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cs.CL2026
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Young-Jun Lee, Seungone Kim, Minki Kang +5
Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search h…
cs.CL2026
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning
Zerui Chen, Qinggang Zhang, Zhishang Xiang +5
Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning…
cs.CL2026
Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation
Linfeng Gao, Qinggang Zhang, Baolong Bi +9
Retrieval-Augmented Generation (RAG) systems often fail to maintain contextual faithfulness, generating responses that conflict with the provided context or fail to fully leverage…