Large Multimodal Models (LMMs) often hallucinate objects and struggle with compositional reasoning in complex visual scenes. Structured Scene Graph (SG) representations explicitly encoding objects, attributes, and relations can mitigate these issues, however finetuning risks catastrophic forgetting. Recent zero-shot approaches prompt LMMs with scene graphs, yet typically rely on a single SG generated in one step, limiting capture of holistic context and question-specific details. We introduce a Dual-Layer Scene Graph Chain-of-Thought DLSG-CoT framework that enriches reasoning by combining two structured SGs: a Global Scene Graph (G-SG) that offers comprehensive image context, and a Query-Specific Scene Graph (Q-SG) produced through a two-step process targeting information relevant to the input query. Extensive experiments demonstrate that DLSG-CoT substantially improves LMM performance on compositional and context-sensitive tasks.

Deep learning is increasingly applied to intraoperative and surgical video analysis to enable real-time workflow recognition, and decision support for improved surgical precision. A key direction is modeling surgical activity as triplets of instrument, action, and target, which provide a richer representation of procedures. However, existing approaches often depend on bounding-box annotations or lack temporal context. We propose TWiST (Temporal Weakly Supervised Triplet detection), a framework that combines weakly supervised instrument localization, temporal attention for triplet prediction, and grounding of triplets with detected instruments. Our experiments show that TWiST outperforms prior weakly supervised baselines.