Graph-PRefLexOR fine-tunes graph-native models with GRPO to organize reasoning into phases, yielding 40-65% gains in traceable hypothesis generation and 2-3x semantic diversity on 100 materials science questions.
, month = apr, year =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
verdicts
UNVERDICTED 4representative citing papers
A survey of RLM use in 28 disciplines reveals uneven adoption and introduces a maturity assessment framework showing larger gaps when limited to public resources.
A category-theoretic model frames scientific discovery as verified regime transitions via left Kan extensions that preserve and compare artifacts across schema changes in agentic AI.
A review paper that surveys AI uses across the food innovation pipeline for sustainable proteins and identifies four strategic priorities for the emerging field.
citing papers explorer
-
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Graph-PRefLexOR fine-tunes graph-native models with GRPO to organize reasoning into phases, yielding 40-65% gains in traceable hypothesis generation and 2-3x semantic diversity on 100 materials science questions.
-
Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
A survey of RLM use in 28 disciplines reveals uneven adoption and introduces a maturity assessment framework showing larger gaps when limited to public resources.
-
Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence
A category-theoretic model frames scientific discovery as verified regime transitions via left Kan extensions that preserve and compare artifacts across schema changes in agentic AI.
-
Artificial Intelligence for Food Innovation
A review paper that surveys AI uses across the food innovation pipeline for sustainable proteins and identifies four strategic priorities for the emerging field.