title: In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR
summary: The pursuit of automated scientific discovery has fueled progress from
  symbolic logic to modern AI, forging new frontiers in reasoning and pattern
  recognition. Transformers function as potential systems, where every possible
  relationship remains latent potentiality until tasks impose constraints, akin
  to measurement. Yet, refining their sampling requires more than probabilistic
  selection: solutions must conform to specific structures or rules, ensuring
  consistency and the invocation of general principles. We present
  Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for
  Exploratory Optimization of Reasoning), a framework that combines graph
  reasoning with symbolic abstraction to dynamically expand domain knowledge.
  Inspired by reinforcement learning, Graph-PReFLexOR defines reasoning as a
  structured mapping, where tasks yield knowledge graphs, abstract patterns, and
  ultimately, final answers. Inspired by category theory, it encodes concepts as
  nodes and their relationships as edges, supporting hierarchical inference and
  adaptive learning through isomorphic representations. Demonstrations include
  hypothesis generation, materials design, and creative reasoning, such as
  discovering relationships between mythological concepts like 'thin places' with
  materials science. We propose a 'knowledge garden growth' strategy that
  integrates insights across domains, promoting interdisciplinary connections.
  Results with a 3-billion-parameter Graph-PReFLexOR model show superior
  reasoning depth and adaptability, underscoring the potential for transparent,
  multidisciplinary AI-driven discovery. It lays the groundwork for general
  autonomous reasoning solutions.
paper_authors._id: 6787b437bbb0287a08f3b32d
paper_authors.name: Markus J. Buehler
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-15T17:16:02.561Z
paper_authors._id: 623ce1c6b66fedf374859fe7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/623ce1c6b66fedf374859fe7/lhbMLg6BxLCb9DD4rgjfx.jpeg
paper_authors.fullname: Markus Buehler
paper_authors.type: user
paper_authors.user: mjbuehler
paper_title: In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR
paper_summary: The pursuit of automated scientific discovery has fueled progress from
  symbolic logic to modern AI, forging new frontiers in reasoning and pattern
  recognition. Transformers function as potential systems, where every possible
  relationship remains latent potentiality until tasks impose constraints, akin
  to measurement. Yet, refining their sampling requires more than probabilistic
  selection: solutions must conform to specific structures or rules, ensuring
  consistency and the invocation of general principles. We present
  Graph-PReFLexOR (Graph-based Preference-based Recursive Language Modeling for
  Exploratory Optimization of Reasoning), a framework that combines graph
  reasoning with symbolic abstraction to dynamically expand domain knowledge.
  Inspired by reinforcement learning, Graph-PReFLexOR defines reasoning as a
  structured mapping, where tasks yield knowledge graphs, abstract patterns, and
  ultimately, final answers. Inspired by category theory, it encodes concepts as
  nodes and their relationships as edges, supporting hierarchical inference and
  adaptive learning through isomorphic representations. Demonstrations include
  hypothesis generation, materials design, and creative reasoning, such as
  discovering relationships between mythological concepts like 'thin places' with
  materials science. We propose a 'knowledge garden growth' strategy that
  integrates insights across domains, promoting interdisciplinary connections.
  Results with a 3-billion-parameter Graph-PReFLexOR model show superior
  reasoning depth and adaptability, underscoring the potential for transparent,
  multidisciplinary AI-driven discovery. It lays the groundwork for general
  autonomous reasoning solutions.
paper_ai_summary: Graph-PReFLexOR integrates graph reasoning and symbolic abstraction to dynamically expand domain knowledge, enhancing reasoning depth and adaptability across interdisciplinary tasks.
paper_ai_keywords: Transformers graph reasoning symbolic abstraction knowledge graphs abstract patterns reinforcement learning category theory nodes edges hierarchical inference isomorphic representations hypothesis generation materials design creative reasoning knowledge garden growth autonomous reasoning