Tootfinder

I have a habbit of making (too) many (small) packages for functionality that might be reused in different context. {box} might be an alternative by making scripts into modlues that can be loaded: #RStats

‘box’: Write Reusable, Composable and Modular R Code
A modern module system for R. Organise code into hierarchical, composable, reusable modules, and use it effortlessly across projects via a flexible, declarative dependency loading syntax.

This https://arxiv.org/abs/2506.06266 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cartridges: Lightweight and general-purpose long context representations via self-study
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the context window and leveraging in-context learning (ICL). Although current models support contexts of 100K-1M tokens, this setup is costly to serve because the memory consumption of the KV cache scales with input length. We explore an alternative: training a smaller KV cache offline on each corpus. At inference time, we loa…

Apparent motion of penumbral grains in a sunspot simulation
Michal Sobotka, Markus Schmassmann
https://arxiv.org/abs/2506.09504 https://

Apparent motion of penumbral grains in a sunspot simulation
Context. The bright heads of penumbral filaments, penumbral grains (PGs), are manifestations of hot plasma flows rising to the surface. They are observed to move horizontally toward the sunspot umbra or away from it. Recent analyses of observations indicate that the direction of this motion is related to the inclination of the surrounding magnetic field. Aims. The penumbra of a sunspot simulated by the radiative magnetohydrodynamic code MURaM is analysed to get typical physical conditions in PG…

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
Iris Bree, Federico Gasparotto, Antonela Matija\v{s}i\'c, Pouria Mazloumi, Dmytro Melnichenko, Sebastian P\"ogel, Toni Teschke, Xing Wang, Stefan Weinzierl, Konglong Wu, Xiaofeng Xu
https://arxiv.org/abs/250…

The geometric bookkeeping guide to Feynman integral reduction and $\varepsilon$-factorised differential equations
We report on three improvements in the context of Feynman integral reduction and $\varepsilon$-factorised differential equations: Firstly, we show that with a specific choice of prefactors, we trivialise the $\varepsilon$-dependence of the integration-by-parts identities. Secondly, we observe that with a specific choice of order relation in the Laporta algorithm, we directly obtain a basis of master integrals, whose differential equation on the maximal cut is in Laurent polynomial form with res…

With great relief we observed that Lisa has condescended to use her fancy new water fountain. She actually seems quite keen on it after ignoring it for the first few hours after installation. #CatsOfMastodon

This https://arxiv.org/abs/2505.14759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
Large Language Models for code often entail significant computational complexity, which grows significantly with the length of the input code sequence. We propose LeanCode for code simplification to reduce training and prediction time, leveraging code contexts in utilizing attention scores to represent the tokens' importance. We advocate for the selective removal of tokens based on the average context-aware attention scores rather than average scores across all inputs. LeanCode uses the attenti…

Who is using AI to code? Global diffusion and impact of generative AI
Simone Daniotti, Johannes Wachs, Xiangnan Feng, Frank Neffke
https://arxiv.org/abs/2506.08945

Who is using AI to code? Global diffusion and impact of generative AI
Generative coding tools promise big productivity gains, but uneven uptake could widen skill and income gaps. We train a neural classifier to spot AI-generated Python functions in 80 million GitHub commits (2018-2024) by 200,000 developers and track how fast--and where--these tools take hold. By December 2024, AI wrote an estimated 30.1% of Python functions from U.S. contributors, versus 24.3% in Germany, 23.2% in France, 21.6% in India, 15.4% in Russia and 11.7% in China. Newer GitHub users use…

This https://arxiv.org/abs/2502.12096 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Token Communications: A Unified Framework for Cross-modal Context-aware Semantic Communications
In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC). TokCom is a new paradigm, motivated by the recent success of generative foundation models and multimodal large language models (GFM/MLLMs), where the communication units are tokens, enabling efficient transformer-based token processing at the transmitter and receiver. In this paper, we introduce the potential opportu…

Recursive Semantic Anchoring in ISO 639:2023: A Structural Extension to ISO/TC 37 Frameworks
Bugra Kilictas, Faruk Alpay
https://arxiv.org/abs/2506.06870 h…

Recursive Semantic Anchoring in ISO 639:2023: A Structural Extension to ISO/TC 37 Frameworks
ISO 639:2023 unifies the ISO language-code family and introduces contextual metadata, but it lacks a machine-native mechanism for handling dialectal drift and creole mixtures. We propose a formalisation of recursive semantic anchoring, attaching to every language entity $χ$ a family of fixed-point operators $ϕ_{n,m}$ that model bounded semantic drift via the relation $ϕ_{n,m}(χ) = χ\oplus Δ(χ)$, where $Δ(χ)$ is a drift vector in a latent semantic manifold. The base anchor $ϕ_{0,0}$ re…

This https://arxiv.org/abs/2506.06091 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

MIRIAD: Augmenting LLMs with millions of medical query-response pairs
LLMs are bound to transform healthcare with advanced decision support and flexible chat assistants. However, LLMs are prone to generate inaccurate medical content. To ground LLMs in high-quality medical knowledge, LLMs have been equipped with external knowledge via RAG, where unstructured medical knowledge is split into small text chunks that can be selectively retrieved and integrated into the LLMs context. Yet, existing RAG pipelines rely on raw, unstructured medical text, which can be noisy,…