To what extent does multimodal input (code + AST graphs) improve the vulnerability reasoning capabilities of S

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed b

Research goal: To what extent does multimodal input (code + AST graphs) improve the vulnerability reasoning capabilities of SecLM-aligned models compared to text-only input, as evaluated by SWE-bench scores and precision-recall metrics?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.