Beyond Relevance: State-Conditioned Consequence Modeling for Adaptive Retrieval-Augmented Generation

The value of retrieved evidence is not fixed: the same candidate can have different consequences depending on what has already been retrieved. Yet sequential retrieval-augmented generation (RAG) commonly evaluates the next candidate without explicitly modeling this dependence on the accumulated retrieval state. In a canonical singleton-generation audit on HotpotQA and 2WikiMultiHopQA, 33.9% of repeated query–candidate groups change consequence category across retrieval states, confirming that this phenomenon persists after controlling for batch-composition effects. Motivated by this observation, we formulate next-evidence selection as state-conditioned consequence prediction and introduce the State-Consequence Model (SCM). SCM learns from offline counterfactual supervision produced by a frozen generator to predict the benefit and harm of adding each remaining candidate, while an independent answer verifier determines when retrieval should stop. A strictly matched state-agnostic ablation degrades both consequence prediction and held-out answer quality without reducing retrieval cost, directly showing that the accumulated state provides useful information for retrieval decisions. Across adaptive RAG evaluations, SCM achieves strong answer quality with efficient retrieval and remains competitive under stricter relevance-matched controls. Together, these results establish retrieval state as a central variable in predicting the consequence of the next evidence addition and motivate consequence-aware evidence selection for sequential RAG.