Tootfinder

No Stupid Questions: An Analysis of Question Query Generation for Citation Recommendation
Brian D. Zimmerman, Julien Aubert-B\'educhaud, Florian Boudin, Akiko Aizawa, Olga Vechtomova
https://arxiv.org/abs/2506.08196

No Stupid Questions: An Analysis of Question Query Generation for Citation Recommendation
Existing techniques for citation recommendation are constrained by their adherence to article contents and metadata. We leverage GPT-4o-mini's latent expertise as an inquisitive assistant by instructing it to ask questions which, when answered, could expose new insights about an excerpt from a scientific article. We evaluate the utility of these questions as retrieval queries, measuring their effectiveness in retrieving and ranking masked target documents. In some cases, generated questions end…

I always wondered why my 3900X with 64 MB of cache performs so much worse than a 5700X3D with 96 MB of cache. It's only ⇓ more cache, after all.
Well, `lstopo` had the answer. The cache is split into 4 chunks (CCXs), so a single thread only really has 16 MB of cache available.
I actually managed to gain a few FPS through isolating a group of cores that share an L3 cache by manually setting CPU affinity.
1/3

AnnoGram: An Annotative Grammar of Graphics Extension
Md Dilshadur Rahman, Md Rahat-uz- Zaman, Andrew McNutt, Paul Rosen
https://arxiv.org/abs/2507.04236 h…

AnnoGram: An Annotative Grammar of Graphics Extension
Annotations are central to effective data communication, yet most visualization tools treat them as secondary constructs -- manually defined, difficult to reuse, and loosely coupled to the underlying visualization grammar. We propose a declarative extension to Wilkinson's Grammar of Graphics that reifies annotations as first-class design elements, enabling structured specification of annotation targets, types, and positioning strategies. To demonstrate the utility of our approach, we develop a …

This https://arxiv.org/abs/2205.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

Approximability Landscape of Welfare Maximization within Fair Allocations
Fair allocation of indivisible goods studies allocating $m$ goods among $n$ agents in a fair manner. While fairness is a fundamental requirement in many real-world applications, it often conflicts with (economic) efficiency. This raises a natural and important question: How can we identify the most welfare-efficient allocation among all fair allocations? This paper answers from the perspective of computational complexity. Specifically, we study the problem of maximizing utilitarian social welfa…

This https://arxiv.org/abs/2505.09795 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Beyond Pairwise Learning-To-Rank At Airbnb
There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch-no algorithm can achieve all three at the same time. We call this limitation the SAT theorem for ranking algorithms. Given the dilemma, how can we design a practical system that meets user needs? Our current work at Airbnb provides an answer, with a working solutio…