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3DCodeVerse introduces a research ecosystem with a 2M-scale dataset of executable 3D code, a multi-agent DAG harness for automated generation and verification of 3D content, and empirical guidance showing fine-tuned open models achieve a 90% win rate against Claude Design (3D Object).
This paper presents Vera, an end-to-end automated safety testing framework for LLM agents that combines literature-driven risk discovery, combinatorial composition of safety cases, and evidence-grounded verification. Evaluations on four agent frameworks reveal substantial safety weaknesses, with average attack success rates reaching 93.9% under multi-channel attacks, and the release of Vera-Bench with 1600 executable safety cases.
This paper presents STG, a structured testbench generation framework for LLM-driven hardware design workflows that reduces token cost and improves verification reliability compared to existing prompt-based approaches.