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This paper introduces PlaceReasoner-Beta, a verifier-guided multi-agent framework that reformulates macro placement as a reasoning problem for VLSI physical design, achieving significant improvements in timing and wirelength. It also presents PlaceReasoner-Bench, an open benchmark for evaluating methods using routed PPA and DRC.
MacroAgent introduces a novel framework using LLMs to design contour algorithms for macro legalization in VLSI circuits, achieving significant improvements in layout regularity and performance.
MAGE is a multimodal multi-agent framework that improves macro placement in VLSI physical design by combining structured floorplanning rules, visual checks, and iterative refinement, outperforming commercial placers and human experts on timing metrics.
The article compares the performance of OpenAI GPT-5.6 Soul and Anthropic Claude Fable 5 in physical 3D printed part replication and autonomous magazine production. Soul slightly outperforms in speed and design precision, but both require significant human intervention in complex real-world tasks, exposing the limitations of current AI in real-world manufacturing tasks.
Proposes MacroDiff+, a physics-guided geometric diffusion framework for macro placement in VLSI design, achieving 6.1–6.2% wirelength reduction on ISPD2005 benchmarks with superior stability and scalability.