Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refin

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2605.01327v1 Announce Type: new Abstract: Existing reinforcement learning approaches for Large Language Models typically perform policy optimization at the granularity of

<!-- SC_OFF --><div class="md"><p>Analog IC layout is a notoriously hard AI benchmark: spatial reasoning, multi-objective optimization (matching, parasitics, routing), and no

arXiv:2604.18789v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical

arXiv:2604.27891v1 Announce Type: new Abstract: Agent orchestration frameworks -- LangGraph, CrewAI, Google ADK, OpenAI Agents SDK, and others -- place an external orchestrator

arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.

arXiv:2603.18859v1 Announce Type: new Abstract: Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models

arXiv:2604.21018v1 Announce Type: new Abstract: While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute