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README.md

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 # In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR 
  
-In-situ graph reasoning and knowledge expansion are important elements in the advancement of automated systems for scientific discovery. 
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-This paper introduces Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning), a generative framework designed to perform dynamic graph reasoning and iteratively expand domain knowledge. Graph-PReFLexOR is trained inspired by reinforcement learning methods, and leverages construct detailed knowledge graphs and abstract representations, enabling hierarchical reasoning and adaptive learning, to achieve in-situ graph generation, symbolic representation of arguments and logical deduction, to ultimately formulate a response to tasks. Critically, Graph-PReFLexOR formalizes reasoning as a structured mapping. Inspired by category theory modeling that emphasizes how objects relate, rather than their internal detail, the graph encodes concepts as nodes and relationships as directed edges. 
+In-situ graph reasoning and knowledge expansion are important elements in the advancement of automated systems for scientific discovery. This work introduces Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning), a generative framework designed to perform dynamic graph reasoning and iteratively expand domain knowledge. Graph-PReFLexOR is trained inspired by reinforcement learning methods, and leverages construct detailed knowledge graphs and abstract representations, enabling hierarchical reasoning and adaptive learning, to achieve in-situ graph generation, symbolic representation of arguments and logical deduction, to ultimately formulate a response to tasks. Critically, Graph-PReFLexOR formalizes reasoning as a structured mapping. Inspired by category theory modeling that emphasizes how objects relate, rather than their internal detail, the graph encodes concepts as nodes and relationships as directed edges. 
 
 By combining in -situ symbolic and contextual inference, the framework generates its own structured representation on the fly and thereby captures complex interdependencies and translates them into domain-specific interpretable insights. Demonstrations include generating and expanding scientific hypotheses and fabricating dynamic transformations in graph topologies, with applications in materials science and engineering, and multi-disciplinary relationship discovery.