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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arXiv preprint arXiv:2501.13958 (2025)
12 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
An iterative feedback-driven GraphRAG architecture produces higher semantic quality and relevance on HotPotQA queries than single-shot baselines.
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.
Opinion-aware RAG with LLM opinion extraction and entity-linked graphs improves retrieval diversity by 26-42% over factual baselines on e-commerce forum data.
MemOS introduces a unified memory management framework for LLMs using MemCubes to handle and evolve different memory types for improved controllability and evolvability.
BIP turns event streams into autonomous insights by modeling journeys as absorbing Markov chains, extracting facts via knowledge graphs, and generating narratives constrained to verified data.
DGMM is proposed as an explicit graph-structured memory architecture for AI that enables persistent episodic memory, cue-based recall, and context-dependent interpretation without retraining.
citing papers explorer
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Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory
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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Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytelling
Repurposing competency questions as runtime executable plans creates a controlled neuro-symbolic RAG architecture that produces evidence-closed stories from knowledge graphs.
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SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory
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.
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LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation
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.
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EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback-driven Backpropagation
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.
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KGiRAG: An Iterative GraphRAG Approach for Responding Sensemaking Queries
An iterative feedback-driven GraphRAG architecture produces higher semantic quality and relevance on HotPotQA queries than single-shot baselines.
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Context Training with Active Information Seeking
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.
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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.
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Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation
Opinion-aware RAG with LLM opinion extraction and entity-linked graphs improves retrieval diversity by 26-42% over factual baselines on e-commerce forum data.
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MemOS: A Memory OS for AI System
MemOS introduces a unified memory management framework for LLMs using MemCubes to handle and evolve different memory types for improved controllability and evolvability.
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Behavioral Intelligence Platforms: From Event Streams to Autonomous Insight via Probabilistic Journey Graphs, Behavioral Knowledge Extraction, and Grounded Language Generation
BIP turns event streams into autonomous insights by modeling journeys as absorbing Markov chains, extracting facts via knowledge graphs, and generating narratives constrained to verified data.
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The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence
DGMM is proposed as an explicit graph-structured memory architecture for AI that enables persistent episodic memory, cue-based recall, and context-dependent interpretation without retraining.