Large reasoning models have recently demonstrated emergent “aha moments” that enable reflective reasoning. However, a non-trivial portion of reflective steps are “low-utility,” where reasoning errors are not corrected, or even correct reasoning trajectories are shifted toward incorrect ones. To address low-utility reflective continuation, this paper proposes RUAR, a utility-guided advantage rescaling method for reflective reasoning that estimates step-level utility from changes in correct-answer probability, without human process labels or a separately learned process reward model. RUAR augments outcome-supervised RL by rescaling the token-level advantages within each reflective reasoning step according to the estimated utility of the reflective continuation, rather than applying a uniform trajectory-level advantage across the reasoning trace. The utility is estimated through answer-forcing utility estimation, which measures how the model’s correct-answer probability changes before and after the reflective step by forcing answer generation from both reasoning states using answer-forcing prompts. Furthermore, RUAR introduces an overthinking-mitigating rescaling mechanism, where utility-guided rescaling is no longer applied from the earliest answer-ready reflection step onward. Experiments demonstrate that RUAR improves the accuracy–efficiency trade-off, substantially reducing generated token usage while often preserving or improving final-answer accuracy.

Existing in-context editing (ICE) methods for multi-hop knowledge editing commonly suffer from paraphrase sensitivity, which refers to the phenomenon where these methods are not sufficiently robust to paraphrased multi-hop questions. To improve retrieval accuracy and knowledge routing to address paraphrase sensitivity, this paper proposes a novel entity-aware inference-time knowledge routing method, referred to as EAIR, which consists of four major steps: 1) Entity-referential query decomposition, which decomposes the original question into multiple entity-referential sub-question instructions; 2) Entity-aware retrieval, which leverages the previously reference-resolved topic entity in the retrieval step; 3) Evidence-conditioned contrastive decoding, which discourages the model from relying on its parametric knowledge and steers the model toward following retrieved edits; 4) Reflection-based knowledge routing, which additionally filters decoding results using refusal-style reflection to mitigate the risk introduced by contrastive decoding. Experimental results across the MQuAKE benchmark family and model scales show that EAIR achieves the highest strict case accuracy in 11 of 12 settings, substantially reducing paraphrase sensitivity.