Tootfinder

This is me (playing bass) with my first band ever called Rapa Nui. This was officially our first gig, although the first one happened a few months before this one, in Sarajevo, in front of the then-demolished Youth House.
Back then, I hated melodic punk, feeling the buzz from bands like Motorhead and different extreme metal and grindcore artists.
However, in 2016, I rediscovered an old favorite.
This is a story of how Bad Religion made me start listening to punk rock, an…

How I Started Believing in Bad Religion
For almost a decade, after their song You, made me fall in love with punk rock, I hated the band called Bad Religion. And then they turned out to be one of my favorites ever.

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Hao Peng, Yunjia Qi, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2506.09942 …

VerIF: Verification Engineering for Reinforcement Learning in Instruction Following
Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing large language models (LLMs), with verification engineering playing a central role. However, best practices for RL in instruction following remain underexplored. In this work, we explore the verification challenge in RL for instruction following and propose VerIF, a verification method that combines rule-based code verification with LLM-based verification from a large reasoning model (e.g., QwQ-32B). …

HBAR entropy of Infalling Atoms into a GUP-corrected Schwarzschild Black Hole and equivalence principle
Ali \"Ovg\"un, Reggie C. Pantig
https://arxiv.org/abs/2506.10621 …

HBAR entropy of Infalling Atoms into a GUP-corrected Schwarzschild Black Hole and equivalence principle
In this work, we have investigated the phenomenon of acceleration radiation exhibited by a two-level atom freely falling into a Generalized Uncertainty Principle (GUP)-corrected Schwarzschild black hole. We derive analytic expressions for the atom's excitation probability with simultaneous emission of a scalar quantum and observe that it satisfies the Einstein equivalence principle when compared to the excitation probability induced by a uniformly accelerating mirror, motivated by studies [10.1…

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Xi Chen, Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane
https://arxiv.org/abs/2506.10675

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Medical images are usually collected from multiple domains, leading to domain shifts that impair the performance of medical image segmentation models. Domain Generalization (DG) aims to address this issue by training a robust model with strong generalizability. Recently, numerous domain randomization-based DG methods have been proposed. However, these methods suffer from the following limitations: 1) constrained efficiency of domain randomization due to their exclusive dependence on image style…

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Samuel Holt, Max Ruiz Luyten, Antonin Berthon, Mihaela van der Schaar
https://arxiv.org/abs/2506.09272

G-Sim: Generative Simulations with Large Language Models and Gradient-Free Calibration
Constructing robust simulators is essential for asking "what if?" questions and guiding policy in critical domains like healthcare and logistics. However, existing methods often struggle, either failing to generalize beyond historical data or, when using Large Language Models (LLMs), suffering from inaccuracies and poor empirical alignment. We introduce G-Sim, a hybrid framework that automates simulator construction by synergizing LLM-driven structural design with rigorous empirical calibration…

Writer Ariel Courage on never feeling finished https://thecreativeindependent.com/people/writer-ariel-courage-on-ne…

Writer Ariel Courage on never feeling finished – The Creative Independent
Author Ariel Courage discusses hyper-individualism, MFAs, and writing as a social act.

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Zerui Yang, Yuwei Wan, Yinqiao Li, Yudai Matsuda, Tong Xie, Linqi Song
https://arxiv.org/abs/2507.07426

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search
Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug discovery. However, their effectiveness remains constrained when reasoning extends beyond the knowledge acquired during pretraining. Conventional approaches, such as fine-tuning or retrieval-augmented generation, face limitations in either imposing high computational overhead or failing to fully exploit structured scientific data. To overcome these challenges, we propose DrugMCTS…

Origin of persistent photoconductivity in surface conducting hydrogenated diamond films
N. Mohasin Sulthana, K. Ganesan, P. K. Ajikumar
https://arxiv.org/abs/2507.06559

Origin of persistent photoconductivity in surface conducting hydrogenated diamond films
The p-type surface conductivity of hydrogen-terminated diamond (HD) has opened up new possibilities for the development of diamond-based electronic devices. However, the origin of the persistent photoconductivity (PPC) observed in surface-conducting HD remains unclear, an understanding that is crucial for advancing HD-based optoelectronic technologies. In this study, we investigate the underlying mechanism of PPC in surface-conducting HD films. A systematic analysis was performed by tuning the …

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Zheng Zhao, Clara Vania, Subhradeep Kayal, Naila Khan, Shay B. Cohen, Emine Yilmaz
https://arxiv.org/abs/2506.09902

PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants
Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization--adapting to individual user preferences while completing tasks--remains challenging. Existing personalization benchmarks focus on chit-chat, non-conversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented assistance. To address this, we introduce PersonaLens, a comprehensive benchmark for evalu…

Efficient broadband terahertz generation by above band-gap excitation of the pyroelectric ZnSnN2
T. S. Seifert, H. Hempel, O. G\"uckstock, R. Schneider, Q. Remy, A. Fioretti, T. Unold, S. Michaelis de Vasconcellos, R. Bratschitsch, R. Eichberger, K. D\"orr, A. Zakutayev, T. Kampfrath
https://arxiv.org/abs/2506.09528

Efficient broadband terahertz generation by above band-gap excitation of the pyroelectric ZnSnN2
Terahertz (THz) radiation is a powerful probe of low-energy excitations in all phases of matter. However, it remains a challenge to find materials that efficiently generate THz radiation in a broad range of frequencies following optical excitation. Here, we investigate a pyroelectric material, ZnSnN2, and find that above-band-gap excitation results in the efficient formation of an ultrafast photocurrent generating THz radiation. The resulting THz electric field spans a frequency range from belo…