Tootfinder

Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation
Behzad Shayegh, Jan-Thorsten Peter, David Vilar, Tobias Domhan, Juraj Juraska, Markus Freitag, Lili Mou
https://arxiv.org/abs/2509.20287

Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation
We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fall within it. Essentially, current metrics generally lean toward adequacy, meaning that their scores correlate more strongly with the adequacy of translations than with fluency. More importantly, we find that this tradeoff also persists at the meta-evaluation level, and that the standard WMT meta-evaluation favors adeq…

J’ai pas le courage d’écouter des dizaines d’heures du Tour de France mais le documentaire Netflix est un bel aperçu (même 1 an en retard!).
Encore plein de choses que je comprends pas, ils pourraient certainement mieux faire pour expliquer certaines stratégies.
#tourdefrance

"Acting is behaving truthfully under imaginary circumstances."
-Sanford Meisner
#acting #coaching #inspiration

Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics
Mohamed A. Abd-Elmagid, Ming Shi, Eylem Ekici, Ness B. Shroff
https://arxiv.org/abs/2509.18654

Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics
We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI perf…

Pareto-optimal Tradeoffs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai, Jiming Chen, Karl H. Johansson
https://arxiv.org/abs/2509.18129

A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff
J\'er\^ome Emery, Ali Hasanzadeh Karkan, Jean-Fran\c{c}ois Frigon, Fran\c{c}ois Leduc-Primeau
https://arxiv.org/abs/2507.18587

A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff
Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propagation environment. However, training such a model requires high-quality, local datasets at the deployment site, which are often difficult to collect. We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-use…

No bookmark function on Bsky so posting here when I want it
https://bsky.app/profile/natefederman.bsky.social/post/3ljxtmqpejs2c

Treedepth Inapproximability and Exponential ETH Lower Bound
\'Edouard Bonnet, Daniel Neuen, Marek Soko{\l}owski
https://arxiv.org/abs/2507.13818 https:…

Treedepth Inapproximability and Exponential ETH Lower Bound
Treedepth is a central parameter to algorithmic graph theory. The current state-of-the-art in computing and approximating treedepth consists of a $2^{O(k^2)} n$-time exact algorithm and a polynomial-time $O(\text{OPT} \log^{3/2} \text{OPT})$-approximation algorithm, where the former algorithm returns an elimination forest of height $k$ (witnessing that treedepth is at most $k$) for the $n$-vertex input graph $G$, or correctly reports that $G$ has treedepth larger than $k$, and $\text{OPT}$ is t…

A general randomized test for Alpha
Daniele Massacci, Lucio Sarno, Lorenzo Trapani, Pierluigi Vallarino
https://arxiv.org/abs/2507.17599 https://arxiv.org/…

A general randomized test for Alpha
We propose a methodology to construct tests for the null hypothesis that the pricing errors of a panel of asset returns are jointly equal to zero in a linear factor asset pricing model -- that is, the null of "zero alpha". We consider, as a leading example, a model with observable, tradable factors, but we also develop extensions to accommodate for non-tradable and latent factors. The test is based on equation-by-equation estimation, using a randomized version of the estimated alphas, which onl…

Training-free Truthfulness Detection via Value Vectors in LLMs
Runheng Liu, Heyan Huang, Xingchen Xiao, Zhijing Wu
https://arxiv.org/abs/2509.17932 https://

Training-free Truthfulness Detection via Value Vectors in LLMs
Large language models often generate factually incorrect outputs, motivating efforts to detect the truthfulness of their content. Most existing approaches rely on training probes over internal activations, but these methods suffer from scalability and generalization issues. A recent training-free method, NoVo, addresses this challenge by exploiting statistical patterns from the model itself. However, it focuses exclusively on attention mechanisms, potentially overlooking the MLP module-a core c…