Tootfinder

This https://arxiv.org/abs/2409.18334 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Decade-long periodicity study of 2FHL blazars with historical optical data
In our recent investigation, we utilized a century's worth of archival optical data to search for a decade-long periodicity from the blazar PG 1553+113, finding a hint of a 22-yr period. Building on this foundation, the current study extends our analysis to include 10 blazars from the Fermi-Large Area Telescope Second Catalog of Hard Sources (2FHL) catalogue to uncover similar long-term periodic behaviour. To ensure the reliability of our findings, we consider the impact of observational limita…

This https://arxiv.org/abs/2505.24034 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Trainin
Reinforcement Learning (RL) has become the most effective post-training approach for improving the capabilities of Large Language Models (LLMs). In practice, because of the high demands on latency and memory, it is particularly challenging to develop an efficient RL framework that reliably manages policy models with hundreds to thousands of billions of parameters. In this paper, we present LlamaRL, a fully distributed, asynchronous RL framework optimized for efficient training of large-scale …

This https://arxiv.org/abs/2406.13945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks
As large language models (LLMs) continue to advance and gain widespread use, establishing systematic and reliable evaluation methodologies for LLMs and vision-language models (VLMs) has become essential to ensure their real-world effectiveness and reliability. There have been some early explorations about the usability of LLMs for limited urban tasks, but a systematic and scalable evaluation benchmark is still lacking. The challenge in constructing a systematic evaluation benchmark for urban re…

This https://arxiv.org/abs/2502.11475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly complex and long-reasoning nature of the task. Even state-of-the-art models often fail in code generation due to small errors, which can drastically affect the overall functionality of code. Our study identifies that current models tend to produce errors concentrated at specific error-prone points, which significantl…

Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Qiufeng Li, Yiwen Liang, Weidong Cao
https://arxiv.org/abs/2506.02311

Unicorn-CIM: Unconvering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Compute-in-memory (CIM) architecture has been widely explored to address the von Neumann bottleneck in accelerating deep neural networks (DNNs). However, its reliability remains largely understudied, particularly in the emerging domain of floating-point (FP) CIM, which is crucial for speeding up high-precision inference and on device training. This paper introduces Unicorn-CIM, a framework to uncover the vulnerability and improve the resilience of high-precision CIM, built on static r…

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
Omid Reza Abbasi, Franz Welscher, Georg Weinberger, Johannes Scholz
https://arxiv.org/abs/2506.00203

The World As Large Language Models See It: Exploring the reliability of LLMs in representing geographical features
As large language models (LLMs) continue to evolve, questions about their trustworthiness in delivering factual information have become increasingly important. This concern also applies to their ability to accurately represent the geographic world. With recent advancements in this field, it is relevant to consider whether and to what extent LLMs' representations of the geographical world can be trusted. This study evaluates the performance of GPT-4o and Gemini 2.0 Flash in three key geospatial …

This https://arxiv.org/abs/2501.08524 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Deep inference of simulated strong lenses in ground-based surveys
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing syste…

This https://arxiv.org/abs/2503.00600 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Semantic Integrity Constraints: Declarative Guardrails for AI-Augmented Data Processing Systems
AI-augmented data processing systems (DPSs) integrate large language models (LLMs) into query pipelines, allowing powerful semantic operations on structured and unstructured data. However, the reliability (a.k.a. trust) of these systems is fundamentally challenged by the potential for LLMs to produce errors, limiting their adoption in critical domains. To help address this reliability bottleneck, we introduce semantic integrity constraints (SICs) -- a declarative abstraction for specifying and …

Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs
Nguyen-Khang Le, Quan Minh Bui, Minh Ngoc Nguyen, Hiep Nguyen, Trung Vo, Son T. Luu, Shoshin Nomura, Minh Le Nguyen
https://arxiv.org/abs/2506.02529

Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs
Web applications are critical to modern software ecosystems, yet ensuring their reliability remains challenging due to the complexity and dynamic nature of web interfaces. Recent advances in large language models (LLMs) have shown promise in automating complex tasks, but limitations persist in handling dynamic navigation flows and complex form interactions. This paper presents an automated system for generating test cases for two key aspects of web application testing: site navigation and form …

This https://arxiv.org/abs/2505.19165 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential of Large Language Models (LLMs) to serve as unified knowledge repositories and intelligent assistants in enterprise settings becomes increasingly apparent, a critical, yet under explored, challenge emerges: \textit{can these models reliably understand and operate within the complex, often nuanced, constraints imposed b…