Tootfinder

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
Anton Changalidis, Aki H\"arm\"a
https://arxiv.org/abs/2506.14704

Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
This paper studies how the model architecture and data configurations influence the empirical memorization capacity of generative transformers. The models are trained using synthetic text datasets derived from the Systematized Nomenclature of Medicine (SNOMED) knowledge graph: triplets, representing static connections, and sequences, simulating complex relation patterns. The results show that embedding size is the primary determinant of learning speed and capacity, while additional layers provi…

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…

What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
Lance Ying, Daniel Xu, Alicia Zhang, Katherine M. Collins, Max H. Siegel, Joshua B. Tenenbaum
https://arxiv.org/abs/2506.14212

What's in the Box? Reasoning about Unseen Objects from Multimodal Cues
People regularly make inferences about objects in the world that they cannot see by flexibly integrating information from multiple sources: auditory and visual cues, language, and our prior beliefs and knowledge about the scene. How are we able to so flexibly integrate many sources of information to make sense of the world around us, even if we have no direct knowledge? In this work, we propose a neurosymbolic model that uses neural networks to parse open-ended multimodal inputs and then applie…

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Mohammed Mehedi Hasan, Hao Li, Emad Fallahzadeh, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2506.13538

Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
Although Foundation Models (FMs), such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models' real-world interaction. To address this, FM providers introduced tool calling-triggering a proliferation of frameworks with distinct tool interfaces. In late 2024, Anthropic introduced the Model Context Protocol (MCP) to standardize this tool ecosystem, which has become the de facto standard with over eight million weekly SD…

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal
https://arxiv.org/abs/2506.14605

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to paired degraded and ground-truth images -- the proposed method operates under minimal assumptions and relies only on small, unpaired datasets. This makes it particularly well-suited for real-world scenarios, where the forward model is often unknown or misspecified, and collecting…

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

Learning, fast and slow: a two-fold algorithm for data-based model adaptation
Laura Boca de Giuli, Alessio La Bella, Riccardo Scattolini
https://arxiv.org/abs/2507.12187

Learning, fast and slow: a two-fold algorithm for data-based model adaptation
This article addresses the challenge of adapting data-based models over time. We propose a novel two-fold modelling architecture designed to correct plant-model mismatch caused by two types of uncertainty. Out-of-domain uncertainty arises when the system operates under conditions not represented in the initial training dataset, while in-domain uncertainty results from real-world variability and flaws in the model structure or training process. To handle out-of-domain uncertainty, a slow learnin…

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…

A look at the Chile-led Latam-GPT project, which involves 30 Latin American and Caribbean institutions collaborating to release an open-source LLM in September (Cristišn Vera-Cruz/Rest of World)
https://restofworld.org/2025/chatgpt-latin-america-alternative-latamgpt…

Fed up with ChatGPT, Latin America is building its own
Dozens of organizations in the region have partnered to develop a large language model that better understands Latin America’s cultural and linguistic nuances.

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…

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…

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…

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 …

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…

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…

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.

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…

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…

Tokopedia sellers say Tokopedia's strengths have eroded since its TikTok Shop merger in Indonesia, driving thousands of sellers to join rivals, including Toco (Michelle Anindya/Rest of World)
https://restofworld.org/2025/tiktok-indonesia-tokopedia-merger-problems…

TikTok’s messy merger in Indonesia could be a preview of what’s to come in the U.S.
A year after merging with local giant Tokopedia, TikTok’s business model is alienating sellers, forcing many to join rival platforms.

Search for High-Energy Neutrinos From the Sun Using Ten Years of IceCube Data
Abbasi, Ackermann, Adams, Agarwalla, Aguilar, Ahlers, Alameddine, Ali, Amin, Andeen, Arg\"uelles, Ashida, Athanasiadou, Axani, Babu, Bai, Baines-Holmes, V., Barwick, Bash, Basu, Bay, Beatty, Tjus, Behrens, Beise, Bellenghi, Benkel, BenZvi, Berley, Bernardini, Besson, Blaufuss, Bloom, Blot, Bodo, Bontempo, Motzkin, Meneguolo, B\"oser, Botner, B\"ottcher, Braun, Brinson, Brisson-Tsavoussis, Burle…

Search for High-Energy Neutrinos From the Sun Using Ten Years of IceCube Data
In this Letter, we present the results of a search for high-energy neutrinos produced by the annihilation of dark matter particles trapped in the Sun. Using 9.3 and 10.4 years of data from the DeepCore and IceCube neutrino detectors, we establish world-best limits for spin-dependent interactions between dark matter and Standard Model particles for dark matter masses from tens of GeV to tens of TeV. We additionally place constraints on the neutrino background produced by interactions of cosmic r…

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…

Universal self-similarity of hierarchical communities formed through a general self-organizing principle
Shruti Tandon (equal), Nidhi Dilip Sonwane (equal), Tobias Braun, Norbert Marwan, Juergen Kurths, R. I. Sujith
https://arxiv.org/abs/2507.11159

Universal self-similarity of hierarchical communities formed through a general self-organizing principle
Emergence of self-similarity in hierarchical community structures is ubiquitous in complex systems. Yet, there is a dearth of universal quantification and general principles describing the formation of such structures. Here, we discover universality in scaling laws describing self-similar hierarchical community structure in multiple real-world networks including biological, infrastructural, and social networks. We replicate these scaling relations using a phenomenological model, where nodes wit…

Russian attacks show with clarity that Putin is mocking Trump, Poland's Sikorski says
Arriving for talks in Rome, Polish foreign minister Radosław Sikorski said the continued Russian attacks show that
“Vladimir Putin of Russia is mocking the peace efforts of president Donald Trump.”
He also stressed that Europe is stepping up its plans for defence, with increased spending.
Talking about the meeting ahead, he said leaders needed to
“strategise about what to do…

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…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

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…

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…

Subtooting since people in the original thread wanted it to be over, but selfishly tagging @… and @… whose opinions I value...
I think that saying "we are not a supply chain" is exactly what open-source maintainers should be doing right now in response to "open source supply chain security" threads.
I can't claim to be an expert and don't maintain any important FOSS stuff, but I do release almost all of my code under open licenses, and I do use many open source libraries, and I have felt the pain of needing to replace an unmaintained library.
There's a certain small-to-mid-scale class of program, including many open-source libraries, which can be built/maintained by a single person, and which to my mind best operate on a "snake growth" model: incremental changes/fixes, punctuated by periodic "skin-shedding" phases where make rewrites or version updates happen. These projects aren't immortal either: as the whole tech landscape around them changes, they become unnecessary and/or people lose interest, so they go unmaintained and eventually break. Each time one of their dependencies breaks (or has a skin-shedding moment) there's a higher probability that they break or shed too, as maintenance needs shoot up at these junctures. Unless you're a company trying to make money from a single long-lived app, it's actually okay that software churns like this, and if you're a company trying to make money, your priorities absolutely should not factor into any decisions people making FOSS software make: we're trying (and to a huge extent succeeding) to make a better world (and/or just have fun with our own hobbies share that fun with others) that leaves behind the corrosive & planet-destroying plague which is capitalism, and you're trying to personally enrich yourself by embracing that plague. The fact that capitalism is *evil* is not an incidental thing in this discussion.
To make an imperfect analogy, imagine that the peasants of some domain have set up a really-free-market, where they provide each other with free stuff to help each other survive, sometimes doing some barter perhaps but mostly just everyone bringing their surplus. Now imagine the lord of the domain, who is the source of these peasants' immiseration, goes to this market secretly & takes some berries, which he uses as one ingredient in delicious tarts that he then sells for profit. But then the berry-bringer stops showing up to the free market, or starts bringing a different kind of fruit, or even ends up bringing rotten berries by accident. And the lord complains "I have a supply chain problem!" Like, fuck off dude! Your problem is that you *didn't* want to build a supply chain and instead thought you would build your profit-focused business in other people's free stuff. If you were paying the berry-picker, you'd have a supply chain problem, but you weren't, so you really have an "I want more free stuff" problem when you can't be arsed to give away your own stuff for free.
There can be all sorts of problems in the really-free-market, like maybe not enough people bring socks, so the peasants who can't afford socks are going barefoot, and having foot problems, and the peasants put their heads together and see if they can convince someone to start bringing socks, and maybe they can't and things are a bit sad, but the really-free-market was never supposed to solve everyone's problems 100% when they're all still being squeezed dry by their taxes: until they are able to get free of the lord & start building a lovely anarchist society, the really-free-market is a best-effort kind of deal that aims to make things better, and sometimes will fall short. When it becomes the main way goods in society are distributed, and when the people who contribute aren't constantly drained by the feudal yoke, at that point the availability of particular goods is a real problem that needs to be solved, but at that point, it's also much easier to solve. And at *no* point does someone coming into the market to take stuff only to turn around and sell it deserve anything from the market or those contributing to it. They are not a supply chain. They're trying to help each other out, but even then they're doing so freely and without obligation. They might discuss amongst themselves how to better coordinate their mutual aid, but they're not going to end up forcing anyone to bring anything or even expecting that a certain person contribute a certain amount, since the whole point is that the thing is voluntary & free, and they've all got changing life circumstances that affect their contributions. Celebrate whatever shows up at the market, express your desire for things that would be useful, but don't impose a burden on anyone else to bring a specific thing, because otherwise it's fair for them to oppose such a burden on you, and now you two are doing your own barter thing that's outside the parameters of the really-free-market.

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…

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
toXiv_bot_toot

DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
Yinsheng Li, Zhen Dong, Yi Shao
https://arxiv.org/abs/2507.11527

DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
Large Language Model (LLM) agents have shown great potential for solving real-world problems and promise to be a solution for tasks automation in industry. However, more benchmarks are needed to systematically evaluate automation agents from an industrial perspective, for example, in Civil Engineering. Therefore, we propose DrafterBench for the comprehensive evaluation of LLM agents in the context of technical drawing revision, a representation task in civil engineering. DrafterBench contains t…

Meta launches V-JEPA 2, an open-source AI "world model" to understand and predict 3D environments and object movements, to help robotics and self-driving cars (Ryan Browne/CNBC)
https://www.cnbc.com/2025/06/11/meta-launc

Meta launches AI 'world model' to advance robotics, self-driving cars
Meta on Wednesday announced it's rolling out a new AI "world model" that can better understand the 3D environment and movements of physical objects.

UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
Peiran Wu, Yunze Liu, Zhengdong Zhu, Enmin Zhou, Shawn Shen
https://arxiv.org/abs/2507.11336

UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and models remain predominantly visual centric, overlooking the crucial role of audio in conveying scene dynamics, speaker intent, and narrative context. This lack of omni datasets and lightweight, capable models hampers progress in fine grained, multimodal video understanding. To address these challenges, we introduce UGC-…

After training, we finetune on real-world data. We observe that the models that have been pre-trained with noise converge very quickly compared to a baseline which is trained from scratch.
Moreover, on the other datasets, the UP models retain their zero-shot performance during finetuning. This suggests that there may be a generalization benefit to using a UP model.
All this is at the expense of much longer training, but that cost can be amortized over many tasks.

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Fengxiao Tang, Huan Li, Ming Zhao, Zongzong Wu, Shisong Peng, Tao Yin
https://arxiv.org/abs/2507.11310

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Verifying the credibility of Cyber Threat Intelligence (CTI) is essential for reliable cybersecurity defense. However, traditional approaches typically treat this task as a static classification problem, relying on handcrafted features or isolated deep learning models. These methods often lack the robustness needed to handle incomplete, heterogeneous, or noisy intelligence, and they provide limited transparency in decision-making-factors that reduce their effectiveness in real-world threat envi…

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…

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 …

Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
Heet Nitinkumar Dalsania
https://arxiv.org/abs/2507.07254 https://a…

Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
Modern deep learning implementations for medical imaging usually rely on large labeled datasets. These datasets are often difficult to obtain due to privacy concerns, high costs, and even scarcity of cases. In this paper, a label-efficient strategy is proposed for chest X-ray diagnosis that seeks to reflect real-world hospital scenarios. The experiments use the NIH Chest X-ray14 dataset and a pre-trained CLIP ViT-B/32 model. The model is adapted via partial fine-tuning of its visual encoder and…

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 …

How long until the internet, which allowed a generation to benefit from a vast wealth of human knowledge, becomes a swamp filled with generated #AI pollution? It may already be too late. https://www.theregist…

The launch of ChatGPT polluted the world forever, like the first atomic weapons tests
Feature: Academics mull the need for the digital equivalent of low-background steel

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…

Tesla shareholders will apparently get to vote on whether Tesla should bail out Xai/Twitter.
Do Tesla shareholders want to give Musk more money in return for Tesla owning part of his nazi AI model and his nazi troll site?
We shall see. My guess is yes! Tesla share owners will vote to dilute themselves in return for the chance to bail out the failing Twitter and Grok.
#xai #grok #twitter #tesla

TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications
Zexin Deng (University of Warwick, UK), Zhenhui Yuan (University of Warwick, UK), Longhao Zou (Pengcheng Laboratory, China)
https://arxiv.org/abs/2507.04425

TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications
Telerobotic technologies are becoming increasingly essential in fields such as remote surgery, nuclear decommissioning, and space exploration. Reliable datasets and testbeds are essential for evaluating telerobotic system performance prior to real-world deployment. However, there is a notable lack of datasets that capture the impact of network delays, as well as testbeds that realistically model the communication link between the operator and the robot. This paper introduces TeleSim, a network-…

DNA Unzipping Transition
Somendra M. Bhattacharjee
https://arxiv.org/abs/2506.24064 https://arxiv.org/pdf/2506.24064

DNA Unzipping Transition
This review focuses on the force-induced unzipping transition of double-stranded DNA. It begins with a brief history of DNA melting, which emerged alongside the growth of the field of molecular biology, juxtaposed with the advancements in physics during the same post-World War II period. The earlier theories of melting of DNA were based on the Ising model and its modifications, but gradually moved towards polymer-based models. The idea of force-induced unzipping was first introduced in 1999 as …

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…

Symmetry Sectors in Chord Space and Relational Holography in the DSSYK
Sergio E. Aguilar-Gutierrez
https://arxiv.org/abs/2506.21447 https://

Symmetry Sectors in Chord Space and Relational Holography in the DSSYK
Can there be multiple bulk theories for the same boundary theory? We answer this affirmatively in the double-scaled SYK (DSSYK) model using the tools of constrained systems. We find different symmetry sectors generated by specific constraints within the chord Hilbert space of the DSSYK with matter. Each sector corresponds to a different bulk description. These include chord parity symmetry, corresponding to End-Of-The-World (ETW) branes and Euclidean wormholes in sine dilaton gravity; and relat…

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…

PBE Meets LLM: When Few Examples Aren't Few-Shot Enough
Shuning Zhang, Yongjoo Park
https://arxiv.org/abs/2507.05403 https://arxi…

PBE Meets LLM: When Few Examples Aren't Few-Shot Enough
Large language models (LLMs) can generate code from natural language descriptions. Their performance is typically evaluated using programming benchmarks that simulate real-world tasks. These benchmarks provide specifications in the form of docstrings, function signatures, or bug reports. The model then generates a program, which is tested against predefined test cases. In contrast, Programming by Example (PBE) uses input-output examples as the specification. Traditional PBE systems rely on sear…

Assessing the Ship Motion Prediction Capabilities of the Open-Source Model NEMOH Against Field Observations
Tianshi Yu, Ziyue Wang, Filippo Nelli, Ying Tan, Guillaume Ducrozet, Alessandro Toffoli
https://arxiv.org/abs/2506.20186

Assessing the Ship Motion Prediction Capabilities of the Open-Source Model NEMOH Against Field Observations
Accurate ship motion prediction is critical for safe and efficient maritime operations, particularly in open ocean environments. This study evaluates the capability of NEMOH, an open-source potential flow boundary element solver, for predicting ship motions in real-world open ocean conditions. A linear model, known as the Response Amplitude Operator (RAO), is obtained using NEMOH, and is driven by the wave directional spectrum obtained from the WaMoS-II marine radar on the research vessel Akade…

A "Good" Regulator May Provide a World Model for Intelligent Systems
Bradly Alicea, Morgan Hough, Amanda Nelson, Jesse Parent
https://arxiv.org/abs/2506.23032

A "Good" Regulator May Provide a World Model for Intelligent Systems
One classic idea from the cybernetics literature is the Every Good Regulator Theorem (EGRT). The EGRT provides a means to identify good regulation, or the conditions under which an agent (regulator) can match the dynamical behavior of a system. We reevaluate and recast the EGRT in a modern context to provide insight into how intelligent autonomous learning systems might utilize a compressed global representation (world model). One-to-one mappings between a regulator (R) and the corresponding sy…

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…

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…

Extended datasamples under the lens of Brane World Theory
Kyra Jacobo, Dorian Araya
https://arxiv.org/abs/2506.15002 https://arxiv.or…

Extended datasamples under the lens of Brane World Theory
This work revises the Brane World Theory known as Randall-Sundrum with the modification of an exponential, redshift-dependent brane tension. This model is studied in a scenario assuming no dark energy, with the aim of determining whether it can reproduce the universe's acceleration on its own, without the addition of a dark energy fluid. Bayesian statistical analysis is performed in order to constrain the free parameters of each scenario using SLS, SNIa, OHD, and BAO data samples, the last two …

Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
Gurjeet Sangra Singh, Maciej Falkiewicz, Alexandros Kalousis
https://arxiv.org/abs/2506.22204

Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport
Physics phenomena are often described by ordinary and/or partial differential equations (ODEs/PDEs), and solved analytically or numerically. Unfortunately, many real-world systems are described only approximately with missing or unknown terms in the equations. This makes the distribution of the physics model differ from the true data-generating process (DGP). Using limited and unpaired data between DGP observations and the imperfect model simulations, we investigate this particular setting by c…

Challenges in Grounding Language in the Real World
Peter Lindes, Kaoutar Skiker
https://arxiv.org/abs/2506.17375 https://arxiv.org/pd…

Challenges in Grounding Language in the Real World
A long-term goal of Artificial Intelligence is to build a language understanding system that allows a human to collaborate with a physical robot using language that is natural to the human. In this paper we highlight some of the challenges in doing this, and propose a solution that integrates the abilities of a cognitive agent capable of interactive task learning in a physical robot with the linguistic abilities of a large language model. We also point the way to an initial implementation of th…

Impact of a Deployed LLM Survey Creation Tool through the IS Success Model
Peng Jiang, Vinicius Cezar Monteiro de Lira, Antonio Maiorino
https://arxiv.org/abs/2506.14809

Impact of a Deployed LLM Survey Creation Tool through the IS Success Model
Surveys are a cornerstone of Information Systems (IS) research, yet creating high-quality surveys remains labor-intensive, requiring both domain expertise and methodological rigor. With the evolution of large language models (LLMs), new opportunities emerge to automate survey generation. This paper presents the real-world deployment of an LLM-powered system designed to accelerate data collection while maintaining survey quality. Deploying such systems in production introduces real-world complex…

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…

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.

Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement
Jiangzhong Cao, Zekai Zeng, Xu Zhang, Huan Zhang, Chunling Fan, Gangyi Jiang, Weisi Lin
https://arxiv.org/abs/2507.06234

Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement
High-quality underwater images are essential for both machine vision tasks and viewers with their aesthetic appeal.However, the quality of underwater images is severely affected by light absorption and scattering. Deep learning-based methods for Underwater Image Enhancement (UIE) have achieved good performance. However, these methods often overlook considering human perception and lack sufficient constraints within the solution space. Consequently, the enhanced images often suffer from diminish…

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…

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/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…

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…

Arbiter PUF: Uniqueness and Reliability Analysis Using Hybrid CMOS-Stanford Memristor Model
Tanvir Rahman, A. B. M. Harun-ur Rashid
https://arxiv.org/abs/2507.04461

Arbiter PUF: Uniqueness and Reliability Analysis Using Hybrid CMOS-Stanford Memristor Model
In an increasingly interconnected world, protecting electronic devices has grown more crucial because of the dangers of data extraction, reverse engineering, and hardware tampering. Producing chips in a third-party manufacturing company can let hackers change the design. As the Internet of Things (IoT) proliferates, physical attacks happen more, and conventional cryptography techniques do not function well. In this paper, we investigate the design and assessment of PUFs using the Stanford Memri…

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 …

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…

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…

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…

Open-closed 3d gravity as a random ensemble
Daniel L. Jafferis, Liza Rozenberg, Diandian Wang
https://arxiv.org/abs/2506.19817 https://

Open-closed 3d gravity as a random ensemble
We investigate an ensemble of boundary CFTs within the framework of a tensor model recently constructed to model 3d quantum gravity. The incorporation of CFT borders introduces new elements to the gravity theory. In particular, it leads to an open-closed extension of Virasoro TQFT, which in the classical limit gives rise to 3d gravity with tensionful end-of-the-world branes. It also provides predictions for off-shell manifolds with bordered asymptotic boundaries, such as the annulus wormhole. A…

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/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 …

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…

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/2505.16422 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…

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware
Sebastian Graf, Keith Moffat, Anurag Mohapatra, Alessandro Chiuso, Florian D\"orfler
https://arxiv.org/abs/2507.02325

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware
Grid-connected inverter control is challenging to implement due to the difficulty of obtaining and maintaining an accurate grid model. Direct Data-Driven Predictive Control provides a model-free alternative to traditional model-based control methods. This paper describes how the recently-proposed Transient Predictive Control (TPC) can be used for real-world, plug-and-play inverter control. The following hypotheses were tested: 1) The TPC algorithm can be run online using standard hardware, and …

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/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…

SAKURAONE: Empowering Transparent and Open AI Platforms through Private-Sector HPC Investment in Japan
Fumikazu Konishi
https://arxiv.org/abs/2507.02124 ht…

SAKURAONE: Empowering Transparent and Open AI Platforms through Private-Sector HPC Investment in Japan
SAKURAONE is a managed high performance computing (HPC) cluster developed and operated by the SAKURA Internet Research Center. It reinforces the ``KOKARYOKU PHY'' configuration of bare-metal GPU servers and is designed as a cluster computing resource optimized for advanced workloads, including large language model (LLM) training. In the ISC 2025 edition of the TOP500 list, SAKURAONE was ranked \textbf{49th} in the world based on its High Performance Linpack (HPL) score, demonstrating its glob…

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…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
C\'edric Bonhomme, Alexandre Dulaunoy
https://arxiv.org/abs/2507.03607 …

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

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…

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
TRI LBM Team, Jose Barreiros, Andrew Beaulieu, Aditya Bhat, Rick Cory, Eric Cousineau, Hongkai Dai, Ching-Hsin Fang, Kunimatsu Hashimoto, Muhammad Zubair Irshad, Masha Itkina, Naveen Kuppuswamy, Kuan-Hui Lee, Katherine Liu, Dale McConachie, Ian McMahon, Haruki Nishimura, Calder Phillips-Grafflin, Charles Richter, Paarth Shah, Krishnan Srinivasan, Blake Wulfe, Chen Xu, Mengchao Zhang, Alex Alspach, Maya …

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
Robot manipulation has seen tremendous progress in recent years, with imitation learning policies enabling successful performance of dexterous and hard-to-model tasks. Concurrently, scaling data and model size has led to the development of capable language and vision foundation models, motivating large-scale efforts to create general-purpose robot foundation models. While these models have garnered significant enthusiasm and investment, meaningful evaluation of real-world performance remains a …

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Samuel Ofosu Mensah, Kerol Djoumessi, Philipp Berens
https://arxiv.org/abs/2507.05852

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
Federated learning (FL) provides a promising paradigm for collaboratively training machine learning models across distributed data sources while maintaining privacy. Nevertheless, real-world FL often faces major challenges including communication overhead during the transfer of large model parameters and statistical heterogeneity, arising from non-identical independent data distributions across clients. In this work, we propose an FL framework that 1) provides inherent interpretations using pro…

WorldVLA: Towards Autoregressive Action World Model
Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, Deli Zhao, Hao Chen
https://arxiv.org/abs/2506.21539

WorldVLA: Towards Autoregressive Action World Model
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework. The world model predicts future images by leveraging both action and image understanding, with the purpose of learning the underlying physics of the environment to improve action generation. Meanwhile, the action model generates the subsequent actions based on image observations,…

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…

Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar, Yann LeCun, Trevor Darrell, Jitendra Malik
https://arxiv.org/abs/2506.21552

Whole-Body Conditioned Egocentric Video Prediction
We train models to Predict Ego-centric Video from human Actions (PEVA), given the past video and an action represented by the relative 3D body pose. By conditioning on kinematic pose trajectories, structured by the joint hierarchy of the body, our model learns to simulate how physical human actions shape the environment from a first-person point of view. We train an auto-regressive conditional diffusion transformer on Nymeria, a large-scale dataset of real-world egocentric video and body pose c…

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…

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…

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 …

In-context learning for the classification of manipulation techniques in phishing emails
Antony Dalmiere (LAAS-TRUST, LAAS), Guillaume Auriol (LAAS-TRUST, INSA Toulouse), Vincent Nicomette (LAAS-TSF, LAAS), Pascal Marchand (LERASS)
https://arxiv.org/abs/2506.22515

In-context learning for the classification of manipulation techniques in phishing emails
Traditional phishing detection often overlooks psychological manipulation. This study investigates using Large Language Model (LLM) In-Context Learning (ICL) for fine-grained classification of phishing emails based on a taxonomy of 40 manipulation techniques. Using few-shot examples with GPT-4o-mini on real-world French phishing emails (SignalSpam), we evaluated performance against a human-annotated test set (100 emails). The approach effectively identifies prevalent techniques (e.g., Baiting, …

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…

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…

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Biao Yi, Tiansheng Huang, Sishuo Chen, Tong Li, Zheli Liu, Zhixuan Chu, Yiming Li
https://arxiv.org/abs/2506.16447

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Backdoor unalignment attacks against Large Language Models (LLMs) enable the stealthy compromise of safety alignment using a hidden trigger while evading normal safety auditing. These attacks pose significant threats to the applications of LLMs in the real-world Large Language Model as a Service (LLMaaS) setting, where the deployed model is a fully black-box system that can only interact through text. Furthermore, the sample-dependent nature of the attack target exacerbates the threat. Instead …

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…

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/2505.06787 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/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…