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PolyUQuest is a verifiable, structure-aware web RAG framework that uses heterogeneous graphs to unify hyperlink topology, DOM hierarchy, and entity-relation knowledge, outperforming existing systems on answer correctness, coverage, and faithfulness.
Proposes M-QCDNet, a structure-aware deep learning architecture that embeds multilayer Q-matrices for enhanced psychometric interpretability in cognitive diagnosis.
Proposes SAOT, a structure-aware optimal transport framework for self-supervised continual graph learning that preserves relational structure across tasks. Achieves significant performance gains over state-of-the-art methods on multiple benchmarks, including up to 15% improvement on Products-CL.
This paper introduces Sesame, a diffusion-based molecular generation model that conditions on partial molecular structure and protein pocket via spatial density maps, enabling both de novo generation and fragment-conditioned lead optimization for drug design.
IV-CoT decomposes visual conditioning into structural and semantic cascades for improved structure-aware image generation, using training-only sketch supervision to guide structural queries. It achieves state-of-the-art results on GenEval and T2I-CompBench.
Proposes CodeBlock, a structure-aware sparse supervision framework for supervised fine-tuning of code LLMs. It selects high-quality instruction-response pairs and partitions code responses into syntactically coherent coding items, applying loss only to selected items to achieve stronger pass@1 rates using only 1.9% of supervised response tokens.
BrickAnything is an autoregressive framework that generates physically buildable brick structures from diverse 3D representations using point clouds and structure-aware tree tokenization, ensuring geometric fidelity and structural stability.
DPR-BAG is a training-free, zero-shot framework that generates coherent biomedical abstracts from full-text articles by decomposing them into rhetorical facets, summarizing each with an LLM, and refining for coherence, achieving better novelty than baselines while maintaining factual consistency.