Generative recommenders decode items from a user's interaction history, but offer no way for users to correct a recommendation that misses their current intent. Adding conversation is natural since items and words share same output space, yet training the model to converse may overwrite the history-to-item mapping it r...
Yuliia Den, Zhi Rui Tam, Yung-Yu Shih et al.· 0 citations
Model merging offers a promising solution for combining multiple fine-tuned checkpoints into a single model through parameter arithmetic. However, finding optimal merging coefficients requires an extensive search that becomes prohibitively expensive as models scale in both size and number, due to high memory requiremen...
Shih-Cheng Huang, Zhi Rui Tam, Chieh-Yen Lin et al.· 0 citations
Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VIEScore2, a unified evaluator for image generation and editing tasks with optional conditioning images. VIEScore2 represents an image as an N x N grid and jointly predic...
Xian-Da Du, Max W.F. Ku, Weiming Ren et al.· 0 citations
A reinforcement learning framework that jointly trains tool creation and tool use inside a single policy, with three separate reward axes that catch schema, code, and outcome failures independently, so each failure mode contributes its own gradient.
Zhi Rui Tam, Chieh-Yen Lin, Yun-Nung Chen et al.· 2 citations
This work revisits incremental GR as an in-context retrieval problem, where newly added documents are supplied as inference-time document-docid evidence and proposes ICICLE, an in-context indexing framework that performs source-aware docid generation over both parametric memory and context-provided document-docid pairs...
Yuliia Den, Yung-Yu Shih, Zhi Rui Tam et al.· arXiv.org· 0 citations
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