Tootfinder

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
David Breitenmoser, Alberto Stabilini, Sabine Mayer
https://arxiv.org/abs/2506.17820

Full-spectrum modeling of mobile gamma-ray spectrometry systems in scattering media
Mobile gamma-ray spectrometry (MGRS) systems are essential for localizing, identifying, and quantifying gamma-ray sources in complex environments. Full-spectrum template matching offers the highest accuracy and sensitivity for these tasks but is limited by the computational cost of generating the required spectral templates. Here, we present a generalized full-spectrum modeling framework for MGRS systems in scattering media, enabling near real-time template generation through dynamic, anisotrop…

A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions
Stephen Mell, Botong Zhang, David Mell, Shuo Li, Ramya Ramalingam, Nathan Yu, Steve Zdancewic, Osbert Bastani
https://arxiv.org/abs/2506.12202

A Fast, Reliable, and Secure Programming Language for LLM Agents with Code Actions
Modern large language models (LLMs) are often deployed as agents, calling external tools adaptively to solve tasks. Rather than directly calling tools, it can be more effective for LLMs to write code to perform the tool calls, enabling them to automatically generate complex control flow such as conditionals and loops. Such code actions are typically provided as Python code, since LLMs are quite proficient at it; however, Python may not be the ideal language due to limited built-in support for p…

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Chia-Heng Yu, Yen-Lung Tsai
https://arxiv.org/abs/2506.13607

Tree-Based Text Retrieval via Hierarchical Clustering in RAGFrameworks: Application on Taiwanese Regulations
Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant c…

Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput
Gabriel Orlanski, Nicholas Roberts, Aws Albarghouthi, Frederic Sala
https://arxiv.org/abs/2506.10056

Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput
The standard paradigm for solving coding tasks via large language models (LLMs) is to generate-then-rank programs, where the latter step uses a verifier in the ranking process. The growing consensus is that a comprehensive verifier (e.g., a full test suite) should be prioritized over an outcome reward model (ORM) whenever possible, with little consideration given to the trade-offs involved. We aim to challenge this assumption by systematically exploring the tradeoff between speed and accuracy. …

First Lasing and Stable Operation of a Direct-Amplification Enabled Harmonic Generation Free-Electron laser
Zheng Qi, Junhao Liu, Lanpeng Ni, Tao Liu, Zhen Wang, Kaiqing Zhang, Hanxiang Yang, Zhangfeng Gao, Nanshun Huang, Si Chen, Hang Luo, Yaozong Xiao, Cheng Yu, Yongmei Wen, Fei Gao, Yangyang Lei, Huan Zhao, Yanyan Zhu, Liping Sun, Weiyi Yin, Xingtao Wang, Taihe Lan, Xiaoqing Liu, Lie Feng, Wenyan Zhang, Ximing Zhang, Bin Li, Chao Feng, Bo Liu, Zhentang Zhao

First Lasing and Stable Operation of a Direct-Amplification Enabled Harmonic Generation Free-Electron laser
Seeded free-electron lasers (FELs) capable of operating at repetition rates up to the MHz level are in high demand for advanced time-resolved spectroscopies, which require both full longitudinal coherence and high average photon flux in the extreme ultraviolet (EUV) and x-ray regimes. However, conventional external-seed laser systems cannot sustain MHz operation with sufficient hundreds of megawatts peak power requirement due to their limited total power. Here, we report the first lasing and st…

This https://arxiv.org/abs/2410.14205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Modelling 1/f Noise in TRNGs via Fractional Brownian Motion
Security of oscillatory true random number generators remains not fully understood due to insufficient understanding of complex $1/f^α$ phase noise. To bridge this gap, we introduce fractional Brownian motion as a comprehensive theoretical framework, capturing power-law spectral densities from white to flicker frequency noise. Our key contributions provide closed-form tractable solutions: (1) a quasi-renewal property showing conditional variance grows with power-law time dependence, enabling…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Mingzhe Du, Luu Tuan Tuan, Yue Liu, Yuhao Qing, Dong Huang, Xinyi He, Qian Liu, Zejun Ma, See-kiong Ng
https://arxiv.org/abs/2505.23387

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

This https://arxiv.org/abs/2505.23387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…