AI Research & Publications on AI Computing Platform | Lambda (original) (raw)

Breakthroughs backed by Lambda

Bold ideas, funded and refined through the Lambda Research Grant. These are the projects shaping how AI learns, reasons, and scales — built by the researchers defining what’s next.

SAEBench

A comprehensive benchmark for sparse autoencoders in language model interpretability

Adam Karvonen, Can Rager, Johnny Lin, Curt Tigges, Joseph Bloom, David Chanin, Yeu-Tong Lau, Eoin Farrell, Callum McDougall, Kola Ayonrinde, Demian Till, Matthew Wearden, Arthur Conmy, Samuel Marks, and Neel Nanda — ICML 2025

VideoHallu

Evaluating and mitigating multi-modal hallucinations on synthetic video understanding

Zongxia Li, Xiyang Wu, Guangyao Shi, Yubin Qin, Hongyang Du, Tianyi Zhou, Dinesh Manocha, and Jordan Lee Boyd-Graber — NeurIPS 2025

VLM2Vec-V2

Advancing multimodal embedding for videos, images, and visual documents

Meng, Rui and Jiang, Ziyan and Liu, Ye and Su, Mingyi and Yang, Xinyi and Fu, Yuepeng and Qin, Can and Chen, Zeyuan and Xu, Ran and Xiong, Caiming, and others — arXiv preprint 2025

Think, prune, train, improve

Scaling reasoning without scaling models

Caia Costello, Simon Guo, Anna Goldie, and Azalia Mirhoseini — ICLR 2025 workshop

NeoBERT

A next-generation BERT

Lola Le Breton, Quentin Fournier, Mariam El Mezouar, and Sarath Chandar — TMLR 2025