{
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  "slug": "ai-research-papers",
  "question": "What new AI research papers appeared this week?",
  "section": "research-expert-networks",
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  "json_url": "https://hangingcontext.com/data/questions/ai-research-papers.json",
  "as_of": "2026-06-11T00:00:11Z",
  "updated_at": "2026-06-11T00:00:11Z",
  "quiet": false,
  "answer": "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.",
  "coverage": {
    "manifests_matched": 1148,
    "manifests_cited": 5,
    "producing_sources": 10,
    "cohort_label": "preprint",
    "cohort_size": null,
    "window": "168h"
  },
  "manifests": [
    {
      "manifest_id": 1780984721674944811,
      "headline": "A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support",
      "summary": "",
      "source_name": "arxiv-ai-infra-inference-ops",
      "source_display": "arXiv AI Infrastructure, Inference & Ops",
      "url": "https://arxiv.org/pdf/2606.09460v1",
      "published": "2026-06-09",
      "claim_count": 12,
      "streams": [
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    {
      "manifest_id": 1780984721689651805,
      "headline": "APEX4: Efficient Pure W4A4 LLM Inference via Intra-SM Compute Rebalancing",
      "summary": "",
      "source_name": "arxiv-ai-infra-inference-ops",
      "source_display": "arXiv AI Infrastructure, Inference & Ops",
      "url": "https://arxiv.org/pdf/2606.08761v1",
      "published": "2026-06-09",
      "claim_count": 15,
      "streams": [
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    },
    {
      "manifest_id": 1780897792400354862,
      "headline": "FP8 is All You Need (Part 1): Debunking Hardware FP64 as the HPC Holy Grail",
      "summary": "",
      "source_name": "arxiv-ai-infra-inference-ops",
      "source_display": "arXiv AI Infrastructure, Inference & Ops",
      "url": "https://arxiv.org/pdf/2606.06510v1",
      "published": "2026-06-08",
      "claim_count": 13,
      "streams": [
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    {
      "manifest_id": 1780981552911232187,
      "headline": "How Small Can You Go? LoRA Fine-Tuning 270M-8B Models for Merchant Information Extraction in Financial Transactions",
      "summary": "",
      "source_name": "arxiv-model-efficiency-engineering",
      "source_display": "arXiv Model Efficiency & Engineering",
      "url": "https://arxiv.org/pdf/2606.08051v1",
      "published": "2026-06-09",
      "claim_count": 12,
      "streams": [
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    },
    {
      "manifest_id": 1781080456553565194,
      "headline": "ConvMemory v2: A Recall-Preserving Top-10 Evidence Reranker for Conversational Memory Retrieval",
      "summary": "",
      "source_name": "arxiv-rag-search-knowledge",
      "source_display": "arXiv RAG, Search & Knowledge Systems",
      "url": "https://arxiv.org/pdf/2606.10842v1",
      "published": "2026-06-10",
      "claim_count": 10,
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}