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Received — 2 April 2026 ⏭

Gut-Brain Axis Dysregulation in Inflammatory Bowel Disease: Implications for Coagulation Abnormalities and Extraintestinal Manifestations

✇(Multiomics OR Omics) AND (Pancreatic)
By: Peng Lu · Maolin Liu · Lei Zhang · Jing-Jing Fan · Guoda Han · Baozhou Hou · Yu Meng · Liang Wang · Yan Sun
31 March 2026 at 18:00

Int J Gen Med. 2026 Mar 24;19:590621. doi: 10.2147/IJGM.S590621. eCollection 2026.

ABSTRACT

Inflammatory bowel disease (IBD) involves chronic intestinal inflammation driven by gut-brain axis imbalance, fostering complications through an "inflammation-neuro-coagulation" triad. Current staging systems inadequately capture the dynamics of this multidimensional network. Therefore, integrated multi-omics analyses-including metagenomics, metabolomics, and single-cell transcriptomics-are essential to construct dynamic models that monitor coagulation, microbiome, and metabolism for precise assessment of disease activity and thrombotic or bleeding risks. Interventions targeting gut-brain axis nodes, such as eliminating tissue factor-positive (TF⁺) T cells or modulating vagal activity, show potential to disrupt the inflammation-coagulation cycle, although rigorous randomized trials are still needed. Artificial intelligence (AI)-assisted systems that integrate real-time biomarker monitoring with multi-omics predictions represent a novel paradigm for managing IBD-related coagulation dysfunction. Key challenges include elucidating gut-brain-liver axis regulation of coagulation and characterizing platelet functional heterogeneity. Future efforts must prioritize ethically compliant multi-omics platforms and racially stratified risk models to advance personalized coagulation management in IBD.

PMID:41913906 | PMC:PMC13033200 | DOI:10.2147/IJGM.S590621

Received — 26 March 2026 ⏭

A multiomics Mendelian randomization study on PANoptosis-related genes and gastric cancer risk

✇Omics in Gastric
By: Yunliang Wang · Yujian Xia · Lin Weng · Jin Wang · Wenzi Meng · Jiahui Zhu · Xin Zhao · Xinguo Zhu
17 March 2026 at 18:00

J Int Med Res. 2026 Mar;54(3):3000605261430163. doi: 10.1177/03000605261430163. Epub 2026 Mar 16.

ABSTRACT

ObjectiveTo explore the potential involvement of PANoptosis-related genes in gastric cancer susceptibility through multiomics analyses.MethodsSummary-data-based Mendelian randomization was performed by integrating blood-derived methylation, gene expression, and protein quantitative trait loci data with genome-wide association study results. The findings were further evaluated in The Cancer Genome Atlas cohort, followed by protein-protein interaction analysis, drug prediction, and molecular docking.ResultsSummary-data-based Mendelian randomization and colocalization analyses identified several traits suggestively associated with gastric cancer risk. Genetically predicted higher expression of apoptosis and caspase activation inhibitor (AVEN) and hepatocyte growth factor (HGF) as well as higher HGF protein levels were associated with increased risk, whereas higher levels of protein phosphatase 2 regulatory subunit B beta (PPP2R2B) appeared to be protective. Multiomics integration suggested epigenetic regulation of HGF and PPP2R2B. The Cancer Genome Atlas analysis corroborated the dysregulation of these candidates, with high AVEN expression associated with poorer survival. Protein-protein interaction and drug prediction analyses highlighted functional networks and potential therapeutics, supported by molecular docking demonstrating strong HGF-binding affinities. However, these associations did not reach statistical significance in the independent validation cohort, possibly due to limited statistical power.ConclusionsThis study identified AVEN, HGF, and PPP2R2B as potential candidate genes for gastric cancer. These findings require further validation in larger cohorts.

PMID:41840829 | DOI:10.1177/03000605261430163

Received — 17 March 2026 ⏭

Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yichen Zhang · Da Peng · Zonghao Guo · Zijian Zhang · Xuesong Yang · Tong Sun · Shichu Sun · Yidan Zhang · Yanghao Li · Haiyan Zhao · Wang Xu · Qi Shi · Yangang Sun · Chi Chen · Shuo Wang · Yukun Yan · Xu Han · Qiang Ma · Wei Ke · Liang Wang · Zhiyuan Liu · Maosong Sun
16 March 2026 at 12:00
arXiv:2603.12793v1 Announce Type: cross Abstract: A recent cutting-edge topic in multimodal modeling is to unify visual comprehension and generation within a single model. However, the two tasks demand mismatched decoding regimes and visual representations, making it non-trivial to jointly optimize within a shared feature space. In this work, we present Cheers, a unified multimodal model that decouples patch-level details from semantic representations, thereby stabilizing semantics for multimodal understanding and improving fidelity for image generation via gated detail residuals. Cheers includes three key components: (i) a unified vision tokenizer that encodes and compresses image latent states into semantic tokens for efficient LLM conditioning, (ii) an LLM-based Transformer that unifies autoregressive decoding for text generation and diffusion decoding for image generation, and (iii) a cascaded flow matching head that decodes visual semantics first and then injects semantically gated detail residuals from the vision tokenizer to refine high-frequency content. Experiments on popular benchmarks demonstrate that Cheers matches or surpasses advanced UMMs in both visual understanding and generation. Cheers also achieves 4x token compression, enabling more efficient high-resolution image encoding and generation. Notably, Cheers outperforms the Tar-1.5B on the popular benchmarks GenEval and MMBench, while requiring only 20% of the training cost, indicating effective and efficient (i.e., 4x token compression) unified multimodal modeling. We will release all code and data for future research.
Received — 5 March 2026 ⏭

LifeBench: A Benchmark for Long-Horizon Multi-Source Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zihao Cheng · Weixin Wang · Yu Zhao · Ziyang Ren · Jiaxuan Chen · Ruiyang Xu · Shuai Huang · Yang Chen · Guowei Li · Mengshi Wang · Yi Xie · Ren Zhu · Zeren Jiang · Keda Lu · Yihong Li · Xiaoliang Wang · Liwei Liu · Cam-Tu Nguyen
5 March 2026 at 13:00
arXiv:2603.03781v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized agents capable of accumulating knowledge, reasoning over user experiences, and adapting across time. However, existing memory benchmarks primarily target declarative memory, specifically semantic and episodic types, where all information is explicitly presented in dialogues. In contrast, real-world actions are also governed by non-declarative memory, including habitual and procedural types, and need to be inferred from diverse digital traces. To bridge this gap, we introduce Lifebench, which features densely connected, long-horizon event simulation. It pushes AI agents beyond simple recall, requiring the integration of declarative and non-declarative memory reasoning across diverse and temporally extended contexts. Building such a benchmark presents two key challenges: ensuring data quality and scalability. We maintain data quality by employing real-world priors, including anonymized social surveys, map APIs, and holiday-integrated calendars, thus enforcing fidelity, diversity and behavioral rationality within the dataset. Towards scalability, we draw inspiration from cognitive science and structure events according to their partonomic hierarchy; enabling efficient parallel generation while maintaining global coherence. Performance results show that top-tier, state-of-the-art memory systems reach just 55.2\% accuracy, highlighting the inherent difficulty of long-horizon retrieval and multi-source integration within our proposed benchmark. The dataset and data synthesis code are available at https://github.com/1754955896/LifeBench.
Received — 25 February 2026 ⏭

Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yousef Emami · Hao Zhou · Radha Reddy · Atefeh Hajijamali Arani · Biliang Wang · Kai Li · Luis Almeida · Zhu Han
24 February 2026 at 13:00
arXiv:2602.19534v1 Announce Type: cross Abstract: Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.
Received — 17 February 2026 ⏭

ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tao Yu · Haopeng Jin · Hao Wang · Shenghua Chai · Yujia Yang · Junhao Gong · Jiaming Guo · Minghui Zhang · Xinlong Chen · Zhenghao Zhang · Yuxuan Zhou · Yufei Xiong · Shanbin Zhang · Jiabing Yang · Hongzhu Yi · Xinming Wang · Cheng Zhong · Xiao Ma · Zhang Zhang · Yan Huang · Liang Wang
17 February 2026 at 13:00
arXiv:2601.23232v3 Announce Type: replace-cross Abstract: In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open-domain video shot retrieval, which involves richer temporal structure and more complex semantics, still lacks systematic benchmarks and analysis. To fill this gap, we introduce ShotFinder, a benchmark that formalizes editing requirements as keyframe-oriented shot descriptions and introduces five types of controllable single-factor constraints: Temporal order, Color, Visual style, Audio, and Resolution. We curate 1,210 high-quality samples from YouTube across 20 thematic categories, using large models for generation with human verification. Based on the benchmark, we propose ShotFinder, a text-driven three-stage retrieval and localization pipeline: (1) query expansion via video imagination, (2) candidate video retrieval with a search engine, and (3) description-guided temporal localization. Experiments on multiple closed-source and open-source models reveal a significant gap to human performance, with clear imbalance across constraints: temporal localization is relatively tractable, while color and visual style remain major challenges. These results reveal that open-domain video shot retrieval is still a critical capability that multimodal large models have yet to overcome.
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