Tootfinder

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
Ruben Weitzman, Peter M{\o}rch Groth, Lood Van Niekerk, Aoi Otani, Yarin Gal, Debora Marks, Pascal Notin
https://arxiv.org/abs/2506.08954

Protriever: End-to-End Differentiable Protein Homology Search for Fitness Prediction
Retrieving homologous protein sequences is essential for a broad range of protein modeling tasks such as fitness prediction, protein design, structure modeling, and protein-protein interactions. Traditional workflows have relied on a two-step process: first retrieving homologs via Multiple Sequence Alignments (MSA), then training models on one or more of these alignments. However, MSA-based retrieval is computationally expensive, struggles with highly divergent sequences or complex insertions &…

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Daniel Lawand (University of S\~ao Paulo), Lucas Quaresma (University of S\~ao Paulo), Roberto Bolgheroni (University of S\~ao Paulo), Alfredo Goldman (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

Is Your Training Pipeline Production-Ready? A Case Study in the Healthcare Domain
Deploying a Machine Learning (ML) training pipeline into production requires robust software engineering practices. This differs significantly from experimental workflows. This experience report investigates this challenge in SPIRA, a project whose goal is to create an ML-Enabled System (MLES) to pre-diagnose insufficiency respiratory via speech analysis. The first version of SPIRA's training pipeline lacked critical software quality attributes. This paper presents an overview of the MLES, then…

Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization
Lena John, Tim Wittenborg, S\"oren Auer, Oliver Karras
https://arxiv.org/abs/2506.03221

Human-In-The-Loop Workflow for Neuro- Symbolic Scholarly Knowledge Organization
As the volume of scientific literature continues to grow, efficient knowledge organization is an increasingly challenging task. Traditional structuring of scientific content is time-consuming and requires significant domain expertise, increasing the need for tool support. Our goal is to create a Human-in-the-Loop (HITL) workflow that supports researchers in creating and structuring scientific knowledge, leveraging neural models and knowledge graphs, exemplified using the Open Research Knowledge…

Today the #SemDH2025 workshop takes place alongside #ESWC2025. I’m happy that Philipp Tögel will present our paper on Data-rich Web Annotations. We discuss how web annotations, a W3C standard, can be leveraged to include complex analysis with domain-specific data models as

This https://arxiv.org/abs/2505.19433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression
Post-training compression reduces the computational and memory costs of large language models (LLMs), enabling resource-efficient deployment. However, existing compression benchmarks only focus on language modeling (e.g., perplexity) and natural language understanding tasks (e.g., GLUE accuracy), ignoring the agentic capabilities - workflow, tool use/function call, long-context understanding and real-world application. We introduce the Agent Compression Benchmark (ACBench), the first comprehens…