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Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. While this token-generation paradigm is expected to surpass traditional transductive methods, potentially generating new items directly based on semantics, we empirically show that GR models predominantly generate items seen during training and struggle to recommend unseen items. In this paper, we propose SpecGR, a plug-and-play framework that enables GR models to recommend new items in an inductive setting. SpecGR uses a drafter model with inductive capability to propose candidate items, which may include both existing items and new items. The GR model then acts as a verifier, accepting or rejecting candidates while retaining its strong ranking capabilities. We further introduce the guided re-drafting technique to make the proposed candidates more aligned with the outputs of generative recommendation models, improving verification efficiency. We consider two variants for drafting: (1) using an auxiliary drafter model for better flexibility, or (2) leveraging the GR model’s own encoder for parameterefficient self-drafting. Extensive experiments on three realworld datasets demonstrate that SpecGR exhibits both strong inductive recommendation ability and the best overall performance among the compared methods. 

Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. While this token-generation paradigm is expected to surpass traditional transductive methods, potentially generating new items directly based on semantics, we empirically show that GR models predominantly generate items seen during training and struggle to recommend unseen items. In this paper, we propose SpecGR, a plug-and-play framework that enables GR models to recommend new items in an inductive setting. SpecGR uses a drafter model with inductive capability to propose candidate items, which may include both existing items and new items. The GR model then acts as a verifier, accepting or rejecting candidates while retaining its strong ranking capabilities. We further introduce the guided re-drafting technique to make the proposed candidates more aligned with the outputs of generative recommendation models, improving verification efficiency. We consider two variants for drafting: (1) using an auxiliary drafter model for better flexibility, or (2) leveraging the GR model’s own encoder for parameterefficient self-drafting. Extensive experiments on three realworld datasets demonstrate that SpecGR exhibits both strong inductive recommendation ability and the best overall performance among the compared methods. 

The scientific literature sometimes considers music an abstract stimulus, devoid of explicit meaning, and at other times considers it a universal language. Here, individuals in three geographically distinct locations spanning two cultures performed a highly unconstrained task: they provided free-response descriptions of stories they imagined while listening to instrumental music. Tools from natural language processing revealed that listeners provide highly similar stories to the same musical excerpts when they share an underlying culture, but when they do not, the generated stories show limited overlap. These results paint a more complex picture of music’s power: music can generate remarkably similar stories in listeners’ minds, but the degree to which these imagined narratives are shared depends on the degree to which culture is shared across listeners. Thus, music is neither an abstract stimulus nor a universal language but has semantic affordances shaped by culture, requiring more sustained attention from psychology. 

Although people across multiple cultures have been shown to experience music narratively, it has proven difficult to disentangle whether narrative dimensions of music derive from learned extramusical associations within a culture or from less experience-dependent elements of the music, such as musical contrast. Toward this end, two experiments investigated factors contributing to listeners’ narrative engagement with music, comparing the narrative experiences of Western and Chinese instrumental music for listeners in two suburban locations in the United States with those of listeners living in a remote rural village in China with different patterns of musical exposure. Supporting an enculturation perspective where learned extramusical associations (i.e., Topicality) play an important role in narrative perceptions of music, results from the first experiment show that for Western listeners, greater Topicality, rather than greater Contrast, increases narrative engagement, as long as listeners have sufficient exposure to its patterns of use within a culture. Strengthening this interpretation, results for the second experiment, which directly manipulated Topicality and Contrast, show that reducing an excerpt’s Topicality, but not its Contrast reduces listeners’ narrative engagement.