One Hypothesis Is Not Enough: Abductive Reasoning with Agentic Hypothesis Refinement over Knowledge Graphs

תקציר מקורי באנגליתarXiv:2605.31370v2 Announce Type: replace Abstract: Abductive reasoning over knowledge graphs (KGs) seeks a first-order logic hypothesis whose answer set explains a given set of observed entities. Since many hypotheses can explain the same observations, controllable hypothesis generators condition generation on entities, relations, or logical patterns, but they treat generation as a single step. A generated hypothesis may be well-formed and satisfy the given conditions yet still fail to explain the observations, and the model has no mechanism to detect or correct this mismatch. Closing the abductive cycle requires revising a discrete, structured hypothesis, which large language models cannot do reliably, even though they iterate readily in natural language. We propose HypoAgent, an agentic