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Estimating Grammatical Gender Directions in Contextual Embeddings under Controlled and Natural Contexts

arXiv:2606.30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish. Existing gender debiasing appro

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Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer

DGX agent

arXiv:2606.28980v1 Announce Type: cross Abstract: Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and sur

safetyarxiv-cs-ai

30 Jun 2026

Experience-Evolving Multi-Turn Tool-Use Agent with Hybrid Episodic-Procedural Memory

arXiv:2512.07287v3 Announce Type: replace-cross Abstract: As intents unfold and environments change, multi-turn agents face continuously shifting decision contexts. Although reusing past experience is

Exploration and Online Transfer with Behavioral Foundation Models

arXiv:2606.29980v1 Announce Type: new Abstract: Zero-shot Transfer in Reinforcement Learning (RL) aims to train an agent that can generate optimal policies for any reward function, without additional

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control

arXiv:2606.28476v1 Announce Type: new Abstract: High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions un

FAIL: Flow Matching Adversarial Imitation Learning for Image Generation

arXiv:2602.12155v2 Announce Type: replace Abstract: Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to imitation learning

Fairness Attacks on Recommender Systems

arXiv:2606.29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works ha

Fast Enough to Act: Spatio-Temporal Visual Token Merging for Low-Latency Robotic VLMs and VLAs

arXiv:2606.29350v1 Announce Type: cross Abstract: Vision-language models and vision-language action models endow the robot with unprecedented capabilities. However, the input of video and high-resolut

FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On

arXiv:2606.29319v1 Announce Type: new Abstract: Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks w

FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification

arXiv:2606.30376v1 Announce Type: cross Abstract: Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods. Existing

Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University

arXiv:2606.28749v1 Announce Type: cross Abstract: Although most undergraduates now use large language models (LLMs), a form of generative artificial intelligence (GenAI) for academic writing, no valid

Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data

arXiv:2606.29793v1 Announce Type: new Abstract: Demand for personalized financial advising is growing, but consistent advisor expertise is difficult to obtain, scale, and encode in LLM systems. Simple

safetyarxiv-cs-cl

FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation

arXiv:2606.30367v1 Announce Type: new Abstract: Vision-and-language navigation (VLN) in continuous environments requires an agent to ground instructions in egocentric observations while maintaining sp

GarmentZoom: Generating Zoomable Images from Garment Listings

arXiv:2606.29535v1 Announce Type: new Abstract: Online product listings for garments often include an overview photo and a close-up to show garment details. However, each photo focuses on either field

General and Efficient Steering of Diffusion Models

arXiv:2602.11395v2 Announce Type: replace-cross Abstract: Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gr

Generalization error of min-norm interpolators in transfer learning

arXiv:2406.13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-ell_2-norm interpolation in transfer learning, where data from diverse distribut

Generative Learning as a Tool to Improve Perception of Emotional Body Motion Expressions

arXiv:2606.28769v1 Announce Type: new Abstract: Emotional body motion expressions are an essential element of non-verbal communication. Effectively conveying these expressions through technology is of

GeoISF: Instance Semantic Forest Inspired Large-Scale Cross-View Geo-Localization via Ground LiDAR-to-Satellite Image

arXiv:2606.28371v1 Announce Type: new Abstract: The problem of localization on a large-scale satellite image given a frame of query ground view point clouds remains challenging. Existing LiDAR-to-imag

Golden Hour Divide: Trauma Care Accessibility and Resource Vulnerability in Sri Lanka

arXiv:2606.29889v1 Announce Type: new Abstract: Timely intensive care dictates survival, yet emergency infrastructure remains unevenly distributed across Sri Lanka. While pre-hospital services have ex

GPC: Large-Scale Generative Pretraining for Transferable Motor Control

arXiv:2606.29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applicat

Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field

arXiv:2606.30474v1 Announce Type: new Abstract: Non-prehensile manipulation is often used as a preparatory step for robotic grasping, yet existing approaches typically require a predefined target obje

HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data

arXiv:2606.29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these 'gold' labels are expensive to collect

HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning

arXiv:2606.29126v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat

Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization

arXiv:2606.28764v1 Announce Type: new Abstract: Hierarchical decision-making frameworks are pivotal for addressing complex control tasks, enabling agents to decompose intricate problems into manageabl

Hierarchical Policy Learning via Spectral Decomposition

arXiv:2606.29570v1 Announce Type: new Abstract: In this paper, we identify a semantic decomposition in robot action sequences, separating task-level motion intent from execution-level refinements. By

High-Dimensional Concentration and Retrieval Instability in Embedding Spaces: Implications for Retrieval-Augmented Generation

arXiv:2606.28330v1 Announce Type: cross Abstract: Embedding-based retrieval systems rely on the assumption that geometric proximity in highdimensional representation spaces reflects semantic relevance

How Anthropomorphic Language Impacts Public Perceptions of AI

arXiv:2606.29121v1 Announce Type: cross Abstract: Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristi

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position

arXiv:2606.15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models

Hyper-Network Neural Functional Maps for Unsupervised Robust 3D Shape Matching

arXiv:2606.30131v1 Announce Type: cross Abstract: Functional maps are the cornerstone of recent non-rigid 3D shape matching methods due to their efficiency and performance. However, existing methods s

In a new interview with the Washington Post's @ben_guggenheim, ControlAI's US Director Connor Leahy explains why nobody really understands h…

In a new interview with the Washington Post's @ben_guggenheim, ControlAI's US Director Connor Leahy explains why nobody really understands how AI works, the race to uncontrollable superintelligence, a

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Inference-time optimization for experiment-grounded protein ensemble generation

arXiv:2602.24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles tha

InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion

arXiv:2512.17504v2 Announce Type: replace-cross Abstract: Recent advances in diffusion models have enabled impressive video editing capabilities, yet production-grade Video Object Insertion (VOI) rema

Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization

arXiv:2606.29386v1 Announce Type: new Abstract: Predicting a patient's physiological trajectory under a planned treatment sequence is a prospective interventional problem, not standard time-series ext

Invariant Reasoning Directions in Latent Trajectories of Language Models

arXiv:2606.29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains

Is Muon as good as they say? We looked beyond training speed and found a hidden cost: Muon loses the simplicity bias of older optimizers lik…

Muon optimizer shows faster training speeds compared to traditional optimizers, but analysis reveals it sacrifices the simplicity bias that older optimizers maintain, potentially impacting model gener

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ITSPACE: Monotone Gaussian Optimal Transport Updates

arXiv:2606.30523v1 Announce Type: new Abstract: Covariance matrices serve as compact descriptors of feature distributions in many machine-learning pipelines, including domain adaptation and Gaussian e

Keypose Exploration: Efficient Automatic Trajectory Labelling and Cross-Embodiment Policy Transfer

arXiv:2606.29028v1 Announce Type: new Abstract: Keypose-based manipulation decomposes tasks into critical waypoints to simplify policy learning for long-horizon tasks, but existing approaches rely on

Knowing Bias, Doing Better: Mitigating Social Bias in LLMs via Know-Bias Neuron Enhancement

arXiv:2601.21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing metho

KYON: Semi-Modular Wheel-Legged Quadruped With Agile Bimanual Capability

arXiv:2606.30243v1 Announce Type: new Abstract: This paper presents KYON, a hybrid wheel-legged quadruped robot equipped with a bimanual upper body for loco-manipulation tasks. The platform features a

L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction

arXiv:2606.29374v1 Announce Type: new Abstract: High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Ga

Latent Actions from Factorized Transition Effects under Agent Ambiguity

arXiv:2606.30544v1 Announce Type: new Abstract: Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual eff

LatentRevise: Learning from Zero-Hit Reasoning

arXiv:2606.29938v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by hard prompts on which correct trajectories have low probability, so sampling mi

safetyarxiv-cs-cl

Leader Reward for POMO-Based Neural Combinatorial Optimization

arXiv:2405.13947v2 Announce Type: replace Abstract: Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown

Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing

arXiv:2606.29541v1 Announce Type: new Abstract: Role-semantic assignments provide priors over how heterogeneous agents may coordinate, but cooperative MARL systems instead settle on conventions throug

Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

arXiv:2606.30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.

Learning Transferable Dynamics Priors from Action to World Modeling

arXiv:2606.29501v1 Announce Type: new Abstract: We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict

LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training

arXiv:2606.30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.

LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics

arXiv:2606.29113v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate strategic interactions through natural language, making semantic control a critical element of commu