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Anders Søgaard

8 papers hereh-index 441 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author6

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.LG2
  • cs.CY1
same name
  • Anders Søgaard — 71 papers, h 46
  • Anders Søgaard — 11 papers, h 6
  • Anders Søgaard — 8 papers
  • Anders Søgaard — 4 papers, h 2
  • Anders Søgaard — 3 papers, h 2
  • Anders Søgaard — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedFederated learning, ethics, and the double black box problem in medical AI

17 citations · 17 across the 7 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

Real-Time Progress Prediction in Reasoning Language Models

Hans Peter Lyngsøe Raaschou-Jensen, Constanza Fierro, Anders Søgaard

Recent reasoning language models, particularly those that employ long latent chains of thought, achieve strong performance on complex agentic tasks. However, as these models operat…

cs.CL2025

Lost at the Beginning of Reasoning

Baohao Liao, Xinyi Chen, Sara Rajaee +5

Recent advancements in large language models (LLMs) have significantly advanced complex reasoning capabilities, particularly through extended chain-of-thought (CoT) reasoning that…

cs.CL2025

Mechanistic Interpretability Needs Philosophy

Iwan Williams, Ninell Oldenburg, Ruchira Dhar +6

Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying mechanisms. As the field grows in influence, it is increasingly important…

cs.LG2025★ 17 cited

Federated learning, ethics, and the double black box problem in medical AI

Joshua Hatherley, Anders Søgaard, Angela Ballantyne +1

Federated learning (FL) is a machine learning approach that allows multiple devices or institutions to collaboratively train a model without sharing their local data with a third-p…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.