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QuantMind is an open-source framework for intelligent knowledge extraction and retrieval in quantitative finance. It can automatically fetch unstructured content like papers and news, build a queryable structured knowledge base, and support natural language retrieval.
This paper constructs the first large-scale citation graph from 100.7 million Ukrainian court decisions, extracting over 500 million citation links. It demonstrates that the citation structure can automatically recover legal domain boundaries and predict legislative importance with near-perfect accuracy, and releases the pipeline and data as open resources.
This paper proposes the Experience Compression Spectrum, a unifying framework that integrates agent memory, skill discovery, and rule-based systems along a single axis of increasing compression (5-20× for episodic memory, 50-500× for procedural skills, 1000×+ for declarative rules). The work identifies a critical gap—the 'missing diagonal'—showing that existing systems operate at fixed compression levels without adaptive cross-level support, and articulates design principles for scalable, full-spectrum agent learning systems.