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Multi-service messaging app Beeper relaunches, introducing premium offerings and the option to switch to an on-device model; Automattic acquired Beeper in 2024 (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/07/16/beep

Beeper's all-in-one messaging app relaunches with an on-device model and premium upgrades | TechCrunch
Multi-service messaging app Beeper, which allows people to connect to all their chat apps from one interface, is relaunching its app on Wednesday to offer a more secure version that no longer requires use of its own cloud services.

The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Avinash Baidya, Kamalika Das, Xiang Gao
https://arxiv.org/abs/2506.12266

The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs
Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and kno…

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Haoyu Zhai, Shuo Wang, Pirouz Naghavi, Qingying Hao, Gang Wang
https://arxiv.org/abs/2506.12344

Restoring Gaussian Blurred Face Images for Deanonymization Attacks
Gaussian blur is widely used to blur human faces in sensitive photos before the photos are posted on the Internet. However, it is unclear to what extent the blurred faces can be restored and used to re-identify the person, especially under a high-blurring setting. In this paper, we explore this question by developing a deblurring method called Revelio. The key intuition is to leverage a generative model's memorization effect and approximate the inverse function of Gaussian blur for face restora…

(2018) Functional MRI of sensory substitution in the blind #blindness

Generative Intelligence Systems in the Flow of Group Emotions
Fernando Koch, Jessica Nahulan, Jeremy Fox, Martin Keen
https://arxiv.org/abs/2507.11831 http…

Generative Intelligence Systems in the Flow of Group Emotions
Emotional cues frequently arise and shape group dynamics in interactive settings where multiple humans and artificial agents communicate through shared digital channels. While artificial agents lack intrinsic emotional states, they can simulate affective behavior using synthetic modalities such as text or speech. This work introduces a model for orchestrating emotion contagion, enabling agents to detect emotional signals, infer group mood patterns, and generate targeted emotional responses. The…

Intermediate Interaction Strategies for Collective Behavior
Y. Kikuchi, M. Iwamoto
https://arxiv.org/abs/2507.09457 https://arxiv.org…

Intermediate Interaction Strategies for Collective Behavior
From bird flocks and fish schools to migrating cell sheets, collective motion is a ubiquitous biological phenomenon that inspires quantitative modeling through self-propelled particle (SPP) frameworks. Conventional SPP models prescribe either distance-based (metric) or rank-based (topological) interactions; however, empirical studies indicate that real groups may blend both types of interaction. Motivated by this graded perception, we introduce a new three-dimensional SPP model in which metric …

Periodicity and Dynamical Systems of Dickson Polynomials in Finite Fields
Wayne Peng, Yen-Ju Chen
https://arxiv.org/abs/2508.08621 https://arxiv.org/pdf/25…

Periodicity and Dynamical Systems of Dickson Polynomials in Finite Fields
This paper investigates the dynamical properties of Dickson polynomials over finite fields, focusing on the periodicity and structural behavior of their iterated sequences. We introduce and analyze the sequence \( [D_n(x, α) \mod (x^q - x)]_n \), where \( D_n(x, α) \) denotes a Dickson polynomial of the first kind, and explore its periodic nature when reduced modulo \( x^q - x \). We derive explicit formulas for the period of these sequences, particularly in the case when \( n \) is coprime t…

AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
Mille Mei Zhen Loo, Gert Luzkov, Paolo Burelli
https://arxiv.org/abs/2508.06348 https:…

AntiCheatPT: A Transformer-Based Approach to Cheat Detection in Competitive Computer Games
Cheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems. This paper presents AntiCheatPT\_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset o…

Impostor Among $\nu$s: Dark Radiation Masquerading as Self-Interacting Neutrinos
Anirban Das, P. S. Bhupal Dev, Christina Gao, Subhajit Ghosh, Taegyun Kim
https://arxiv.org/abs/2506.08085

Impostor Among $ν$s: Dark Radiation Masquerading as Self-Interacting Neutrinos
Multiple cosmological observations hint at neutrino self-interactions beyond the Standard Model, yet such interactions face severe constraints from terrestrial experiments. We resolve this tension by introducing a model where active neutrinos resonantly convert to self-interacting dark radiation after BBN but before CMB epoch. This exploits the fact that cosmological observables cannot distinguish between neutrinos and dark radiation with the same abundance and free-streaming properties. Our me…

Attack Effect Model based Malicious Behavior Detection
Limin Wang, Lei Bu, Muzimiao Zhang, Shihong Cang, Kai Ye
https://arxiv.org/abs/2506.05001 https://…

Attack Effect Model based Malicious Behavior Detection
Traditional security detection methods face three key challenges: inadequate data collection that misses critical security events, resource-intensive monitoring systems, and poor detection algorithms with high false positive rates. We present FEAD (Focus-Enhanced Attack Detection), a framework that addresses these issues through three innovations: (1) an attack model-driven approach that extracts security-critical monitoring items from online attack reports for comprehensive coverage; (2) effic…