026

To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentation

arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy

To Each Language Its Tokenizer: Modular Tokenizers for Efficient Multilingual LLMs

arXiv:2609.15528v1 Announce Type: new Abstract: Multilingual Large Language Models (LLMs) traditionally rely on a single vocabulary shared by all supported languages, which can lead to uneven compress

Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing

arXiv:2609.15921v1 Announce Type: new Abstract: Dexterous manipulation of deformable objects demands continuous fingertip-level regulation of pressure, friction, and incipient slip. We study one of th

Towards Identifying the Dataset Biases Causing Phantom Transfer

arXiv:2609.14449v1 Announce Type: new Abstract: Recent work has shown that a teacher model can transfer a bias to a student through a dataset from which every explicit reference to that bias has been

TRACE: Two-Stage Detector-Response Estimation With Angular Cosine Expansion for Ring Artifact Correction in Photon-Counting CT

arXiv:2609.15834v1 Announce Type: new Abstract: Detector response nonuniformity introduces systematic projection errors and ring artifacts in photon-counting detector computed tomography (PCD-CT). In

Trust-Region Neural Moving Horizon Estimation for Robots

arXiv:2309.05955v5 Announce Type: replace Abstract: Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which use

TryOnReward: Learning Foveated Consistency for Reinforcement Fine-Tuning of Virtual Try-On

arXiv:2609.13259v1 Announce Type: cross Abstract: Virtual Try-On (VTON) aims to dress a person with the reference garment, producing visually reasonable results aligned with human preferences. Turning

Two-Stage Personalized Gait Phase Estimation in Stroke Survivors During Exoskeleton-Assisted Walking: An Offline Feasibility Study

arXiv:2609.14984v1 Announce Type: new Abstract: This study evaluated personalized gait phase estimation for stroke survivors using functional inertial measurement unit (IMU) alignment and two-stage se

Unfair Utilities and First Steps Towards Improving Them

arXiv:2306.00636v3 Announce Type: replace-cross Abstract: Many fairness criteria constrain the policy or choice of predictors, which can have unwanted consequences, in particular, when optimizing the

UniCAR-RL: Seeing Better before Thinking Deeper in Visual Mathematics

arXiv:2609.13849v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often struggle with complex mathematical visual reasoning primarily due to a lack of fine-grained perception, c

Unsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals

arXiv:2505.17763v2 Announce Type: replace Abstract: The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially du

Utility-Guided Agent Orchestration for Efficient LLM Tool Use

arXiv:2603.19896v2 Announce Type: replace Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable

Validating Hybrid-State Cache Recovery for GLM-5.3-Flash with vLLM and LMCache

arXiv:2609.15030v1 Announce Type: cross Abstract: External cache transfers can succeed while a hybrid language model resumes from an inconsistent state. We examine the full 45-layer GLM-5.3-Flash mode

VGFM: Expressive Robot Policies via Dense Value Guidance in Flow Matching

arXiv:2609.14261v1 Announce Type: cross Abstract: Recent robot learning paradigms increasingly rely on large offline datasets of robotic interactions to train control policies. Expressive generative m

ViperQ: Order Flow Pattern Recognition via Auction Market Theory for Reinforcement Learning Trading

arXiv:2609.13825v1 Announce Type: new Abstract: Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) or

VLBiMan++: Expanding the Generalization Boundary of Vision-Language Anchored One-Shot Bimanual Manipulation

arXiv:2609.14310v1 Announce Type: new Abstract: Generalizable bimanual robotic manipulation requires a reusable task prior that can persist across increasingly diverse tasks, objects, scenes, embodime

Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading

arXiv:2609.15150v1 Announce Type: new Abstract: Unpaired cross-modal distillation transfers grade structure from histopathology into a micro-ultrasound (micro-US) encoder by aligning a pooled needle-r

When Do Learned Priors Help Visual Inertial Estimation? A Controlled Study of Prior Integration, Calibration, Initialization, and Backend Consistency

arXiv:2609.13777v1 Announce Type: cross Abstract: Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence c

When Edit Localization Amplifies Relative Selection Bias: Gradient Geometry, Target Mismatch, and Importance Weighting

arXiv:2609.13709v1 Announce Type: new Abstract: Human corrections identify editable spans, but the examples receiving corrections may come from a selective feedback channel. We analyze this interactio

When Greedy Sampling Explores: KL-Regularized Contextual Bandits without Eluder-Dimension Dependence

arXiv:2609.13564v1 Announce Type: new Abstract: We study KL-regularized contextual bandits under both reward and preference feedback. We show that greedy sampling can achieve logarithmic regret withou

Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition

arXiv:2609.07474v2 Announce Type: replace Abstract: Language models compute over tokens: language is their input, their output, and increasingly their internal representation. Whether language should

Who Teaches Which Token? Verifier-Gated Multi-Expert On-Policy Distillation for Scientific Reasoning

arXiv:2609.15404v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (OPD) is becoming the standard way to integrate specialist capabilities into one model: train experts with RL, then

Why oh why should we trust the companies to do so, @DavidSacks? I missed the part where they have earned our trust.

Why oh why should we trust the companies to do so, @DavidSacks? I missed the part where they have earned our trust. Top AI companies Anthropic and OpenAI should make products and pace their own progre

safetygary-marcus--x

Will Anthropic and OpenAI succeed in meaningfully slowing frontier AI progress, whether through US government restrictions, voluntary restra…

In a September 2026 poll titled “Will Anthropic and OpenAI succeed in meaningfully slowing frontier AI progress…”, participants were asked whether these companies could significantly slow development

safetygary-marcus--x

X-WBC: A Cross-Embodiment Foundation Model for Humanoid Whole-Body Control

arXiv:2609.15213v1 Announce Type: new Abstract: Scaling humanoid whole-body control toward general-purpose deployment requires large human motion corpora and training experience shared across robot bo

You are what you eat and AI models especially so https://x.com/EMostaque/status/2099580512675262512?s=20

You are what you eat and AI models especially so https://x.com/EMostaque/status/2099580512675262512?s=20 The Labs need to focus on 10x or 100x their effort on ALIGNMENT. Stop training the models on th

safetyemad-mostaque--x

3D CT-to-PET Translation via Latent Brownian Bridge Diffusion

arXiv:2609.12860v1 Announce Type: new Abstract: Computed tomography (CT) and positron emission tomography (PET) provide complementary anatomical and functional information for cancer diagnosis and tre

A lot of alignment issues would be sorted if companies had to be 100% transparent on datasets used for training models. No cyber or biologic…

A lot of alignment issues would be sorted if companies had to be 100% transparent on datasets used for training models. No cyber or biological (outside standard medical) data allowed for base models T

safetyemad-mostaque--x

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

arXiv:2609.13050v1 Announce Type: new Abstract: Regularization-based methods have become a standard approach for training Deep Reinforcement Learning policies against adversarial input perturbations.

Agent as Policy for Robotic Manipulation

arXiv:2609.12541v1 Announce Type: new Abstract: We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-speci

AI agents blew the whistle on their cheating colleagues

A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent

safetymit-tech-review

Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, partic

ArtManip: Category-Level Articulated In-Hand Manipulation

arXiv:2609.12498v1 Announce Type: new Abstract: Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stem

Balancing Emotional Alignment and Semantic Consistency in Image Generation via Reinforcement Learning with Valence-Arousal Anchoring

arXiv:2609.12830v1 Announce Type: new Abstract: Continuous emotion control in text-to-image generation requires a model to improve affective alignment without changing the objects, layout, or scene de

Bias-Corrected Data Synthesis for Imbalanced Learning

arXiv:2510.26046v3 Announce Type: replace-cross Abstract: Class imbalance complicates probabilistic classification because standard training objectives emphasize majority-class performance. Synthetic

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases

arXiv:2504.07133v2 Announce Type: replace-cross Abstract: We revisit the problem of estimating k linear regressors with self-selection bias in d dimensions with the maximum selection criterion, as int

Can We Trust LLM Judges: A Study of Capability-Dependent Biases and Multi-Judge Ensemble for Bias Calibration

arXiv:2609.12002v1 Announce Type: new Abstract: LLMs are increasingly used as automated judges for model training and evaluation, yet individual judges exhibit systematic biases that undermine reliabi

Chain-SLAM: Globally Consistent Backend for Multi-Session LiDAR SLAM via Chained Loop Closure

arXiv:2609.12221v1 Announce Type: new Abstract: Maintaining consistency over long spatial and temporal horizons remains a fundamental challenge in large-scale LiDAR SLAM, particularly when integrating

DCRA: Diffusion-Conditioned Representation Alignment for Robust Time-Series Learning

arXiv:2609.11997v1 Announce Type: new Abstract: Learning robust representations for time-series signals under noise and distribution shifts remains challenging, especially in clinical applications suc

Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions

arXiv:2603.27510v2 Announce Type: replace Abstract: Statistical fairness metrics in AI-driven credit decisions conflate two causally distinct mechanisms: discrimination operating directly from a prote

DIA: Denoising Intermediate Advantage for Diffusion Policy Optimization

arXiv:2609.12245v1 Announce Type: new Abstract: Diffusion-based robot policies have become widely used in robotic manipulation, where they are typically trained with behavior cloning. However, policie

Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking

arXiv:2609.12674v1 Announce Type: new Abstract: Advanced large language models (LLMs) with long context windows can substantially reduce input truncation in document-level machine translation (DocMT).

DRS-VPT: Directly Relocalizing in a Scan with Vision Point Transformers

arXiv:2609.12557v1 Announce Type: new Abstract: We present DRS-VPT, a feed-forward transformer architecture for foundational image-to-scan registration. Given query images and a reference 3D point clo

Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control

arXiv:2607.10625v2 Announce Type: replace Abstract: Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. Howe

DWMP: Leveraging Dual World Models for Humanoid Obstacle Traversal

arXiv:2609.12347v1 Announce Type: new Abstract: Humanoid robots must traverse cluttered obstacle fields using onboard proprioceptive and visual observations, yet existing methods usually process multi

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

arXiv:2609.13053v1 Announce Type: cross Abstract: Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependen