MemCoE learns memory organization guidelines via contrastive feedback and then trains a guideline-aligned RL policy for memory updates, yielding consistent gains on personalization benchmarks.
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A survey of graph retrieval-augmented generation for customized large language models
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Repurposing competency questions as runtime executable plans creates a controlled neuro-symbolic RAG architecture that produces evidence-closed stories from knowledge graphs.
AtomicRAG replaces chunk-based and triple-based GraphRAG with atom-entity graphs that store facts as atomic units and use personalized PageRank plus relevance filtering to achieve higher retrieval accuracy and reasoning robustness on five benchmarks.
M³KG-RAG improves multimodal reasoning in large language models by constructing multi-hop knowledge graphs and selectively pruning retrieved context with GRASP.
MemGraphRAG uses a memory-based multi-agent system for globally consistent graph construction from fragmented corpora plus a memory-aware hierarchical retriever, claiming better benchmark performance than prior GraphRAG methods at similar cost.
MoG uses hub graphs for shared context and sparsely activates expert graphs with a topology-aware router, reporting over 20% relative gains on MuSiQue.
TERGAD augments graph anomaly detection by converting node topological properties into LLM-generated semantic embeddings that are fused with original attributes via a gated dual-branch autoencoder for joint reconstruction-based anomaly scoring.
TeleCom-Bench reveals LLMs reach 90% on telecom intent and entity tasks but drop to 30% on solution generation and root cause analysis in live network scenarios.
SAGE is a self-evolving agentic graph-memory engine that dynamically constructs and refines structured memory graphs via writer-reader feedback, yielding performance gains on multi-hop QA, open-domain retrieval, and long-term agent benchmarks.
LARAG improves RAG answer quality on hyperlinked technical documentation by using author-defined links for retrieval, achieving higher BERTScore while using fewer chunks and tokens than standard embedding-based RAG.
EvoRAG adds a feedback-driven backpropagation step that attributes response quality to individual knowledge-graph triplets and updates the graph to raise reasoning accuracy by 7.34 percent over prior KG-RAG methods.
CS-RAG is a GraphRAG framework that plans queries as ordered atomic constraints, uses anchor-relation aware retrieval, applies sufficiency checks, and falls back to text recovery to reduce drift and hallucination from imperfect KGs.
An iterative feedback-driven GraphRAG architecture produces higher semantic quality and relevance on HotPotQA queries than single-shot baselines.
KaSLA applies knapsack optimization hierarchically to schema linking for LLM text-to-SQL, claiming better results than large models and improved SQL generation on Spider and BIRD.
CS-PQ optimizes PQ construction on CPUs via vectorized SIMD across centroids and cache-restructured pipelines, claiming 10.7x speedup over prior CPU methods with no accuracy loss on large datasets.
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
Active information seeking via search tools, when combined with multi-candidate context pruning during training, produces consistent gains on translation, health, and reasoning tasks over naive tool addition or no-tool baselines.
AVA is a specialized GenAI platform for development policy research that provides verifiable syntheses from World Bank reports and is associated with 2.4-3.9 hours of weekly time savings in a large-scale user evaluation.
STAR is a semantic-tuned and tail-adaptive retriever for GraphRAG that uses cross-attention interaction learning and path-weighted contrastive learning to mitigate Semantic Shortcut Bias and Long-Tail Path Bias, reporting 1.8% retrieval and 2.2% QA gains.
MemOS introduces a unified memory management framework for LLMs using MemCubes to handle and evolve different memory types for improved controllability and evolvability.
FeLoG achieves 27.9x average speedup and over 53% communication reduction in distributed graph embedding by using feedback-coupled sampling, activity-aware communication, and round-interleaved pipelining.
Didact is a RAG-based prototype with an Evidence Rail for conversational capability discovery from integrated Australian defence documents and research publications.
A survey of RDF validation users finds the technology is valued for data quality but needs better documentation, tool support, performance, and expressiveness for complex tasks.
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
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Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research
AVA is a specialized GenAI platform for development policy research that provides verifiable syntheses from World Bank reports and is associated with 2.4-3.9 hours of weekly time savings in a large-scale user evaluation.