T cell receptor (TCR) recognition prediction and receptor generation are traditionally modelled separately, leaving vast TCR sequence collections disconnected from smaller TCR–peptide–MHC datasets. Here we present OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records. Sequence-type tokens and complementary component orders enable joint learning from individual TCR chains and partial or complete TCR–pMHC associations. On unseen epitopes, OmniTCR achieved AUPRCs of 0.7009 for peptide– TCRβ recognition and 0.8235 for TCR–pMHC interaction prediction, exceeding the strongest evaluated comparators by 0.3396 and 0.3451, respectively. It distinguishes cancer from healthy repertoires across 11 independent pan-cancer cohorts (mean AUROC, 0.9436). The model achieved the highest sequence recovery on internal and external generation benchmarks. Structural modelling supported the plausibility of selected pMHC-conditioned CDR3β candidates. OmniTCR bridges heterogeneous immune sequence data, providing a foundation for computational immunology and receptor design.
Fei-Ran Zeng, Duanyu Feng, Dan-Dan Song et al.· bioRxiv· 0 citations
DREAMS is proposed, a novel tree-structured context modeling framework that explicitly captures user preference evolution throughout multi-turn interactions and introduces two specialized node types to support the two fundamental objectives of CRSs: preference elicitation and preference exploitation.
Jin-Cheng Zhang, Chen Huang, Wen-Qiang Lei et al.· 0 citations
Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constraints required for valid CAD construction, a challenge that has been largely overlooked by existing methods. In this paper, we argue that missing spatial constraints should be inferred with respect to the underlying construction structure and informed by reusable design experience. Based on this insight, we propose ExpConCAD, an experience-enhanced framework for implicit spatial constraint completion. ExpConCAD first recovers the intended construction structure and constraint scopes, then retrieves relevant constraint-completion experience for similar scopes to complete the missing spatial constraints, and finally generates executable CadQuery programs. Extensive experiments demonstrate the effectiveness of ExpConCAD and provide insights into the role of construction structure understanding and experience memory in spatial constraint completion. Our code is available at: https://github.com/Hotjiashell/ExpConCAD.
Jingyao Liu, Jin Tang, Chen Huang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
