Tootfinder

As I thought of the game of Go this morning, I remembered that when Wikipedia started in 2001, I thought "oh, it’s something like Sensei‘s Library“, because that was the biggest and most active wiki I knew at that time.
https://senseis.xmp.net/

[2025-06-09 Mon (UTC), 7 new articles found for eess.IV Image and Video Processing]
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[2025-06-09 Mon (UTC), 5 new articles found for math-ph Mathematical Physics]
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Monitorability for the Modal mu-Calculus over Systems with Data: From Practice to Theory
Luca Aceto, Antonis Achilleos, Duncan Paul Attard, L\'eo Exibard, Adrian Francalanza, Anna Ing\'olfsd\'ottir, Karoliina Lehtinen
https://arxiv.org/abs/2506.06172

Monitorability for the Modal mu-Calculus over Systems with Data: From Practice to Theory
Runtime verification, also known as runtime monitoring, consists of checking whether a system satisfies a given specification by observing the trace it produces during its execution. It is used as a lightweight verification technique to complement or substitute costlier methods such as model-checking. In the regular setting, Hennessy-Milner logic with recursion, a variant of the modal mu-calculus, provides a versatile formalism for expressing linear- and branching-time specifications of the c…

[2025-06-09 Mon (UTC), 12 new articles found for eess.SY Systems and Control]
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$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Yifan Zeng
https://arxiv.org/abs/2506.05844

$\text{C}^{2}\text{BNVAE}$: Dual-Conditional Deep Generation of Network Traffic Data for Network Intrusion Detection System Balancing
Network Intrusion Detection Systems (NIDS) face challenges due to class imbalance, affecting their ability to detect novel and rare attacks. This paper proposes a Dual-Conditional Batch Normalization Variational Autoencoder ($\text{C}^{2}\text{BNVAE}$) for generating balanced and labeled network traffic data. $\text{C}^{2}\text{BNVAE}$ improves the model's adaptability to different data categories and generates realistic category-specific data by incorporating Conditional Batch Normalization (C…

[2025-06-09 Mon (UTC), 2 new articles found for math.QA Quantum Algebra]
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[2025-06-09 Mon (UTC), 7 new articles found for q-bio.NC Neurons and Cognition]
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Novel browser extension idea: Site bookmarks
Sometimes bookmarks are only useful when you're on a specific website, when visiting a specific page, etc. This extension would let you create a bookmark that would only show up as a suggestion on specific pages
Extra credit: allow users to configure include/exclude rules for showing a bookmark

[2025-06-09 Mon (UTC), 2 new articles found for econ.EM Econometrics]
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[2025-06-09 Mon (UTC), 1 new article found for physics.bio-ph Biological Physics]
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Fuck it, im all on the style #anarchist,

[2025-06-09 Mon (UTC), 15 new articles found for cond-mat.mes-hall Mesoscale and Nanoscale Physics]
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Band of topological groups
Sunil Kumar Maity, Monika Paul
https://arxiv.org/abs/2506.05956 https://arxiv.org/pdf/2506.05956

Band of topological groups
In this article, we construct a band of topological groups from a cryptogroup. Also, we prove that a band of topological groups is metrizable if and only if each $\mathcal{H}$-class is metrizable. Finally, we demonstrate that if $S$ is a band of topological groups and $N$ is a full normal subcryptogroup of $S$, then $S/N$ is Hausdorff if and only if $ρ_{_N}$ is closed in $S \times S$ if and, $ρ_{_N}$ is closed in $S \times S$ if and only if $N$ is closed in $S$.

[2025-06-09 Mon (UTC), 9 new articles found for physics.chem-ph Chemical Physics]
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[2025-06-09 Mon (UTC), 1 new article found for econ.TH Theoretical Economics]
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Understanding Community-Level Blocklists in Decentralized Social Media
Owen Xingjian Zhang, Sohyeon Hwang, Yuhan Liu, Manoel Horta Ribeiro, Andr\'es Monroy-Hern\'andez
https://arxiv.org/abs/2506.05522

Understanding Community-Level Blocklists in Decentralized Social Media
Community-level blocklists are key to content moderation practices in decentralized social media. These blocklists enable moderators to prevent other communities, such as those acting in bad faith, from interacting with their own -- and, if shared publicly, warn others about communities worth blocking. Prior work has examined blocklists in centralized social media, noting their potential for collective moderation outcomes, but has focused on blocklists as individual-level tools. To understand h…

pH-Dependent Zeta Potential Induces Diffusiophoretic Focusing in an Acid-Base Reaction
Ethan Coleman, Ankur Gupta
https://arxiv.org/abs/2506.05731 https://…

pH-Dependent Zeta Potential Induces Diffusiophoretic Focusing in an Acid-Base Reaction
Diffusiophoresis of charged particles in the presence of electrolytes has been extensively studied in the literature. However, in these setups, particles typically move in a single direction, either up or down the electrolyte gradient. Here, we theoretically investigate the conditions under which a particle can reverse its diffusiophoretic direction within the same setup, leading to the formation of a focusing band under steady-state concentration gradients. Using multi-ion diffusiophoresis cal…

[2025-06-09 Mon (UTC), 1 new article found for physics.app-ph Applied Physics]
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[2025-06-09 Mon (UTC), 2 new articles found for q-bio.PE Populations and Evolution]
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[2025-06-09 Mon (UTC), no new articles found for physics.data-an Data Analysis, Statistics and Probability]
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This https://arxiv.org/abs/2505.24765 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantag…

This https://arxiv.org/abs/2504.12527 has been replaced.
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Analysis of the MICCAI Brain Tumor Segmentation - Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Despite continuous advancements in cancer treatment, brain metastatic disease remains a significant complication of primary cancer and is associated with an unfavorable prognosis. One approach for improving diagnosis, management, and outcomes is to implement algorithms based on artificial intelligence for the automated segmentation of both pre- and post-treatment MRI brain images. Such algorithms rely on volumetric criteria for lesion identification and treatment response assessment, which are …

Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models
Yuke Lin, Ming Cheng, Ze Li, Beilong Tang, Ming Li
https://arxiv.org/abs/2506.05796

Diarization-Aware Multi-Speaker Automatic Speech Recognition via Large Language Models
Multi-speaker automatic speech recognition (MS-ASR) faces significant challenges in transcribing overlapped speech, a task critical for applications like meeting transcription and conversational analysis. While serialized output training (SOT)-style methods serve as common solutions, they often discard absolute timing information, limiting their utility in time-sensitive scenarios. Leveraging recent advances in large language models (LLMs) for conversational audio processing, we propose a novel…

This https://arxiv.org/abs/2505.01651 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Human-AI Governance (HAIG): A Trust-Utility Approach
This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rat…

This https://arxiv.org/abs/2505.21091 has been replaced.
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Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs)
System prompts in Large Language Models (LLMs) are predefined directives that guide model behaviour, taking precedence over user inputs in text processing and generation. LLM deployers increasingly use them to ensure consistent responses across contexts. While model providers set a foundation of system prompts, deployers and third-party developers can append additional prompts without visibility into others' additions, while this layered implementation remains entirely hidden from end-users. As…

A Covariant Framework for Generalized Spinor Dual Structures
Rodolfo Jos\'e Bueno Rogerio, Rogerio Teixeira Cavalcanti, Luca Fabbri
https://arxiv.org/abs/2506.05819

A Covariant Framework for Generalized Spinor Dual Structures
In this work, we propose a novel framework for defining the dual structure of a spinor. This construction relies on the basis elements of the Clifford algebra, leading to a covariant structure that embeds the dual. The formulation includes free parameters that may be adjusted to meet specific requirements. Remarkably, it enables the explicit construction of representatives for each class within a recently proposed general classification of spinors. In addition to recovering known results, the f…

This https://arxiv.org/abs/2402.14287 has been replaced.
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Tropical Fermat-Weber Polytropes
We study the geometry of tropical Fermat-Weber points in terms of the symmetric tropical metric over the tropical projective torus. It is well-known that a tropical Fermat-Weber point of a given sample is not unique and we show that the set of all possible Fermat-Weber points forms a polytrope. To prove this, we show that the tropical Fermat-Weber is the dual of a minimum-cost flow problem, and that its polytrope is a bounded cell of a tropical hyperplane arrangement given by both max- and min-…

This https://arxiv.org/abs/2505.06459 has been replaced.
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Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles
Physics-Informed Neural Networks (PINNs) have been widely used to obtain solutions to various physical phenomena modeled as Differential Equations. As PINNs are not naturally equipped with mechanisms for Uncertainty Quantification, some work has been done to quantify the different uncertainties that arise when dealing with PINNs. In this paper, we use a two-step procedure to train Bayesian Neural Networks that provide uncertainties over the solutions to differential equation systems provided by…

I made this a while ago and wanted to share it with you.
If you have epilepsy, please be careful before visiting because it has lots of rainbow spinning effects. It’s that duck spinning to Geometry Dash Practice Mode, with rainbow-colored text overlaid.
https://midtsveen.codeberg.page/duck.h

This https://arxiv.org/abs/2505.20597 has been replaced.
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LSZ ghostbusters in the quadratic gravity stage
The present letter considers the quantization method developed in [1]-[9], which postulates that, in several situations, negative norm or ghost states can be avoided in order to give positive probabilities. These authors also postulate a candidate for a path integral for those theories, following pioneer works initiated by Dirac [10]} and Pauli \[11]. However, taking into account the non standard oscillator algebra inherent to this method, in order to be sure that unitarity is not violated, one…

Symmetry Classification for Alternating Excitons in Two-Dimensional Altermagnets
Jiayu David Cao, Konstantin S. Denisov, Yuntian Liu, Igor Zutic
https://arxiv.org/abs/2506.05753

Symmetry Classification for Alternating Excitons in Two-Dimensional Altermagnets
Excitons, bound electron-holes states, often dominate the optical response of two-dimensional (2D) materials and reflect their inherent properties, including spin-orbit coupling, magnetic ordering, or band topology. By focusing on a growing class of collinear antiferromagnets with a nonrelativistic spin splitting, referred to also as altermagnets (AM), we propose a theoretical framework based on the spin space group (SSG) to elucidate their resulting excitons. Our approach is illustrated on 2D …

This https://arxiv.org/abs/2503.10524 has been replaced.
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On the computation of the vanishing locus of a finitely presented functor
We discuss invariants which are helpful for the computation of the vanishing locus of a finitely presented functor $\mathcal{G}$, i.e., the set of points in the Ziegler spectrum on which $\mathcal{G}$ vanishes. These invariants are: the rank of $\mathcal{G}$, the supports of its co- and contravariant defect, and the class of $\mathcal{G}$ in the Grothendieck group of the category of finitely presented functors. We show that these invariants determine the vanishing locus in the case of a finitel…

This https://arxiv.org/abs/2505.23576 has been replaced.
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Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms
Small Uncrewed Aerial Systems (sUAS) are increasingly deployed as autonomous swarms in search-and-rescue and other disaster-response scenarios. In these settings, they use computer vision (CV) to detect objects of interest and autonomously adapt their missions. However, traditional CV systems often struggle to recognize unfamiliar objects in open-world environments or to infer their relevance for mission planning. To address this, we incorporate large language models (LLMs) to reason about dete…

TikTok expands Manage Topics, a feature that lets users customize how often specific topics appear in the For You feed, and adds AI-powered keyword filtering (Emma Roth/The Verge)
https://www.theverge.com/news/678779/tiktok-for-you-feed-manage-…

TikTok gives everyone more control over what’s on their For You page
TikTok is rolling out Manage Topics globally, a feature that lets you control how often you see certain kinds of videos in your For You feed, such as those related to sports, travel, humor, or dance.

Meta-analysis of Life Cycle Assessments for Li-Ion Batteries Production Emissions
Maurizio Clemente, Prapti Maharjan, Mauro Salazar, Theo Hofman
https://arxiv.org/abs/2506.05531

Meta-analysis of Life Cycle Assessments for Li-Ion Batteries Production Emissions
This paper investigates the environmental impact of Li-Ion batteries by quantifying manufacturing-related emissions and analyzing how electricity mix and production scale affect emission intensity. To this end, we conduct a meta-analysis of life cycle assessments on lithium-ion batteries published over the past two decades, categorizing them by year, battery chemistry, functional unit, system boundaries, and electricity mix. We then carry out a cradle-to-gate assessment for a nickel manganese c…

This https://arxiv.org/abs/2504.18019 has been replaced.
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A search for $B_{s0}^{*}$ and $B^{*}_{s1}$ through the $K^{-}p$ interaction
The formation and properties of hadrons, particularly those involving heavy quarks, remain a central topic in particle physics due to the nonperturbative nature of Quantum Chromodynamics (QCD) at low energies. Heavy-quark symmetry (HQS) provides a valuable framework for understanding the behavior of heavy quarks in hadronic systems, offering insights into spin and flavor symmetries. However, discrepancies between HQS predictions and experimental observations -- particularly the absence of predi…

I may need to get back to doing illustrations again since it's been about a year that I took a break...
Hand drawn stuff (even if digitally) might be the future of human art with all this AI bullshit going on.
➡️ https://rasterweb.net/raster/projects/illustrations/

Illustrations
In March 2024 I started doing sketches on an iPad using Procreate and an Apple Pencil. As of writing this it's May 2024 and I'm still doing it. I'll try to keep this page updated with new illustrations as they are completed. Each illustration links to a page with more info about it, and some [...]

This https://arxiv.org/abs/2503.09920 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

Black-bounce solutions in a k-essence theory under the effects of bumblebee gravity
In the present study, we analyze the effects of violation of Lorentz symmetry for black-bounce solutions in a $k$-essence theory that has the form of a power law for the configuration $n=1/3$. We perform such analysis for a known model explored in previous work $Σ^2=x^2+a^2$ and complement the proposal with a new black-bounce model for the area functions $Σ^2_1=\sqrt{x^4+d^4}$. This model has the Schwarzschild-de Sitter asymptomatic behavior for $x\to{-\infty}$, and we investigate the scalar …

This https://arxiv.org/abs/2504.09567 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Conditional Independence Test Based on Transport Maps
Testing conditional independence between two random vectors given a third is a fundamental and challenging problem in statistics, particularly in multivariate nonparametric settings due to the complexity of conditional structures. We propose a innovative framework for testing conditional independence using transport maps. At the population level, we show that two well-defined transport maps can transform the conditional independence test into an unconditional independence test, this substantial…

This https://arxiv.org/abs/2503.20197 has been replaced.
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Enhancing the Robustness of LLM-Generated Code: Empirical Study and Framework
Ensuring the robustness of code generated by large language models (LLMs) is crucial for real-world reliability. However, existing evaluations predominantly focus on correctness, often neglecting key robustness concerns such as missing input validation and insufficient error handling. In this paper, we present the first empirical study on the robustness of LLM-generated code. We introduce novel robustness metrics and analyze four state-of-the-art code LLMs, revealing that, on average, 43.1% of …

This https://arxiv.org/abs/2506.00834 has been replaced.
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Söze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weighted bandwidth allocation is a powerful abstraction that has a wide range of use cases in modern data center networks. However, realizing highly agile and precise weighted bandwidth allocation for large-scale cloud environments is fundamentally challenging. In this paper, we propose Söze, a lightweight decentralized weighted bandwidth allocation system that leverages simple network telemetry features of commodity Ethernet switches. Given the flow weights, Söze can effectively use the tele…

This https://arxiv.org/abs/2406.07575 has been replaced.
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On the largest prime factor of quadratic polynomials
Let $x$ denote a sufficiently large integer. We show that the recent result of Grimmelt and Merikoski actually yields the largest prime factor of $n^2 +1$ is greater than $x^{1.317}$ infinitely often. As an application, we give a new upper bound for the number of integers $n \leqslant x$ which $n^2 +1$ has a primitive divisor.

This https://arxiv.org/abs/2505.17212 has been replaced.
initial toot: https://mastoxiv.page/@a…

Engineering Altermagnetism via Layer Shifts and Spin Order in Bilayer MnPS$_3$
We investigate how stacking configuration and magnetic ordering determine the magnetic phases of bilayer MnPS$_3$, using density-functional theory and spin-Laue symmetry analysis. While monolayer MnPS$_3$ is a collinear antiferromagnet with no spin splitting, our calculations show that the bilayer can host either Type II (AFM) or Type III (altermagnetic) phases depending sensitively on stacking and spin alignment. We focus on two representative stackings: AA, where the top layer sits directly a…

This https://arxiv.org/abs/2311.01835 has been replaced.
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Surjective rational maps and del Pezzo surfaces
We study surjective (not necessarily regular) rational endomorphisms $f$ of smooth del Pezzo surfaces $X$. We prove that under certain natural non\,-\,degeneracy condition $f$ can have degree bigger than $1$ only when $(-K_X^2) > 5$. Some structural properties of $f$ in the case $X = \p^2$ are also established.

This https://arxiv.org/abs/2505.24718 has been replaced.
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Reinforcing Video Reasoning with Focused Thinking
Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning. In this paper, we propos…

#ActuallyAutistic logging offline, lmao!
https://midtsveen.codeberg.page

This https://arxiv.org/abs/2405.10886 has been replaced.
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Three-mode tunable coupler for superconducting two-qubit gates
Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction control. We experimentally demonstrate the native CZ gate with the pulse duration of 60 ns achieving the two-qubit gate fidelity above 98%,…

This https://arxiv.org/abs/2503.00555 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
Safety alignment is an important procedure before the official deployment of a Large Language Model (LLM). While safety alignment has been extensively studied for LLM, there is still a large research gap for Large Reasoning Models (LRMs) that equip with improved reasoning capability. We in this paper systematically examine a simplified pipeline for producing safety aligned LRMs. With our evaluation of various LRMs, we deliver two main findings: i) Safety alignment can be done upon the LRM to re…

This https://arxiv.org/abs/2310.17451 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability. We propose the Abductive visual Generation (AbdGen) approach to build such logic-integrated models. A vector-quantized symbol grounding mechanism and the corresponding disentanglement training method are introduced to enhance the controllability of logical symbols over generation. Furthermore, we propose two logical abduction methods to ma…

[2025-05-09 Fri (UTC), no new articles found for q-bio.OT Other Quantitative Biology]
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This https://arxiv.org/abs/2505.15724 has been replaced.
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Gravitational Bremsstrahlung in Black-Hole Scattering at $\mathcal{O}(G^3)$: Quadratic-in-Spin Effects
We are employing a supersymmetric variant of the worldline quantum field theory (WQFT) formalism to compute the far-field momentum-space gravitational waveform emitted during the scattering of two spinning black holes at next-to-leading order (NLO) in the post-Minkowskian expansion. Our results are accurate up to quadratic-in-spin contributions, which means we report for the very first time the waveform observable at the order $\mathcal{O}(G^3\mathcal{S}^2)$. Our computation is based on mapping…

This https://arxiv.org/abs/2505.15053 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Owicki--Gries Logic for Timestamp Semantics
Whereas an extension with non-interference of Hoare logic for sequential programs Owicki--Gries logic ensures the correctness of concurrent programs on strict consistency, it is unsound to weak memory models adopted by modern computer architectures and specifications of programming languages. This paper proposes a novel non-interference notion and provides concurrent program logic sound to timestamp semantics corresponding to a weak memory model that allows delays in the effects of store instru…

This https://arxiv.org/abs/2303.17475 has been replaced.
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Learning distributed representations with efficient SoftMax normalization
Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the …

This https://arxiv.org/abs/2504.04209 has been replaced.
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Axion Superradiance in Dipole Magnetic Fields of Pulsars
We consider constraints on the axion-photon coupling by superradiance due to a plasma instability in the magnetospheres of millisecond pulsars. We compute the growth rate of a superradiant axion cloud in a dipole magnetic field, and give a semi-analytical formula for the superradiance rate for the lowest state. By requiring the associated instability time to be longer than the characteristic age of the supermassive black-widow millisecond pulsar PSR J0952-0607, we examine the pulsar-timing arra…

This https://arxiv.org/abs/2503.03076 has been replaced.
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Non-Affine Extensions of the Raychaudhuri Equation in the K-essence Framework
We present a new avenue of the Raychaudhuri Equation (RE) by introducing a non-affine parametrization within the k-essence framework. This modification accounts for non-geodesic flow curves, leading to emergent repulsive effects in cosmic evolution. Using a DBI-type k-essence Lagrangian, we derive a modified RE and demonstrate its ability to address the Hubble tension while predicting a natural emergence of a dynamical dark energy equation of state. Our Bayesian analysis, constrained by cosmolo…

This https://arxiv.org/abs/2506.01950 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling effi…

This https://arxiv.org/abs/2503.04091 has been replaced.
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Generalization in Federated Learning: A Conditional Mutual Information Framework
Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and the participation gap, which quantifies the risk difference between participating and non-participating clients. In this work, we apply an i…

This https://arxiv.org/abs/2504.10370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Further Comments on Yablo's Construction
We continue our analysis of Yablo's coding of the liar paradox by infinite acyclic graphs. The present notes are based on and continue the author's previous results on the problem. In particular, our approach is often more systematic than before.

This https://arxiv.org/abs/2504.09200 has been replaced.
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A Datagram Extension to DNS over QUIC: Proven Resource Conservation in the Internet of Things
In this paper, we investigate the Domain Name System (DNS) over QUIC (DoQ) and propose a non-disruptive extension, which can greatly reduce DoQ's resource consumption. This extension can benefit all DNS clients - especially Internet of Things (IoT) devices. This is important because even resource-constrained IoT devices can generate dozens of DNS requests every hour. DNS is a crucial service that correlates IP addresses and domain names. It is traditionally sent as plain-text, favoring low-late…

[2025-05-08 Thu (UTC), no new articles found for q-bio.OT Other Quantitative Biology]
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This https://arxiv.org/abs/2504.21131 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

A Formalism for Optimal Search with Dynamic Heuristics (Extended Version)
While most heuristics studied in heuristic search depend only on the state, some accumulate information during search and thus also depend on the search history. Various existing approaches use such dynamic heuristics in $\mathrm{A}^*$-like algorithms and appeal to classic results for $\mathrm{A}^*$ to show optimality. However, doing so ignores the complexities of searching with a mutable heuristic. In this paper we formalize the idea of dynamic heuristics and use them in a generic algorithm fr…

This https://arxiv.org/abs/2408.17151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Investigating Privacy Leakage in Dimensionality Reduction Methods via Reconstruction Attack
This study investigates privacy leakage in dimensionality reduction methods through a novel machine learning-based reconstruction attack. Employing an informed adversary threat model, we develop a neural network capable of reconstructing high-dimensional data from low-dimensional embeddings. We evaluate six popular dimensionality reduction techniques: principal component analysis (PCA), sparse random projection (SRP), multidimensional scaling (MDS), Isomap, $t$-distributed stochastic neighbor…

This https://arxiv.org/abs/2502.14578 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_grqc_…

The massive BMS character in 3D quantum gravity
We derive the one-loop partition function for three-dimensional quantum gravity in a finite-radius thermal twisted flat space with a conical defect, reproducing the massive BMS$_3$ character. We perform the computation in both discrete and continuum geometry formulations, showing consistency between them. In the discrete case, we integrate out bulk degrees of freedom in a Regge gravity framework, while in the continuum, we construct a dual non-local boundary field theory encoding geodesic lengt…

This https://arxiv.org/abs/2505.17371 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

An End-to-End Approach for Child Reading Assessment in the Xhosa Language
Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure the effectiveness of these programs and AI can be a reliable and economical tool to support educators with this task. Developing accurate a…

This https://arxiv.org/abs/2411.16666 has been replaced.
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CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to quantify the feature importance, and constructs a vector-formed mirror statistic for FDR control with the Gaussian Mirror algorithm. To avoid instability due to nonlinear or temporal correlations among features, we also propose a new kernel-based independence measure. CatNet performs robustly on different model settings …

[2025-06-03 Tue (UTC), 92 new articles found for cs.AI Artificial Intelligence]
#toXiv_bot_toot

This https://arxiv.org/abs/2410.17401 has been replaced.
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AdvAgent: Controllable Blackbox Red-teaming on Web Agents
Foundation model-based agents are increasingly used to automate complex tasks, enhancing efficiency and productivity. However, their access to sensitive resources and autonomous decision-making also introduce significant security risks, where successful attacks could lead to severe consequences. To systematically uncover these vulnerabilities, we propose AdvAgent, a black-box red-teaming framework for attacking web agents. Unlike existing approaches, AdvAgent employs a reinforcement learning-ba…

This https://arxiv.org/abs/2501.11694 has been replaced.
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Gravitational Wave Propagation in a Geometric Condensate in Starobinsky Cosmology
In this paper we propose a new paradigm for cosmology: a time dependent scalar condensate background originated from the quadratic $(R + αR^2)$ Starobinski model, where $R$ is the Ricci scalar and $α$ the coupling constant. In weak gravity limit the system decouples into a conventional graviton and a higher derivative scalar. It was shown earlier through works from our group, \cite{ssg,sg,us}, that the latter can sustain an oscillatory lowest energy configuration or a {\it{Geometric Condensat…

This https://arxiv.org/abs/2505.07453 has been replaced.
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How well do LLMs reason over tabular data, really?
Large Language Models (LLMs) excel in natural language tasks, but less is known about their reasoning capabilities over tabular data. Prior analyses devise evaluation strategies that poorly reflect an LLM's realistic performance on tabular queries. Moreover, we have a limited understanding of the robustness of LLMs towards realistic variations in tabular inputs. Therefore, we ask: Can general-purpose LLMs reason over tabular data, really?, and focus on two questions 1) are tabular reasoning cap…

Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API
Pierre Tholoniat, Alison Caulfield, Giorgio Cavicchioli, Mark Chen, Nikos Goutzoulias, Benjamin Case, Asaf Cidon, Roxana Geambasu, Mathias L\'ecuyer, Martin Thomson
https://arxiv.org/abs/2506.05290

Big Bird: Privacy Budget Management for W3C's Privacy-Preserving Attribution API
Privacy-preserving advertising APIs like Privacy-Preserving Attribution (PPA) are designed to enhance web privacy while enabling effective ad measurement. PPA offers an alternative to cross-site tracking with encrypted reports governed by differential privacy (DP), but current designs lack a principled approach to privacy budget management, creating uncertainty around critical design decisions. We present Big Bird, a privacy budget manager for PPA that clarifies per-site budget semantics and in…

This https://arxiv.org/abs/2504.14870 has been replaced.
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Acting Less is Reasoning More! Teaching Model to Act Efficiently
Tool-integrated reasoning (TIR) augments large language models (LLMs) with the ability to invoke external tools during long-form reasoning, such as search engines and code interpreters, to solve tasks beyond the capabilities of internal reasoning. While reinforcement learning (RL) has shown promise in training such agents, most of existing approaches typically optimize only for final correctness without considering the efficiency or necessity of external tool use. This often leads to excessive …

This https://arxiv.org/abs/2503.23487 has been replaced.
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Large Language and Reasoning Models are Shallow Disjunctive Reasoners
Large Language Models (LLMs) have been found to struggle with systematic reasoning. Even on tasks where they appear to perform well, their performance often depends on shortcuts, rather than on genuine reasoning abilities, leading them to collapse on out-of-distribution (OOD) examples. Post-training strategies based on reinforcement learning and chain-of-thought prompting have recently been hailed as a step change. However, little is known about the potential of the resulting ``Large Reasoning …

This https://arxiv.org/abs/2503.19990 has been replaced.
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LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?
Multi-step spatial reasoning entails understanding and reasoning about spatial relationships across multiple sequential steps, which is crucial for tackling complex real-world applications, such as robotic manipulation, autonomous navigation, and automated assembly. To assess how well current Multimodal Large Language Models (MLLMs) have acquired this fundamental capability, we introduce LEGO-Puzzles, a scalable benchmark designed to evaluate both spatial understanding and sequential reasoning …

This https://arxiv.org/abs/2503.18938 has been replaced.
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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/2503.12181 has been replaced.
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Value Gradients with Action Adaptive Search Trees in Continuous (PO)MDPs
Solving Partially Observable Markov Decision Processes (POMDPs) in continuous state, action and observation spaces is key for autonomous planning in many real-world mobility and robotics applications. Current approaches are mostly sample based, and cannot hope to reach near-optimal solutions in reasonable time. We propose two complementary theoretical contributions. First, we formulate a novel Multiple Importance Sampling (MIS) tree for value estimation, that allows to share value information b…

This https://arxiv.org/abs/2503.07792 has been replaced.
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Efficient Neural Clause-Selection Reinforcement
Clause selection is arguably the most important choice point in saturation-based theorem proving. Framing it as a reinforcement learning (RL) task is a way to challenge the human-designed heuristics of state-of-the-art provers and to instead automatically evolve -- just from prover experiences -- their potentially optimal replacement. In this work, we present a neural network architecture for scoring clauses for clause selection that is powerful yet efficient to evaluate. Following RL principle…

This https://arxiv.org/abs/2502.18744 has been replaced.
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ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose ZEBRA - a model behavior-wise zero-annotation framework that constructs preference data by leveraging model behavior knowledge derived from benchmark performances. ZEBRA binarizes response pairs by …

This https://arxiv.org/abs/2502.17701 has been replaced.
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From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

This https://arxiv.org/abs/2502.13943 has been replaced.
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AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence
Current approaches for training Process Reward Models (PRMs) often involve breaking down responses into multiple reasoning steps using rule-based techniques, such as using predefined placeholder tokens or setting the reasoning step's length into a fixed size. These approaches overlook the fact that specific words do not typically mark true decision points in a text. To address this, we propose AdaptiveStep, a method that divides reasoning steps based on the model's confidence in predicting the …

This https://arxiv.org/abs/2501.15056 has been replaced.
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Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select quest…

This https://arxiv.org/abs/2501.07071 has been replaced.
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Value Compass Leaderboard: A Platform for Fundamental and Validated Evaluation of LLMs Values
As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify the underlying values of LLMs precisely and comprehensively, while current evaluations focus narrowly on safety risks such as bias and toxicity. (2) Evaluation Validity: Existing …

This https://arxiv.org/abs/2412.18148 has been replaced.
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Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media
Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, it remains unclear how prevalent AIGTs are on social media. To address this gap, this paper aims to quantify and monitor the AIGTs on online social media platforms. We first collect a dataset (SM-D) with around 2.4M posts from 3 major social…

This https://arxiv.org/abs/2412.13631 has been replaced.
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Mind Your Theory: Theory of Mind Goes Deeper Than Reasoning
Theory of Mind (ToM) capabilities in LLMs have recently become a central object of investigation. Cognitive science distinguishes between two steps required for ToM tasks: 1) determine whether to invoke ToM, which includes the appropriate Depth of Mentalizing (DoM), or level of recursion required to complete a task; and 2) applying the correct inference given the DoM. In this position paper, we first identify several lines of work in different communities in AI, including LLM benchmarking, ToM …

This https://arxiv.org/abs/2412.13147 has been replaced.
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Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performances and real-world applications. We attribute this gap primarily to current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, especially in complex reasoning tasks where both accuracy and consistency are essential. In this paper, we introduce G-Pass@$k$, a novel evaluat…

This https://arxiv.org/abs/2412.11934 has been replaced.
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Stepwise Reasoning Error Disruption Attack of LLMs
Large language models (LLMs) have made remarkable strides in complex reasoning tasks, but their safety and robustness in reasoning processes remain underexplored. Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. To address these challenges, we propose the Stepwise rEasoning Error Disruption (SEED) attack, which subtly injects errors into prior reasoning steps to mislead the model into producing i…

This https://arxiv.org/abs/2412.05718 has been replaced.
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RLZero: Direct Policy Inference from Language Without In-Domain Supervision
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward signal is notoriously difficult, as humans are often unable to predict the optimal behavior corresponding to a reward function. Natural language offers an intuitive alternative for instructing reinforcement learning (RL) agents, yet previous language-conditioned approaches either require costly supervision or test-time tr…

This https://arxiv.org/abs/2412.03824 has been replaced.
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Towards Data Governance of Frontier AI Models
Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier …

This https://arxiv.org/abs/2412.01948 has been replaced.
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The Evolution and Future Perspectives of Artificial Intelligence Generated Content
Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone co…

This https://arxiv.org/abs/2411.09689 has been replaced.
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Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge
LLM hallucination, where unfaithful text is generated, presents a critical challenge for LLMs' practical applications. Current detection methods often resort to external knowledge, LLM fine-tuning, or supervised training with large hallucination-labeled datasets. Moreover, these approaches do not distinguish between different types of hallucinations, which is crucial for enhancing detection performance. To address such limitations, we introduce hallucination probing, a new task that classifies …

This https://arxiv.org/abs/2411.02006 has been replaced.
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Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and process multimodal data have grown. This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction. Recent evaluation benchmarks have been developed better to capture the static and interactive environments of mobile…

This https://arxiv.org/abs/2410.04444 has been replaced.
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Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement
The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven agents across various tasks. However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restriction of human-designed components, and thus might miss the globally optimal agent design. In this paper, we introduce Gödel Agent, a self-evolving framework inspired by the Göde…

This https://arxiv.org/abs/2409.14457 has been replaced.
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Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
With the rapid advancement of large models (LMs), the development of general-purpose intelligent agents powered by LMs has become a reality. It is foreseeable that in the near future, LM-driven general AI agents will serve as essential tools in production tasks, capable of autonomous communication and collaboration without human intervention. This paper investigates scenarios involving the autonomous collaboration of future LM agents. We review the current state of LM agents, the key technologi…

This https://arxiv.org/abs/2409.13825 has been replaced.
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A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics
Understanding the structure and motion of the heart is crucial for diagnosing and managing cardiovascular diseases, the leading cause of global death. There is wide variation in cardiac shape and motion patterns, influenced by demographic, anthropometric and disease factors. Unravelling normal patterns of shape and motion, and understanding how each individual deviates from the norm, would facilitate accurate diagnosis and personalised treatment strategies. To this end, we developed a condition…

This https://arxiv.org/abs/2407.18950 has been replaced.
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Unexplainability of Artificial Intelligence Judgments in Kant's Perspective
Kant's Critique of Pure Reason, a major contribution to the history of epistemology, proposes a table of categories to elucidate the structure of the a priori principles underlying human judgment. Artificial intelligence (AI) technology, grounded in functionalism, claims to simulate or replicate human judgment. To evaluate this claim, it is necessary to examine whether AI judgments exhibit the essential characteristics of human judgment. This paper investigates the unexplainability of AI judgme…

This https://arxiv.org/abs/2407.15325 has been replaced.
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Odyssey: Empowering Minecraft Agents with Open-World Skills
Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving basic programmatic tasks, e.g., material collection and tool-crafting following the Minecraft tech-tree, treating the ObtainDiamond task as the ultimate goal. This limitation stems from the narrowly defined set of actions available to agents, requiring them to learn effective long-horizon strategies from scratch. Con…

This https://arxiv.org/abs/2406.13948 has been replaced.
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CityGPT: Empowering Urban Spatial Cognition of Large Language Models
Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life geospatial tasks within urban environments. This limitation stems from a lack of physical world knowledge and relevant data during training. To address this gap, we propose \textit{CityGPT}, a systematic framework designed to enhance LLMs' understanding of urb…

This https://arxiv.org/abs/2406.13945 has been replaced.
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CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
As large language models (LLMs) continue to advance and gain widespread use, establishing systematic and reliable evaluation methodologies for LLMs and vision-language models (VLMs) has become essential to ensure their real-world effectiveness and reliability. There have been some early explorations about the usability of LLMs for limited urban tasks, but a systematic and scalable evaluation benchmark is still lacking. The challenge in constructing a systematic evaluation benchmark for urban re…

This https://arxiv.org/abs/2307.14634 has been replaced.
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Fact-Checking of AI-Generated Reports
With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the dev…

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Chenjun Xu, Bingbing Wen, Bin Han, Robert Wolfe, Lucy Lu Wang, Bill Howe
https://arxiv.org/abs/2506.00582

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas -- e.g., expert vs laym…