Tootfinder

Sekai: A Video Dataset towards World Exploration
Zhen Li, Chuanhao Li, Xiaofeng Mao, Shaoheng Lin, Ming Li, Shitian Zhao, Zhaopan Xu, Xinyue Li, Yukang Feng, Jianwen Sun, Zizhen Li, Fanrui Zhang, Jiaxin Ai, Zhixiang Wang, Yuwei Wu, Tong He, Jiangmiao Pang, Yu Qiao, Yunde Jia, Kaipeng Zhang
https://arxiv.org/abs/2506.15675…

Sekai: A Video Dataset towards World Exploration
Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning ``world'' in Japanese), a high-quality first-person view worldwide video dataset with rich…

Reasoning with Exploration: An Entropy Perspective
Daixuan Cheng, Shaohan Huang, Xuekai Zhu, Bo Dai, Wayne Xin Zhao, Zhenliang Zhang, Furu Wei
https://arxiv.org/abs/2506.14758

Reasoning with Exploration: An Entropy Perspective
Balancing exploration and exploitation is a central goal in reinforcement learning (RL). Despite recent advances in enhancing language model (LM) reasoning, most methods lean toward exploitation, and increasingly encounter performance plateaus. In this work, we revisit entropy -- a signal of exploration in RL -- and examine its relationship to exploratory reasoning in LMs. Through empirical analysis, we uncover strong positive correlations between high-entropy regions and three types of explora…

On 17 June the European Space Agency (ESA) and OHB System AG sealed their agreement to build ESA's Laser Interferometer Space Antenna, #LISA: https://www.esa.int/Science_Exploration/Space_Science/LISA/Construction_of_ESA_s_ambitious_LISA_mission_begins - a bold scientific enterprise, the triple-spacecraft mission will be the first space-based observatory to study gravitational waves.

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

GHAR: GeoPose-based Handheld Augmented Reality for Architectural Positioning, Manipulation and Visual Exploration
Sabahat Israr, Dawar Khan, Zhanglin Cheng, Mukhtaj Khan, Kiyoshi Kiyokawa
https://arxiv.org/abs/2506.14414

GHAR: GeoPose-based Handheld Augmented Reality for Architectural Positioning, Manipulation and Visual Exploration
Handheld Augmented Reality (HAR) is revolutionizing the civil infrastructure application domain. The current trend in HAR relies on marker tracking technology. However, marker-based systems have several limitations, such as difficulty in use and installation, sensitivity to light, and marker design. In this paper, we propose a markerless HAR framework with GeoPose-based tracking. We use different gestures for manipulation and achieve 7 DOF (3 DOF each for translation and rotation, and 1 DOF for…

Beam aboard! Tonight's stream features Star Trek Online. Join me for missions, exploration, and maybe a little Tribble trouble, all on my Bazzite Linux rig! #StarTrek

Draw an Ugly Person An Exploration of Generative AIs Perceptions of Ugliness
Garyoung Kim, Huisung Kwon, Seoju Yun, Yu-Won Youn
https://arxiv.org/abs/2507.12212

Draw an Ugly Person An Exploration of Generative AIs Perceptions of Ugliness
Generative AI does not only replicate human creativity but also reproduces deep-seated cultural biases, making it crucial to critically examine how concepts like ugliness are understood and expressed by these tools. This study investigates how four different generative AI models understand and express ugliness through text and image and explores the biases embedded within these representations. We extracted 13 adjectives associated with ugliness through iterative prompting of a large language m…

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2506.14224

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large …

Zero Forcing on Iterated Graph Models
Christopher Brice, Erin Meger, Nhat-Dinh Nguyen, Allen Rakhamimov, Abigail Raz
https://arxiv.org/abs/2507.12579 https…

Zero Forcing on Iterated Graph Models
Modeling how information travels throughout a network has vast applications across social sciences, cybersecurity, and graph-based neural networks. In this paper, we consider the zero forcing model for information diffusion on iterative deterministic complex network models. In particular, we continue the exploration of the Iterative Local Transitive (ILT) model and the Iterative Local Anti-Transitive (ILAT) model, both introduced by Bonato et. al. in 2009 and 2017, respectively. These models us…

Öoo is a novel puzzle platformer where you can't jump, but you can bomb yourself around. Love the aesthetic and demo takes 15 min and has some great puzzles. It's from the developer of the much-lauded ElecHead.
Also has the funniest implementation of Clear Save Data I've ever seen.
https://store…

Öoo on Steam
A Bomb Caterpillar's Big Adventure! "Öoo" is a charming yet mysterious exploration puzzle platformer where you discover creative ways to use bombs!

Great #DH2025 talk from @luanamcosta.bsky.social on using CLIP to analyse images of the 'New Woman'. Loved the idea of networks of queries, eg women working, voting, doing domestic activities, and querying the intersection of 'women working' and 'women in a public space'. Thus far CLIP was good for insights and exploration rather than definitive analysis - excellent for a …

LASANA: Large-scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration
Jason Ho, James A. Boyle, Linshen Liu, Andreas Gerstlauer
https://arxiv.org/abs/2507.10748 …

LASANA: Large-scale Surrogate Modeling for Analog Neuromorphic Architecture Exploration
Neuromorphic systems using in-memory or event-driven computing are motivated by the need for more energy-efficient processing of artificial intelligence workloads. Emerging neuromorphic architectures aim to combine traditional digital designs with the computational efficiency of analog computing and novel device technologies. A crucial problem in the rapid exploration and co-design of such architectures is the lack of tools for fast and accurate modeling and simulation. Typical mixed-signal des…

Advancing Loss Functions in Recommender Systems: A Comparative Study with a R\'enyi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
https://arxiv.org/abs/2506.15120

Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective. Their theoretical connections and differences warrant in-depth exploration. This work conducts comprehensive analyses of these losses, yielding significant insights: 1) Common strengths -- both can be viewed as augmentations of traditional losses with Distributional Robust Optimization (DRO), enhancing robustness to…

A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
Zhong Yang, Zhengqiu Zhu, Yong Zhao, Yonglin Tian, Changjun Fan, Runkang Guo, Wenhao Lu, Jingwei Ge, Bin Chen, Yin Zhang, Guohua Wu, Rui Wang, Gyorgy Eigner, Guangquan Cheng, Jincai Huang, Zhong Liu, Jun Zhang, Imre J. Rudas, Fei-Yue Wang

A Comprehensive Survey on Underwater Acoustic Target Positioning and Tracking: Progress, Challenges, and Perspectives
Underwater target tracking technology plays a pivotal role in marine resource exploration, environmental monitoring, and national defense security. Given that acoustic waves represent an effective medium for long-distance transmission in aquatic environments, underwater acoustic target tracking has become a prominent research area of underwater communications and networking. Existing literature reviews often offer a narrow perspective or inadequately address the paradigm shifts driven by emergi…

Flips Reveal the Universal Impact of Memory on Random Explorations
Julien Br\'emont, L\'eo R\'egnier, Alex Barbier--Chebbah, Olivier B\'enichou, Rapha\"el Voituriez
https://arxiv.org/abs/2506.15642

Flips Reveal the Universal Impact of Memory on Random Explorations
Quantifying space exploration is a central question in random walk theory, with direct applications ranging from animal foraging, diffusion-limited reactions, and intracellular transport to stock markets. In particular, the explored domain by one or many simple memoryless (or Markovian) random walks has received considerable attention . However, the physical systems mentioned above typically involve significant memory effects, and so far, no general framework exists to analyze such systems. We …

The New Black Hole Solution with Anisotropic Fluid in f(R,Lm, T) Gravity: Thermodynamics
Aniruddha Ghosh, Ujjal Debnath
https://arxiv.org/abs/2506.15240 ht…

The New Black Hole Solution with Anisotropic Fluid in f(R,Lm, T) Gravity: Thermodynamics
In this work, we derive a new class of analytic black hole solutions within the framework of f(R,Lm, T) gravity, where the black hole is surrounded by an anisotropic fluid acting as the matter source. We consider both linear and nonlinear forms of the function f(R,Lm, T), enabling a detailed exploration of how anisotropic pressures and different f(R,Lm, T) influence the spacetime structure. Furthermore, we derive the conditions on the coupling parameters (\b{eta}1, \b{eta}2, \b{eta}3) under whi…

Technosignature Searches with Real-time Alert Brokers
Eleanor M. Gallay, James R. A. Davenport, Steve Croft
https://arxiv.org/abs/2506.14744 https://

Technosignature Searches with Real-time Alert Brokers
We present an exploration of technosignature research that is possible using real-time alert brokers from surveys such as the Zwicky Transient Facility (ZTF) and the upcoming Legacy Survey of Space and Time (LSST). Nine alert brokers currently stream up to 1 million alerts each night from ZTF, and LSST is projected to increase this volume by an order of magnitude. While these brokers are primarily designed to facilitate real-time follow-up of explosive transients such as supernovae, they offer …

Circum-nuclear eccentric gas flow in the Galactic Center revealed by ALMA CMZ Exploration Survey (ACES)
Yoshiaki Sofue, Tomoharu Oka, Steven N. Longmore, Daniel Walker, Adam Ginsburg, Jonathan D. Henshaw, John Bally, Ashley T. Barnes, Cara Battersby, Laura Colzi, Paul Ho, Izaskun Jimenez-Serra, J. M. Diederik Kruijssen, Elizabeth Mills, Maya A. Petkova, Mattia C. Sormani, Jennifer Wallace, Jairo Armijos-Abenda\~no, Katarzyna M. Dutkowska, Rei Enokiya, Pablo Garc\'ia, Savannah Gramz…

Circum-nuclear eccentric gas flow in the Galactic Center revealed by ALMA CMZ Exploration Survey (ACES)
We analyze the CS (J=2-1) line cube from the internal data release obtained by the large-scale program "ALMA CMZ Exploration Survey (ACES)" to investigate the kinematic structure of the innermost $\sim 10$ pc region of the Galaxy, which contains the high-velocity compact cloud (HVCC) at $(l,b,v_{\rm lsr})\sim(+0^\circ.02,-0^\circ.02, 100 {\rm km~s}^{-1})$ (hereafter G0.02). The longitude-velocity diagram (LVD) of the cloud draws an elliptical structure, which is interpreted as an orbital trajec…

Is Selection All You Need in Differential Evolution?
Tomofumi Kitamura, Alex Fukunaga
https://arxiv.org/abs/2506.14425 https://arxiv.…

Is Selection All You Need in Differential Evolution?
Differential Evolution (DE) is a widely used evolutionary algorithm for black-box optimization problems. However, in modern DE implementations, a major challenge lies in the limited population diversity caused by the fixed population size enforced by the generational replacement. Population size is a critical control parameter that significantly affects DE performance. Larger populations inherently contain a more diverse set of individuals, thereby facilitating broader exploration of the search…

Highly anisotropic magnetic phase diagram of the ferromagnetic rare-earth diboride HoB$_{2}$
Takafumi D. Yamamoto, Hiroyuki Takeya, Kensei Terashima, Akiko T. Saito, Yoshihiko Takano
https://arxiv.org/abs/2506.15302

Highly anisotropic magnetic phase diagram of the ferromagnetic rare-earth diboride HoB$_{2}$
Rare-earth (RE) compounds have been of enormous interest in condensed matter physics as a platform for the exploration of interesting physical phenomena. Here, we report the successful single crystal growth of HoB$_{2}$, which exhibits a paramagnetic (PM)-ferromagnetic (FM) phase transition at 15 K and another phase transition at 11 K, and the discovery of a highly anisotropic magnetic phase diagram of this FM diboride. Magnetization measurements suggest that the ferromagnetically ordered momen…

Multimodal "Puppeteer": An Exploration of Robot Teleoperation Via Virtual Counterpart with LLM-Driven Voice and Gesture Interaction in Augmented Reality
Yuchong Zhang, Bastian Orthmann, Shichen Ji, Michael Welle, Jonne Van Haastregt, Danica Kragic
https://arxiv.org/abs/2506.13189…

Multimodal "Puppeteer": An Exploration of Robot Teleoperation Via Virtual Counterpart with LLM-Driven Voice and Gesture Interaction in Augmented Reality
The integration of robotics and augmented reality (AR) holds transformative potential for advancing human-robot interaction (HRI), offering enhancements in usability, intuitiveness, accessibility, and collaborative task performance. This paper introduces and evaluates a novel multimodal AR-based robot puppeteer framework that enables intuitive teleoperation via virtual counterpart through large language model (LLM)-driven voice commands and hand gesture interactions. Utilizing the Meta Quest 3,…

Misfit layered superconductor (PbSe)1.14(NbSe2)3 with possible layer-selective FFLO state
Yuki M. Itahashi, Yamato Nohara, Michiya Chazono, Hideki Matsuoka, Koichiro Arioka, Tetsuya Nomoto, Yoshimitsu Kohama, Youichi Yanase, Yoshihiro Iwasa, Kaya Kobayashi
https://arxiv.org/abs/2506.14106

Misfit layered superconductor (PbSe)1.14(NbSe2)3 with possible layer-selective FFLO state
Two-dimensional (2D) superconductors are known for their novel emergent phenomena, however, lack of experimental probes beyond resistivity has hindered further exploration of diverse superconducting states. Bulk 2D superconductors, with superconducting layers separated by non-superconducting layers, offer a unique opportunity to break this limit. Here, we synthesized a single crystal of misfit layered compound (PbSe)1.14(NbSe2)3, composed of alternately stacked tri-layer NbSe2 and non-supercond…

Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
Andreas Schlaginhaufen, Reda Ouhamma, Maryam Kamgarpour
https://arxiv.org/abs/2506.09508

Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in this setting is to design algorithms that select informative preference queries to identify the underlying reward while ensuring theoretical guarantees. We propose a meta-algorithm based on randomized exploration, which avoids the computational challenges associated with optimistic approaches and remains tractable. We e…

Tuning spin-density separation via finite-range interactions: Dimensionality-driven signatures in dynamic structure factors
Xiaoran Ye, Yi Zhang, Ziheng Zhou, Zhaoxin Liang
https://arxiv.org/abs/2506.15092

Tuning spin-density separation via finite-range interactions: Dimensionality-driven signatures in dynamic structure factors
Spin-density separation, marked by distinct propagation velocities of spin and density excitations, epitomizes strong correlations, historically confined to one-dimensional (1D) systems. Recent ultracold quantum gas experiments, however, demonstrate its emergence in higher dimensions through precise tuning of intra- to interspecies interaction ratios, inspiring exploration of how dimensionality and interatomic interactions govern quantum correlations. We investigate this in two-component bosoni…

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
Xingyu Liu, Yubin Li, Guozhang Chen
https://arxiv.org/abs/2506.11062

Decoding Cortical Microcircuits: A Generative Model for Latent Space Exploration and Controlled Synthesis
A central idea in understanding brains and building artificial intelligence is that structure determines function. Yet, how the brain's complex structure arises from a limited set of genetic instructions remains a key question. The ultra high-dimensional detail of neural connections vastly exceeds the information storage capacity of genes, suggesting a compact, low-dimensional blueprint must guide brain development. Our motivation is to uncover this blueprint. We introduce a generative model, t…

Slip-tendency analysis of a potential CO2 storage site in the central German North Sea
Hendrawan D. B. Aji, Fabian J\"ahne-Klingberg, Heidrun L. St\"uck, Frank Wuttke, Petia Dineva
https://arxiv.org/abs/2507.12897

Slip-tendency analysis of a potential CO2 storage site in the central German North Sea
Underground CO2 storage operations may lead to induced seismicity, which can be partially related to nearby affected faults. Considering the storage potential in the Middle Buntsandstein sandstone formation on the West Schleswig Block within the German North Sea, we investigate the reactivation and seismicity potential of existing faults in the vicinity of a selected study area. For this, we develop a 3D fault data set based on roughly 80 2D seismic lines, combining 60 years of exploration hist…

Oh yeah, my copy of the Psygnosis book is being shipped ✌️
#psygnosis

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen
https://arxiv.org/abs/2506.12444

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling techniques with the well-known variance-reduced algorithm SARAH while dynamically adjusting the stochastic gradient weights in each update to enhance exploration. Our method achieves the best-known gradient complexity for shuffling variance reduction methods in a strongly convex setting. This result applies to any shuffling technique, which narrows the gap in the complexity analysis of variance reduc…

"Brazil to auction oil exploration rights months before hosting Cop30"
#Brazil #Climate #ClimateChange #COP30

Foundation of Affective Computing and Interaction
Changzeng Fu
https://arxiv.org/abs/2506.15497 https://arxiv.org/pdf/2506.15497

Foundation of Affective Computing and Interaction
This book provides a comprehensive exploration of affective computing and human-computer interaction technologies. It begins with the historical development and basic concepts of human-computer interaction, delving into the technical frameworks and practical applications of emotional computing, visual interaction, voice interaction, brain-computer interfaces, physiological electrical signal analysis, and social robotics. The book covers a wide range of topics, including the psychological and ne…

SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
Zhixin Guo, Qi Shi, Xiaofan Xu, Sixiang Shan, Limin Qin, Linqiang Ge, Rui Zhang, Ya Dai, Hua Zhu, Guowei Jiang
https://arxiv.org/abs/2506.13034

SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
With the rapid advancement of aerospace technology and the large-scale deployment of low Earth orbit (LEO) satellite constellations, the challenges facing astronomical observations and deep space exploration have become increasingly pronounced. As a result, the demand for high-precision orbital data on space objects-along with comprehensive analyses of satellite positioning, constellation configurations, and deep space satellite dynamics-has grown more urgent. However, there remains a notable l…

La palette #Wikipedia "Apprentissage automatique et exploration de données" me donne une vue d'ensemble du sujet.
https://notes.sklein.xyz/2025-06-15_0943/

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
Jiaqi Lv, Xufeng He, Yanchen Liu, Xu Dai, Yang Hu, Shouyi Yin
https://arxiv.org/abs/2506.10401

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration
The rapid growth of deep learning has driven exponential increases in model parameters and computational demands. NVIDIA GPUs and their CUDA-based software ecosystem provide robust support for parallel computing, significantly alleviating computational bottlenecks. Meanwhile, due to the cultivation of user programming habits and the high performance of GPUs, the CUDA ecosystem has established a dominant position in the field of parallel software. This dominance requires other hardware platforms…

Trump Wants $1 Billion For Private-Sector-Led Mars Exploration - Slashdot
https://science.slashdot.org/story/25/06/03/0335217/trump-wants-1-billion-for-private-sector-led-mars-exploration

Formal Verification of Neural Certificates Done Dynamically
Thomas A. Henzinger, Konstantin Kueffner, Emily Yu
https://arxiv.org/abs/2507.11987 https://

Formal Verification of Neural Certificates Done Dynamically
Neural certificates have emerged as a powerful tool in cyber-physical systems control, providing witnesses of correctness. These certificates, such as barrier functions, often learned alongside control policies, once verified, serve as mathematical proofs of system safety. However, traditional formal verification of their defining conditions typically faces scalability challenges due to exhaustive state-space exploration. To address this challenge, we propose a lightweight runtime monitoring fr…

OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads
Xinxin Wang, Lixian Yan, Shuhan Liu, Luke Upton, Zhuoqi Cai, Yiming Tan, Shengman Li, Koustav Jana, Peijing Li, Jesse Cirimelli-Low, Thierry Tambe, Matthew Guthaus, H. -S. Philip Wong
https://arxiv.org/abs/2507.10849

OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads
Gain Cell memory (GCRAM) offers higher density and lower power than SRAM, making it a promising candidate for on-chip memory in domain-specific accelerators. To support workloads with varying traffic and lifetime metrics, GCRAM also offers high bandwidth, ultra low leakage power and a wide range of retention times, which can be adjusted through transistor design (like threshold voltage and channel material) and on-the-fly by changing the operating voltage. However, designing and optimizing GCRA…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
Imagine, Verify, Execute: Memory-Guided Agentic Exploration with Vision-Language Models

HEMA: A Hands-on Exploration Platform for MEMS Sensor Attacks
Bhagawat Baanav Yedla Ravi, Md Rafiul Kabir, Sandip Ray
https://arxiv.org/abs/2507.06439 http…

HEMA: A Hands-on Exploration Platform for MEMS Sensor Attacks
Automotive safety and security are paramount in the rapidly advancing landscape of vehicular technology. Building safe and secure vehicles demands a profound understanding of automotive systems, particularly in safety and security. Traditional learning approaches, such as reading materials or observing demonstrations, often fail to provide the practical, hands-on experience essential for developing this expertise. For novice users, gaining access to automotive-grade systems and mastering their …

A Fast Method for Planning All Optimal Homotopic Configurations for Tethered Robots and Its Extended Applications
Jinyuan Liu, Minglei Fu, Ling Shi, Chenguang Yang, Wenan Zhang
https://arxiv.org/abs/2507.11880

A Fast Method for Planning All Optimal Homotopic Configurations for Tethered Robots and Its Extended Applications
Tethered robots play a pivotal role in specialized environments such as disaster response and underground exploration, where their stable power supply and reliable communication offer unparalleled advantages. However, their motion planning is severely constrained by tether length limitations and entanglement risks, posing significant challenges to achieving optimal path planning. To address these challenges, this study introduces CDT-TCS (Convex Dissection Topology-based Tethered Configuration …

Start from the End: A Framework for Computational Policy Exploration to Inform Effective and Geospatially Consistent Interventions applied to COVID-19 in St. Louis
David O'Gara, Matt Kasman, Matthew D. Haslam, Ross A. Hammond
https://arxiv.org/abs/2507.10870

Start from the End: A Framework for Computational Policy Exploration to Inform Effective and Geospatially Consistent Interventions applied to COVID-19 in St. Louis
Mathematical models are a powerful tool to study infectious disease dynamics and intervention strategies against them in social systems. However, due to their detailed implementation and steep computational requirements, practitioners and stakeholders are typically only able to explore a small subset of all possible intervention scenarios, a severe limitation when preparing for disease outbreaks. In this work, we propose a parameter exploration framework utilizing emulator models to make uncert…

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Xinyuan Liu, Jiahui Chen, Bocheng Hu, Yu Sun, Xinyang Chen, Shaoxu Song
https://arxiv.org/abs/2507.10934

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Data quality remains an important challenge in data-driven systems, as errors in tabular data can severely compromise downstream analytics and machine learning performance. Although numerous error detection algorithms have been proposed, the lack of diverse, real-world error datasets limits comprehensive evaluation. Manual error annotation is both time-consuming and inconsistent, motivating the exploration of synthetic error generation as an alternative. In this work, we introduce TableEG, a fr…

AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Geonwoo Cho, Jaemoon Lee, Jaegyun Im, Subi Lee, Jihwan Lee, Sundong Kim
https://arxiv.org/abs/2506.05980

AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Skill-based reinforcement learning (SBRL) enables rapid adaptation in environments with sparse rewards by pretraining a skill-conditioned policy. Effective skill learning requires jointly maximizing both exploration and skill diversity. However, existing methods often face challenges in simultaneously optimizing for these two conflicting objectives. In this work, we propose a new method, Adaptive Multi-objective Projection for balancing Exploration and skill Diversification (AMPED), which expli…

An Exploration of Vacuum-Decay Valleys
J. R. Espinosa, T. Konstandin
https://arxiv.org/abs/2506.06154 https://arxiv.org/pdf/2506.0615…

An Exploration of Vacuum-Decay Valleys
In the standard lore the decay of the false vacuum of a single-field potential is described by a semi-classical Euclidean bounce configuration that can be found using overshoot/undershoot algorithms, and whose action suppresses exponentially the decay rate. While this is generically correct, we show in a few concrete examples of potentials, previously studied in the literature for other purposes, that the vacuum decay structure can be far richer. In some cases there is no bounce and decay proce…

Internal Heat Flux and Energy Imbalance of #Uranus: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025GL115660 -> Discovery is Unlocking Planetary Mysteries to Advance Space Exploration and Earth Science: https://uh.edu/news-events/stories/2025/july/07142025-uranus-internal-heat-study.php

Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
Benjamin Schiffer, Mark Sellke
https://arxiv.org/abs/2506.01685 h…

Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
In the incentivized exploration model, a principal aims to explore and learn over time by interacting with a sequence of self-interested agents. It has been recently understood that the main challenge in designing incentive-compatible algorithms for this problem is to gather a moderate amount of initial data, after which one can obtain near-optimal regret via posterior sampling. With high-dimensional contexts, however, this \emph{initial exploration} phase requires exponential sample complexity…

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Ziwei Liu, Borui Kang, Wei Li, Hangjie Yuan, Yanbing Yang, Wenbin Li, Jun Luo, Yifan Zhu, Tao Feng
https://arxiv.org/abs/2506.12409

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
Continual learning in vision-language models (VLMs) faces critical challenges in balancing parameter efficiency, memory consumption, and optimization stability. While First-Order (FO) optimization (e.g., SGD) dominate current approaches, their deterministic gradients often trap models in suboptimal local minima and incur substantial memory overhead. This paper pioneers a systematic exploration of Zeroth-Order (ZO) optimization for vision-language continual learning (VLCL). We first identify the…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Fine-grained Stateful Knowledge Exploration: Effective and Efficient Graph Retrieval with Large L...
Dehao Tao, Congqi Wang, Feng Huang, Junhao Chen, Yongfeng Huang, Minghu Jiang

I mean, nothing yet beats the Brazilian Institute of Oriental Studies bit the trend is there and it fits
https://velvetshark.com/ai-company-logos-that-look-like-buttholes

Exploration of features in the black hole mass spectrum inspired by non-parametric analyses of gravitational wave observations
Stefano Rinaldi, Yajie Liang, Gabriele Demasi, Michela Mapelli, Walter Del Pozzo
https://arxiv.org/abs/2506.05929

Exploration of features in the black hole mass spectrum inspired by non-parametric analyses of gravitational wave observations
Current gravitational-wave data reveal structures in the mass function of binary compact objects. Properly modelling and deciphering such structures is the ultimate goal of gravitational-wave population analysis: in this context, non-parametric models are a powerful tool to infer the distribution of black holes from gravitational waves without committing to any specific functional form. Here, we aim to quantitatively corroborate the findings of non-parametric methods with parametrised models in…

Ukraine's Naftogaz brings new high-yield gas well online as country reserves run low: https://benborges.xyz/2025/07/08/ukraines-naftogaz-brings-new-highyield.html

Tonight's adventure: streaming some #LOTRO from my #TuxPoweredPC! Come join me for some Middle-earth exploration and see what quests we can conquer. #LordOfTheRingsOnline

Uncertainty band evaluation of optical potentials and differential cross-sections. Application to $^8$Li $^{58}$Ni elastic scattering
O. C. B. Santos, J. G\'omez-Camacho
https://arxiv.org/abs/2507.11591

Uncertainty band evaluation of optical potentials and differential cross-sections. Application to $^8$Li + $^{58}$Ni elastic scattering
A statistical method is presented to evaluate the uncertainty bands in the optical nucleus-nucleus potential and in differential cross sections. The starting point is the least square fit of a set of experimental values of elastic differential cross sections, varying the relevant optical potential parameters. This is done using standard $χ^2$ minimization codes, that provide the covariance matrix of the parameters. A maximum likelihood exploration of the $χ^2$ surface in parameter space allow…

gotta get involved in sea exploration shit and start wearing this all the time

"We shall not cease from exploration and the end of all our exploring will be to arrive where we started and know the place for the first time." — T.S. Eliot
(No better way to summarize my own research of the past four months...)
#QuoteOfTheDay

This https://arxiv.org/abs/2412.13664 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Skeleton-Based Topological Planner for Exploration in Complex Unknown Environments
The capability of autonomous exploration in complex, unknown environments is important in many robotic applications. While recent research on autonomous exploration have achieved much progress, there are still limitations, e.g., existing methods relying on greedy heuristics or optimal path planning are often hindered by repetitive paths and high computational demands. To address such limitations, we propose a novel exploration framework that utilizes the global topology information of observed …

DELVE-ing into the Milky Way's Globular Clusters: Assessing extra-tidal features in NGC 5897, NGC 7492, and testing detectability with deeper photometry
A. Chiti, K. Tavangar, P. S. Ferguson, J. A. Carballo-Bello, A. M. Senkevich, D. Erkal, A. Drlica-Wagner, A. B. Pace, A. P. Ji, D. J. Sand, G. Limberg, A. Chaturvedi, D. Crnojevi\'c, G. E. Medina, A. H. Riley, N. Shipp, A. K. Vivas, M. Wertheim, Y. Choi, C. E. Mart\'inez-V\'azquez, B. Mutlu-Pakdil, M. Navabi, J. D. Sako…

DELVE-ing into the Milky Way's Globular Clusters: Assessing extra-tidal features in NGC 5897, NGC 7492, and testing detectability with deeper photometry
Extra-tidal features around globular clusters (GCs) are tracers of their disruption, stellar stream formation, and their host's gravitational potential. However, these features remain challenging to detect due to their low surface brightness. We conduct a systematic search for such features around 19 GCs in the DECam Local Volume Exploration (DELVE) survey Data Release 2, discovering a new extra-tidal envelope around NGC 5897 and find tentative evidence for an extended envelope surrounding NGC …

🤾 Building a giant catcher's mitt on the moon
#moon

Building a giant catcher's mitt on the moon
Members of the space exploration community are always coming up with novel ideas to solve problems that they view as holding back humanity's expansion into the cosmos. One such problem that has become more noticeable of late, due to the failure of several powered lunar landers, is the difficulty of landing on the moon.

"Researchers call for urgent exploration of methods to cool Earth"
#Climate #ClimateChange

Ooh! From the Makers of Psychonauts...
▶️ Keeper: Double Fine’s Otherworldly Adventure, Arriving October 17  - Xbox Wire
https://news.xbox.com/en-us/2025/06/08/keeper-double-fine-xbox-games-showcase/

Double Fine’s Otherworldly Adventure, Arriving October 17  - Xbox Wire
Keeper is a third-person adventure filled with exploration, puzzle solving, and unexpected departures from reality, coming October 17, 2025.

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Ashish Kumar
https://arxiv.org/abs/2507.07302 https://arxiv.org/…

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi-agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi-agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large-language models as expert …

LLM-Guided Scenario-based GUI Testing
Shengcheng Yu, Yuchen Ling, Chunrong Fang, Quan Zhou, Chunyang Chen, Shaomin Zhu, Zhenyu Chen
https://arxiv.org/abs/2506.05079

LLM-Guided Scenario-based GUI Testing
The assurance of mobile app GUI is more and more significant. Automated GUI testing approaches of different strategies have been developed, while there are still huge gaps between the approaches and the app business logic, not taking the completion of specific testing scenarios as the exploration target, leading to the exploration missing of critical app functionalities. Learning from the manual testing, which takes testing scenarios with app business logic as the basic granularity, in this pap…

Computational algorithm for downward continuation of gravity anomalies
D. K. Ivanov, L. N. Temirbekova, P. N. Vabishchevich
https://arxiv.org/abs/2507.08506

Computational algorithm for downward continuation of gravity anomalies
The downward continuation of potential fields from the Earth's surface into the subsurface is a critical task in gravity exploration, as it helps to identify the sources of gravity anomalies. This problem is often addressed by solving a first-kind integral equation using regularization techniques to stabilize an inherently unstable process. A similar approach is used in our work, where the continued field is represented as the potential of a simple layer or its vertical derivative. The constanc…

from my link log —
Why do AI company logos look like arseholes?
https://velvetshark.com/ai-company-logos-that-look-like-buttholes
saved 2025-04-11

Why do AI company logos look like buttholes?
A humorous exploration of the uncanny resemblance between AI company logos and human anatomy. Discover why circular, gradient-based designs dominate the AI industry, and what this design convergence tells us about branding in tech.

Check out activities, videos and resources from Shelly Fryer’s “Rocket Camp” this week:
#stem

Mrs. Fryer's Classroom - Rocket Camp
Description July 7-11, 2025 Summer Camp: The Summer Rocket Lab is a week-long adventure into the exciting world of rocketry and space exploration. Designed for young minds eager to learn and create, the camp offers two age-specific sessions tailored to 2nd and 3rd graders, as well as incoming 4th

Granular jamming and rheology in microgravity
Qing Yu, Thorsten P\"oschel, Olfa D'Angelo
https://arxiv.org/abs/2507.08674 https://

Granular jamming and rheology in microgravity
Understanding how granular materials behave in low gravity is crucial for planetary science and space exploration. It can also help us understand granular phenomena usually hidden by gravity. On Earth, gravity dominates granular behavior, but disentangling its role from intrinsic particle interactions is challenging. We present a series of compression and shear experiments conducted in microgravity using the Center of Applied Space Technology and Microgravity (ZARM) drop tower and GraviTower Br…

Unraveling the Potential of Diffusion Models in Small Molecule Generation
Peining Zhang, Daniel Baker, Minghu Song, Jinbo Bi
https://arxiv.org/abs/2507.08005

Unraveling the Potential of Diffusion Models in Small Molecule Generation
Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and ch…

Epistemically-guided forward-backward exploration
N\'uria Armengol Urp\'i, Marin Vlastelica, Georg Martius, Stelian Coros
https://arxiv.org/abs/2507.05477

Epistemically-guided forward-backward exploration
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward representations (FB) have emerged as a promising method for learning optimal policies in absence of rewards via a factorization of the policy occupancy measure. However, up until now, FB and many similar zero-shot reinforcement learning algorithms have been decoupled from the exploration problem, generally relying on other …

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials
Rui Sheng, Xingbo Wang, Jiachen Wang, Xiaofu Jin, Zhonghua Sheng, Zhenxing Xu, Suraj Rajendran, Huamin Qu, Fei Wang
https://arxiv.org/abs/2507.12298

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials
Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations…

Learning Safe Control via On-the-Fly Bandit Exploration
Alexandre Capone, Ryan Cosner, Aaaron Ames, Sandra Hirche
https://arxiv.org/abs/2506.10279 https://…

Learning Safe Control via On-the-Fly Bandit Exploration
Control tasks with safety requirements under high levels of model uncertainty are increasingly common. Machine learning techniques are frequently used to address such tasks, typically by leveraging model error bounds to specify robust constraint-based safety filters. However, if the learned model uncertainty is very high, the corresponding filters are potentially invalid, meaning no control input satisfies the constraints imposed by the safety filter. While most works address this issue by assu…

ZettaLith: An Architectural Exploration of Extreme-Scale AI Inference Acceleration
Kia Silverbrook
https://arxiv.org/abs/2507.02871 https://

ZettaLith: An Architectural Exploration of Extreme-Scale AI Inference Acceleration
The high computational cost and power consumption of current and anticipated AI systems present a major challenge for widespread deployment and further scaling. Current hardware approaches face fundamental efficiency limits. This paper introduces ZettaLith, a scalable computing architecture designed to reduce the cost and power of AI inference by over 1,000x compared to current GPU-based systems. Based on architectural analysis and technology projections, a single ZettaLith rack could potential…

Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
Frederic Gmeiner, Kaitao Luo, Ye Wang, Kenneth Holstein, Nikolas Martelaro
https://arxiv.org/abs/2506.12879

Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. …

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Alberto Caron, Chris Hicks, Vasilios Mavroudis
https://arxiv.org/abs/2507.02639 htt…

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motivation. Specifically, we focus on a class of exploration bonuses that targets epistemic uncertainty rather than the aleatoric noise inherent in the environment. We prove that these bonuses naturally signal epistemic information gains and converge to zero once the agent becomes sufficiently certain about the environment…

Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging
Lennart Busch, Daniel Tebernum, Gissel Velarde
https://arxiv.org/abs/2507.05282

Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging
Efficient data exploration is crucial as data becomes increasingly important for accelerating processes, improving forecasts and developing new business models. Data consumers often spend 25-98 % of their time searching for suitable data due to the exponential growth, heterogeneity and distribution of data. Data catalogs can support and accelerate data exploration by using metadata to answer user queries. However, as metadata creation and maintenance is often a manual process, it is time-consum…

Adaptive Exploration in Lenia with Intrinsic Multi-Objective Ranking
Niko Lorantos, Lee Spector
https://arxiv.org/abs/2506.02990 https://

Adaptive Exploration in Lenia with Intrinsic Multi-Objective Ranking
Artificial life aims to understand the fundamental principles of biological life by creating computational models that exhibit life-like properties. Although artificial life systems show promise for simulating biological evolution, achieving open-endedness remains a central challenge. This work investigates mechanisms to promote exploration and unbounded innovation within evolving populations of Lenia continuous cellular automata by evaluating individuals against each other with respect to dist…

Bounomodes: the grazing ox algorithm for exploration of clustered anomalies
Samuel Matloob, Ayan Dutta, O. Patrick Kreidl, Swapnonel Roy, Ladislau B\"ol\"oni
https://arxiv.org/abs/2507.06960

Bounomodes: the grazing ox algorithm for exploration of clustered anomalies
A common class of algorithms for informative path planning (IPP) follows boustrophedon ("as the ox turns") patterns, which aim to achieve uniform area coverage. However, IPP is often applied in scenarios where anomalies, such as plant diseases, pollution, or hurricane damage, appear in clusters. In such cases, prioritizing the exploration of anomalous regions over uniform coverage is beneficial. This work introduces a class of algorithms referred to as bounomōdes ("as the ox grazes"), which al…

MIGHTEE-HI: The direct detection of neutral hydrogen in galaxies at $z>0.25$
Matt J. Jarvis, Madalina N. Tudorache, I. Heywood, Anastasia A. Ponomareva, M. Baes, Natasha Maddox, Kristine Spekkens, Andreea Varasteanu, C. L. Hale, Mario G. Santos, R. G. Varadaraj, Elizabeth A. K. Adams, Alessandro Bianchetti, Barbara Catinella, Jacinta Delhaize, M. Maksymowicz-Maciata, Pavel E. Mancera Pi\~na, Hengxing Pan, Am\'elie Saintonge, Gauri Sharma, O. Ivy Wong

MIGHTEE-HI: The direct detection of neutral hydrogen in galaxies at $z>0.25$
Atomic hydrogen constitutes the gas reservoir from which molecular gas and star formation in galaxies emerges. However, the weakness of the line means it has been difficult to directly detect in all but the very local Universe. Here we present results from the first search using the MeerKAT International Tiered Extragalactic Exploration (MIGHTEE) Survey for high-redshift ($z>0.25$) H{\sc i} emission from individual galaxies. By searching for 21-cm emission centered on the position and redshift …

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng
https://arxiv.org/abs/2506.09404

Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
Combinatorial optimization problems are notoriously challenging due to their discrete structure and exponentially large solution space. Recent advances in deep reinforcement learning (DRL) have enabled the learning heuristics directly from data. However, DRL methods often suffer from limited exploration and susceptibility to local optima. On the other hand, evolutionary algorithms such as Genetic Algorithms (GAs) exhibit strong global exploration capabilities but are typically sample inefficien…

No certainty of a Milky Way–#Andromeda collision: https://www.nature.com/articles/s41550-025-02563-1 -> Hubble and Gaia revisit fate of our galaxy: https://www.esa.int/Science_Exploration/Space_Science/Hubble_and_Gaia_revisit_fate_of_our_galaxy / Apocalypse When? Hubble Casts Doubt on Certainty of Galactic Collision: https://science.nasa.gov/missions/hubble/apocalypse-when-hubble-casts-doubt-on-certainty-of-galactic-collision/

IDEA: Augmenting Design Intelligence through Design Space Exploration
Chuer Chen, Xiaoke Yan, Xiaoyu Qi, Nan Cao
https://arxiv.org/abs/2506.10587 https://

IDEA: Augmenting Design Intelligence through Design Space Exploration
Design spaces serve as a conceptual framework that enables designers to explore feasible solutions through the selection and combination of design elements. However, effective decision-making remains heavily dependent on the designer's experience, and the absence of mathematical formalization prevents computational support for automated design processes. To bridge this gap, we introduce a structured representation that models design spaces with orthogonal dimensions and discrete selectable elem…

Gaussian Process-Based Active Exploration Strategies in Vision and Touch
Ho Jin Choi, Nadia Figueroa
https://arxiv.org/abs/2507.05522 https://

Gaussian Process-Based Active Exploration Strategies in Vision and Touch
Robots struggle to understand object properties like shape, material, and semantics due to limited prior knowledge, hindering manipulation in unstructured environments. In contrast, humans learn these properties through interactive multi-sensor exploration. This work proposes fusing visual and tactile observations into a unified Gaussian Process Distance Field (GPDF) representation for active perception of object properties. While primarily focusing on geometry, this approach also demonstrates …

Learning to Explore: An In-Context Learning Approach for Pure Exploration
Alessio Russo, Ryan Welch, Aldo Pacchiano
https://arxiv.org/abs/2506.01876 https:…

Learning to Explore: An In-Context Learning Approach for Pure Exploration
In this work, we study the active sequential hypothesis testing problem, also known as pure exploration, where the goal is to actively control a data collection process to efficiently identify the correct hypothesis underlying a decision problem. While relevant across multiple domains, devising adaptive exploration strategies remains challenging, particularly due to difficulties in encoding appropriate inductive biases. Existing Reinforcement Learning (RL)-based methods often underperform when …

Improving population size adapting CMA-ES algorithm on step-size blow-up in weakly-structured multimodal functions
Chandula Fernando, Kushani De Silva
https://arxiv.org/abs/2506.00825

Improving population size adapting CMA-ES algorithm on step-size blow-up in weakly-structured multimodal functions
Multimodal optimization requires both exploration and exploitation. Exploration identifies promising attraction basins, while exploitation finds the best solutions within these basins. The balance between exploration and exploitation can be maintained by adjusting parameter settings. The population size adaptation covariance matrix adaption evolutionary strategy algorithm (PSA-CMA-ES) achieves this balance by dynamically adjusting population size. PSA-CMA-ES performs well on well-structured mul…

DTECT: Dynamic Topic Explorer & Context Tracker
Suman Adhya, Debarshi Kumar Sanyal
https://arxiv.org/abs/2507.07910 https://arxiv…

DTECT: Dynamic Topic Explorer & Context Tracker
The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines that lack robust support for interpretation and user-friendly exploration. We introduce DTECT (Dynamic Topic Explorer & Context Tracker), an end-to-end system that bridges the gap between raw textual data and meaningful temporal insights. DTECT provides a unified workflow that sup…

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Lala Shakti Swarup Ray, Bo Zhou, Paul Lukowicz
https://arxiv.org/abs/2507.05532 …

W2W: A Simulated Exploration of IMU Placement Across the Human Body for Designing Smarter Wearable
Inertial measurement units (IMUs) are central to wearable systems for activity recognition and pose estimation, but sensor placement remains largely guided by heuristics and convention. In this work, we introduce Where to Wear (W2W), a simulation-based framework for systematic exploration of IMU placement utility across the body. Using labeled motion capture data, W2W generates realistic synthetic IMU signals at 512 anatomically distributed surface patches, enabling high-resolution, task-specif…

Close-in planet induces flares on its host star / Searching for planet-induced radio signal from the young close-in planet host star HIP 67522: #planets can trigger own doom, suspect Cheops and TESS: https://www.esa.int/Science_Exploration/Space_Science/Cheops/Clingy_planets_can_trigger_own_doom_suspect_Cheops_and_TESS

PlayerOne: Egocentric World Simulator
Yuanpeng Tu, Hao Luo, Xi Chen, Xiang Bai, Fan Wang, Hengshuang Zhao
https://arxiv.org/abs/2506.09995 https://

PlayerOne: Egocentric World Simulator
We introduce PlayerOne, the first egocentric realistic world simulator, facilitating immersive and unrestricted exploration within vividly dynamic environments. Given an egocentric scene image from the user, PlayerOne can accurately construct the corresponding world and generate egocentric videos that are strictly aligned with the real scene human motion of the user captured by an exocentric camera. PlayerOne is trained in a coarse-to-fine pipeline that first performs pretraining on large-scale…

Designing Visualization Widgets for Tangible Data Exploration: A Systematic Review
Haonan Yao, Lingyun Yu, Lijie Yao
https://arxiv.org/abs/2507.00775 https…

Designing Visualization Widgets for Tangible Data Exploration: A Systematic Review
We present a systematic review on tasks, interactions, and visualization widgets (refer to tangible entities that are used to accomplish data exploration tasks through specific interactions) in the context of tangible data exploration. Tangible widgets have been shown to reduce cognitive load, enable more natural interactions, and support the completion of complex data exploration tasks. Yet, the field lacks a structured understanding of how task types, interaction methods, and widget designs a…

This https://arxiv.org/abs/2409.18122 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

RT-GuIDE: Real-Time Gaussian splatting for Information-Driven Exploration
We propose a framework for active mapping and exploration that leverages Gaussian splatting for constructing dense maps. Further, we develop a GPU-accelerated motion planning algorithm that can exploit the Gaussian map for real-time navigation. The Gaussian map constructed onboard the robot is optimized for both photometric and geometric quality while enabling real-time situational awareness for autonomy. We show through simulation experiments that our method yields comparable Peak Signal-to-No…

Reinforcement Learning with Action Chunking
Qiyang Li, Zhiyuan Zhou, Sergey Levine
https://arxiv.org/abs/2507.07969 https://arxiv.org/pdf/2507.07969 https://arxiv.org/html/2507.07969
arXiv:2507.07969v1 Announce Type: new
Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks. Our recipe is designed for the offline-to-online RL setting, where the goal is to leverage an offline prior dataset to maximize the sample-efficiency of online learning. Effective exploration and sample-efficient learning remain central challenges in this setting, as it is not obvious how the offline data should be utilized to acquire a good exploratory policy. Our key insight is that action chunking, a technique popularized in imitation learning where sequences of future actions are predicted rather than a single action at each timestep, can be applied to temporal difference (TD)-based RL methods to mitigate the exploration challenge. Q-chunking adopts action chunking by directly running RL in a 'chunked' action space, enabling the agent to (1) leverage temporally consistent behaviors from offline data for more effective online exploration and (2) use unbiased $n$-step backups for more stable and efficient TD learning. Our experimental results demonstrate that Q-chunking exhibits strong offline performance and online sample efficiency, outperforming prior best offline-to-online methods on a range of long-horizon, sparse-reward manipulation tasks.
toXiv_bot_toot

This https://arxiv.org/abs/2409.17641 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Scene Exploration by Vision-Language Models
Active perception enables robots to dynamically gather information by adjusting their viewpoints, a crucial capability for interacting with complex, partially observable environments. In this paper, we present AP-VLM, a novel framework that combines active perception with a Vision-Language Model (VLM) to guide robotic exploration and answer semantic queries. Using a 3D virtual grid overlaid on the scene and orientation adjustments, AP-VLM allows a robotic manipulator to intelligently select opt…

PSZ2 G181.06 48.47 - X-Ray Exploration of / Radio Analysis of / Weak-lensing Analysis and Merging Scenario Reconstruction of a Low-mass Cluster with Exceptionally Distant Radio Relics: #GalaxyClusters on Course to Crash Again, NASA's Chandra Finds: https://chandra.si.edu/photo/2025/g181/ / https://www.nasa.gov/image-article/galaxy-clusters-on-course-to-crash-again-nasas-chandra-finds/

This https://arxiv.org/abs/2407.09206 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Cooperative Indoor Exploration Leveraging a Mixed-Size UAV Team with Heterogeneous Sensors
Heterogeneous teams of Unmanned Aerial Vehicles (UAVs) can enhance the exploration capabilities of aerial robots by exploiting different strengths and abilities of varying UAVs. This paper presents a novel method for exploring unknown indoor spaces with a team of UAVs of different sizes and sensory equipment. We propose a frontier-based exploration with two task allocation strategies: a greedy strategy that assigns Points of Interest (POIs) based on Euclidean distance and UAV priority and an op…

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Gaurav Chaudhary, Wassim Uddin Mondal, Laxmidhar Behera
https://arxiv.org/abs/2506.09574

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected datasets, has emerged as a promising alternative. However, offline RL is constrained by issues such as out-of-distribution (OOD) actions that limit policy performance and generalization. To overcome these limitations, we propose Meta Offline-Online Reinforcement Learning (MOORL), …

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
Abhinav Sood, Maria Teresa Llano, Jon McCormack
https://arxiv.org/abs/2507.08260

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
We present a graphical, node-based system through which users can visually chain generative AI models for creative tasks. Research in the area of chaining LLMs has found that while chaining provides transparency, controllability and guardrails to approach certain tasks, chaining with pre-defined LLM steps prevents free exploration. Using cognitive processes from creativity research as a basis, we create a system that addresses the inherent constraints of chat-based AI interactions. Specifically…

TritonZ: A Remotely Operated Underwater Rover with Manipulator Arm for Exploration and Rescue Operations
Kawser Ahmed, Mir Shahriar Fardin, Md Arif Faysal Nayem, Fahim Hafiz, Swakkhar Shatabda
https://arxiv.org/abs/2506.18343

TritonZ: A Remotely Operated Underwater Rover with Manipulator Arm for Exploration and Rescue Operations
The increasing demand for underwater exploration and rescue operations enforces the development of advanced wireless or semi-wireless underwater vessels equipped with manipulator arms. This paper presents the implementation of a semi-wireless underwater vehicle, "TritonZ" equipped with a manipulator arm, tailored for effective underwater exploration and rescue operations. The vehicle's compact design enables deployment in different submarine surroundings, addressing the need for wireless system…

An Exploration of Internal States in Collaborative Problem Solving
Sifatul Anindho, Videep Venkatesha, Mariah Bradford, Anne M. Cleary, Nathaniel Blanchard
https://arxiv.org/abs/2507.02229

An Exploration of Internal States in Collaborative Problem Solving
Collaborative problem solving (CPS) is a complex cognitive, social, and emotional process that is increasingly prevalent in educational and professional settings. This study investigates the emotional states of individuals during CPS using a mixed-methods approach. Teams of four first completed a novel CPS task. Immediately after, each individual was placed in an isolated room where they reviewed the video of their group performing the task and self-reported their internal experiences throughou…

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
Rifat Mehreen Amin, Oliver Hans K\"uhle, Daniel Buschek, Andreas Butz
https://arxiv.org/abs/2506.03741

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
We introduce PromptCanvas, a concept that transforms prompting into a composable, widget-based experience on an infinite canvas. Users can generate, customize, and arrange interactive widgets representing various facets of their text, offering greater control over AI-generated content. PromptCanvas allows widget creation through system suggestions, user prompts, or manual input, providing a flexible environment tailored to individual needs. This enables deeper engagement with the creative proce…

Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
Lorin Achey, Alec Reed, Brendan Crowe, Bradley Hayes, Christoffer Heckman
https://arxiv.org/abs/2506.20049

Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis
We present a novel approach for enhancing robotic exploration by using generative occupancy mapping. We introduce SceneSense, a diffusion model designed and trained for predicting 3D occupancy maps given partial observations. Our proposed approach probabilistically fuses these predictions into a running occupancy map in real-time, resulting in significant improvements in map quality and traversability. We implement SceneSense onboard a quadruped robot and validate its performance with real-worl…

Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
Octavio Arriaga, Rebecca Adam, Melvin Laux, Lisa Gutzeit, Marco Ragni, Jan Peters, Frank Kirchner
https://arxiv.org/abs/2506.08756

Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
Real-world robotic applications, from autonomous exploration to assistive technologies, require adaptive, interpretable, and data-efficient learning paradigms. While deep learning architectures and foundation models have driven significant advances in diverse robotic applications, they remain limited in their ability to operate efficiently and reliably in unknown and dynamic environments. In this position paper, we critically assess these limitations and introduce a conceptual framework for com…