Contact-rich manipulation remains challenging because successful control depends on physical interaction cues that are often weakly observable from vision alone. Recent tactile world action models jointly model future visual observations and tactile signals to guide action generation, but how such futures should be structured for effective use by the action expert remains underexplored. Directly studying this question with learned world action models is difficult because end-to-end behavior entangles physically invalid visual futures, unreliable predictions, inaccurate or cross-modally inconsistent tactile forecasts, and an unreadable future-to-action interface. To make this interface independently studyable, we introduce Oracle Visuo-Tactile Foresight (OVTF), a controlled framework that supplies paired RGB and tactile futures from successful trajectories verified in simulation. By fixing the future provider, OVTF isolates the interface and asks a cleaner question: if the future is successful and physically executable, what representation allows the action expert to absorb its benefit? Within OVTF, we propose Asymmetric Phase-Local Future Memory (AFM), in which visual memory reads future vision, each tactile memory jointly attends to its own tactile stream and phase-aligned future vision, and cross-tactile access is blocked. We compare AFM with Modality-Isolated Future Memory (IFM), which removes visual-to-tactile access and processes each future modality independently. Across seven tasks on the UniVTAC simulation benchmark, AFM achieves 32.0% average success, compared with 23.7% for IFM and 14.9% for UniVTAC-ACT. This controlled comparison shows that selective phase-aligned visual-tactile routing provides a more actionable future-to-action bridge than complete modality isolation.
Zihang Yao, Chaoyue Ding, Yingying Yu Brigham Young University et al.· 0 citations
Cross-domain sequential recommendation (CDSR) improves target-domain prediction by leveraging multi-domain interaction histories. Most CDSR methods rely on shared entities or co-occurrence signals, which become unreliable when overlap is limited, and atomic ID representations further generalize poorly to long-tail or unseen items as cross-domain distribution shifts exacerbate this problem. Recent generative CDSR methods enable cross-domain transfer without relying on raw ID alignment by generating content-grounded semantic IDs (SIDs) for cross-domain alignment. However, two challenges remain, including (i) user-item asymmetry, with items discretized for generation whereas user preferences are encoded only implicitly in sequence representations, limiting semantic-level preference control; and (ii) selective transfer, making it difficult to assess source-domain signals against the target preference representation without an explicit discrete user anchor aligned with item IDs, which can lead to unintended transfer of irrelevant signals. This work presents UniGCRec, which constructs a cross-domain user profile from multi-domain histories and quantizes both users and items into CSC-IDs that integrate semantic and collaborative signals. This symmetric quantization places user and item representations in the same discrete CSC-ID space, effectively mitigating user-item asymmetry and enabling preference-aware selective transfer under low-overlap settings. The generator is conditioned on a user CSC-ID prefix and the target domain item CSC-ID history for next-item generation, with trie-constrained decoding ensuring target domain validity. Experiments on public multi-domain benchmarks show consistent gains over strong baselines, with particularly strong gains on several target domains.
Chaoyue Ding, Jiahao Liu, Dongsheng Li et al.· Proceedings of the 32nd ACM...· 0 citations
