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Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics

arXiv:2505.15998v4 Announce Type: replace Abstract: We present a curiosity-driven AI scientist method for discovering system-level dynamics in Flow-Lenia, a continuous cellular automaton (CA) with mas

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Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

DGX agent

arXiv:2606.06800v1 Announce Type: cross Abstract: Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transi

applicationsarxiv-cs-ai

8 Jun 2026

Extending Responsibility-Sensitive Safety for the Assessment of Offloaded Autonomous Driving Services

arXiv:2606.07067v1 Announce Type: new Abstract: Safety is a fundamental requirement in the development of autonomous driving (AD) systems. While function offloading has demonstrated significant benefi

safetyarxiv-cs-ro

Extracting Recurring Vulnerabilities from Black-Box LLM-Generated Software

arXiv:2602.04894v4 Announce Type: replace-cross Abstract: LLMs are increasingly used for code generation, but their outputs often follow recurring templates that can induce predictable vulnerabilities

FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

arXiv:2606.06547v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a 'stability lag' where early decisions rem

Fast and Robust Convergence Rate for TD(0) with Linear Function Approximation, Universal Learning Steps and I.I.D. Samples

arXiv:2606.05967v2 Announce Type: replace-cross Abstract: In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA). We consider

Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions

arXiv:2505.17739v2 Announce Type: replace-cross Abstract: Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as auto

agentsarxiv-cs-ro

Applications

Federated Foundation Models over Vehicular Networks

arXiv:2606.06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular

applicationsarxiv-cs-lg

FIGMA: Towards FIne-Grained Music retrievAl

arXiv:2606.06615v1 Announce Type: cross Abstract: Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited

Finding Most Influential Sets

model-releasesarxiv-cs-lg

arXiv:2606.05919v2 Announce Type: replace-cross Abstract: Identifying most influential sets (MIS) - size-k subsets whose removal maximally changes a target estimand - is typically infeasible because i

Flatland: The Adventures of Gradient Descent with Large Step Sizes

arXiv:2606.06722v1 Announce Type: new Abstract: The training of neural networks often entails objective functions that are not globally L-smooth. For these functions, it is both theoretically and prac

FLOWREADER: Min-Cost Flow Optimization for Multi-Modal Long Document Q&A

arXiv:2606.07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken acros

Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting

arXiv:2603.02220v2 Announce Type: replace-cross Abstract: Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends.

ForensicConcept: Transferable Forensic Concepts for AIGI Detection

arXiv:2606.07034v1 Announce Type: new Abstract: AI-generated image detectors achieve high accuracy on in-distribution data but often fail on unseen generators. A key obstacle to understanding this fai

safetyarxiv-cs-cv

Hardware

FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail

arXiv:2606.06510v1 Announce Type: cross Abstract: Conventional HPC dogma holds that native hardware FP64 silicon is the irreducible foundation of scientific computing -- the 'holy grail' of double-pre

hardwarearxiv-cs-ai

FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising

arXiv:2606.06885v1 Announce Type: cross Abstract: Human Image Animation has seen significant advancements, primarily driven by diffusion models. However, existing methods typically demand substantial

Local Ai

From Correctness to Utility: Gain-Based Prefix Evaluation for LLM Reasoning

arXiv:2606.07190v1 Announce Type: new Abstract: Reasoning prefixes shape the future trajectory of LLM problem solving, yet existing process reward models usually evaluate them through local step corre

local-aiarxiv-cs-cl

From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video

arXiv:2606.06631v1 Announce Type: new Abstract: Joint contact forces govern implant longevity, cartilage health, and rehabilitation outcomes, shaping who develops osteoarthritis, who recovers well fro

researcharxiv-cs-cv

From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator

arXiv:2509.11740v2 Announce Type: replace Abstract: Autonomous stocking in retail environments, particularly supermarkets, presents challenges due to dynamic human interactions, constrained spaces, an

agentsarxiv-cs-ro

From Privacy to Workflow Integrity: Communication-Graph Metadata in Autonomous Agent Interoperability

arXiv:2606.07150v1 Announce Type: cross Abstract: Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such

agentsarxiv-cs-ai

From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing

arXiv:2606.06924v1 Announce Type: new Abstract: Existing LLM routing methods typically treat a model's single response to a query as its capability label for training routers. However, because LLM gen

From Vision to Text: A Compact Multimodal Approach for Robust, Cross-Domain Presentation Attack Detection on ID Cards

model-releasesarxiv-cs-cv

arXiv:2606.06966v1 Announce Type: new Abstract: Cross-domain shifts challenge Presentation Attack Detection (PAD) on ID Cards, given the restricted data available due to privacy concerns. This work pr

Front-to-Attractors: Modifying the Front-to-Front Heuristic in Bidirectional Search

arXiv:2606.07047v1 Announce Type: new Abstract: Heuristics play a central role in the performance of bidirectional search algorithms, which commonly rely on two main classes. Front-to-end (F2E) heuris

FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness

arXiv:2606.06856v1 Announce Type: new Abstract: Dynamic vision sensors (DVS) offer exceptional temporal resolution and dynamic range by asynchronously reporting pixel-level intensity changes. However,

researcharxiv-cs-cv

Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems

arXiv:2606.06576v1 Announce Type: new Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in

Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods

arXiv:2606.06772v1 Announce Type: cross Abstract: Understanding the generalization performance of over-parameterized neural networks has become a central topic in deep learning theory. While recent ad

Local Ai

Generalization of Diffusion Models Arises with a Balanced Representation Space

arXiv:2512.20963v3 Announce Type: replace-cross Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training object

local-aiarxiv-cs-cv

Generative Modeling of Discrete Latent Structures via Dynamic Policy Gradients

arXiv:2606.07400v1 Announce Type: new Abstract: Many scientific problems require inferring unobserved mechanistic latent states from indirect observations. While classical approaches, including expect

Generative Models Erode Human Temporal Learning Through Market Selection

arXiv:2606.06572v1 Announce Type: cross Abstract: We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define

Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport

arXiv:2606.07239v1 Announce Type: new Abstract: The success of generative molecular design hinges on a model's steerability toward high-reward samples. Because many molecular properties are intrinsica

GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios

arXiv:2606.06967v1 Announce Type: new Abstract: Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous

Applications

Geodesics of Dynamic Graphs for Regime Change Detection

arXiv:2606.07151v1 Announce Type: new Abstract: Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evoluti

applicationsarxiv-cs-lg

Geometric-Aware Hypergraph Reasoning for Novel Class Discovery in Point Cloud Segmentation

arXiv:2606.07280v1 Announce Type: new Abstract: Novel class discovery in point cloud segmentation aims to transfer knowledge from known classes to automatically identify and segment unlabeled novel cl

researcharxiv-cs-cv

Geometric Second-Order Feature Correlation Learning for Self-Supervised Speech Emotion Recognition

arXiv:2606.06550v1 Announce Type: cross Abstract: Self-supervised learning (SSL) yields powerful, context-rich representations for speech emotion recognition (SER), yet aggregating these representatio

Tutorials

Geometry of Semantic Space: Comparative Study of Discrete and Continuous Models

arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence g

tutorialsarxiv-cs-cl

GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting

model-releasesarxiv-cs-lg

arXiv:2606.06881v1 Announce Type: new Abstract: Blood glucose forecasting models are foundational for modern diabetes management systems, as reliable short-term predictions can enable proactive interv

GOPAgen: Motion-Aware and Efficient Agentic Long-Video Understanding with Structural Memory and Hierarchical Reasoning

arXiv:2606.06532v1 Announce Type: new Abstract: Despite significant progress in agentic long video understanding, existing methods still lack detailed motion comprehension coupled with an efficient me

agentsarxiv-cs-cv

GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection

arXiv:2606.07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty m

Applications

Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs

arXiv:2606.07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node class

applicationsarxiv-cs-ai

GRASP: Geometry-aware Residual Alignment for Scalable Pretraining Data Attribution

arXiv:2606.06892v1 Announce Type: new Abstract: Scalable data attribution methods typically assign isolated utility scores to individual training examples. This prevalent additive assumption fundament

GS-KAN: Parameter-Efficient Kolmogorov-Arnold Networks via Sprecher-Type Shared Basis Functions

model-releasesarxiv-cs-lg

arXiv:2512.09084v3 Announce Type: replace Abstract: The Kolmogorov-Arnold representation theorem offers a theoretical alternative to Multi-Layer Perceptrons (MLPs) by placing learnable univariate func

GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding

model-releasesarxiv-cs-cv

arXiv:2606.07024v1 Announce Type: new Abstract: Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we pro

Tutorials

Hard labels sampled from sparse targets mislead rotation invariant algorithms

arXiv:2603.20967v2 Announce Type: replace-cross Abstract: One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final pre

tutorialsarxiv-cs-lg

Hearing the Unspoken: Language Model Priors for Acoustic Adversarial Attacks

arXiv:2606.06833v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) systems operating in real-time settings must process acoustic input under strict temporal constraints, where transc

Hermes: Accelerating Long-Latency Load Requests via Perceptron-Based Off-Chip Load Prediction

arXiv:2209.00188v4 Announce Type: replace-cross Abstract: Long-latency load requests continue to limit the performance of high-performance processors. To increase the latency tolerance of a processor,

Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China

arXiv:2606.06986v1 Announce Type: new Abstract: While green finance has become a key instrument for low-carbon city transitions, its actual decarbonization effects and transmission mechanisms remain u

Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration

arXiv:2606.07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-langua

Hierarchical Forecast Reconciliation for Urban Rail Transit Demand Prediction under Operational Disruptions

arXiv:2606.07044v1 Announce Type: new Abstract: Accurate and coherent passenger demand forecasting is essential for Urban Rail Transit (URT) operations. Passenger demand has a hierarchical structure i