Tootfinder

What Generative Search Engines Like and How to Optimize Web Content Cooperatively
Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong
https://arxiv.org/abs/2510.11438 https://

What Generative Search Engines Like and How to Optimize Web Content Cooperatively
By employing large language models (LLMs) to retrieve documents and generate natural language responses, Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences and have rapidly become the new form of search. Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them. In this paper, we introduce AutoGEO, a framework to automatically learn generative engine …

How Students Use Generative AI for Software Testing: An Observational Study
Baris Ardic, Quentin Le Dilavrec, Andy Zaidman
https://arxiv.org/abs/2510.10551 https://

How Students Use Generative AI for Software Testing: An Observational Study
The integration of generative AI tools like ChatGPT into software engineering workflows opens up new opportunities to boost productivity in tasks such as unit test engineering. However, these AI-assisted workflows can also significantly alter the developer's role, raising concerns about control, output quality, and learning, particularly for novice developers. This study investigates how novice software developers with foundational knowledge in software testing interact with generative AI for e…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e
https://arxiv.org/abs/2510.11269 https://…

From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini
Generative AI (GenAI) chatbots are now pervasive in digital ecosystems, yet their network traffic remains largely underexplored. This study presents an in-depth investigation of traffic generated by three leading chatbots (ChatGPT, Copilot, and Gemini) when accessed via Android mobile apps for both text and image generation. Using a dedicated capture architecture, we collect and label two complementary workloads: a 60-hour generic dataset with unconstrained prompts, and a controlled dataset bui…

We have no idea how this #AI cycle is affecting jobs. The numbers are all over the place.
Relatedly, I've found myself regularly explaining the concept of an "AI winter" and that most academics expect that we're coming up on the next one. Nobody is connecting these two concepts because they're both so…

From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?
Thomas Serre, Ellie Pavlick
https://arxiv.org/abs/2509.17280 https://a…

From Prediction to Understanding: Will AI Foundation Models Transform Brain Science?
Generative pretraining (the "GPT" in ChatGPT) enables language models to learn from vast amounts of internet text without human supervision. This approach has driven breakthroughs across AI by allowing deep neural networks to learn from massive, unstructured datasets. We use the term foundation models to refer to large pretrained systems that can be adapted to a wide range of tasks within and across domains, and these models are increasingly applied beyond language to the brain sciences. These …