Tootfinder

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CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in visual reasoning and text generation. While previous studies have explored the application of MLLM for detecting out-of-context (OOC) misinformation, our empirical analysis reveals two persisting challenges of this paradigm. Evaluating the representative GPT-4o model on direct reasoning and evidence augmented reasoning, results indicate that MLLM struggle to capture the deeper relationships-specifically, cases…

Context, Credibility, and Control: User Reflections on AI Assisted Misinformation Tools
Varun Sangwan, Heidi Makitalo
https://arxiv.org/abs/2506.22940 http…

Context, Credibility, and Control: User Reflections on AI Assisted Misinformation Tools
This paper investigates how collaborative AI systems can enhance user agency in identifying and evaluating misinformation on social media platforms. Traditional methods, such as personal judgment or basic fact-checking, often fall short when faced with emotionally charged or context-deficient content. To address this, we designed and evaluated an interactive interface that integrates collaborative AI features, including real-time explanations, source aggregation, and debate-style interaction. T…

Inside Job: Defending Kubernetes Clusters Against Network Misconfigurations
Jacopo Bufalino, Jose Luis Martin-Navarro, Mario Di Francesco, Tuomas Aura
https://arxiv.org/abs/2506.21134

Inside Job: Defending Kubernetes Clusters Against Network Misconfigurations
Kubernetes has emerged as the de facto standard for container orchestration. Unfortunately, its increasing popularity has also made it an attractive target for malicious actors. Despite extensive research on securing Kubernetes, little attention has been paid to the impact of network configuration on the security of application deployments. This paper addresses this gap by conducting a comprehensive analysis of network misconfigurations in a Kubernetes cluster with specific reference to lateral…

MOA-2022-BLG-091Lb and KMT-2024-BLG-1209Lb: Microlensing planets detected through weak caustic-crossing signals
Cheongho Han, Chung-Uk Lee, Andrzej Udalski, Ian A. Bond, Hongjing Yang, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Youn Kil Jung, Kyu-Ha Hwang, Yoon-Hyun Ryu, Yossi Shvartzvald, In-Gu Shin, Jennifer C. Yee, Weicheng Zang, Tanagodchaporn Inyanya, Sang-Mok Cha, Doeon Kim, Dong-Jin Kim, Seung-Lee Kim, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge, Przemek Mr…

MOA-2022-BLG-091Lb and KMT-2024-BLG-1209Lb: Microlensing planets detected through weak caustic-crossing signals
The light curves of the microlensing events MOA-2022-BLG-091 and KMT-2024-BLG-1209 exhibit anomalies with very similar features. These anomalies appear near the peaks of the light curves, where the magnifications are moderately high, and are distinguished by weak caustic-crossing features with minimal distortion while the source remains inside the caustic. To achieve a deeper understanding of these anomalies, we conducted a comprehensive analysis of the lensing events. We carried out binary-len…

How online misinformation works: a costly signalling perspective
Neri Marsili
https://arxiv.org/abs/2506.17158 https://arxiv.org/pdf/…

How online misinformation works: a costly signalling perspective
This chapter explores how online communication, particularly on social media, reshapes the reputational incentives that motivate speakers to communicate truthfully. Drawing on costly signalling theory (CST), it examines how online contexts alter the social mechanisms that sustain honest communication. Key characteristics of online spaces are identified and discussed, namely (i) the presence of novel speech acts like reposting, (ii) the gamification of communication, (iii) information overload, …

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Fanxiao Li, Jiaying Wu, Canyuan He, Wei Zhou
https://arxiv.org/abs/2505.23449

CMIE: Combining MLLM Insights with External Evidence for Explainable Out-of-Context Misinformation Detection
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in visual reasoning and text generation. While previous studies have explored the application of MLLM for detecting out-of-context (OOC) misinformation, our empirical analysis reveals two persisting challenges of this paradigm. Evaluating the representative GPT-4o model on direct reasoning and evidence augmented reasoning, results indicate that MLLM struggle to capture the deeper relationships-specifically, cases…

Real-space visualization of orbital-selective superconductivity in FeSe
Sang Yong Song, G\'abor B. Hal\'asz, Jiaqiang Yan, Benjamin J. Lawrie, Petro Maksymovych
https://arxiv.org/abs/2506.16384

Real-space visualization of orbital-selective superconductivity in FeSe
We investigate the orbitally resolved superconducting properties of bulk FeSe using scanning tunneling microscopy (STM). We find that the spectral weights of both the large $Δ_1$ and small $Δ_2$ superconducting gaps remain nearly unchanged at the top Se sites as the STM tip approaches atomic contact. In contrast, the spectral weight of $Δ_2$ increases significantly at the Fe and bottom Se sites. These results suggest that the gap $Δ_2$ is localized in the xy-plane and likely associated with…

Social Media Can Reduce Misinformation When Public Scrutiny is High
Gavin Wang, Haofei Qin, Xiao Tang, Lynn Wu
https://arxiv.org/abs/2506.16355 https://

Social Media Can Reduce Misinformation When Public Scrutiny is High
Misinformation poses a growing global threat to institutional trust, democratic stability, and public decision-making. While prior research has often portrayed social media as a channel for spreading falsehoods, less is known about the conditions under which it may instead constrain misinformation by enhancing transparency and accountability. Here we show this dual potential in the context of local governments' GDP reporting in China, where data falsifications are widespread. Analyzing official…

Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
Tomas Peterka, Matyas Bohacek
https://arxiv.org/abs/2506.09847 https://

Dataset of News Articles with Provenance Metadata for Media Relevance Assessment
Out-of-context and misattributed imagery is the leading form of media manipulation in today's misinformation and disinformation landscape. The existing methods attempting to detect this practice often only consider whether the semantics of the imagery corresponds to the text narrative, missing manipulation so long as the depicted objects or scenes somewhat correspond to the narrative at hand. To tackle this, we introduce News Media Provenance Dataset, a dataset of news articles with provenance-…

Large-Scale-Structure Observables in General Relativity Validated at Second Order
Antoine Villey, Yonadav Barry Ginat, Vincent Desjacques, Donghui Jeong, Fabian Schmidt
https://arxiv.org/abs/2506.11260

Large-Scale-Structure Observables in General Relativity Validated at Second Order
We present a second-order calculation of relativistic large-scale-structure observables in cosmological perturbation theory, specifically the "cosmic rulers and clock", which are the building-blocks of any other large-scale-structure observable, including galaxy number counts, on large scales. We calculate the scalar rulers (longitudinal perturbation and magnification) and the cosmic clock to second order, using a fully non-linear covariant definition of the observables. We validate our formula…