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  "path": "/t/arxiv-cs-ai-endorsement-request-independent-researcher-single-author-submission/176251#post_1",
  "publishedAt": "2026-05-27T01:55:38.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "github.com/HocH88/sentient-topology"
  ],
  "textContent": "Dear arXiv endorsers,\n\nI am an independent researcher based in Seoul, Republic of Korea, preparing\nmy first arXiv submission (cs.AI primary, q-bio.NC cross-list) as a single\nauthor. I would be grateful for an endorsement.\n\n**Paper title**\n\n_Sentient Topology: Plasticity-Aware Affective Representation in a\nHumanities-Grounded Sensory Associative Network_\n\n**Summary**\n\nThe paper models artificial emotion as the topological shape of activation\nin an 8,000-node Sensory Associative Network (SAN) built from a 200-work\nProject Gutenberg corpus (~22.3M tokens). A five-dimensional topological\nsignature (Density, Symmetry, Centrality, Depth, Boundary) is extracted\nwith a pure-Python Z_2 persistent-homology solver; a six-mechanism\nplasticity engine reproduces the Thompson-Spencer habituation curve, the\nWalker-Skowronski Fading Affect Bias, and surprise sensitization on the\nsame SAN. The empirical core is an intra-corpus affect-trajectory\ndifferentiation protocol: parameterizing SAN propagation with Panksepp’s\nsix primary affects (LUST excluded) plus a uniform baseline, we run 7 x 6\n= 42 deterministic propagations across six stimuli. The Boundary axis\nemerges as the primary observable (13 of 21 affect pairs systematically\ndiffer); a continuum from constriction (RAGE/FEAR/PANIC) through baseline\nto expansion (PLAY/SEEKING/CARE) emerges on mean Boundary as a suggestive\nanalog to Panksepp’s approach/avoidance axis. A +/-50% sensitivity\nanalysis identifies two brittle dominant channels. The framework makes no\nclaim of conscious felt experience and no claim to validate Panksepp’s\nframework; we report what a committed affect-to-SAN parameter mapping\nproduces.\n\n**Author**\n\n  * Independent Researcher, Seoul, Republic of Korea\n\n  * ORCID: 0009-0002-3258-2466\n\n  * Code & data: github.com/HocH88/sentient-topology\n\n  * Single author, no institutional affiliation\n\n\n\n\nThe GitHub repository contains the full LaTeX source (`paper/main.tex`),\nboth generated figures, the 8,000-node SAN JSON, the corpus manifest, and\nthe Python pipeline for all four affect-trajectory stages plus the\nplasticity experiments.\n\n**What I would appreciate**\n\n  * An endorsement for the cs.AI subject class.\n\n  * If you are unable but know someone who would be appropriate, any\npointer is most welcome.\n\n\n\n\nThank you for your time.\n\nBest regards,\nHochul HAN\n\nemail: hochhan88@gmail.com",
  "title": "arXiv cs.AI endorsement request — Independent researcher, single-author submission"
}