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.
arXiv preprint arXiv:2501.15228 , year=
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Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
RELOOP unifies retrieval across text, tables, and KGs via hierarchical sequences and dual-agent guided iteration, reporting EM/F1 gains over baselines on HotpotQA, HybridQA/TAT-QA, and MetaQA.
RioRAG uses nugget-centric verification with cross-source checks to create dense verifiable rewards for RL-based optimization of long-form RAG, yielding higher factual recall and faithfulness on LongFact and RAGChecker.
Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
citing papers explorer
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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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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
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RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA
RELOOP unifies retrieval across text, tables, and KGs via hierarchical sequences and dual-agent guided iteration, reporting EM/F1 gains over baselines on HotpotQA, HybridQA/TAT-QA, and MetaQA.
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Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation
RioRAG uses nugget-centric verification with cross-source checks to create dense verifiable rewards for RL-based optimization of long-form RAG, yielding higher factual recall and faithfulness on LongFact and RAGChecker.
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Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
Proposes five foundational pillars and architectural patterns for building robust GenAI-native systems by combining AI with software engineering principles.
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