Search: “safety”

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

arXiv:2607.29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circu

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Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints

DGX agent

arXiv:2501.04426v2 Announce Type: replace-cross Abstract: Offline diversity maximization under imitation constraints can transform demonstration data into a set of distinct behavioral policies, improv

safetyarxiv-cs-ai

3 Aug 2026

DuetHOI: Language-Guided Bimanual Hand--Object Motion Generation with Articulation Planning and Contact Refinement

arXiv:2603.08390v3 Announce Type: replace-cross Abstract: Bimanual articulated-object interaction generation requires a model to capture the evolution of object articulation, coordination between the

DynoDINO: Harnessing Dynamic Latent Information from DINO Features for Multi-Phase Medical Image Segmentation

arXiv:2607.29568v1 Announce Type: new Abstract: Multi-phase Contrast-Enhanced Computed Tomography (CECT) plays a central role in the diagnosis and characterization of focal lesions by capturing tempor

End-to-End Fairness Optimization with Fair Decision-Focused Learning

arXiv:2607.29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages. We introd

Expert-Data Alignment Governs Generation Quality in Decentralized Diffusion Models

arXiv:2602.02685v3 Announce Type: replace Abstract: Decentralized Diffusion Models (DDMs) route denoising through experts trained independently on disjoint data clusters, which can strongly disagree i

FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

arXiv:2607.28945v1 Announce Type: new Abstract: Synthetic tabular data is increasingly used in privacy-preserving data sharing, data augmentation, and to mitigate downstream classifier bias. State-of-

fascinating comments on possible slowdown – but probably a red herring. - Anthropic and OpenAI presumably aren’t colluding to slowdown, they…

fascinating comments on possible slowdown – but probably a red herring. - Anthropic and OpenAI presumably aren’t colluding to slowdown, they are asking governments to build a framework such that a slo

Fisher Information, Training and Bias in Fourier Regression Models

arXiv:2510.06945v2 Announce Type: replace Abstract: Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evalua

Flow Matching with Missing Data

arXiv:2607.28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide. We propose Missing-Data Flow Matching, which trea

future generations will have no idea what was real and what was not.

Gary Marcus warns that, with increasingly sophisticated generative‑AI tools, future generations may struggle to tell what is real from what is fabricated. Matt Stoller adds that generative AI will tra

GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System

arXiv:2607.29213v1 Announce Type: cross Abstract: Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhan

Gated Q-learning: Add Off-Policy Bias to Taste

arXiv:2607.28916v1 Announce Type: cross Abstract: Multistep credit assignment is critical for sample-efficient reinforcement learning, yet managing off-policy bias in Q-learning remains a fundamental

HierDoc: Hierarchical Page-to-Region Evidence Routing for Long-Document Visual Question Answering

arXiv:2607.29638v1 Announce Type: new Abstract: Multi-page document visual question answering requires locating sparse evidence at both the page and region levels. Existing approaches typically emphas

Homotopy-Aware Corridor Generation without Predefined Reference Paths

arXiv:2607.29513v1 Announce Type: new Abstract: Generating safe corridors is essential for collision-free robotic motion planning, yet most existing methods rely on predefined reference paths, which b

safetyarxiv-cs-ro

How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories

arXiv:2607.28674v1 Announce Type: new Abstract: Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpr

Hypergradient-based Bilevel Reinforcement Learning with Improved Sample Complexity

arXiv:2607.28849v1 Announce Type: cross Abstract: Bilevel reinforcement learning (RL) is an important framework within the literature of RL that can be used to formalize various categories of problems

Implicit Reasoning for Large Language Model-based Generative Recommendation

arXiv:2606.14142v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world kn

In-situ Autoguidance: Eliciting Self-Correction in Diffusion Models

arXiv:2510.17136v2 Announce Type: replace Abstract: The generation of high-quality, diverse, and prompt-aligned images is a central goal in image-generating diffusion models. The popular classifier-fr

Incorporating data drift to perform survival analysis on credit risk

arXiv:2601.20533v2 Announce Type: replace-cross Abstract: Survival analysis has become a standard approach for modelling time to default by time-varying covariates in credit risk. Unlike most existing

JoVA: Unified Multimodal Learning for Joint Video-Audio Generation and Editing

arXiv:2512.13677v2 Announce Type: replace Abstract: In this paper, we present JoVA, a streamlined framework that unifies joint video-audio generation and editing. While existing methods often rely on

Kinodynamic Motion Retargeting for Humanoid Locomotion via Multi-Contact Whole-Body Trajectory Optimization

arXiv:2603.09956v2 Announce Type: replace Abstract: We present the KinoDynamic Motion Retargeting (KDMR) framework, a novel approach for humanoid locomotion that models the retargeting process as a mu

safetyarxiv-cs-ro

Learning Latent Reasoning Traces for Scalar Reward Models End-to-End

arXiv:2607.29185v1 Announce Type: new Abstract: Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs ena

safetyarxiv-cs-cl

Learning Stateful Predictive Knowledge From Experience

arXiv:2607.28638v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed

safetyarxiv-cs-cl

MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation

arXiv:2607.29320v1 Announce Type: new Abstract: Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, ex

metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making

arXiv:2607.29093v1 Announce Type: cross Abstract: Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measur

Mirror Learning

arXiv:2607.28737v1 Announce Type: cross Abstract: We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable polic

Model Releases

Model or Harness? An Interaction-Centric Taxonomy for Localizing Agent Failures

model-releasesarxiv-cs-ai

arXiv:2607.28802v1 Announce Type: new Abstract: Existing evaluations often reduce agent failures to system-level outcomes, obscuring where the fault originated and which intervention would improve the

MPP-GNN: Subject-Adaptive Community Detection for fMRI-Based Alzheimer's Disease Classification

arXiv:2607.28681v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNN

Multi-Modal Object Re-Identification with Dual Semantic Guidance and Global-Local Mutual Modulation

arXiv:2607.29207v1 Announce Type: new Abstract: Multi-modal object Re-Identification (ReID) aims to retrieve target instances by leveraging complementary information across modalities. However, existi

Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI

arXiv:2607.29531v1 Announce Type: new Abstract: Cross-site identification of major depressive disorder (MDD) from resting-state functional magnetic resonance imaging (rs-fMRI) is hindered by inter-sit

NeuroSynth: A Biologically Inspired Continual Reinforcement Learning Architecture for Mitigating Catastrophic Forgetting

arXiv:2607.28663v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems often perform well on isolated tasks but struggle under continual learning conditions, where training on new task

On the Efficacy of Self-Supervised Point Cloud Encoders for Efficient 3D Large Language Models

arXiv:2607.29136v1 Announce Type: new Abstract: 3D point cloud-language models (3D-LLMs) enable 3D understanding by pairing point cloud encoders with large language models, but existing methods rely o

Model Releases

On the Generalization of Steering Vectors for Chain-of-Thought Faithfulness

model-releasesarxiv-cs-ai

arXiv:2607.29062v1 Announce Type: new Abstract: Model capabilities have improved in large part due to scaling chain of thought. This has been a promising development for AI safety--where models verbal

OpenAI distilled Google’s name lol

Gary Marcus commented on Twitter that OpenAI “distilled” Google’s brand name, prompting a humorous response from Tony Carden. Carden’s reply referenced Google DeepMind’s Project Astra and included a s

OpenAI: No Anthropic: Unlikely

Gary Marcus tweeted “OpenAI: No Anthropic: Unlikely,” suggesting that the company does not expect Anthropic to satisfy particular requirements. The tweet references a discussion about the “RPO obligat

Part of the challenge with the AI math advances is the current air of secrecy around them. We don't know how Astra/Fable/Mythos etc are trai…

Part of the challenge with the AI math advances is the current air of secrecy around them. We don't know how Astra/Fable/Mythos etc are trained. We know many mathematicians have been paid to create tr

Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization

arXiv:2607.29008v1 Announce Type: cross Abstract: Modern opaque AI models prize performance over interpretability, which makes testing difficult. However, formal statistical tests conducted on a model

Physics-Aligned Self-Supervised Learning for Scientific Imaging

arXiv:2607.28868v1 Announce Type: new Abstract: Data augmentations define the invariances learned by self-supervised learning (SSL). Standard augmentation pipelines were designed for natural images, y

“poorly evidenced sensationalism makes for bad policymaking, politics, philanthropy and much else.” beautiful coda for a weekend in which pe…

“poorly evidenced sensationalism makes for bad policymaking, politics, philanthropy and much else.” beautiful coda for a weekend in which people went completely nuts without even asking for a control

QASP: Query-Adaptive Robust Vector Search Policy

arXiv:2607.29606v1 Announce Type: cross Abstract: A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs. Fixed search parameters cause sig

QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models

arXiv:2607.29445v1 Announce Type: cross Abstract: Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering

RePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment

arXiv:2507.22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention. However, existing tools tend

Rethinking Detection Calibration: A Coordinate and Direction Perspective

arXiv:2607.29040v1 Announce Type: new Abstract: Deep learning based object detectors require trustworthiness beyond competitive detection performance, but deep neural networks are prone to overconfide

Robust Bidirectional Associative Memory via Regularization Inspired by the Subspace Rotation Algorithm

arXiv:2511.11902v2 Announce Type: replace-cross Abstract: Bidirectional Associative Memory (BAM) trained with Bidirectional Backpropagation (B-BP) often suffers from poor robustness and high sensitivi

RTLCurator: Label-Efficient Data Curation for RTL Generation

arXiv:2607.29283v1 Announce Type: cross Abstract: Training large language models (LLMs) to write register-transfer level (RTL) requires large corpora of paired specifications and code, and such data i

SAF-OPD: Stable Advantage Fusion for On-Policy Distillation

arXiv:2607.29209v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) broadcasts a single response-level reward to every token, while on-policy distillation (OPD) sco

SAGP: Semantic Affordance-Guided Grasp Planning via Coarse-Zone VLM Reasoning

arXiv:2607.29374v1 Announce Type: new Abstract: Geometry-based grasp planners ensure physically valid grasps but ignore functional semantics, often generating grasps that are antipodal and collision-f

safetyarxiv-cs-ro