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1 Introduction

https://arxiv.org/html/2506.01844v1

While complementary in terms of modalities, such advances in developing multi-modal foundation models stem from (i) the adoption of scalable architectures, such as the Transformer (Vaswani, 2017) and (ii) internet-scale training datasets

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual

https://arxiv.org/html/2407.04503v4

Molinaro Inria, Flowers team, Université de Bordeaux, France Department of Psychology, University of California, Berkeley, Berkeley, CA, USA Maxime Derex Institute for Advanced Study in Toulouse, Toulouse, France Pierre-Yves Oudeyer Inria, Flowers team, Université de Bordeaux, France Clément Moulin-Frier Inria

Software Engineering

https://arxiv.org/list/cs.SE/new

Through an analysis of 1,966 open source projects, 1,355 Github issues, 444 CVE reports, and 361 StackOverflow posts, we systematically map the feature support and real-world usage of globs across six common ecosystems that utilize globs

[2608.03883] DS@GT-ARC at eRisk 2026 Task 3: Sparse, Semantic, and LLM

https://arxiv.org/abs/2608.03883

All submitted systems follow a staged retrieval design in which BM25 retrieves candidates at scale and semantic or LLM rerankers refine the final rankings

[2104.08663] BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of

https://arxiv.org/abs/2104.08663

Benchmark for Zero-shot Evaluation of Information Retrieval Models, by Nandan Thakur and 4 other authors View PDF HTML (experimental) Abstract:Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of