Tootfinder

The past few months of hand-coding MastoGizmos and RSS Gizmos have both given me more knowledge and more comfort around working with CSS,
so last week I decided to see if I could create a single-page application that would allow the user to both find information about a person listed on Wikipedia and create informed, context-added searches of external sources like Google News, Bing, and Chronicling America.
And y’all, I think I made something pretty nice.
I haven’t put all m…

Wikimedia Deutschland launches the Wikidata Embedding Project, a vector-based semantic search database with nearly 120M entries, to make data accessible to AI (Russell Brandom/TechCrunch)
https://techcrunch.com/2025/10/01/new-project-makes-wikipedia-…

New project makes Wikipedia data more accessible to AI | TechCrunch
A new database will make Wikipedia's wealth of knowledge more accessible to AI models.

Cool little adapted tale on AI, by Alison Gopnik:
https://simons.berkeley.edu/news/stone-soup-ai

Stone Soup AI
For some time, I’ve argued that a common conception of AI is misguided. This is the idea that AI systems like large language and vision models are individual intelligent agents, analogous to human agents. Instead, I’ve argued that these models are “cultural technologies” like writing, print, pictures, libraries, internet search engines, and Wikipedia.

A Case Study of Balanced Query Recommendation on Wikipedia
Harshit Mishra, Sucheta Soundarajan
https://arxiv.org/abs/2508.20399 https://arxiv.org/pdf/2508.…

A Case Study of Balanced Query Recommendation on Wikipedia
Modern IR systems are an extremely important tool for seeking information. In addition to search, such systems include a number of query reformulation methods, such as query expansion and query recommendations, to provide high quality results. However, results returned by such methods sometimes exhibit undesirable or wrongful bias with respect to protected categories such as gender or race. Our earlier work considered the problem of balanced query recommendation, where instead of re-ranking a l…

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Zhenyun Yin, Shujie Wang, Xuhong Wang, Xingjun Ma, Yinchun Wang
https://arxiv.org/abs/2507.16727

Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with constraints
Improving the reliability of large language models (LLMs) is critical for deploying them in real-world scenarios. In this paper, we propose \textbf{Deliberative Searcher}, the first framework to integrate certainty calibration with retrieval-based search for open-domain question answering. The agent performs multi-step reflection and verification over Wikipedia data and is trained with a reinforcement learning algorithm that optimizes for accuracy under a soft reliability constraint. Empirical …