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Transformer-Based Multitask Framework Integrating Habitat and Deep Learning for Predicting Early Disease Control and Survival in Immunotherapy-Treated Hepatocellular Carcinoma

By: Xiaona Fu · Yusheng Guo · Bingxin Gong · Jie Lou · Ning Wang · Xuejun Chen · Hongtao Hu · Rundong Wang · Xiaofang Guo · Shanmei Li · Yangyang Xie · Shichao Long · Mengsi Li · Shubo Pan · Wen Shen · Zhaogang Zhang · Zifang Song · Pengchen Liang · Xiaoyun Liang · Peng Sun · Guilin Zhang · Shanshan Jiang · Jinxiang Zhang · Lian Yang

27 September 2026 at 18:00

Adv Sci (Weinh). 2026 Sep 27:e78005. doi: 10.1002/advs.78005. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS-Net, a transformer-based multitask network integrating CT-derived habitat and 2.5-dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI-treated patients were used for model development. ECOS-Net fused features using multi-head attention and generated early DC probabilities and OS risk scores. ECOS-DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS-OS yielded C-indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C-index: 0.741). Patients with higher ECOS-DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS-OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS-DC probability and lower ECOS-OS risk groups were associated with immune-active tumor microenvironment features. Therefore, ECOS-Net shows potential as a non-invasive imaging-based risk stratification framework for simultaneously predicting early DC and OS in ICI-treated HCC patients.

PMID:42801546 | PMC:PMC13616327 | DOI:10.1002/advs.78005

Coupled Variational Reinforcement Learning for Language Model General Reasoning

cs.AI, q-bio.NC updates on arXiv.org

By: Xueru Wen · Jie Lou · Yanjiang Liu · Hongyu Lin · Ben He · Xianpei Han · Le Sun · Yaojie Lu · Debing Zhang

26 May 2026 at 12:00

arXiv:2512.12576v3 Announce Type: replace-cross Abstract: While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable rewards. Recent verifier-free RL methods address this limitation by utilizing the probabilities that LLMs generate reference answers as reward signals. However, these approaches typically sample reasoning traces conditioned only on the question. This design decouples reasoning-trace sampling from answer information, leading to inefficient exploration and incoherence between traces and final answers. In this paper, we propose \textit{\b{Co}upled \b{V}ariational \b{R}einforcement \b{L}earning} (CoVRL), which bridges variational inference and reinforcement learning by coupling prior and posterior distributions through a hybrid sampling strategy. By constructing and optimizing a composite distribution that integrates these two distributions, CoVRL enables efficient exploration while preserving strong thought-answer coherence. Extensive experiments on mathematical and general reasoning benchmarks show that CoVRL improves performance by 12.4\% over the base model and achieves an additional 2.3\% improvement over state-of-the-art verifier-free RL baselines, providing a principled framework for enhancing the general reasoning capabilities of language models.