mlsanityData to 14 Oct 2026

Aegix Pulse: A Traceable Three-Stage Architecture for Personalized Content Generation and Context-Preserving Revision

arXiv 2609.07672 ↗First posted 7 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

Production content-generation systems must integrate a user's immediate task, long-term brand identity, historical evidence, and revision feedback. We present Aegix Pulse, a production-oriented three-stage architecture that separates current-task clarification and Task Persona finalization, long-term Account Profile (Brand DNA) assembly, and controlled generation and revision while preserving provenance across content versions. We evaluate four preregistered claims using 96 synthetic social-media generation tasks. Four initial-generation conditions progressively introduced a Task Persona, Account Profile, and successful-history style evidence, while two revision conditions compared plain and context-preserving revision. The experiment produced 480 completed generation records and 1,440 blinded LLM-Judge evaluations, supplemented by human review. Adding the Account Profile increased mean brand-consistency scores by 0.1562 points on a five-point scale compared with Task Persona alone (Holm-adjusted p=.1224). Preserving task and brand context during revision increased mean task-preservation scores by 0.2917 points compared with plain revision (Holm-adjusted p=.2432). Neither improvement was statistically conclusive after multiple-comparison correction. Task Persona alone showed a small observed effect, while successful-history evidence provided no additional improvement in brand consistency under the current setting. Human validation did not consistently reproduce the LLM-Judge effect directions and showed low inter-reviewer agreement. These findings provide preliminary evidence for persistent brand context and context-preserving revision while identifying priorities for stronger evidence processing and evaluation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 10 references that are in the map. First posted on arXiv 7 Sep 2026.

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8 of 10 references.

References · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  4. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  5. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
  6. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  7. 2023PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traitscited by 234
  8. 2024Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalizationcited by 208

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