Tootfinder

I don’t understand why we must spend billions of dollars and waste so much water for AI.
I just created an “AI-generated image” from the original using a few dabs of the retouch tool in the Photos app.
Might have taken a sip of water afterwards but that was it.
(Is there a word/phrase for taking a regular image and making it look like it was AI generated? There should be.)

»DuckDuckGo now lets you hide AI-generated images in search results:
Privacy-focused browser DuckDuckGo is rolling out a new setting that lets users filter out AI images in search results. The company says it’s launching the feature in response to feedback from users who said AI images can get in the way of finding what they’re looking for.«
I don't prefer this as my main search engine but if this could not trigger ain anti-AI hype?
🔎

DuckDuckGo now lets you hide AI-generated images in search results | TechCrunch
DuckDuckGo's new search feature comes as the internet is being flooded with AI-generated slop.

A researcher found 88 TikTok accounts that used AI-generated versions of Jorge Ramos and other Spanish-language journalists to spread misinformation for profit (Nicole Acevedo/NBC News)
https://www.nbcnews.com/news/latino/fake-ai-vide…

What's behind the TikTok accounts using AI-generated versions of real Latino journalists?
The accounts point to the challenge of stopping or controlling the surge in fake images and misinformation targeting Spanish-speakers in the U.S., as AI technology advances.

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Shengchao Liu, Xiaoming Liu, Chengzhengxu Li, Zhaohan Zhang, Guoxin Ma, Yu Lan, Shuai Xiao
https://arxiv.org/abs/2508.13768

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generated Text (MGT) perform well when they are trained and tested in the same domain but generalize poorly to unseen domains, due to domain shift between data from different sources. In this work, we propose MGT-Prism, an MGT detection method from the perspective of the frequency domain for better domain generalization. Our…

The BBC's "Verify" team has done a good job in explaining that this viral image of European leaders waiting for Trump has been generated by AI.
But quite why they've slapped an "AI GENERATED" warning over the image, rather than just simply "FAKE" is puzzling.

Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text
Zixin Rao, Youssef Mohamed, Shang Liu, Zeyan Liu
https://arxiv.org/abs/2508.14190 https://…

Two Birds with One Stone: Multi-Task Detection and Attribution of LLM-Generated Text
Large Language Models (LLMs), such as GPT-4 and Llama, have demonstrated remarkable abilities in generating natural language. However, they also pose security and integrity challenges. Existing countermeasures primarily focus on distinguishing AI-generated content from human-written text, with most solutions tailored for English. Meanwhile, authorship attribution--determining which specific LLM produced a given text--has received comparatively little attention despite its importance in forensic…

Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
Abbas Sabra, Olivier Schmitt, Joseph Tyler
https://arxiv.org/abs/2508.14727 https://

Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
This study presents a quantitative evaluation of the code quality and security of five prominent Large Language Models (LLMs): Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4o, Llama 3.2 90B, and OpenCoder 8B. While prior research has assessed the functional performance of LLM-generated code, this research tested LLM output from 4,442 Java coding assignments through comprehensive static analysis using SonarQube. The findings suggest that although LLMs can generate functional code, they also introduce…

Apparently an older boy generated the ads with AI and ran away.
https://www.rockpapershotgun.com/nexon-respond-to-claims-theyre-using-ai-generated-tiktok-streamers-to-advertise-the-first-descendant…

Nexon respond to claims they're using AI-generated Tiktok streamers to advertise The First Descendant
The First Descendant publishers Nexon have addressed AI-looking streamer adverts for the game on TikTok, saying they and the platform are investigating.

One of the great things about #mastodon is that my feed isn’t spammed with #AI generated shite. AI generated content gives me the rage.

I don't think AI slop is a new problem, I think it just accelerated a trend that was already ongoing.
It wasn't generated so easily, but the internet was already full of slop, just human generated.

Leveraging Pre-Trained Visual Models for AI-Generated Video Detection
Keerthi Veeramachaneni, Praveen Tirupattur, Amrit Singh Bedi, Mubarak Shah
https://arxiv.org/abs/2507.13224

Leveraging Pre-Trained Visual Models for AI-Generated Video Detection
Recent advances in Generative AI (GenAI) have led to significant improvements in the quality of generated visual content. As AI-generated visual content becomes increasingly indistinguishable from real content, the challenge of detecting the generated content becomes critical in combating misinformation, ensuring privacy, and preventing security threats. Although there has been substantial progress in detecting AI-generated images, current methods for video detection are largely focused on deep…

Hilarious and horrifyingly awful thread of bad images generated by chatGPT prompts for pregnancy. Damn I hope kids aren't using this for sex ed or anatomy lessons...
https://masto.ai/@vagina_museum/115231068737126436

A look at the Dor Brothers, a video production studio that has gained over 100M views across platforms by creating viral subversive videos using only AI tools (Stuart A. Thompson/New York Times)
https://www.nytimes.com/2025/07/18/techn…

How a Video Studio Embraced A.I. and Stormed the Internet
The Dor Brothers are indie filmmakers whose viral videos are generated entirely by artificial intelligence.

I normally would not share any AI slop, but this was an attack graphic an opponent tried to use on State Rep. Francesca Hong but once someone cropped out the racist crap it sort of works!
(Hong supports workers and would hire a human to make graphics, not use AI generated crap.)
…

Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
https://arxiv.org/abs/2507.13705

Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS). In this paper, we evaluate these recommendations and explanations by comparing them to social choice-based aggregation strategies. Our results indicate that LLM-generated recommendations often resembled those produced by Additive Utilitarian (ADD) aggregation. However, the explanations typically referred to averaging ratings (resembling but not…

Seen elsewhere.

Russian State TV Launches AI-Generated News Satire Show https://www.404media.co/russian-state-tv-launches-ai-generated-news-satire-show/

A thread by fediverse favorites the Vagina museum on AI-generated inaccuracies.
They're mostly right to highlight this, but it shows an issue in the way people reason about AI's reliability: they look at the dream-logic nonsense that comes out of image generators and generalize it to the rest of AI. 1/n
https://

ai but with mood swings to better understand context smh.
science more interested in how menstruation affects ai than it ever was in how it affects women.

Indeed it does.
Just tested it. Not entirely accurate, but it does hide some stuff.
https://mstdn.social/@TechCrunch/114875173097890841

TechCrunch (@TechCrunch@mstdn.social)
Attached: 1 image DuckDuckGo now lets you hide AI-generated images in search results https://techcrunch.com/2025/07/18/duckduckgo-now-lets-you-hide-ai-generated-images-in-search-results/?utm_source=dlvr.it&utm_medium=mastodon

I very briefly contemplated whether I should replace a (technically copyrighted) meme template with an AI generated lookalike.
What was that Rostand quote again?
Steal from one person, and you are a thief. Steal from 100, and you are a rebel. Steal from millions, and you are an innovator. (Or something like that).

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
Xinxing Wu
https://arxiv.org/abs/2508.11872 https://

Singing Syllabi with Virtual Avatars: Enhancing Student Engagement Through AI-Generated Music and Digital Embodiment
In practical teaching, we observe that few students thoroughly read or fully comprehend the information provided in traditional, text-based course syllabi. As a result, essential details, such as course policies and learning outcomes, are frequently overlooked. To address this challenge, in this paper, we propose a novel approach leveraging AI-generated singing and virtual avatars to present syllabi in a format that is more visually appealing, engaging, and memorable. Especially, we leveraged t…

Students' Perceptions to a Large Language Model's Generated Feedback and Scores of Argumentation Essays
Winter Allen, Anand Shanker, N. Sanjay Rebello
https://arxiv.org/abs/2508.14759

Students' Perceptions to a Large Language Model's Generated Feedback and Scores of Argumentation Essays
Students in introductory physics courses often rely on ineffective strategies, focusing on final answers rather than understanding underlying principles. Integrating scientific argumentation into problem-solving fosters critical thinking and links conceptual knowledge with practical application. By facilitating learners to articulate their scientific arguments for solving problems, and by providing real-time feedback on students' strategies, we aim to enable students to develop superior problem…

Characterization of deterministically recognizable weighted tree languages over commutative semifields by finitely generated and cancellative scalar algebras
Zolt\'an F\"ul\"op, Heiko Vogler
https://arxiv.org/abs/2509.14914

Characterization of deterministically recognizable weighted tree languages over commutative semifields by finitely generated and cancellative scalar algebras
Due to the works of S. Bozapalidis and A. Alexandrakis, there is a well-known characterization of recognizable weighted tree languages over fields in terms of finite-dimensionality of syntactic vector spaces. Here we prove a characterization of bottom-up deterministically recognizable weighted tree languages over commutative semifields in terms of the requirement that the respective m-syntactic scalar algebras are finitely generated. The concept of scalar algebra is introduced in this paper; it…

Scaling behaviour of charged particles generated in Xe$-$Xe collisions at $\sqrt{s_{\rm{NN}}}$ = 5.44 TeV using the AMPT model
Zarina Banoo, Ramni Gupta, Salman K. Malik, Fakhar Ul Haider, Balwan Singh, Sheetal Sharma
https://arxiv.org/abs/2508.12796

Scaling behaviour of charged particles generated in Xe$-$Xe collisions at $\sqrt{s_{\rm{NN}}}$ = 5.44 TeV using the AMPT model
The spatial configurations of particles produced in the kinematic phase space during a heavy-ion collision reflect the characteristics of the system created in the collision. The scaling behaviour of the multiplicity fluctuations is studied for the charged particles generated in Xe--Xe collisions at $\sqrt{s_{\rm{NN}}}$ = 5.44 TeV using the String Melting (SM) mode of the AMPT (A Multi-Phase Transport) model. The scaling behaviour of the normalized factorial moments ($F_\text{q}$) give signific…

Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systems
Dennis Schiese, Aleksandr Perevalov, Andreas Both
https://arxiv.org/abs/2508.14553

Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systems
Over time, software systems have reached a level of complexity that makes it difficult for their developers and users to explain particular decisions made by them. In this paper, we focus on the explainability of component-based systems for Question Answering (QA). These components often conduct processes driven by AI methods, in which behavior and decisions cannot be clearly explained or justified, s.t., even for QA experts interpreting the executed process and its results is hard. To address …

Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
Aaron Councilman, David Fu, Aryan Gupta, Chengxiao Wang, David Grove, Yu-Xiong Wang, Vikram Adve
https://arxiv.org/abs/2507.13290

Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
In the past few years LLMs have emerged as a tool that can aid programmers by taking natural language descriptions and generating code based on it. However, LLMs often generate incorrect code that users need to fix and the literature suggests users often struggle to detect these errors. In this work we seek to offer formal guarantees of correctness to LLM generated code; such guarantees could improve the experience of using AI Code Assistants and potentially enable natural language programming …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- MIRAGE: Towards AI-Generated Image Detection in the Wild
Xia, Lin, Tan, Du, Qiu, Zheng, Kong, Jiang, Zheng

Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study
Hanna Woloszyn, Benjamin Gagl
https://arxiv.org/abs/2508.13769 https://

Can Large Language Models (LLMs) Describe Pictures Like Children? A Comparative Corpus Study
The role of large language models (LLMs) in education is increasing, yet little attention has been paid to whether LLM-generated text resembles child language. This study evaluates how LLMs replicate child-like language by comparing LLM-generated texts to a collection of German children's descriptions of picture stories. We generated two LLM-based corpora using the same picture stories and two prompt types: zero-shot and few-shot prompts specifying a general age from the children corpus. We con…

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Kai Ye, Yuhang Wu, Shuyuan Hu, Junliang Li, Meng Liu, Yongquan Chen, Rui Huang
https://arxiv.org/abs/2509.14178

\textsc{Gen2Real}: Towards Demo-Free Dexterous Manipulation by Harnessing Generated Video
Dexterous manipulation remains a challenging robotics problem, largely due to the difficulty of collecting extensive human demonstrations for learning. In this paper, we introduce \textsc{Gen2Real}, which replaces costly human demos with one generated video and drives robot skill from it: it combines demonstration generation that leverages video generation with pose and depth estimation to yield hand-object trajectories, trajectory optimization that uses Physics-aware Interaction Optimization M…

Classifying localizing subcategories of a Grothendieck category
Reza Sazeedeh
https://arxiv.org/abs/2507.13749 https://arxiv.org/pdf/…

Classifying localizing subcategories of a Grothendieck category
Let $\mathcal{A}$ be a Grothendieck category, with Sp$\mathcal{A}$ denoting the set of isomorphism classes of indecomposable injective objects of $A$, and ASpec$\mathcal{A}$ the atom spectrum of $\mathcal{A}$. In this paper, we introduce new topologies on Sp$\mathcal{A}$ and ASpec$\mathcal{A}$, and classify localizing subcategories of: (i) locally monoform categories, (ii) locally finitely generated categories, and (iii) locally coherent categories via open subsets of Sp$\mathcal{A}$ an…

In 1999, 307 people were killed in Moscow apartment bombings that propelled putin into control of russia.
Perhaps we should be glad that as potus moved toward control of the U.S., fewer people were immediately killed?
USAID cuts alone have already lead to tens of thousands of preventable deaths.
Gosh, this post got dark.

In 2020, Scotland generated 32TWh of electricity from wind alone.
https://www.scottishrenewables.com/our-industry/statistics
In 2023, Scotland consumed 21.8TWh of electricity in total.

Renewable Energy Facts & Statistics | Scottish Renewables
Scotland's renewable energy sector in numbers. Scotland’s renewable energy sector is growing and developing year-on-year. Discover facts, statistics and figures from Scotland's renewable energy sector.

Genuine multipartite entanglement verification with convolutional neural networks
Yi-Jun Luo, Xuan Leng, Chengjie Zhang
https://arxiv.org/abs/2508.13463 https://

Genuine multipartite entanglement verification with convolutional neural networks
In recent years, the detection of genuine multipartite entanglement (GME) via machine learning has received scant attention. Here, we employ convolutional neural networks (CNNs), as well as CNNs enhanced with squeeze-and-excitation (SE) to detect GME. We randomly generated GME states with 4 to 6 qubits and GHZ-diagonal states ranging from 4 to 20 qubits using the semidefinite programming approach. Subsequently, we assessed their classification accuracy. Our results demonstrate that the integrat…

Precision over Noise: Tailoring S3 Public Access Detection to Reduce False Positives in Cloud Security Platforms
Dikshant, Geetika Verma
https://arxiv.org/abs/2508.14402 https:/…

Precision over Noise: Tailoring S3 Public Access Detection to Reduce False Positives in Cloud Security Platforms
Excessive and spurious alert generation by cloud security solutions is a root cause of analyst fatigue and operational inefficiencies. In this study, the long-standing issue of false positives from publicly accessible alerts in Amazon S3, as generated by a licensed cloud-native security solution, is examined. In a simulated production test environment, which consisted of over 1,000 Amazon S3 buckets with diverse access configurations, it was discovered that over 80\% of the alerts generated by …

NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understanding
Running Zhao, Zhihan Jiang, Xinchen Zhang, Chirui Chang, Handi Chen, Weipeng Deng, Luyao Jin, Xiaojuan Qi, Xun Qian, Edith C. H. Ngai
https://arxiv.org/abs/2508.14395

NoteIt: A System Converting Instructional Videos to Interactable Notes Through Multimodal Video Understanding
Users often take notes for instructional videos to access key knowledge later without revisiting long videos. Automated note generation tools enable users to obtain informative notes efficiently. However, notes generated by existing research or off-the-shelf tools fail to preserve the information conveyed in the original videos comprehensively, nor can they satisfy users' expectations for diverse presentation formats and interactive features when using notes digitally. In this work, we present …

Holographic Extended Thermodynamics of deformed AdS-Schwarzschild black hole
Kamal L. Panigrahi, Balbeer Singh
https://arxiv.org/abs/2508.14873 https://arx…

Holographic Extended Thermodynamics of deformed AdS-Schwarzschild black hole
We investigate the thermodynamics and phase structure of a deformed AdS-Schwarzschild black hole, generated via the gravitational decoupling (GD) method as a minimal geometric deformation. In the bulk canonical ensemble, our results exhibit a van der Waals-type first-order phase transition in addition to the Hawking-Page transition, in the suitable parameter regime. Further, we compute the critical exponents characterising the bulk transition, confirming their consistency with mean-field theory…

A look at the 2025 AI Film Festival, backed by Runway AI and screened by Imax; some cinephiles deride it while others say it's part of a natural tech evolution (John Semley/Wired)
https://www.wired.com/story/cream-of-the-s

I Went to an AI Film Festival Screening and Left With More Questions Than Answers
Runway AI paired up with Imax to screen 10 AI-generated winning selections. The festival has been derided by some cinephiles, while others insist it’s part of a natural technological evolution.

reading LLM-generated code is exhausting. locally plausible, globally incoherent, and most of the problems with it are uncanny mistakes that humans don't make

Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases
Ishaan Mahapatra, Nihar R. Mahapatra
https://arxiv.org/abs/2508.14089

Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases
Voice biomarkers--human-generated acoustic signals such as speech, coughing, and breathing--are promising tools for scalable, non-invasive detection and monitoring of mental health and neurodegenerative diseases. Yet, their clinical adoption remains constrained by inconsistent quality and limited usability of publicly available datasets. To address this gap, we present the first systematic FAIR (Findable, Accessible, Interoperable, Reusable) evaluation of 27 publicly available voice biomarker d…

On the homology of simplicial and cubical sets with symmetries
Curtis Greene, Volkmar Welker, Georg Wille
https://arxiv.org/abs/2508.14639 https://arxiv.or…

On the homology of simplicial and cubical sets with symmetries
We study the homology of simplicial and cubical sets with symmetries. These are simplicial and cubical sets with additional maps expressing the symmetries of simplices and cubes. We consider the chain complex computing the homology groups in either case. We show for coefficients in fields of characteristic $0$ that the sub-complex generated by degeneracies (simplicial case) or connections (cubical case) together with all $x - sgn(t)tx$ for symmetries $t$ and chains $x$ is acyclic. In particular…

On a Super-analogue of Carrollian Manifolds
Andrew James Bruce
https://arxiv.org/abs/2508.14240 https://arxiv.org/pdf/2508.14240

On a Super-analogue of Carrollian Manifolds
Inspired by Carrollian geometry, we define super-Carrollian manifolds as a supermanifold with an even degenerate metric such that the kernel is generated by a non-singular odd vector field that is a supersymmetry generator. Alongside other results, we show that compatible affine connections always exist, albeit they must carry torsion.

Evaluating the Quality of Open Building Datasets for Mapping Urban Inequality: A Comparative Analysis Across 5 Cities
Franz Okyere, Meng Lu, Ansgar Brunn
https://arxiv.org/abs/2508.12872

Evaluating the Quality of Open Building Datasets for Mapping Urban Inequality: A Comparative Analysis Across 5 Cities
While informal settlements lack focused development and are highly dynamic, the quality of spatial data for these places may be uncertain. This study evaluates the quality and biases of AI-generated Open Building Datasets (OBDs) generated by Google and Microsoft against OpenStreetMap (OSM) data, across diverse global cities including Accra, Nairobi, Caracas, Berlin, and Houston. The Intersection over Union (IoU), overlap analysis and a positional accuracy algorithm are used to analyse the simil…

Preprint: Did I Just Browse A Website Written by LLMs?
Sichang "Steven" He, Ramesh Govindan, Harsha V. Madhyastha
https://arxiv.org/abs/2507.13933

Preprint: Did I Just Browse A Website Written by LLMs?
Increasingly, web content is automatically generated by large language models (LLMs) with little human input. We call this "LLM-dominant" content. Since LLMs plagiarize and hallucinate, LLM-dominant content can be unreliable and unethical. Yet, websites rarely disclose such content, and human readers struggle to distinguish it. Thus, we must develop reliable detectors for LLM-dominant content. However, state-of-the-art LLM detectors are insufficient, because they perform well mainly on clean, p…

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Scott Blyth, Sherlock A. Licorish, Christoph Treude, Markus Wagner
https://arxiv.org/abs/2508.14419

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We in…

On the Dynamical System Generated by the M\"obius Transformation at Smooth Times
L\'aszl\'o M\'erai, Igor E. Shparlinski
https://arxiv.org/abs/2507.12160

On the Dynamical System Generated by the Möbius Transformation at Smooth Times
We study the distribution of the sequence of the first $N$ elements of the discrete dynamical system generated by the Möbius transformation $x \mapsto (αx + β)/(γx + δ)$ over a finite field of $p$ elements at the moments of time that correspond to $Q$-smooth numbers, that is, to numbers composed out of primes up to $Q$. In particular, we obtain nontrivial estimates of exponential sums with such sequences.

A Monte Carlo simulation on the scattering coefficients of solar radio wave propagation
Jiazhen Gan, Chuanbing Wang
https://arxiv.org/abs/2508.13494 https://

A Monte Carlo simulation on the scattering coefficients of solar radio wave propagation
Radio waves undergo scattering by small-scale density fluctuations during propagation through the solar-terrestrial environment, substantially affecting the observed characteristics of solar radio bursts. This scattering process can be effectively modeled as photon diffusion in phase space. In this study, we present a comprehensive comparison between the quasilinear diffusion coefficients and those calculated by ray-tracing the photon trajectories in numerically generated, broadband, isotropic …

Data Fusion for High-Resolution Estimation
Amy Guan, Marissa Reitsma, Roshni Sahoo, Joshua Salomon, Stefan Wager
https://arxiv.org/abs/2508.14858 https://a…

Data Fusion for High-Resolution Estimation
High-resolution estimates of population health indicators are critical for precision public health. We propose a method for high-resolution estimation that fuses distinct data sources: an unbiased, low-resolution data source (e.g. aggregated administrative data) and a potentially biased, high-resolution data source (e.g. individual-level online survey responses). We assume that the potentially biased, high-resolution data source is generated from the population under a model of sampling bias wh…

The Hidden Role of Anisotropies in Shaping Structure Formation in Cosmological N-Body Simulations
Francesco Sylos Labini
https://arxiv.org/abs/2508.13765 https://

The Hidden Role of Anisotropies in Shaping Structure Formation in Cosmological N-Body Simulations
Initial conditions in cosmological $N$-body simulations are typically generated by displacing particles from a regular cubic lattice using a correlated field derived from the linear power spectrum, often via the Zel'dovich approximation. While this procedure reproduces the target two-point statistics (e.g., the power spectrum or correlation function), it introduces subtle anisotropies due to the underlying lattice structure. These anisotropies, invisible to angle-averaged diagnostics, become ev…

Learning Iterated Function Systems from Time Series of Partial Observations
Emilia Gibson, Jeroen S. W. Lamb
https://arxiv.org/abs/2508.13794 https://arxiv…

Learning Iterated Function Systems from Time Series of Partial Observations
We develop a methodology to learn finitely generated random iterated function systems from time-series of partial observations using delay embeddings. We obtain a minimal model representation for the observed dynamics, using a hidden variable representation, that is diffeomorphic to the original system.

Recent Chemo-morphological Coma Evolution of Comet 67P/Churyumov-Gerasimenko
Brian P. Murphy, Cyrielle Opitom, Colin Snodgrass, Sophie E. Deam, L\'ea Ferellec, Matthew Knight, Bin Yang, Vincent Okoth
https://arxiv.org/abs/2507.13979

Recent Chemo-morphological Coma Evolution of Comet 67P/Churyumov-Gerasimenko
We present VLT/MUSE observations of comet 67P/Churyumov-Gerasimenko during its 2021 perihelion passage, from which we generated simultaneous maps of dust, [OI], C$_2$, NH$_2$, and CN comae across 12 pre- and post-perihelion epochs. These maps reveal the evolutionary and compositional trends of 67P's coma and further enrich the context and findings of ESA's Rosetta mission. Dust and gas species displayed distinct structures, where NH$_2$ and CN signals were uniquely associated with known dust fa…

On Hilbert series of Koszul operads and a classification result for set-operads
Paul Laubie
https://arxiv.org/abs/2509.14419 https://arxiv.org/pdf/2509.144…

On Hilbert series of Koszul operads and a classification result for set-operads
Motivated by numerous examples in the literature, we state a conjecture on the Hilbert series of Koszul symmetric operads generated by one element of arity $2$. We prove this conjecture for all Koszul symmetric set-operads generated by one element of arity $2$ by explicitly classifying those. There are $11$ such operads; $4$ of them are new.

Appfigures: ChatGPT's app for iOS and Android hit $2B in global consumer spending, including $1.35B so far in 2025, up 673% YoY; Grok made ~$25.6M this year (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/08/15/chatgpts-mobile-a…

ChatGPT's mobile app has generated $2B to date, earns $2.91 per install | TechCrunch
ChatGPT is topping its rivals on mobile with $2B in global consumer spending to date.

Identification and Denoising of Radio Signals from Cosmic-Ray Air Showers using Convolutional Neural Networks
R. Abbasi, et al.
https://arxiv.org/abs/2508.14711 https://

Identification and Denoising of Radio Signals from Cosmic-Ray Air Showers using Convolutional Neural Networks
Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from…

Effect of transient shell formation on Shock-induced atomization of an evaporating nanofluid droplet
Gautham Vadlamudi, Bal Krishan, Akarsh Choudhary, Saptarshi Basu
https://arxiv.org/abs/2508.14733

Effect of transient shell formation on Shock-induced atomization of an evaporating nanofluid droplet
This study investigates the shock-induced atomisation dynamics of an acoustically levitated TM-10 nanofluid droplet subjected to a coaxially propagating blast wave and subsequent compressible vortex ring, generated using a compact wire-explosion shock source. The blast wave imposes a sharp velocity discontinuity, followed by a decaying flow field and a vortex-dominated interaction that drives droplet disintegration. Laser-induced heating promotes evaporation, increasing nanoparticle concentra…

Die englischsprachige Wikipedia hat einen neuen Schnelllöschgrund eingeführt: „LLM-generated pages without human review“. Damit können nun Artikel ohne längere Löschdiskussion sofort gelöscht werden, wenn aus dem Text eine Erstellung durch generative KI erkennbar wird oder die Belege unplausibel, klar halluziniert oder falsch zugeordnet sind. In der deutschsprachigen Wikipedia ist eine entsprechende Praxis bereits Konsens.

Image Realness Assessment and Localization with Multimodal Features
Lovish Kaushik, Agnij Biswas, Somdyuti Paul
https://arxiv.org/abs/2509.13289 https://ar…

Image Realness Assessment and Localization with Multimodal Features
A reliable method of quantifying the perceptual realness of AI-generated images and identifying visually inconsistent regions is crucial for practical use of AI-generated images and for improving photorealism of generative AI via realness feedback during training. This paper introduces a framework that accomplishes both overall objective realness assessment and local inconsistency identification of AI-generated images using textual descriptions of visual inconsistencies generated by vision-lang…

A look at the Dor Brothers, a video production studio that has gained over 100M views across platforms by creating viral subversive videos using only AI tools (Stuart A. Thompson/New York Times)
https://www.nytimes.com/2025/07/18/techn…

How a Video Studio Embraced A.I. and Stormed the Internet
The Dor Brothers are indie filmmakers whose viral videos are generated entirely by artificial intelligence.

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein, Todd Millstein, Benjamin Pierce, Guy Van den Broeck
https://arxiv.org/abs/2508.14394

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is via generators that describe how to sample test inputs through random choices. To achieve a good distribution over test inputs, users must tune their generators, i.e., decide on the weights of these individual random choices. Unfortunately, it is very difficult to understand how to choose individual generator weights…

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
Katharina Stein, Nils Hodel, Daniel Fi\v{s}er, J\"org Hoffmann, Michael Katz, Alexander Koller
https://arxiv.org/abs/2508.13876

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the LLM first generates a summary and then a strategy for the domain, both in natural language, and then implements that strategy as a Python program, that gets debugged on example planning tasks. In that work, only one strategy is generated and passed directly …

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Xin Chen, Junchao Wu, Shu Yang, Runzhe Zhan, Zeyu Wu, Ziyang Luo, Di Wang, Min Yang, Lidia S. Chao, Derek F. Wong
https://arxiv.org/abs/2508.13152

RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns
Detecting content generated by large language models (LLMs) is crucial for preventing misuse and building trustworthy AI systems. Although existing detection methods perform well, their robustness in out-of-distribution (OOD) scenarios is still lacking. In this paper, we hypothesize that, compared to features used by existing detection methods, the internal representations of LLMs contain more comprehensive and raw features that can more effectively capture and distinguish the statistical patte…

Human-Like Trajectories Generation via Receding Horizon Tracking Applied to the TickTacking Interface
Daniele Masti, Stefano Menchetti, \c{C}a\u{g}r{\i} Erdem, Giorgio Gnecco, Davide Rocchesso
https://arxiv.org/abs/2507.13528

Human-Like Trajectories Generation via Receding Horizon Tracking Applied to the TickTacking Interface
TickTacking is a rhythm-based interface that allows users to control a pointer in a two-dimensional space through dual-button tapping. This paper investigates the generation of human-like trajectories using a receding horizon approach applied to the TickTacking interface in a target-tracking task. By analyzing user-generated trajectories, we identify key human behavioral features and incorporate them in a controller that mimics these behaviors. The performance of this human-inspired controller …

Chiral effective potential in $4D$, $\mathcal{N}=1$ supersymmetric gauge theories
I. L. Buchbinder, R. M. Iakhibbaev, A. I. Mukhaeva, D. I. Kazakov, D. M. Tolkachev
https://arxiv.org/abs/2508.14002

Chiral effective potential in $4D$, $\mathcal{N}=1$ supersymmetric gauge theories
We calculate the chiral effective superpotential in $4D$ $\mathcal{N}=1$, $SU(N)$ super Yang-Mills theory coupled to chiral matter in one- and two-loop approximations. It is found that the one-loop contribution to the chiral effective potential is always finite and is expressed in terms of a specific triangle integral. The two-loop contributions generated by purely chiral vertices turned out to be finite as well. The chiral effective potential stipulated by supergraphs with gauge superfield sub…

AI at the World’s Biggest Games Event Booked Random Meetings for Attendees https://www.404media.co/ai-at-the-worlds-biggest-games-event-booked-random-meetings-for-attendees/

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
Ken Deng, Yunhan Yang, Jingxiang Sun, Xihui Liu, Yebin Liu, Ding Liang, Yan-Pei Cao
https://arxiv.org/abs/2508.14036

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation
Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present DetailGen3D, a generative approach specifically designed to enhance these generated 3D shapes. Our key insight is to model the coarse-to-fine transformation directly through data-dependent flows in latent space, avoiding the computational overhead of large-scale 3D generative models. We introduce a token matching strategy …

The Lovelace Test of Intelligence: Can Humans Recognise and Esteem AI-Generated Art?
Ewelina Gajewska
https://arxiv.org/abs/2509.11371 https://arxiv.org/pd…

The Lovelace Test of Intelligence: Can Humans Recognise and Esteem AI-Generated Art?
This study aims to evaluate machine intelligence through artistic creativity by employing a modified version of the Turing Test inspired by Lady Lovelace. It investigates two hypotheses: whether human judges can reliably distinguish AI-generated artworks from human-created ones and whether AI-generated art achieves comparable aesthetic value to human-crafted works. The research contributes to understanding machine creativity and its implications for cognitive science and AI technology. Particip…

DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang, Wenfu Wang, Meng Yu, Ruibo Fu, Zheng Lin, Weiping Wang, Dong Yu
https://arxiv.org/abs/2508.13786

DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and onset and offset timestamps. This enables precise control over both the content and temporal structure of the generated audio. Despite recent progress, existing methods still face inherent trade-offs among accurate temporal localization, open-vocabulary scalability, and practical efficiency. To address these challeng…

A news station, real enough for the US DoD to have someone on it, was knowingly using Grok-generated images without labelling them as such.
https://edition.cnn.com/2025/08/15/media/oan-gaetz-ai-images-women-military-kingsley-wilson

A Trump-favored MAGA network praised an increase in women soldiers. Its images were fake
The images of female soldiers appear to have been generated using Elon Musk’s Grok.

Freeze-in gravitational waves and dark matter in warm inflation
Quan Chen, Siyu Jiang, Dayun Qiu, Peilin Chen, Fa Peng Huang
https://arxiv.org/abs/2507.13916

Freeze-in gravitational waves and dark matter in warm inflation
Recent study[1] has suggested that warm inflation may be realized with Standard Model gauge interaction alone. Motivated by this framework, we investigate the gravitational wave spectrum and graviton-portal dark matter production through the freeze-in process generated during warm inflation scenarios. We perform a comparative analysis for different dissipation terms, focusing on their distinct gravitational wave signatures in the high-frequency regime. Our findings reveal qualitative and quanti…

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
Chi Wang, Min Gao, Zongwei Wang, Junwei Yin, Kai Shu, Chenghua Lin
https://arxiv.org/abs/2508.12632 …

Prompt-Induced Linguistic Fingerprints for LLM-Generated Fake News Detection
With the rapid development of large language models, the generation of fake news has become increasingly effortless, posing a growing societal threat and underscoring the urgent need for reliable detection methods. Early efforts to identify LLM-generated fake news have predominantly focused on the textual content itself; however, because much of that content may appear coherent and factually consistent, the subtle traces of falsification are often difficult to uncover. Through distributional di…

Who Sees What? Structured Thought-Action Sequences for Epistemic Reasoning in LLMs
Luca Annese, Sabrina Patania, Silvia Serino, Tom Foulsham, Silvia Rossi, Azzurra Ruggeri, Dimitri Ognibene
https://arxiv.org/abs/2508.14564

Who Sees What? Structured Thought-Action Sequences for Epistemic Reasoning in LLMs
Recent advances in large language models (LLMs) and reasoning frameworks have opened new possibilities for improving the perspective -taking capabilities of autonomous agents. However, tasks that involve active perception, collaborative reasoning, and perspective taking (understanding what another agent can see or knows) pose persistent challenges for current LLM-based systems. This study investigates the potential of structured examples derived from transformed solution graphs generated by the…

Nvidia debuts the Nemotron-Nano-9B-v2, a hybrid Mamba-transformer model, saying it achieves scores comparable to or better than Qwen3-8B on reasoning benchmarks (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/nvidia-rele

Nvidia releases a new small, open model Nemotron-Nano-9B-v2 with toggle on/off reasoning
Developers are free to create and distribute derivative models. Importantly, Nvidia does not claim ownership of any outputs generated...

On lattices generated by algebraic conjugates of prime degree
Lenny Fukshansky, Evelyne Knight
https://arxiv.org/abs/2507.12770 https://

On lattices generated by algebraic conjugates of prime degree
We consider Euclidean lattices spanned by images of algebraic conjugates of an algebraic number under Minkowski embedding, investigating their rank, properties of their automorphism groups and sets of minimal vectors. We are especially interested in situations when the resulting lattice is well-rounded. We show that this happens for large Pisot numbers of prime degree, demonstrating infinite families of such lattices. We also fully classify well-rounded lattices from algebraic conjugates in the…

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Viscous influences on impulsively generated focused jets
Xianggang Cheng, Xiao-Peng Chen, Hang Ding, Chun-Yu Zhang, Haibao Hu, Laibing Jia

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Is The Watermarking Of LLM-Generated Code Robust?
Tarun Suresh, Shubham Ugare, Gagandeep Singh, Sasa Misailovic

Fundamentals of Next-generation Network Planning
M. Umar Khan
https://arxiv.org/abs/2508.13469 https://arxiv.org/pdf/2508.13469

Fundamentals of Next-generation Network Planning
The fifth-generation (5G) of cellular communications is expected to be deployed in the next years to support a wide range of services with different demands of peak data rates, latency and quality of experience (QoE). To support higher data rates and latency requirements third-generation partnership project (3GPP) has introduced numerology and bandwidth parts (BWPs), via new radio (NR) for service-tailored resource allocation. Legacy 4G networks have generated extensive data, which combined wit…

"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns
Samra Zafar, Shifa Yousaf, Muhammad Shaheer Minhas
https://arxiv.org/abs/2508.13543 …

"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns
As large language models (LLMs) increasingly assist in evaluating student writing, researchers have begun questioning whether these models can be cognitively grounded, that is, whether they can attend not just to the final product, but to the process by which it was written. In this study, we explore how incorporating writing process data, specifically keylogs and time-stamped snapshots, affects the quality of LLM-generated feedback. We conduct an ablation study on 52 student essays comparing f…

SAGA: Learning Signal-Aligned Distributions for Improved Text-to-Image Generation
Paul Grimal, Micha\"el Soumm, Herv\'e Le Borgne, Olivier Ferret, Akihiro Sugimoto
https://arxiv.org/abs/2508.13866

SAGA: Learning Signal-Aligned Distributions for Improved Text-to-Image Generation
State-of-the-art text-to-image models produce visually impressive results but often struggle with precise alignment to text prompts, leading to missing critical elements or unintended blending of distinct concepts. We propose a novel approach that learns a high-success-rate distribution conditioned on a target prompt, ensuring that generated images faithfully reflect the corresponding prompts. Our method explicitly models the signal component during the denoising process, offering fine-grained …

Detecting LLM-generated Code with Subtle Modification by Adversarial Training
Xin Yin, Xinrui Li, Chao Ni, Xiaodan Xu, Xiaohu Yang
https://arxiv.org/abs/2507.13123

Detecting LLM-generated Code with Subtle Modification by Adversarial Training
With the rapid development of Large Language Models (LLMs), their powerful code-generation capabilities have been widely applied in tasks like code completion and automated development, demonstrating the value of improving coding efficiency. However, the extensive use of LLM-generated code also raises several new challenges. On the one hand, issues such as the regulation of code provenance, copyright disputes, and code quality have become increasingly concerning. How to effectively detect LLM-g…

Revisiting the $\Lambda_c^ \to \Lambda \eta \pi^ $ and the roles of intermediate resonances
En Wang, Wen-Tao Lyu, Man-Yu Duan, Chu-Wen Xiao, Dian-Yong Chen, Ju-Jun Xie, Eulogio Oset
https://arxiv.org/abs/2507.13738

Revisiting the $Λ_c^+\to Ληπ^+$ and the roles of intermediate resonances
In this paper, we first review the theoretical and experimental studies of the process $Λ_c^+ \to π^+ ηΛ$. Motivated by the recent BESIII and Belle measurements, we have conducted a theoretical study of the process $Λ_c^+ \to π^+ ηΛ$, where the $a_0(980)$ and $Λ(1670)$ resonances are dynamically generated from the $S$-wave meson meson and meson baryon interaction, respectively. Our results give a reasonable description of the invariant mass distributions, which implies that the spin fl…

Using AI for User Representation: An Analysis of 83 Persona Prompts
Joni Salminen, Danial Amin, Bernard Jansen
https://arxiv.org/abs/2508.13047 https://arx…

Using AI for User Representation: An Analysis of 83 Persona Prompts
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, a…

A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives
Juepeng Zheng, Zi Ye, Yibin Wen, Jianxi Huang, Zhiwei Zhang, Qingmei Li, Qiong Hu, Baodong Xu, Lingyuan Zhao, Haohuan Fu
https://arxiv.org/abs/2508.14558

A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives
Powered by advances in multiple remote sensing sensors, the production of high spatial resolution images provides great potential to achieve cost-efficient and high-accuracy agricultural inventory and analysis in an automated way. Lots of studies that aim at providing an inventory of the level of each agricultural parcel have generated many methods for Agricultural Parcel and Boundary Delineation (APBD). This review covers APBD methods for detecting and delineating agricultural parcels and syst…

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
Marc Brinner, Sina Zarriess
https://arxiv.org/abs/2507.13105

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts
We introduce SemCSE, an unsupervised method for learning semantic embeddings of scientific texts. Building on recent advances in contrastive learning for text embeddings, our approach leverages LLM-generated summaries of scientific abstracts to train a model that positions semantically related summaries closer together in the embedding space. This resulting objective ensures that the model captures the true semantic content of a text, in contrast to traditional citation-based approaches that do…

Measuring LLM Code Generation Stability via Structural Entropy
Yewei Song, Tiezhu Sun, Xunzhu Tang, Prateek Rajput, Tegawende F. Bissyande, Jacques Klein
https://arxiv.org/abs/2508.14288

Measuring LLM Code Generation Stability via Structural Entropy
Assessing the stability of code generation from large language models (LLMs) is essential for judging their reliability in real-world development. We extend prior "structural-entropy concepts" to the program domain by pairing entropy with abstract syntax tree (AST) analysis. For any fixed prompt, we collect the multiset of depth-bounded subtrees of AST in each generated program and treat their relative frequencies as a probability distribution. We then measure stability in two complementary way…

Moodifier: MLLM-Enhanced Emotion-Driven Image Editing
Jiarong Ye, Sharon X. Huang
https://arxiv.org/abs/2507.14024 https://arxiv.org/…

Moodifier: MLLM-Enhanced Emotion-Driven Image Editing
Bridging emotions and visual content for emotion-driven image editing holds great potential in creative industries, yet precise manipulation remains challenging due to the abstract nature of emotions and their varied manifestations across different contexts. We tackle this challenge with an integrated approach consisting of three complementary components. First, we introduce MoodArchive, an 8M+ image dataset with detailed hierarchical emotional annotations generated by LLaVA and partially valid…

Removal Attack and Defense on AI-generated Content Latent-based Watermarking
De Zhang Lee, Han Fang, Hanyi Wang, Ee-Chien Chang
https://arxiv.org/abs/2509.11745 https://

Removal Attack and Defense on AI-generated Content Latent-based Watermarking
Digital watermarks can be embedded into AI-generated content (AIGC) by initializing the generation process with starting points sampled from a secret distribution. When combined with pseudorandom error-correcting codes, such watermarked outputs can remain indistinguishable from unwatermarked objects, while maintaining robustness under whitenoise. In this paper, we go beyond indistinguishability and investigate security under removal attacks. We demonstrate that indistinguishability alone does n…

Confidence Estimation for Text-to-SQL in Large Language Models
Sepideh Entezari Maleki, Mohammadreza Pourreza, Davood Rafiei
https://arxiv.org/abs/2508.14056 https://

Confidence Estimation for Text-to-SQL in Large Language Models
Confidence estimation for text-to-SQL aims to assess the reliability of model-generated SQL queries without having access to gold answers. We study this problem in the context of large language models (LLMs), where access to model weights and gradients is often constrained. We explore both black-box and white-box confidence estimation strategies, evaluating their effectiveness on cross-domain text-to-SQL benchmarks. Our evaluation highlights the superior performance of consistency-based methods…

Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
Shaz Furniturewala, Arkaitz Zubiaga
https://arxiv.org/abs/2509.12672

Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
The volume of machine-generated content online has grown dramatically due to the widespread use of Large Language Models (LLMs), leading to new challenges for content moderation systems. Conventional content moderation classifiers, which are usually trained on text produced by humans, suffer from misclassifications due to LLM-generated text deviating from their training data and adversarial attacks that aim to avoid detection. Present-day defence tactics are reactive rather than proactive, sinc…

$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection
Daniil Orel, Indraneil Paul, Iryna Gurevych, Preslav Nakov
https://arxiv.org/abs/2507.10583

$\texttt{Droid}$: A Resource Suite for AI-Generated Code Detection
In this work, we compile $\textbf{$\texttt{DroidCollection}$}$, the most extensive open data suite for training and evaluating machine-generated code detectors, comprising over a million code samples, seven programming languages, outputs from 43 coding models, and over three real-world coding domains. Alongside fully AI-generated samples, our collection includes human-AI co-authored code, as well as adversarial samples explicitly crafted to evade detection. Subsequently, we develop $\textbf{$\t…

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
Xiaohang Tang, Sam Wong, Zicheng He, Yalong Yang, Yan Chen
https://arxiv.org/abs/2507.11470

REVA: Supporting LLM-Generated Programming Feedback Validation at Scale Through User Attention-based Adaptation
This paper introduces REVA, a human-AI system that expedites instructor review of voluminous AI-generated programming feedback by sequencing submissions to minimize cognitive context shifts and propagating instructor-driven revisions across semantically similar instances. REVA introduces a novel approach to human-AI collaboration in educational feedback by adaptively learning from instructors' attention in the review and revision process to continuously improve the feedback validation process. …

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Xiancheng Li, Georgios D. Karampatakis, Helen E. Wood, Chris J. Griffiths, Borislava Mihaylova, Neil S. Coulson, Alessio Pasinato, Pietro Panzarasa, Marco Viviani, Anna De Simoni
https://arxiv.org/abs/2508.14032

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised know…

GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
Haozhen Yan, Yan Hong, Suning Lang, Jiahui Zhan, Yikun Ji, Yujie Gao, Jun Lan, Huijia Zhu, Weiqiang Wang, Jianfu Zhang
https://arxiv.org/abs/2509.10250

GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
With generative models becoming increasingly sophisticated and diverse, detecting AI-generated images has become increasingly challenging. While existing AI-genereted Image detectors achieve promising performance on in-distribution generated images, their generalization to unseen generative models remains limited. This limitation is largely attributed to their reliance on generation-specific artifacts, such as stylistic priors and compression patterns. To address these limitations, we propose G…

Cognitive Surgery: The Awakening of Implicit Territorial Awareness in LLMs
Yinghan Zhou, Weifeng Zhu, Juan Wen, Wanli Peng, Zhengxian Wu, Yiming Xue
https://arxiv.org/abs/2508.14408

Cognitive Surgery: The Awakening of Implicit Territorial Awareness in LLMs
Large language models (LLMs) have been shown to possess a degree of self-recognition capability-the ability to identify whether a given text was generated by themselves. Prior work has demonstrated that this capability is reliably expressed under the Pair Presentation Paradigm (PPP), where the model is presented with two texts and asked to choose which one it authored. However, performance deteriorates sharply under the Individual Presentation Paradigm (IPP), where the model is given a single t…

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
Shaohua Duan, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun
https://arxiv.org/abs/2508.13993

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization
Long-context modeling is critical for a wide range of real-world tasks, including long-context question answering, summarization, and complex reasoning tasks. Recent studies have explored fine-tuning Large Language Models (LLMs) with synthetic data to enhance their long-context capabilities. However, the effectiveness of such approaches is often limited by the low diversity and factual inconsistencies in the generated data. To address these challenges, we propose LongMab-PO, a novel framework t…

Evaluating Generated Commit Messages with Large Language Models
Qunhong Zeng, Yuxia Zhang, Zexiong Ma, Bo Jiang, Ningyuan Sun, Klaas-Jan Stol, Xingyu Mou, Hui Liu
https://arxiv.org/abs/2507.10906

Evaluating Generated Commit Messages with Large Language Models
Commit messages are essential in software development as they serve to document and explain code changes. Yet, their quality often falls short in practice, with studies showing significant proportions of empty or inadequate messages. While automated commit message generation has advanced significantly, particularly with Large Language Models (LLMs), the evaluation of generated messages remains challenging. Traditional reference-based automatic metrics like BLEU, ROUGE-L, and METEOR have notable…

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain
Bohao Chu, Meijie Li, Sameh Frihat, Chengyu Gu, Georg Lodde, Elisabeth Livingstone, Norbert Fuhr
https://arxiv.org/abs/2508.13798

TracSum: A New Benchmark for Aspect-Based Summarization with Sentence-Level Traceability in Medical Domain
While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist, especially in the medical domain. Tracing evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace b…

AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences
Jieyu Li, Xin Zhang, Joey Tianyi Zhou
https://arxiv.org/abs/2508.10771 https://ar…

AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences
Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the …

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
Haitong Luo, Weiyao Zhang, Suhang Wang, Wenji Zou, Chungang Lin, Xuying Meng, Yujun Zhang
https://arxiv.org/abs/2508.11343

SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis
The proliferation of high-quality text from Large Language Models (LLMs) demands reliable and efficient detection methods. While existing training-free approaches show promise, they often rely on surface-level statistics and overlook fundamental signal properties of the text generation process. In this work, we reframe detection as a signal processing problem, introducing a novel paradigm that analyzes the sequence of token log-probabilities in the frequency domain. By systematically analyzing …

Benchmark of stylistic variation in LLM-generated texts
Ji\v{r}\'i Mili\v{c}ka, Anna Marklov\'a, V\'aclav Cvr\v{c}ek
https://arxiv.org/abs/2509.10179 https://…

Benchmark of stylistic variation in LLM-generated texts
This study investigates the register variation in texts written by humans and comparable texts produced by large language models (LLMs). Biber's multidimensional analysis (MDA) is applied to a sample of human-written texts and AI-created texts generated to be their counterparts to find the dimensions of variation in which LLMs differ most significantly and most systematically from humans. As textual material, a new LLM-generated corpus AI-Brown is used, which is comparable to BE-21 (a Brown fam…