{
  "id": 16652,
  "url": "https://arxiv.org/abs/2608.04999v1",
  "title": "ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration",
  "summary": "Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort. However, many existing RL-based methods focus on single-objective optimization. Even methods designed for multi-objective (MO) problems often reduce multiple design specifications to a single scalar reward. This simplification limits the ability to capture the true Pareto trade-off among competing objectives and often leads to suboptimal designs. Moreover, requiring",
  "authors": "Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi",
  "category": "research",
  "topics": "jobs-economy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-05T16:09:12.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16652",
  "original_url": "https://arxiv.org/abs/2608.04999v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}