Tootfinder

Ramping Up Open-Source RISC-V Cores: Assessing the Energy Efficiency of Superscalar, Out-of-Order Execution
Zexin Fu, Riccardo Tedeschi, Gianmarco Ottavi, Nils Wistoff, C\'esar Fuguet, Davide Rossi, Luca Benini
https://arxiv.org/abs/2505.24363

Ramping Up Open-Source RISC-V Cores: Assessing the Energy Efficiency of Superscalar, Out-of-Order Execution
Open-source RISC-V cores are increasingly demanded in domains like automotive and space, where achieving high instructions per cycle (IPC) through superscalar and out-of-order (OoO) execution is crucial. However, high-performance open-source RISC-V cores face adoption challenges: some (e.g. BOOM, Xiangshan) are developed in Chisel with limited support from industrial electronic design automation (EDA) tools. Others, like the XuanTie C910 core, use proprietary interfaces and protocols, including…

Did lunar tides sustain the early Earth's geodynamo?
J\'er\'emie Vidal, David C\'ebron
https://arxiv.org/abs/2506.19039 https://

Did lunar tides sustain the early Earth's geodynamo?
Geological data show that, early in its history, the Earth had a large-scale magnetic field with an amplitude comparable to that of the present geomagnetic field. However, its origin remains enigmatic and various mechanisms have been proposed to explain the Earth's field over geological time scales. Here, we critically evaluate whether tidal forcing could explain the early Earth's geodynamo, by combining constraints from geophysical models of the Earth-Moon system and predictions from rotating …

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
Nhi Hoai Doan, Tatsuya Hiraoka, Kentaro Inui
https://arxiv.org/abs/2507.07810

Understanding and Controlling Repetition Neurons and Induction Heads in In-Context Learning
This paper investigates the relationship between large language models' (LLMs) ability to recognize repetitive input patterns and their performance on in-context learning (ICL). In contrast to prior work that has primarily focused on attention heads, we examine this relationship from the perspective of skill neurons, specifically repetition neurons. Our experiments reveal that the impact of these neurons on ICL performance varies depending on the depth of the layer in which they reside. By comp…

Language bubbles in online social networks
Alessandro Bellina, Donald Ruggiero Lo Sardo, Emanuele Brugnoli, Fabio Saracco, Pietro Gravino, Vittorio Loreto, Gabriele Di Bona
https://arxiv.org/abs/2507.13068

Language bubbles in online social networks
Social media platforms have become essential spaces for public discourse. While political polarisation and limited communication across different groups are widely acknowledged, the connection between social network fragmentation and the language features and quality used by various communities has received insufficient attention. This study aims to fill this gap by examining the social structure and linguistic richness of the Italian debate on Twitter/X. We analyse tweets and retweets from Ita…

This https://arxiv.org/abs/2504.06550 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Blameocracy: Causal Attribution in Political Communication
We propose a supervised method to detect causal attribution in political texts, distinguishing between expressions of merit and blame. Analyzing four million tweets shared by U.S. Congress members from 2012 to 2023, we document a pronounced shift toward causal attribution following the 2016 presidential election. The shift reflects changes in rhetorical strategy rather than compositional variation in the actors or topics of the political debate. Within causal communication, a trade-off emerges …