Pith Number

pith:FXGAQ32Q

pith:2025:FXGAQ32Q2N2MDFWGNCKUUUR6KV

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Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis

Dmitry Ignatov, Radu Timofte, Yash Mittal

Fractal templates generate over 1,200 neural network variants that maintain strong performance on CIFAR-10 while remaining computationally efficient.

arxiv:2511.07329 v4 · 2025-11-10 · cs.LG · cs.CV

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The outcomes show that fractal-based architectures are capable of strong performance and are computationally efficient.

C2weakest assumption

That recursive fractal templates combined with layer permutations will reliably produce deeper and wider models that maintain strong performance without additional regularization or longer training.

C3one line summary

Fractal templates enable systematic creation of more than 1,200 neural network variants that show strong performance and computational efficiency when trained on CIFAR-10 for five epochs.

Cited by

3 papers in Pith

Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design

Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models

Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs

Receipt and verification

First computed	2026-05-20T00:02:58.380810Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2dcc086f50d374c196c668954a523e556221e5bc988a1e849182583f4f56c123

Aliases

arxiv: 2511.07329 · arxiv_version: 2511.07329v4 · doi: 10.48550/arxiv.2511.07329 · pith_short_12: FXGAQ32Q2N2M · pith_short_16: FXGAQ32Q2N2MDFWG · pith_short_8: FXGAQ32Q

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FXGAQ32Q2N2MDFWGNCKUUUR6KV \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2dcc086f50d374c196c668954a523e556221e5bc988a1e849182583f4f56c123

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5ff035973712a018bdcbf26a2451f9835912eb8e105b12c8c7c5f07a10275e95",
    "cross_cats_sorted": [
      "cs.CV"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-11-10T17:31:39Z",
    "title_canon_sha256": "b8c460672a7fecdcd0ae888ae9269b98577eb843d0444b29ef8680078a001531"
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  "source": {
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    "kind": "arxiv",
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}