Zining Liu, Yunhai Hu, Tianhua Xia, BO Bao, Eric Sather, Vithursan Thangarasa, Sai Qian Zhang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Survival prediction in cancer diagnosis is a critical research task. Current methods often employ the multimodal feature fusion of pathological images and genomics data within a weakly-supervised learning paradigm. However, these approaches fail to efficiently learn the intrinsic features of large amount of unlabeled WSIs and neglect the strong associations between genomics data and pathological images, resulting in reduced prognostic accuracy. To address these challenges, we propose a novel Genomics-Aware Multimodal Self-Supervised Learning model that designs a multimodal pretext task, improving learning of intra-modal features and inter-modal correlations without additional annotations. Specifically, we randomly mask pathological patch features and fuse unmasked pathology representations with genomics representations via a cross-modal attention module. Then we add mask tokens to the genomicsguided pathology representation and reconstruct the missing parts via a reconstruction decoder. Experimental results on four TCGA datasets demonstrate the superior performance of our method compared to state-of-the-art methods, highlighting its potential for advancing survival prediction. Our code is available at https://github.com/sunkevin101/GMSL.

Speculative decoding (SD) has emerged as a powerful method for accelerating autoregressive generation in large language models (LLMs), yet its integration into vision-language models (VLMs) remains underexplored. We introduce DREAM, a novel speculative decoding framework tailored for VLMs that combines three key innovations: (1) a cross-attention-based mechanism to inject intermediate features from the target model into the draft model for improved alignment, (2) adaptive intermediate feature selection based on attention entropy to guide efficient draft model training, and (3) visual token compression to reduce draft model latency. DREAM enables efficient, accurate, and parallel multimodal decoding with significant throughput improvement. Experiments across a diverse set of recent popular VLMs, including LLaVA, Pixtral, SmolVLM and Gemma3, demonstrate up to 3.6x speedup over conventional decoding and significantly outperform prior SD baselines in both inference throughput and speculative draft acceptance length across a broad range of multimodal benchmarks.

Allocating resources to customers in the customer service is a difficult problem, because designing an optimal strategy to achieve an optimal trade-off between available resources and customers' satisfaction is non-trivial. In this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning (RL) to address this problem. To make it more practical, a demo is provided to show and compare different models, which visualizes all decision process, and in particular, the system shows how the optimal strategy is reached. Besides, our demo system also ships with a variety of models that users can choose based on their needs.