-д хадгалсан:
| Үндсэн зохиолч: | |
|---|---|
| Формат: | Recurso digital |
| Хэл сонгох: | англи |
| Хэвлэсэн: |
Zenodo
2025
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| Нөхцлүүд: | |
| Онлайн хандалт: | https://doi.org/10.5281/zenodo.17609974 |
| Шошгууд: |
Шошго нэмэх
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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Агуулга:
- <p>This work proposes a unified multi-layer causal framework connecting the geometric structure of large language model (LLM) embeddings, the information-flow properties of attention mechanisms, and the emergence of semantic meaning during reasoning.</p> <p>The study introduces three interacting layers—<strong>physical (geometry/topology), information (directed correlations), and meaning (causal constraints)</strong>—and demonstrates that standard transformer embeddings can be analyzed using tools such as persistent homology, local intrinsic-dimension estimation, and density-matrix-like representations.</p> <p>Experimental results using GPT-2 small reveal:</p> <ul> <li> <p>non-trivial H1H1 loops in embedding space,</p> </li> <li> <p>non-uniform low-dimensional structure via TwoNN,</p> </li> <li> <p>attention matrices acting as stochastic operators with meaningful spectral structure.</p> </li> </ul> <p>All code necessary for full reproducibility is included in the appendix.<br>The aim of this work is to encourage further exploration of the deep structure of learned representations and their potential connections to physics, information theory, and semantic emergence.<br><br></p> <p>For readers interested in the broader theoretical context of this work, a companion whitepaper provides an integrated view of how quantum phase structure, geometric manifolds, and topological invariants may relate to semantic stability in AI systems:</p> <p><strong><em>Meaning Unification Framework: A Dual-Tier Whitepaper Connecting Quantum–Geometric Structures and AI Semantic Stability</em></strong><br>URL: <a href="https://zenodo.org/records/17650669" target="_new" rel="noopener">https://zenodo.org/records/17650669</a></p> <p>This whitepaper positions the present paper as part of a larger two-tier research program, linking foundational mathematical structures (Tier I) with applications to alignment, semantic stability, and internal phenomenology (Tier II).</p>