RNA’s conformational flexibility enables diverse biological functions but makes structure-guided design difficult. Although precise control of 3D structure is essential, existing methods often overlook the molecular surface, which critically shapes intermolecular interactions and functional outcomes. To address this, we propose gRFold, a multimodal framework that incorporates molecular surface information as a core design constraint. gRFold employs two geometric encoders: a Frame-Averaged Geometric Surface Encoder (FA-GSE) to learn E(3)-invariant surface features, and an Equivariant Backbone Graph Perceptron (EBGP) to capture backbone topology. The structural representations are integrated via hierarchical cross-attention to guide autoregressive generation; at inference, candidates are re-ranked by agreement between their predicted folds and the target base-pairing pattern. Across three RNA inverse-folding benchmarks covering RNA-protein complexes, monomeric RNAs and independently curated RNA-Puzzles targets, gRFold achieves strong sequence recovery and structural fidelity under both secondary-structure and 3D refolding evaluations, and ablations show that the surface and backbone branches contribute complementary gains. These results highlight the potential of surface-aware geometric modeling for structure-guided RNA design.

Existing works on dialogue summarization often follow the common practice in document summarization and view the dialogue, which comprises utterances of different speakers, as a single utterance stream ordered by time. However, this single-stream approach without specific attention to the speaker-centered points has limitations in fully understanding the dialogue. To better capture the dialogue information, we propose a 2D view of dialogue based on a time-speaker perspective, where the time and speaker streams of dialogue can be obtained as strengthened input. Based on this 2D view, we present an effective two-stream model called ATM to combine the two streams. Extensive experiments on various summarization datasets demonstrate that ATM significantly surpasses other models regarding diverse metrics and beats the state-of-the-art models on the QMSum dataset in ROUGE scores. Besides, ATM achieves great improvements in summary faithfulness and human evaluation. Moreover, results on machine reading comprehension datasets show the generalization ability of the proposed methods and shed light on other dialogue-based tasks. Our code will be publicly available online.