A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support
This week, five new AI research papers were published across arXiv's AI Infrastructure, Inference & Ops, Model Efficiency & Engineering, and RAG, Search & Knowledge Systems categories. On June 8, "FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail" appeared in AI Infrastructure, Inference & Ops. The following day, June 9, saw three new papers: "A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support" and "APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing" were published in AI Infrastructure, Inference & Ops, while "How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions" was released in Model Efficiency & Engineering. Finally, on June 10, "ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval" was published in RAG, Search & Knowledge Systems. Synthesized from 1148 manifests produced by 10 monitored preprint sources in the last 7 days, including arXiv.
Answer updated Jun 11, 2026 00:00 UTC · rebuilt twice daily from the rolling 168-hour window
A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support
APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing
FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail
How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions
ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval