Tootfinder

Turns out that if you model online spaces after real world ones, it works pretty well. Online spaces let you be in many places at once which changes the dynamics, but as an example of something that ports well,
https://www.patternlanguageindex.com/patterns/intimacy-gradient
This pattern works not just for the design of houses but online spaces. Let people get to know a group in less-intimate space before they end up in the more-intimate space. Having a few gradations works really well, and it doesn't have to be a power play or status game.

Bounded Memory in Distributed Networks
Ran Ben Basat, Keren Censor-Hillel, Yi-Jun Chang, Wenchen Han, Dean Leitersdorf, Gregory Schwartzman
https://arxiv.org/abs/2506.11644

Bounded Memory in Distributed Networks
The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $μ$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $μ$ words, in line with rea…

I read "Then I Am Myself the World: What Consciousness Is and How to Expand It" by Christof Koch.
Interesting book which spends like 8 or 9 chapters detailing all the experiments which prove beyond much doubt that consciousness, and self awareness, is a thing done by a brain.
It describes how perception is a construction of a description, has a chapter called "computational mind"
And then spends the last two chapters describing why he thinks the mind can't be computed, because drugs have made him think experience is some kind of magic associated with highly interconnected causal structures.
Apparently, he thinks, once things become interconnected enough they become able to cause things independently of the physics running those connections.
Which is crazy, obviously. There's nothing causal in direct connections between neurons that isn't equally causal in modeled connections between virtual neurons.
All his evidence in the book from neural MRI scans to the effects of psychedelic drugs and symptoms of strokes and disease point to the brain simulating a virtual reality which is the basis of perception.
That simulated world in which we live is full of colour and shape and sounds and emotions and millions of mental constructs that are built to be correlated by the senses with the outside world, but are not equal to the world itself. We live in a dream constructed to correlate with reality.
But then instead of taking the next step: That consciousness itself is a property of a simulated being inside that mental model of the universe, a property which the brain simulates and applies to the virtual self that's doing the experiencing inside that model, he jumps towards some magic implying pan-psychism or that sufficiently interconnected networks become causally self-complete for some reason nobody can fathom.
Sure, colour and shape and emotions are all made up by the brain but experience can't be! For some reason.
You see in truth dualism is false, in that there is no spirit realm in which ghosts animate the matter of the body somehow.
Yet also, dualism is true, in that there is a simulated mental reality which we live in, computed by the brain in which all perception and experience are created, which is related-to but separate-from the unfolding complicated dance of energy that is the universe our bodies interact with.
People take some DMT trip, and the model of the universe emulated by their brain collapses and breaks. Their virtual simulated self inside their mind has these experiences of being one with the universe or the experience of feeling dead yet conscious or whatever, and these hippies think that the broken down simulated experience is real and reflects how consciousness is more fundamental than the atoms that make up the neurons in their brain.
Instead of realizing it shows them that their experienced universe is a simulacrum, they think they get a more direct experience of reality somehow. A consciousness more pure than any mere base atom.
"Then I am myself the world" is a great title. Everything you ever experience is created and simulated in your brain like a dream, the whole universe is inside your head. Even the fact of experience itself.
But that isn't the conclusion Koch reaches somehow, he just jumps from describing the evidence that this is so straight into ascribing super-causal magic consciousness to particular arrangements of atoms that integrated information theory suggest have high correlation, and thinks therefore conciousness is itself the entire universe.
Ah well, fun book. I like arguing in my head with authors that are wrong.
#reading #books #consciousness #thenIAmMyselfTheWorld

BattBee: Equivalent Circuit Modeling and Early Detection of Thermal Runaway Triggered by Internal Short Circuits for Lithium-Ion Batteries
Sangwon Kang, Hao Tu, Huazhen Fang
https://arxiv.org/abs/2506.13577

BattBee: Equivalent Circuit Modeling and Early Detection of Thermal Runaway Triggered by Internal Short Circuits for Lithium-Ion Batteries
Lithium-ion batteries are the enabling power source for transportation electrification. However, in real-world applications, they remain vulnerable to internal short circuits (ISCs) and the consequential risk of thermal runaway (TR). Toward addressing the challenge of ISCs and TR, we undertake a systematic study that extends from dynamic modeling to fault detection in this paper. First, we develop {\em BattBee}, the first equivalent circuit model to specifically describe the onset of ISCs and t…

Bias and Identifiability in the Bounded Confidence Model
Claudio Borile, Jacopo Lenti, Valentina Ghidini, Corrado Monti, Gianmarco De Francisci Morales
https://arxiv.org/abs/2506.11751

Bias and Identifiability in the Bounded Confidence Model
Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a principled way to tackle it. Here, our goal is to outline the properties of statistical estimators of th…

Impact of Tariff Wars on Global Economy
N. S. Gonchar, O. P. Dovzhyk, A. S. Zhokhin, W. H. Kozyrsky, A. P. Makhort
https://arxiv.org/abs/2505.05576 https:/…

Impact of Tariff Wars on Global Economy
The Ricardian model of world trade based on comparative advantage is not sufficient to justify equal trade relations.The existing model of trade relations does not explain the distribution of income among trading countries. This paper presents a method for building equitable trade relations. Its essence is to present an algorithm for building such trade relations, based on the previously proposed model of world trade, that the trade balance of each country would be equal to zero. Under such con…

The Model Context Protocol acts as a universal translator between LLMs and data sources, eliminating complex platform-specific requirements. Our new open source MCP server for ODBC (mcp-odbc-server) enables seamless integration of any ODBC-accessible data into RAG pipelines.
Read more in my latest newsletter!

What's The Model Context Protocol & Why Is It Important?
The way Large Language Models (LLMs) interact with the vast world of data sources and APIs (Application Programming Interface) is undergoing a significant transformation, thanks to the emergence of the Model Context Protocol (MCP). MCP servers act as a crucial middleware layer, a kind of "universal

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Maximilian Du, Shuran Song
https://arxiv.org/abs/2506.13922 https://

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base pol…

Implied Probabilities and Volatility in Credit Risk: A Merton-Based Approach with Binomial Trees
Jagdish Gnawali, Abootaleb Shirvani, Svetlozar T. Rachev
https://arxiv.org/abs/2506.12694

Implied Probabilities and Volatility in Credit Risk: A Merton-Based Approach with Binomial Trees
We explore credit risk pricing by modeling equity as a call option and debt as the difference between the firm's asset value and a put option, following the structural framework of the Merton model. Our approach proceeds in two stages: first, we calibrate the asset volatility using the Black-Scholes-Merton (BSM) formula; second, we recover implied mean return and probability surfaces under the physical measure. To achieve this, we construct a recombining binomial tree under the real-world (natu…

CipherMind: The Longest Codebook in the World
Ming Nie, Zhixiong Yang, Bingsheng Wei
https://arxiv.org/abs/2506.15117 https://arxiv.o…

CipherMind:The Longest Codebook in the World
In recent years, the widespread application of large language models has inspired us to consider using inference for communication encryption. We therefore propose CipherMind, which utilizes intermediate results from deterministic fine-tuning of large model inferences as transmission content. The semantic parameters of large models exhibit characteristics like opaque underlying implementations and weak interpretability, thus enabling their use as an encryption method for data transmission. This…

Empirical Validation of the Independent Chip Model
Juho Kim
https://arxiv.org/abs/2506.00180 https://arxiv.org/pdf/2506.00180

Empirical Validation of the Independent Chip Model
The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM perfor…

Abstract Sound Fusion with Unconditioned Inversion Model
Jing Liu, EnQi Lian
https://arxiv.org/abs/2506.11811 https://arxiv.org/pdf/2…

Abstract Sound Fusion with Unconditioned Inversion Model
An abstract sound is defined as a sound that does not disclose identifiable real-world sound events to a listener. Sound fusion aims to synthesize an original sound and a reference sound to generate a novel sound that exhibits auditory features beyond mere additive superposition of the sound constituents. To achieve this fusion, we employ inversion techniques that preserve essential features of the original sample while enabling controllable synthesis. We propose novel SDE and ODE inversion mod…

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, Yunhuai Liu
https://arxiv.org/abs/2506.06001

LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids
Scientific computing for large deformation of elastic-plastic solids is critical for numerous real-world applications. Classical numerical solvers rely primarily on local discrete linear approximation and are constrained by an inherent trade-off between accuracy and efficiency. Recently, deep learning models have achieved impressive progress in solving the continuum mechanism. While previous models have explored various architectures and constructed coefficient-solution mappings, they are desig…

Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
Soumen Garai, Suman Samui
https://arxiv.org/abs/2506.11169

Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms
Small-Footprint Keyword Spotting (SF-KWS) has gained popularity in today's landscape of smart voice-activated devices, smartphones, and Internet of Things (IoT) applications. This surge is attributed to the advancements in Deep Learning, enabling the identification of predefined words or keywords from a continuous stream of words. To implement the SF-KWS model on edge devices with low power and limited memory in real-world scenarios, a efficient Tiny Machine Learning (TinyML) framework is essen…

Impact of the WHO's 90-70-90 Strategy on HPV-Related Cervical Cancer Control: A Mathematical Model Evaluation in China
Hua Liu, Chunya Liu, Yumei Wei, Qibin Zhang, Jingyan Ma
https://arxiv.org/abs/2506.06405

Impact of the WHO's 90-70-90 Strategy on HPV-Related Cervical Cancer Control: A Mathematical Model Evaluation in China
In August 2020, the World Health Assembly approved the Global Strategy to eliminate cervical cancer, marking the first time that numerous countries committed to eliminating a form of cancer. China introduced the HPV vaccine in 2016 and has made significant advancements in both prevention and treatment strategies. However, due to the relatively late introduction of the vaccine, the burden of cervical cancer in China continues to rise. In light of this, we develop a compartmental model to assess …

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Yuantao Wang, Haowei Yang, Wei Zhang, Shijian Lu
https://arxiv.org/abs/2506.12324 …

UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
Real-world object detection is a challenging task where the captured images/videos often suffer from complex degradations due to various adverse weather conditions such as rain, fog, snow, low-light, etc. Despite extensive prior efforts, most existing methods are designed for one specific type of adverse weather with constraints of poor generalization, under-utilization of visual features while handling various image degradations. Leveraging a theoretical analysis on how critical visual details…

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Yupeng Zhou, Can Cui, Juntong Peng, Zichong Yang, Juanwu Lu, Jitesh H Panchal, Bin Yao, Ziran Wang
https://arxiv.org/abs/2506.14100

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
Vision-Language Models (VLMs) have demonstrated notable promise in autonomous driving by offering the potential for multimodal reasoning through pretraining on extensive image-text pairs. However, adapting these models from broad web-scale data to the safety-critical context of driving presents a significant challenge, commonly referred to as domain shift. Existing simulation-based and dataset-driven evaluation methods, although valuable, often fail to capture the full complexity of real-world …

Dive into semantic reranking at Berlin Buzzwords 2025! Athanasios Papaoikonomou will explore how different models and reranking depths impact search performance, revealing important patterns and the real-world efficiency vs. effectiveness trade-off.
Learn more: https://

Exploring reranking depth in modern search pipelines
The use of semantic reranking on top of a ‘cheaper’ retrieval step becomes more and more common in modern search applications. It offers a different cost quality profile to semantic retrieval trading indexing time compute for retrieval time compute. The depth represents the number of documents that we select to retrieve and feed into the reranking model in order to optimise their ordering. Intuitively, there is a “natural” trade-off between the uplift we can achieve by operating on an i…

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Ruiyi Zhang, Albert Cheu, Adria Gascon, Daniel Moghimi, Phillipp Schoppmann, Michael Schwarz, Octavian Suciu
https://arxiv.org/abs/2506.15924

FARFETCH'D: A Side-Channel Analysis Framework for Privacy Applications on Confidential Virtual Machines
Confidential virtual machines (CVMs) based on trusted execution environments (TEEs) enable new privacy-preserving solutions. Yet, they leave side-channel leakage outside their threat model, shifting the responsibility of mitigating such attacks to developers. However, mitigations are either not generic or too slow for practical use, and developers currently lack a systematic, efficient way to measure and compare leakage across real-world deployments. In this paper, we present FARFETCH'D, an ope…

Dynamic Evolution of Cooperation Based on Adaptive Reputation Threshold and Game Transition
Hongyu Yue, Xiaojin Xiong, Minyu Feng, Attila Szolnoki
https://arxiv.org/abs/2506.13319

Dynamic Evolution of Cooperation Based on Adaptive Reputation Threshold and Game Transition
In real-world social systems, individual interactions are frequently shaped by reputation, which not only influences partner selection but also affects the nature and benefits of the interactions themselves. We propose a heterogeneous game transition model that incorporates a reputation-based dynamic threshold mechanism to investigate how reputation regulates game evolution. In our framework, individuals determine the type of game they engage in according to their own and their neighbors' reput…

DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
Chieh Hubert Lin, Zhaoyang Lv, Songyin Wu, Zhen Xu, Thu Nguyen-Phuoc, Hung-Yu Tseng, Julian Straub, Numair Khan, Lei Xiao, Ming-Hsuan Yang, Yuheng Ren, Richard Newcombe, Zhao Dong, Zhengqin Li
https://arxiv.org/abs/2506.09997

DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene. Feed-forward scene reconstruction has gained significant attention for its ability to rapidly create digital replicas of real-world environments. However, most existing models are limited to static scenes and fail to reconstruct the motion of moving objects. Developing a feed-forward model for …

OpenAI's o3-pro is much smarter than o3 and amazing at using tools, but the model requires extensive context to perform optimally and may overthink without it (Ben Hylak/Latent.Space)
https://www.latent.space/p/o3-pro

God is hungry for Context: First thoughts on o3 pro
OpenAI dropped o3 pricing 80% today and launched o3-pro. Ben Hylak of Raindrop.ai returns with the world's first early review.

PerfTracker: Online Performance Troubleshooting for Large-scale Model Training in Production
Yu Guan, Zhiyu Yin, Haoyu Chen, Sheng Cheng, Chaojie Yang, Tianyin Xu, Yang Zhang, Hanyu Zhao, Yong Li, Dennis Cai, Ennan Zhai
https://arxiv.org/abs/2506.08528

PerfTracker: Online Performance Troubleshooting for Large-scale Model Training in Production
Troubleshooting performance problems of large model training (LMT) is immensely challenging, due to unprecedented scales of modern GPU clusters, the complexity of software-hardware interactions, and the data intensity of the training process. Existing troubleshooting approaches designed for traditional distributed systems or datacenter networks fall short and can hardly apply to real-world training systems. In this paper, we present PerfTracker, the first online troubleshooting system utilizing…

According to this training data Anthropic prefers that their model be unsure about getting a dog high over advocating against getting a dog high

RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments
Yuchuan Fu, Xiaohan Yuan, Dongxia Wang
https://arxiv.org/abs/2506.15253

RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments
The rapid deployment of Large language model (LLM) agents in critical domains like healthcare and finance necessitates robust security frameworks. To address the absence of standardized evaluation benchmarks for these agents in dynamic environments, we introduce RAS-Eval, a comprehensive security benchmark supporting both simulated and real-world tool execution. RAS-Eval comprises 80 test cases and 3,802 attack tasks mapped to 11 Common Weakness Enumeration (CWE) categories, with tools implemen…

Rounding error analysis of randomized CholeskyQR2 for sparse matrices
Haoran Guan, Yuwei Fan
https://arxiv.org/abs/2506.04208 https://

Rounding error analysis of randomized CholeskyQR2 for sparse matrices
This work focuses on rounding error analysis of randomized CholeskyQR2 (RCholeskyQR2) for sparse matrices. We form RCholeskyQR2 with CholeskyQR2 and matrix sketching in order to accelerate CholeskyQR2 and improve its applicability by compressing the input matrix $X \in \mathbb{R}^{m\times n}$. In many real-world applications, the input matrix $X$ is always sparse. In this work, we follow the model of sparse matrices proposed in \cite{CSparse} and provide an improved rounding error analysis of R…

This https://arxiv.org/abs/2502.04053 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Proof of principle for a light dark matter search with low-energy positron beams at NA64
Thermal light dark matter (LDM) with particle masses in the 1 MeV - 1 GeV range could successfully explain the observed dark matter abundance as a relic from the primordial Universe. In this picture, a new feeble interaction acts as a "portal" between the Standard Model and LDM particles, allowing for the exploration of this paradigm at accelerator experiments. In the last years, the "missing energy" experiment NA64e at CERN SPS (Super Proton Synchrotron) has set world-leading constraints in th…

Model-Driven Graph Contrastive Learning
Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
https://arxiv.org/abs/2506.06212 https://…

Model-Driven Graph Contrastive Learning
We propose $\textbf{MGCL}$, a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive learning by accounting for the data's underlying generative process. GCL has emerged as a powerful self-supervised framework for learning expressive node or graph representations without relying on annotated labels, which are often scarce in real-world data. By contrasting augmented views of graph data, GCL has demonstrat…

This https://arxiv.org/abs/2506.02576 has been replaced.
link: https://scholar.google.com/scholar?q=a

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
Passenger demand forecasting helps optimize vehicle scheduling, thereby improving urban efficiency. Recently, attention-based methods have been used to adequately capture the dynamic nature of spatio-temporal data. However, existing methods that rely on heuristic masking strategies cannot fully adapt to the complex spatio-temporal correlations, hindering the model from focusing on the right context. These works also overlook the high-level correlations that exist in the real world. Effectively …

This https://arxiv.org/abs/2502.03663 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Fast Geographic Routing in Fixed-Growth Graphs
In the 1960s, the social scientist Stanley Milgram performed his famous "small-world" experiments where he found that people in the US who are far apart geographically are nevertheless connected by remarkably short chains of acquaintances. Since then, there has been considerable work to design networks that accurately model the phenomenon that Milgram observed. One well-known approach was Barab{á}si and Albert's preferential attachment model, which has small diameter yet lacks an algorithm tha…

Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki, Mykel J. Kochenderfer
https://arxiv.org/abs/2506.08459

Diffusion Models for Safety Validation of Autonomous Driving Systems
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capt…

This https://arxiv.org/abs/2505.10640 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

The Hitchhikers Guide to Production-ready Trustworthy Foundation Model powered Software (FMware)
Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KDD 2025 tutorial, we present a comprehensive exploration of FMware that combines a curated catalogue of challenges with real-world production concerns. We first discuss the state of research and practice in building FMware. We further examine the difficulties in selecting suitable models, aligning high-quality domain-s…

A Bayesian analysis of home advantage in professional squash
Philip Greengard, Samer Takriti
https://arxiv.org/abs/2506.09287 https://

A Bayesian analysis of home advantage in professional squash
We estimate the effect of playing in one's home country in professional squash using a Bayesian hierarchical model applied to men's and women's Professional Squash Association matches from 2018-2024. The model incorporates players' world rankings and whether they are competing in their home country. Using margin of victory in games as our outcome, we estimate that home advantage adds 0.4 games for men and 0.3 games for women to the expected margin, with standard errors of 0.1. For evenly matche…

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Wei-Hsiang Huang, Chen-Wei Ke, Wei-Ning Chiu, Yu-Xuan Su, Chun-Chun Yang, Chieh-Yuan Cheng, Yun-Nung Chen, Pu-Jen Cheng
https://arxiv.org/abs/2505.23053

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this study, we propose a systematic taxonomy that classifies existing approaches into two categories: (1) Pure LLM Recommenders, which rely solely on LLMs, and (2) Augmented LLM Recommenders, which integrate additional non-LLM techniques to enhance performance. This taxonomy provides a novel lens through which to examine the …

Probing Audio-Generation Capabilities of Text-Based Language Models
Arjun Prasaath Anbazhagan, Parteek Kumar, Ujjwal Kaur, Aslihan Akalin, Kevin Zhu, Sean O'Brien
https://arxiv.org/abs/2506.00003

Probing Audio-Generation Capabilities of Text-Based Language Models
How does textual representation of audio relate to the Large Language Model's (LLMs) learning about the audio world? This research investigates the extent to which LLMs can be prompted to generate audio, despite their primary training in textual data. We employ a three-tier approach, progressively increasing the complexity of audio generation: 1) Musical Notes, 2) Environmental Sounds, and 3) Human Speech. To bridge the gap between text and audio, we leverage code as an intermediary, prompting …

Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein
https://arxiv.org/abs/2506.13024

Position: Certified Robustness Does Not (Yet) Imply Model Security
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the paradox of detection without distinction, the lack of clear criteria for practitioners to evaluate certification schemes, and the potential security risks arising from users' expectations surrounding ``guara…

This https://arxiv.org/abs/2505.22124 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Nurse Staffing and Scheduling Problem with Bounded Flexibility and Demand Uncertainty
Nurse staffing and scheduling are persistent challenges in healthcare due to demand fluctuations and individual nurse preferences. This study introduces the concept of bounded flexibility, balancing nurse satisfaction with strict rostering rules, particularly a real-world time regularity policy from a major hospital in Singapore. We model the problem as a multi-stage stochastic program to address evolving demand, optimizing both aggregate staffing and detailed scheduling decisions. A reformulat…

GAF: Gaussian Action Field as a Dvnamic World Model for Robotic Mlanipulation
Ying Chai, Litao Deng, Ruizhi Shao, Jiajun Zhang, Liangjun Xing, Hongwen Zhang, Yebin Liu
https://arxiv.org/abs/2506.14135

GAF: Gaussian Action Field as a Dvnamic World Model for Robotic Mlanipulation
Accurate action inference is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V-A) paradigm, predicting actions directly from visual inputs, or a Vision-to-3D-to-Action (V-3D-A) paradigm, leveraging intermediate 3D representations. However, these methods often struggle with action inaccuracies due to the complexity and dynamic nature of manipulation scenes. In this paper, we propose a V-4D-A framework that enables direct action reas…

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Yefeng Fan, Simon Richard White
https://arxiv.org/abs/2506.04558

A Scalable Exponential Random Graph Model: Amortised Hierarchical Sequential Neural Posterior Estimation with Applications in Neuroscience
Exponential Random Graph Models (ERGMs) are an inferential model for analysing statistical networks. Recent development in ERGMs uses hierarchical Bayesian setup to jointly model a group of networks, which is called a multiple-network Exponential Random Graph Model (MN-ERGMs). MN-ERGM has been successfully applied on real-world resting-state fMRI data from the Cam-CAN project to infer the brain connectivity on aging. However, conventional Bayesian ERGM estimation approach is computationally int…

Unmasking real-world audio deepfakes: A data-centric approach
David Combei, Adriana Stan, Dan Oneata, Nicolas M\"uller, Horia Cucu
https://arxiv.org/abs/2506.09606

Unmasking real-world audio deepfakes: A data-centric approach
The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific purposes. To address this gap, we introduce a novel dataset of real-world audio deepfakes. Our analysis reveals that these real-world examples pose significant challenges, even for the most performant detection models. Rather than increasing model complexity or exhaustively search for a better alternative, in this work w…

Governments Should Mandate Tiered Anonymity on Social-Media Platforms to Counter Deepfakes and LLM-Driven Mass Misinformation
David Khachaturov, Roxanne Schnyder, Robert Mullins
https://arxiv.org/abs/2506.12814

Governments Should Mandate Tiered Anonymity on Social-Media Platforms to Counter Deepfakes and LLM-Driven Mass Misinformation
This position paper argues that governments should mandate a three-tier anonymity framework on social-media platforms as a reactionary measure prompted by the ease-of-production of deepfakes and large-language-model-driven misinformation. The tiers are determined by a given user's $\textit{reach score}$: Tier 1 permits full pseudonymity for smaller accounts, preserving everyday privacy; Tier 2 requires private legal-identity linkage for accounts with some influence, reinstating real-world accou…

Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew C. Cullen, Paul Montague, Sarah M. Erfani, Benjamin I. P. Rubinstein
https://arxiv.org/abs/2506.13024

Position: Certified Robustness Does Not (Yet) Imply Model Security
While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the paradox of detection without distinction, the lack of clear criteria for practitioners to evaluate certification schemes, and the potential security risks arising from users' expectations surrounding ``guara…

Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation
Yuxin Liu, Zhenghao Peng, Xuanhao Cui, Bolei Zhou
https://arxiv.org/abs/2506.09485

Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation
Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-critical scenarios in existing datasets collected in the real world. To tackle the data issue, we propose the Adv-BMT framework, which augments real-world scenarios with diverse and realistic adversarial interactions. The core component of Adv-BMT is a bidirectional motion transformer (BMT) model to perform inverse traffic…

This https://arxiv.org/abs/2505.16422 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach
The automatic control of mobile devices is essential for efficiently performing complex tasks that involve multiple sequential steps. However, these tasks pose significant challenges due to the limited environmental information available at each step, primarily through visual observations. As a result, current approaches, which typically rely on reactive policies, focus solely on immediate observations and often lead to suboptimal decision-making. To address this problem, we propose \textbf{For…

Conformance Checking for Less: Efficient Conformance Checking for Long Event Sequences
Eli Bogdanov, Izack Cohen, Avigdor Gal
https://arxiv.org/abs/2505.21506

Conformance Checking for Less: Efficient Conformance Checking for Long Event Sequences
Long event sequences (termed traces) and large data logs that originate from sensors and prediction models are becoming increasingly common in our data-rich world. In such scenarios, conformance checking-validating a data log against an expected system behavior (the process model) can become computationally infeasible due to the exponential complexity of finding an optimal alignment. To alleviate scalability challenges for this task, we propose ConLES, a sliding-window conformance checking appr…

This https://arxiv.org/abs/2505.09999 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Practical workloads play an essential role in this process: they are critical for motivating and benchmarking serving techniques and systems. However, the existing understanding of real-world LLM serving workloads is limited due to the lack of a comprehensive workload characterization. Prior analyses remain insufficient …

This https://arxiv.org/abs/2503.18938 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

AdaWorld: Learning Adaptable World Models with Latent Actions
World models aim to learn action-controlled future prediction and have proven essential for the development of intelligent agents. However, most existing world models rely heavily on substantial action-labeled data and costly training, making it challenging to adapt to novel environments with heterogeneous actions through limited interactions. This limitation can hinder their applicability across broader domains. To overcome this limitation, we propose AdaWorld, an innovative world model learni…

This https://arxiv.org/abs/2505.14884 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
Accelerating large language model (LLM) inference is critical for real-world deployments requiring high throughput and low latency. Contextual sparsity, where each token dynamically activates only a small subset of the model parameters, shows promise but does not scale to large batch sizes due to union of active neurons quickly approaching dense computation. We introduce Polar Sparsity, highlighting a key shift in sparsity importance from MLP to Attention layers as we scale batch size and seque…

Compressing Hypergraphs using Suffix Sorting
Enno Adler, Stefan B\"ottcher, Rita Hartel
https://arxiv.org/abs/2506.05023 https://

Compressing Hypergraphs using Suffix Sorting
Hypergraphs model complex, non-binary relationships like co-authorships, social group memberships, and recommendations. Like traditional graphs, hypergraphs can grow large, posing challenges for storage, transmission, and query performance. We propose HyperCSA, a novel compression method for hypergraphs that maintains support for standard queries over the succinct representation. HyperCSA achieves compression ratios of 26% to 79% of the original file size on real-world hypergraphs - outperformi…

This https://arxiv.org/abs/2410.19214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

BTS: A Comprehensive Benchmark for Tie Strength Prediction
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodolog…

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Ne\c{s}et \"Unver Akmandor, Sarvesh Prajapati, Mark Zolotas, Ta\c{s}k{\i}n Pad{\i}r
https://arxiv.org/abs/2506.08344

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this paper, we propose a novel multi-model motion planning pipeline, termed Re4MPC, which computes trajectories using Nonlinear Model Predictive Control (NMPC). Re4MPC generates trajectories in a computationally efficient manner by reactively selecting the model, cost, and constraints of the NMPC problem depending on the comp…

Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Niv Eckhaus, Uri Berger, Gabriel Stanovsky
https://arxiv.org/abs/2506.05309 ht…

Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are inherently asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns; therefore, the decision of when to speak forms a crucial part of the participant's decision making. In this work, we develop an adaptive asynchronous LLM-agent which, in addition to determining what to s…

This https://arxiv.org/abs/2505.23419 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

SWE-bench Goes Live!
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability a…

Clustering scientific publications: lessons learned through experiments with a real citation network
Vu Thi Huong, Thorsten Koch
https://arxiv.org/abs/2505.18180

Clustering scientific publications: lessons learned through experiments with a real citation network
Clustering scientific publications can reveal underlying research structures within bibliographic databases. Graph-based clustering methods, such as spectral, Louvain, and Leiden algorithms, are frequently utilized due to their capacity to effectively model citation networks. However, their performance may degrade when applied to real-world data. This study evaluates the performance of these clustering algorithms on a citation graph comprising approx. 700,000 papers and 4.6 million citations ex…

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
https://arxiv.org/abs/2506.10287

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these limi…

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Xueqi Cheng, Minxing Zheng, Shixiang Zhu, Yushun Dong
https://arxiv.org/abs/2506.02362

MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models
Model extraction attacks aim to replicate the functionality of a black-box model through query access, threatening the intellectual property (IP) of machine-learning-as-a-service (MLaaS) providers. Defending against such attacks is challenging, as it must balance efficiency, robustness, and utility preservation in the real-world scenario. Despite the recent advances, most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt …

This https://arxiv.org/abs/2505.08944 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Toward Cost-Efficient Serving of Mixture-of-Experts with Asynchrony
Mixture-of-Experts (MoE) architectures offer the promise of larger model capacity without the prohibitive costs of fully dense designs. However, in real-world inference serving, load skew across experts often leads to suboptimal device utilization and excessive synchronization overheads. This paper introduces Asynchronous Expert Parallelism (AEP), a new paradigm that decouples layer execution from barrier-style synchronization. By dynamically queuing tokens at each layer (referred to as $μ$-qu…

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity
Yiqun Yao, Xiang Li, Xin Jiang, Xuezhi Fang, Naitong Yu, Aixin Sun, Yequan Wang
https://arxiv.org/abs/2506.01934

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity
Humans naturally process real-world multimodal information in a full-duplex manner. In artificial intelligence, replicating this capability is essential for advancing model development and deployment, particularly in embodied contexts. The development of multimodal models faces two primary challenges: (1) effectively handling more than three modalities-such as vision, audio, and text; and (2) delivering full-duplex responses to rapidly evolving human instructions. To facilitate research on mode…

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
Oriyomi Ahmad Hassan, Aisha Tunrayo Maradesa, Abdulazeez Toyosi Alabi, Oyejide Surajudeen Salam, Ajani Busari, Akinwale Victor Famotire, Habeeb Abiodun Afolabi, Solomon Adeleke, Abayomi Ayodele Akomolafe
https://arx…

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
This study introduces the Exponentiated-Exponential-Pareto-Half Normal Mixture Distribution (EEPHND), a novel hybrid model developed to overcome the limitations of classical distributions in modeling complex real-world data. By compounding the Exponentiated-Exponential-Pareto (EEP) and Half-Normal distributions through a mixture mechanism, EEPHND effectively captures both early-time symmetry and long-tail behavior, features which are commonly observed in survival and reliability data. The model…

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Kuo Yang, Xingjie Yang, Linhui Yu, Qing Xu, Yan Fang, Xu Wang, Zhengyang Zhou, Yang Wang
https://arxiv.org/abs/2506.08507

MasHost Builds It All: Autonomous Multi-Agent System Directed by Reinforcement Learning
Large Language Model (LLM)-driven Multi-agent systems (Mas) have recently emerged as a powerful paradigm for tackling complex real-world tasks. However, existing Mas construction methods typically rely on manually crafted interaction mechanisms or heuristic rules, introducing human biases and constraining the autonomous ability. Even with recent advances in adaptive Mas construction, existing systems largely remain within the paradigm of semi-autonomous patterns. In this work, we propose MasHos…

Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
https://arxiv.org/abs/2506.08149 https://

Ego-centric Learning of Communicative World Models for Autonomous Driving
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial observability} and \textit{non-stationarity} issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. By making use of generative AI embodied in world model together with its latent …

This https://arxiv.org/abs/2505.21938 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting their relevance to real-world systems. We propose a more practical threat model, Fake Data Injection, which reflects realistic adversarial constraints: the attacker can inject only a limited number of bounded fake feedback samples into the learner's history, simulating legitimate interactions. We design efficient attack …

This https://arxiv.org/abs/2410.19214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

BTS: A Comprehensive Benchmark for Tie Strength Prediction
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodolog…

SWE-bench Goes Live!
Linghao Zhang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Chengxing Xie, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
https://arxiv.org/abs/2505.23419

SWE-bench Goes Live!
The issue-resolving task, where a model generates patches to fix real-world bugs, has emerged as a critical benchmark for evaluating the capabilities of large language models (LLMs). While SWE-bench and its variants have become standard in this domain, they suffer from key limitations: they have not been updated since their initial releases, cover a narrow set of repositories, and depend heavily on manual effort for instance construction and environment setup. These factors hinder scalability a…

Multi-Distillation from Speech and Music Representation Models
Jui-Chiang Wei, Yi-Cheng Lin, Fabian Ritter-Gutierrez, Hung-yi Lee
https://arxiv.org/abs/2506.07237

Multi-Distillation from Speech and Music Representation Models
Real-world audio often mixes speech and music, yet models typically handle only one domain. This paper introduces a multi-teacher distillation framework that unifies speech and music models into a single one while significantly reducing model size. Our approach leverages the strengths of domain-specific teacher models, such as HuBERT for speech and MERT for music, and explores various strategies to balance both domains. Experiments across diverse tasks demonstrate that our model matches the per…

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Kwanwoo Lee, Gyeongjae Park, Jaeheung Park
https://arxiv.org/abs/2506.03856

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Balance control for humanoid robots has been extensively studied to enable robots to navigate in real-world environments. However, balance controllers that explicitly optimize the durations of both the single support phase, also known as step timing, and the Double Support Phase (DSP) have not been widely explored due to the inherent nonlinearity of the associated optimization problem. Consequently, many recent approaches either ignore the DSP or adjust its duration based on heuristics or on li…

This https://arxiv.org/abs/2411.04564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Generalisation of Voter Model: Influential Nodes and Convergence Properties
Consider an undirected graph G, representing a social network, where each node is blue or red, corresponding to positive or negative opinion on a topic. In the voter model, in discrete time rounds, each node picks a neighbour uniformly at random and adopts its colour. Despite its significant popularity, this model does not capture some fundamental real-world characteristics such as the difference in the strengths of individuals connections, individuals with neutral opinion on a topic, and indiv…

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
Hongyan Zhi, Peihao Chen, Siyuan Zhou, Yubo Dong, Quanxi Wu, Lei Han, Mingkui Tan
https://arxiv.org/abs/2506.06199

3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
Manipulation has long been a challenging task for robots, while humans can effortlessly perform complex interactions with objects, such as hanging a cup on the mug rack. A key reason is the lack of a large and uniform dataset for teaching robots manipulation skills. Current robot datasets often record robot action in different action spaces within a simple scene. This hinders the robot to learn a unified and robust action representation for different robots within diverse scenes. Observing how …

This https://arxiv.org/abs/2505.21906 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge
Vision-language-action (VLA) models have emerged as the next generation of models in robotics. However, despite leveraging powerful pre-trained Vision-Language Models (VLMs), existing end-to-end VLA systems often lose key capabilities during fine-tuning as the model adapts to specific robotic tasks. We argue that a generalizable VLA model should retain and expand upon the VLM's core competencies: 1) Open-world embodied reasoning - the VLA should inherit the knowledge from VLM, i.e., recognize a…

Sparse Imagination for Efficient Visual World Model Planning
Junha Chun, Youngjoon Jeong, Taesup Kim
https://arxiv.org/abs/2506.01392 https://

Sparse Imagination for Efficient Visual World Model Planning
World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices. However, ensuring the prediction accuracy of world models often demands substantial computational resources, posing a major challenge for real-time applications. This computational burden is particularly restrictive in robotics, where resources are severely constrained. To address this limitation, we propose a Sparse Imagination fo…

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz B\"Acher, Marco Hutter
https://arxiv.org/abs/2506.09588

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack preci…

This https://arxiv.org/abs/2502.13172 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Unveiling Privacy Risks in LLM Agent Memory
Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically investigate the vulnerability of LLM agents to our proposed Memory EXTRaction Attack (MEXTRA) under a black-box setting. To extract private information from memory, we propose an effective attacking …

This https://arxiv.org/abs/2505.21432 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancement in boosting Large Language Models (LLMs) to solve complex tasks in digital domains. However, the potential of slow thinking remains largely unexplored for robotic foundation models interacting with the physical world. In this work, we propose Hume: a dual-system Vision-Language-Action (VLA) model with value-guided…

This https://arxiv.org/abs/2505.12583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Comprehensive Survey on Physical Risk Control in the Era of Foundation Model-enabled Robotics
Recent Foundation Model-enabled robotics (FMRs) display greatly improved general-purpose skills, enabling more adaptable automation than conventional robotics. Their ability to handle diverse tasks thus creates new opportunities to replace human labor. However, unlike general foundation models, FMRs interact with the physical world, where their actions directly affect the safety of humans and surrounding objects, requiring careful deployment and control. Based on this proposition, our survey co…

This https://arxiv.org/abs/2505.06787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin
The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation proces…

This https://arxiv.org/abs/2503.03480 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robot itself, and humans. How can safety constraints be explicitly integrated into VLAs? We address this by exploring an integrated safety approach (ISA), systematically modeling safety requirements, then actively eliciting diverse unsafe behaviors, effectively constraining VLA poli…

This https://arxiv.org/abs/2501.07985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing
Robotic systems are increasingly employed for industrial automation, with contact-rich tasks like polishing requiring dexterity and compliant behaviour. These tasks are difficult to model, making classical control challenging. Deep reinforcement learning (RL) offers a promising solution by enabling the learning of models and control policies directly from data. However, its application to real-world problems is limited by data inefficiency and unsafe exploration. Adaptive hybrid RL methods blen…