---
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id: news-20260720-d35206
url: http://arxiv.org/abs/2603.29979v1
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canonical_url: http://arxiv.org/abs/2603.29979v1
source: arxiv.org
category: news/tech
category_raw: it_ai
region: null
tags: []
lang: null
published_at: null
fetched_at: '2026-07-20T12:38:31.877929Z'
updated_at: '2026-07-20T12:38:50Z'
status: published
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license_note: link-only
summary: null
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entities:
- name: 構造的リスク(structural risks
  type: concept
- name: Build feature
  type: method
- name: FORBES JAPAN
  type: organization
- name: Generative AI
  type: UNKNOWN
- name: ex-engineer
  type: person
- name: optimization_technology
  type: UNKNOWN
- name: arXiv
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key_facts: []
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- name: An Approach to Technical AGI Safety and Security
  type: event
  weight: 2.0
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  weight: 1.0
- name: AI駆動ディープフェイク技術
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title: 学術論文『Structural Feature Engineering for Generative Engine Optimization』（arXiv:2603.29979
  / 2026年3月）
---

# 学術論文『Structural Feature Engineering for Generative Engine Optimization』（arXiv:2603.29979 / 2026年3月）

## TL;DR
（要約なし・原文はリンク先を参照 / No summary available; see the source link.）

## Key Points
- (なし)

## Details
(本文なし。リンク先参照)

## Source
元記事: [学術論文『Structural Feature Engineering for Generative Engine Optimization』（arXiv:2603.29979 / 2026年3月）](http://arxiv.org/abs/2603.29979v1)