Multi-Hop Knowledge Editing via Intermediate-Entity Representation Alignment

Even when a single-hop edit succeeds, related multi-hop predictions may remain unchanged. We investigate whether access to the updated intermediate-entity representation can help the edited knowledge participate in downstream computation. Through layer-wise hidden-state interventions, we find that introducing the desired-object representation at the subject position can increase the target probability and redirect the final predictive state toward direct next-hop recall. This intervention identifies intermediate-entity reuse as an intervention-sensitive representational factor associated with multi-hop propagation. Based on this observation, we propose Intermediate-Entity Representation Editing (IERE), which converts the temporary intervention into persistent parameter updates. IERE schedules interacting edits from downstream to upstream, recursively re-extracts the latest desired-object representations, aligns subject representations accordingly, and finally applies batched factual editing. It uses only single-hop edit requests and requires no edit-specific multi-hop questions, next-hop relations, reasoning paths, or target answers. Experiments on Llama-3-8B-Instruct and GPT-J-6B show that IERE preserves strong direct editing, achieves the highest forward multi-hop accuracy among single-hop-only methods in all six settings, and remains competitive with methods that use auxiliary multi-hop information. Relation-specific evaluation further shows subject-level locality comparable to MEMIT.