59 citations · 80 across the 7 of their papers we have counts for
7 papers
Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems
Maximilian Schall, Sedigheh Eslami, Markus Krimmel +4
Evaluating first-stage retrievers in large-scale production RAG requires a benchmark that pairs a large-scale corpus with a large set of agent-reformulated search queries based on…
Diffusion-Pretrained Dense and Contextual Embeddings
Sedigheh Eslami, Maksim Gaiduk, Markus Krimmel +3
In this report, we introduce pplx-embed, a family of multilingual embedding models that employ multi-stage contrastive learning on a diffusion-pretrained language model backbone fo…
jina-embeddings-v3: Multilingual Embeddings With Task LoRA
Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram +8
We introduce jina-embeddings-v3, a novel text embedding model with 570 million parameters, achieves state-of-the-art performance on multilingual data and long-context retrieval tas…
Gymnasium: A Standard Interface for Reinforcement Learning Environments
Mark Towers, Ariel Kwiatkowski, Jordan Terry +13
Reinforcement Learning (RL) is a continuously growing field that has the potential to revolutionize many areas of artificial intelligence. However, despite its promise, RL research…
Attention Normalization Impacts Cardinality Generalization in Slot Attention
Markus Krimmel, Jan Achterhold, Joerg Stueckler
Object-centric scene decompositions are important representations for downstream tasks in fields such as computer vision and robotics. The recently proposed Slot Attention module,…
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
Isabelle Mohr, Markus Krimmel, Saba Sturua +16
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of process…
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning
Jan Achterhold, Markus Krimmel, Joerg Stueckler
Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introd…