{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreigwnxxo5x3s3peyneopbtjn4wbq5hdryzcviy2d57hqlfnad4vatm",
    "uri": "at://did:plc:pgryn3ephfd2xgft23qokfzt/app.bsky.feed.post/3mim3rgaqaml2"
  },
  "path": "/t/seeking-arxiv-cs-ai-endorsement-neuroscience-inspired-memory-architecture-for-ai-agents/174918#post_1",
  "publishedAt": "2026-04-03T07:44:06.000Z",
  "site": "https://discuss.huggingface.co",
  "textContent": "Hi everyone,\n\nI’m an independent researcher (Zensation AI) seeking endorsement for my first arXiv submission in cs.AI.\n\n**Paper:** “ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems”\n\n**Summary:** ZenBrain is the first AI memory system grounded in cognitive neuroscience. It implements 7 memory layers (working, short-term, episodic, semantic, procedural, core, cross-context) with 12 algorithms including Hebbian learning, FSRS spaced repetition, sleep-time consolidation (Stickgold & Walker 2013), and Bayesian confidence propagation.\n\n**Prior art:** Published as defensive publication on TDCommons (dpubs_series/9683) and archived on Zenodo (DOI: 10.5281/zenodo.19353663). Open-source npm packages with 9,000+ tests.\n\n**Why this matters:** Recent surveys (arxiv:2603.07670) identify “deeper neuroscience integration” as a key open challenge in AI agent memory. No existing system (MemGPT, Mem0, A-Mem, Zep) implements neuroscience-grounded mechanisms. ZenBrain addresses this gap.\n\nI’d be very grateful for endorsement in cs.AI. Happy to share the full manuscript.\n\nBest regards,\n\nAlexander Bering\n\nZensation AI",
  "title": "Seeking arXiv cs.AI endorsement — neuroscience-inspired memory architecture for AI agents"
}