Tootfinder

Ich habe Mojib Latif letzte Woche beim Forum für Verantwortung (viel zu kurz) getroffen und ihn gebeten, mit seiner Arbeit für den Klimaschutz noch eine ganze Weile weiterzumachen. Denn die Welt braucht Menschen, die sich als Identifikationspersonen für den Erhalt unseres Planeten einsetzen. https://…

On ill-posedness for the Gabitov--Turitsyn equation
Matthew Kowalski
https://arxiv.org/abs/2510.11887 https://arxiv.org/pdf/2510.11887

On ill-posedness for the Gabitov--Turitsyn equation
We investigate the well- and ill-posedness theory for the Gabitov--Turitsyn equation, which models the long-time dynamics of pulses in dispersion-managed optical fibers. We identify two critical regularities, corresponding to two scaling pseudo-symmetries, that demarcate regimes of ill-posedness. First, we identify $s_m = \tfrac{d}{2} - \tfrac{2}{p},$ coinciding with the monomial NLS. For $s \geq \max(s_m,0)$, local well-posedness is known to hold in $H^s$, while for $s < s_m$, we show that the…

Troy Nehls' Identical Twin Brother Wants to Replace Him in Congress (Amelia Benavides-Colón/NOTUS)
https://www.notus.org/campaigns/troy-nehls-identical-twin-brother-trever-campaign-congress-replace-texas
http://www.memeorandum.com/251130/p45#a251130p45

Trump endorses identical twin of retiring Rep. Troy Nehls to fill his seat (Faith Wardwell/Politico)
https://www.politico.com/live-updates/2025/12/04/congress/trump-endorses-trever-nehls-00677128
http://www.memeorandum.com/251204/p95#a251204p95

Scalable In-context Ranking with Generative Models
Nilesh Gupta, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Inderjit Dhillon, Felix Yu
https://arxiv.org/abs/2510.05396 https…

Scalable In-context Ranking with Generative Models
In-context Ranking (ICR) is an emerging paradigm for Information Retrieval (IR), which leverages contextual understanding of LLMs by directly incorporating the task description, candidate documents, and the query into the model's input prompt and tasking the LLM to identify relevant document(s). While it is effective, efficiency is a significant challenge in this paradigm, especially as the candidate list grows due to quadratic/super-linear scaling of attention operation with context length. To…

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger, Sepp Hochreiter
https://arxiv.org/abs/2510.02279

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of hallucinations, known as confabulations, which arise due to predictive uncertainty of LLMs. To detect confabulations, various methods for estimating predictive uncertainty in natural language generation (NLG) have been developed. These methods are typically evaluated by correlating uncertainty estimates with the correctness of generated text, with…

Interpreting Language Models Through Concept Descriptions: A Survey
Nils Feldhus, Laura Kopf
https://arxiv.org/abs/2510.01048 https://arxiv.org/pdf/2510.01…

Interpreting Language Models Through Concept Descriptions: A Survey
Understanding the decision-making processes of neural networks is a central goal of mechanistic interpretability. In the context of Large Language Models (LLMs), this involves uncovering the underlying mechanisms and identifying the roles of individual model components such as neurons and attention heads, as well as model abstractions such as the learned sparse features extracted by Sparse Autoencoders (SAEs). A rapidly growing line of work tackles this challenge by using powerful generator mod…