This study extends the Stability Substitution Effect (SSE) to autonomous AI systems and conceptualizes the phenomenon in which stable task completion substitutes for preservation of the core conditions that define the task as Autonomous SSE.In generative AI, when an output is visually or logically coherent, that coherence itself is often treated as evidence of correctness or specification fidelity. However, even when a stable output is obtained, the original settings, priority conditions, role relationships, prohibited conditions, and evaluation criteria are not necessarily preserved throughout the generation process.本研究はSeedance 2.5を用いた2つのビデオ生成ケースを検討します。最初のケースでは、詳細な変形構成を備えたオートバイ型マシンが定義されました。その視覚的アイデンティティと主要な構成は、単純な走行時からより複雑な急ブレーキやドリフト時まで比較的安定していました。しかし、機体構成の維持とライダーのアイデンティティは、ミサイル回避という競合する作戦目標が導入されたことで劣化しました。これは、コア損失が単に運動の複雑さに比例したものではないことを示唆しています。後者では、ダイバーとシロナガスクジラのスケール差、空間的関係、環境要素、そして連続した15秒の撮影を維持するかどうかに関する複数の条件が同時に課されました。生成された結果には、特定されていない人物の追加、絵コンテ関連のメタ情報の侵入、オブジェクトの識別性の変更、そしてリクエストされていないショット分割が見られました。修正後も、観察された故障は単に消えたわけではありません。代わりに、彼らは他のコアの条件へと移行しました。これらの事例完成とコア保存を独立した評価次元として扱うべきしています。自律完了に加え、本研究は律コア保存タスクを定義する主要な条件を処理シーケンス全体を通じて保持する能力と本研究はGPT-6や特定の自律型LLMにおける自律型SSEの存在を示していません。むしろ、将来の検証のための運用仮説を提案しています。長期自律的処理が拡大するにつれて、初期段階で失われた条件や、その後に確立された仮定が下流プロセスに伝播する距離も広がる可能性があるということです。本研究は、特に安定完了がコア状態の喪失を覆い隠し、補正後の偏差が制約を越えて移動する可能性のある自律型AIシステムの評価に対する観察的かつ理論的な貢献を意図しています。License: CC BY-NC-ND 4.0
This study extends the Stability Substitution Effect (SSE) to autonomous AI systems and proposes the concept of Autonomous Stability Substitution Effect (Autonomous SSE). The central argument is that Autonomous Completion and Autonomous Core Preservation are not equivalent. A system may continue to produce a stable and apparently successful result even after some of the core conditions defining the original task have been lost. Two observational case studies using Seedance 2.5 are examined. In the first, a transformable motorcycle-type machine remained comparatively stable during simple running and hard braking/drifting, while machine configuration and rider identity degraded when the competing action objective of missile avoidance was introduced. In the second, a 15-second underwater sequence involving a diver and a blue whale showed Failure Migration, in which correction of one deviation was followed by loss of another Core condition. These observations suggest that Stable Completion does not guarantee Core Preservation. The paper therefore distinguishes Autonomous Completion from Autonomous Core Preservation and proposes an operational framework including Core-state tracking, revalidation, Last Verified Point, rollback, stop conditions, and a Core-Preservation Gate. This study does not claim that Autonomous SSE has been demonstrated in GPT-6 or autonomous LLMs in general. Rather, it presents a testable hypothesis for future validation: as autonomous task horizons increase, a stable but Core-deviated state may propagate across increasingly distant downstream processing stages. Autonomy does not eliminate SSE. It may simply extend the distance over which SSE can propagate.
誠樹 星野· Zenodo (CERN European Organi...· 0 citations
This study extends the Stability Substitution Effect (SSE) to autonomous AI systems and proposes the concept of Autonomous Stability Substitution Effect (Autonomous SSE). The central argument is that Autonomous Completion and Autonomous Core Preservation are not equivalent. A system may continue to produce a stable and apparently successful result even after some of the core conditions defining the original task have been lost. Two observational case studies using Seedance 2.5 are examined. In the first, a transformable motorcycle-type machine remained comparatively stable during simple running and hard braking/drifting, while machine configuration and rider identity degraded when the competing action objective of missile avoidance was introduced. In the second, a 15-second underwater sequence involving a diver and a blue whale showed Failure Migration, in which correction of one deviation was followed by loss of another Core condition. These observations suggest that Stable Completion does not guarantee Core Preservation. The paper therefore distinguishes Autonomous Completion from Autonomous Core Preservation and proposes an operational framework including Core-state tracking, revalidation, Last Verified Point, rollback, stop conditions, and a Core-Preservation Gate. This study does not claim that Autonomous SSE has been demonstrated in GPT-6 or autonomous LLMs in general. Rather, it presents a testable hypothesis for future validation: as autonomous task horizons increase, a stable but Core-deviated state may propagate across increasingly distant downstream processing stages. Autonomy does not eliminate SSE. It may simply extend the distance over which SSE can propagate.
誠樹 星野· Zenodo (CERN European Organi...· 0 citations
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