Tootfinder

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Lauren Deason, Adam Bali, Ciprian Bejean, Diana Bolocan, James Crnkovich, Ioana Croitoru, Krishna Durai, Chase Midler, Calin Miron, David Molnar, Brad Moon, Bruno Ostarcevic, Alberto Peltea, Matt Rosenberg, Catalin Sandu, Arthur Saputkin, Sagar Shah, Daniel Stan, Ernest Szocs, Shengye Wan, Spencer Whitman, Sven Krasser, Joshua Saxe

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models (LLMs) have the potential to automate and scale Security Operations Center (SOC) operations, existing evaluations do not fully assess the scenarios most relevant to real-world defenders. This lack of informed evaluation impacts both AI developers and those apply…

The Role of High-Speed Rail in Reshaping Chinese County-Level Economic Structures
Mingzhi Xiao, Yuki Takayama
https://arxiv.org/abs/2508.16079 https://arxi…

The Role of High-Speed Rail in Reshaping Chinese County-Level Economic Structures
As high-speed rail (HSR) investment accelerates across China, the question of whether such large-scale infrastructure can promote balanced regional development or exacerbate spatial inequality has become central for policymakers and scholars. This study provides systematic micro-level evidence by analyzing a balanced panel of 353 county-level divisions, including urban districts, county-level cities, and counties, along the Shanghai-Kunming and Xuzhou-Lanzhou HSR corridors from 2008 to 2019. Us…

Astra Nova, which develops no-code tools that enable creators to launch blockchain-based entertainment experiences, raised $48.3M (CoinDesk)
https://www.coindesk.com/business/2025/10/17/astra-nova-raises-usd48-3m-to-grow-web3-ai…

Astra Nova Raises $48.3M to Grow Web3, AI Entertainment Ecosystem
The company develops no-code tools that enable creators to launch blockchain-based entertainment experiences.

Spatial Balancing: Harnessing Spatial Reasoning to Balance Scientific Exposition and Narrative Engagement in LLM-assisted Science Communication Writing
Kexue Fu, Jiaye Leng, Yawen Zhang, Jingfei Huang, Yihang Zuo, Runze Cai, Zijian Ding, Ray LC, Shengdong Zhao, Qinyuan Lei
https://arxiv.org/abs/2509.13742

Spatial Balancing: Harnessing Spatial Reasoning to Balance Scientific Exposition and Narrative Engagement in LLM-assisted Science Communication Writing
Balancing scientific exposition and narrative engagement is a central challenge in science communication. To examine how to achieve balance, we conducted a formative study with four science communicators and a literature review of science communication practices, focusing on their workflows and strategies. These insights revealed how creators iteratively shift between exposition and engagement but often lack structured support. Building on this, we developed SpatialBalancing, a co-writing syste…

Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs
Fanyu Wang, Chetan Arora, Yonghui Liu, Kaicheng Huang, Chakkrit Tantithamthavorn, Aldeida Aleti, Dishan Sambathkumar, David Lo
https://arxiv.org/abs/2508.06888

Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs
Acceptance criteria (ACs) play a critical role in software development by clearly defining the conditions under which a software feature satisfies stakeholder expectations. However, manually creating accurate, comprehensive, and unambiguous acceptance criteria is challenging, particularly in user interface-intensive applications, due to the reliance on domain-specific knowledge and visual context that is not always captured by textual requirements alone. To address these challenges, we propose …

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
Youssef Ait El Mahjoub, Jean-Michel Fourneau, Salma Alouah
https://arxiv.org/abs/2508.00816

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
Solving Markov Decision Processes (MDPs) remains a central challenge in sequential decision-making, especially when dealing with large state spaces and long-term optimization criteria. A key step in Bellman dynamic programming algorithms is the policy evaluation, which becomes computationally demanding in infinite-horizon settings such as average-reward or discounted-reward formulations. In the context of Markov chains, aggregation and disaggregation techniques have for a long time been used to…

AISysRev -- LLM-based Tool for Title-abstract Screening
Aleksi Huotala, Miikka Kuutila, Olli-Pekka Turtio, Mika M\"antyl\"a
https://arxiv.org/abs/2510.06708 https://…

AISysRev - LLM-based Tool for Title-abstract Screening
Systematic reviews are a standard practice for summarizing the state of evidence in software engineering. Conducting systematic reviews is laborious, especially during the screening or study selection phase, where the number of papers can be overwhelming. During this phase, papers are assessed against inclusion and exclusion criteria based on their titles and abstracts. Recent research has demonstrated that large language models (LLMs) can perform title-abstract screening at a level comparable …

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity
Zhiyuan Zou, Bangchao Wang, Peng Liang, Tingting Bi, Huan Jin
https://arxiv.org/abs/2509.05585

Natural Language-Programming Language Software Traceability Link Recovery Needs More than Textual Similarity
In the field of software traceability link recovery (TLR), textual similarity has long been regarded as the core criterion. However, in tasks involving natural language and programming language (NL-PL) artifacts, relying solely on textual similarity is limited by their semantic gap. To this end, we conducted a large-scale empirical evaluation across various types of TLR tasks, revealing the limitations of textual similarity in NL-PL scenarios. To address these limitations, we propose an approac…

From Law to Gherkin: A Human-Centred Quasi-Experiment on the Quality of LLM-Generated Behavioural Specifications from Food-Safety Regulations
Shabnam Hassani, Mehrdad Sabetzadeh, Daniel Amyot
https://arxiv.org/abs/2508.20744

From Law to Gherkin: A Human-Centred Quasi-Experiment on the Quality of LLM-Generated Behavioural Specifications from Food-Safety Regulations
Context: Laws and regulations increasingly affect software design and quality assurance, but legal texts are written in technology-neutral language. This creates challenges for engineers who must develop compliance artifacts such as requirements and acceptance criteria. Manual creation is labor-intensive, error-prone, and requires domain expertise. Advances in Generative AI (GenAI), especially Large Language Models (LLMs), offer a way to automate deriving such artifacts. Objective: We present…