2982 papers
This paper demonstrates that in restricted multimodal oncology settings with limited and heterogeneous data, auditable evidence-role organization—assigning specific, compact roles to molecular, phenotypic, and clinical inputs—is a more critical determinant of cross-domain transfer performance than simply increasing representation scale.
This study proposes a GraphRAG-based methodology for automated meta-analysis of Transcranial Magnetic Stimulation (TMS) literature, which utilizes title-based chunking, paper-level knowledge graph construction, and hybrid retrieval to achieve high-accuracy parameter extraction and significantly reduce the time required for evidence synthesis.
StructFix is a structure-aware automated program repair framework that enhances masked language models by integrating Code Property Graphs to better capture control and data dependencies, thereby improving repair effectiveness and cross-language robustness compared to existing token-sequence-based approaches.
The paper proposes FedVIB–AGP, a post-aggregation repair framework for federated learning that combines Variational Information Bottleneck regularization during training with Activation-Gap Pruning during repair to effectively suppress distributed backdoor attacks while maintaining high clean-task accuracy.
This paper demonstrates that while systematically varying prompt engineering elements like label descriptions, instructional nudges, and few-shot examples can significantly improve LLM classification accuracy for social science texts, performance gains are often marginal beyond minimal context increases, can sometimes decline with excessive context, and vary substantially across models and tasks, necessitating individual validation rather than reliance on general rules.
This paper proposes a practical, multi-layer deep learning framework utilizing CNNs and LSTMs to detect advanced cybersecurity threats by addressing critical engineering challenges like data imbalance and providing a guide for scalable, real-time implementation in Security Operations Centers.
This paper proposes an intelligent, real-time drone detection and early warning framework for critical infrastructure surveillance using the YOLO26 deep learning model, which is trained on a custom multi-scenario database and evaluated against baseline models to demonstrate superior accuracy and speed in identifying small, distant, and challenging drone targets.
This paper demonstrates that Recursive Indexing (RIX) analysis of latent transport topology can detect structural regime transitions in world models that precede behavioral failure, revealing that representations can remain pointwise stable while their underlying organizational structure collapses under stress.
Tamarin (HSQ) introduces a reversible, three-tier hierarchical KV-cache compression scheme that routes tokens into a focal tier, learned summary vectors, and a CPU-based archive to achieve 12–28× GPU memory reduction for long-context LLM inference while maintaining near-baseline perplexity and retrieval accuracy.
This paper proposes a graded-autonomy framework, adapted from the automotive J3016 standard, to govern agentic AI in healthcare by classifying systems across three domains and six levels to separate capability from authorization, thereby addressing safety gaps in current evaluation metrics through a worked example of autonomous sepsis management.