Causal Gated Collision Networks for Chinese Character-Level Language Modeling: An Exploratory Technical Report

This exploratory technical report documents a causal gated pairwise-interaction language-model prototype, controlled component ablations, a rising-and-falling state-increment regularization trial, and a larger Chinese Wikipedia corpus pilot. It archives code, configurations, raw metrics, generated samples, provenance manifests and selected checkpoints. Positive predictive results and negative generation/QA observations are both reported. The experiments are small, largely single-seed, and use repeatedly inspected evaluation splits. No general superiority over Transformers, semantic closure, physical-theory derivation, or dependable factual QA capability is claimed.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-30
DOI
https://doi.org/10.5281/zenodo.23795998
Primary Topic
Topic Modeling
Type
preprint
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Causal Gated Collision Networks for Chinese Character-Level Language Modeling: An Exploratory Technical Report

Zhongren Wang
3 links
Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
preprint

Causal Gated Collision Networks for Chinese Character-Level Language Modeling: An Exploratory Technical Report

Zhongren Wang
preprint en

Abstract

This exploratory technical report documents a causal gated pairwise-interaction language-model prototype, controlled component ablations, a rising-and-falling state-increment regularization trial, and a larger Chinese Wikipedia corpus pilot. It archives code, configurations, raw metrics, generated samples, provenance manifests and selected checkpoints. Positive predictive results and negative generation/QA observations are both reported. The experiments are small, largely single-seed, and use repeatedly inspected evaluation splits. No general superiority over Transformers, semantic closure, physical-theory derivation, or dependable factual QA capability is claimed.

Zenodo (CERN European Organization for Nuclear Research)
Topic Modeling
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