Tootfinder

from my link log —
Incremental lambda calculus.
https://inc-lc.github.io/
saved 2026-04-09 https://dotat.at/:/K0US5.html

If by some effect all the mathematicians of the world would instead of the usual good mathematics suddenly start to only produce mediocre and meaningless self-referential and mildly incremental work --
How long would it take until the general public notices ?
And then what would politics do about it?
[ I mean from now on. In this scenario we still have access to all the good math from the past!]

Had to remove --incremental from my Eleventy watch command because my RSS feed were not being regenerated after a post creation on this blog, it’s a bit sad because full rebuild just to update the RSS feed is a lot.
https://rmendes.net/content/notes/2026-02-05-4be88

Ricardo Mendes
5 February 2026 Eleventy Had to remove --incremental from my Eleventy watch command because my RSS feed were not being regenerated after a post creation on this blog, it’s a bit sad because full rebui...

Incremental improvement coming to the Evans bike lane!
https://carfree.city/@sfmtadocsbot/116060871073512395

Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
toXiv_bot_toot

Sources: Nvidia delays the release of its incremental gaming GPU upgrade, codenamed Kicker, marking the first year in three decades without a new GPU for gaming (The Information)
https://www.theinformation.com/articles/nvidia-delay-new-gaming-chi…

Nvidia to Delay New Gaming Chip Due to Memory Chip Shortage
Nvidia won’t release a new graphics chip for gamers this year due to a deepening global shortage of memory chips, prompted by the AI boom, according to two people with direct knowledge of the matter. It would be the first year in three decades that Nvidia hasn’t released a new graphics ...

Bounded Local Generator Classes for Deterministic State Evolution
R. Jay Martin II
https://arxiv.org/abs/2602.11476 https://arxiv.org/pdf/2602.11476 https://arxiv.org/html/2602.11476
arXiv:2602.11476v1 Announce Type: new
Abstract: We formalize a constructive subclass of locality-preserving deterministic operators acting on graph-indexed state systems. We define the class of Bounded Local Generator Classes (BLGC), consisting of finite-range generators operating on bounded state spaces under deterministic composition. Within this class, incremental update cost is independent of total system dimension. We prove that, under the BLGC assumptions, per-step operator work satisfies W_t = O(1) as the number of nodes M \to \infty, establishing a structural decoupling between global state size and incremental computational effort. The framework admits a Hilbert-space embedding in \ell^2(V; \mathbb{R}^d) and yields bounded operator norms on admissible subspaces. The result applies specifically to the defined subclass and does not claim universality beyond the stated locality and boundedness constraints.
toXiv_bot_toot

AI Gave Investors a Glimpse of the Future This Month.
And Then They Sold Their Stocks.
“The main story is still tech and AI uncertainty,” said Ross Mayfield, investment strategist at Baird.
“It is making investors question the fundamental underpinning of the profitability of a lot of industries.” 
Those worries have triggered several nauseating stock swings in recent weeks, some with relatively innocuous catalysts
—an incremental update to a particular AI tool, …

AI Gave Investors a Glimpse of the Future This Month. And They Sold Their Stocks. — The Wall Street Journal
The S&P 500 and Nasdaq composite fell in February, dragged down by firms whose businesses might be disrupted

TIL: Incremental selection is available by default in Nvim 0.12, see `:help v_in`.
#neovim

From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Hangda Liu, Zhulin An, Fei Wang, Yongjun Xu
https://arxiv.org/abs/2602.20911 https://arxiv.org/pdf/2602.20911 https://arxiv.org/html/2602.20911
arXiv:2602.20911v1 Announce Type: new
Abstract: Class-Incremental Learning (CIL) requires models to learn new classes without forgetting old ones. A common method is to freeze a pre-trained model and train a new, lightweight adapter for each task. While this prevents forgetting, it treats the learned knowledge as a simple, unstructured collection and fails to use the relationships between tasks. To this end, we propose the Semantic-guided Adaptive Expert Forest (SAEF), a new method that organizes adapters into a structured hierarchy for better knowledge sharing. SAEF first groups tasks into conceptual clusters based on their semantic relationships. Then, within each cluster, it builds a balanced expert tree by creating new adapters from merging the adapters of similar tasks. At inference time, SAEF finds and activates a set of relevant experts from the forest for any given input. The final prediction is made by combining the outputs of these activated experts, weighted by how confident each expert is. Experiments on several benchmark datasets show that SAEF achieves SOTA performance.
toXiv_bot_toot

Bridging Distant Ideas: the Impact of AI on R&D and Recombinant Innovation
Emanuele Bazzichi, Massimo Riccaboni, Fulvio Castellacci
https://arxiv.org/abs/2604.02189 https://arxiv.org/pdf/2604.02189 https://arxiv.org/html/2604.02189
arXiv:2604.02189v1 Announce Type: new
Abstract: We study how artificial intelligence (AI) affects firms' incentives to pursue incremental versus radical knowledge recombinations. We develop a model of recombinant innovation embedded in a Schumpeterian quality-ladder framework, in which innovation arises from recombining ideas across varying distances in a knowledge space. R&D consists of multiple tasks, a fraction of which can be performed by AI. AI facilitates access to distant knowledge domains, but at the same time it also increases the aggregate rate of creative destruction, shortening the monopoly duration that rewards radical innovations. Moreover, excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts. We obtain three main results. First, higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals. Second, the effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations. Third, in the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate.
toXiv_bot_toot

Samsung unveils the $1,299 Galaxy S26 Ultra with a Privacy Display feature that limits the screen legibility, an all-new agentic AI, improved night mode, more (Prakhar Khanna/ZDNET)
https://www.zdnet.com/article/samsung-galaxy-s26-ultra-hands-on-unpacked-2026/…

I tried the Samsung Galaxy S26 Ultra and need the Privacy Display feature on my iPhone ASAP
Samsung's flagship phone of 2026 offers incremental upgrades across its design, cameras, and performance, but one display feature sets it apart from the competition.

from my link log —
Fast incremental compilation of Kotlin.
https://blog.jetbrains.com/kotlin/2020/09/the-dark-secrets-of-fast-compilation-for-kotlin/
saved 2020-09-24