Unverified50% confidenceFactExact time
Reflected Anchored Langevin Dynamics (RALD) extends the sampling capability to sampling problems on constrained domains.
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Long-term
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10/9/2026
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UnverifiedThe paper arXiv:2610.09522v1 proposes Reflected Anchored Langevin Dynamics (RALD), a reflected diffusion process that can converge to non-differentiable target distributions on constrained domains.76% similarUnverifiedRALD introduces a smooth anchoring reference potential to provide a numerically friendly anchor point that allows the dynamics to advance stably.70% similarUnverifiedBy attaching RAG as an independent agent node into a LangGraph workflow, adding new capabilities only requires adding graph nodes and routing conditions without refactoring existing pipelines, keeping the growth of system complexity linear and manageable.68% similarUnverifiedIn loosely coupled architectures such as AI·rete·RAG, the interface design for converting rule trigger signals into RAG queries becomes a key bottleneck for system robustness.67% similarUnverified在Agent技能路由场景中，通过RAG向量检索可以将上下文占用从数万token压缩到几百token，同时保持极高的召回准确率64% similar
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