Progressive memory layer for AI agents with Header+Content indexing, chain recall, and file-first governance.
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Updated
Apr 11, 2026 - Python
Progressive memory layer for AI agents with Header+Content indexing, chain recall, and file-first governance.
Structured memory for agents: weighted retrieval and replayable evidence paths
DRESS: A Continuous Framework for Structural Graph Refinement
RAG-based method for mapping variables to PrimeKG subgraphs with textual graph descriptions.
The Memory of your Agent
Research-grade neuro-symbolic RAG framework where retrieval is a policy, not a vector search, built for evaluation, ablation, and reliability analysis.
Parameter inference of a synthetic graph generator for real-world multilayer networks
Unified Hybrid Retrieval–Generation Architecture for Structured Domain Reasoning
Topology-Aware Sparse Distributed Memory for knowledge graph retrieval. Binary 256-bit addresses combining SimHash content + weighted majority vote over 1-hop neighbors, plus classical quantum walk refinement. MRR=0.919 globally, MRR=1.000 in 50-node subgraphs. Python stdlib, no GPU, no API, no training. DOI: 10.5281/zenodo.19645323
Analyze and visualize graph structures efficiently across Python, Rust, JavaScript, and R with dress-graph’s cross-platform tools.
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