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Received — 15 September 2026 ⏭

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gengxian Zhou · Jian Xu · Zichen Tang · Shiming Xiang · Haihong E · Cheng-Lin Liu
14 September 2026 at 12:00
arXiv:2609.12791v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (large-scale knowledge graphs and text corpora) for complex reasoning remains a challenge. Existing methods typically employ reactive, graph-driven exploration strategies, which blindly follow graph topology without adapting to the question context or evolving exploration progress, and lack deep bidirectional synergy between graph and text. To address these limitations, we propose CoG (Cognition on Graph), a cognitive-inspired, training-free framework for adaptive knowledge exploration. Drawing inspiration from human problem-solving, CoG performs a continuous plan-explore-reflect cycle, where it proactively formulates investigation plans, performs dual-source retrieval, and dynamically reflects on progress to adjust strategies. Crucially, it establishes deep bidirectional synergy between structured graph and unstructured text, where entities extracted from text dynamically guide graph exploration to bridge knowledge gaps. Extensive experiments on seven multi-hop QA benchmarks demonstrate that CoG significantly outperforms state-of-the-art methods while achieving superior exploration efficiency. Our code and datasets are available at https://github.com/zhougengxian/CoG.
Received — 10 September 2026 ⏭

EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Muchen Li · Anglin Liu · Xuetian Gao · Ruijian Xu · Jintai Chen
10 September 2026 at 12:00
arXiv:2609.09728v1 Announce Type: cross Abstract: Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by subject variability, class imbalance, and imperfect multimodal context. We present EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification. EEGBind treats EEG as the primary modality and binds synchronized video-context features around an EEG-centric representation. Instead of relying on early or overly strong multimodal fusion, which may perturb the source-sensitive EEG representation, EEGBind uses video context as auxiliary evidence for robust classification. A view-consistent repair stage is further used to improve hidden-set robustness while preserving the learned source-class boundary. On the NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED benchmark, EEGBind achieves 0.8395 on weighted-F1 and outperforms strong competitors. These results support EEG-centric multimodal binding as a practical strategy for source-level IED classification. The open-source code is available at https://github.com/HKUSTGZ-ML4Health-Lab/NeuroMM2026_IED_Detection.

DHCR24<sup>+</sup> tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling

✇Oncogene - Issue - nature.com science feeds
By: Yuanqiao Zhao · Zhuo Xing · Mengmeng Wang · Liangmin Fu · Ruilin Liu · Zexian Ding · Yuan Li · Yinhuai Wang · Bin Yan · Xueqiang Zhu
29 August 2026 at 08:00

Oncogene, Published online: 29 August 2026; doi:10.1038/s41388-026-03967-7

DHCR24+ tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling
Received — 27 May 2026 ⏭

Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chengwei Li · Junlin Liu · Yang Gao
26 May 2026 at 12:00
arXiv:2605.25091v1 Announce Type: new Abstract: As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aerial vehicles (UCAVs) faces significant challenges due to high-dimensional state spaces, discrete action commands, and strongly adversarial dynamic environments. To overcome the limitations of existing multi-agent reinforcement learning (MARL) methods in such settings, namely insufficient exploration efficiency, low sample utilization, and poor policy generalization, we propose Adversarial Curriculum and Evolutionary-enhanced Multi-agent Proximal Policy Optimization (ACE-MAPPO), a hybrid learning framework that integrates evolutionary algorithms with MAPPO. Specifically, a genetic soft update mechanism is introduced to enhance population diversity and mitigate convergence to local optima. An evolutionary-augmented prioritized trajectory replay strategy is further employed to improve the utilization of sparse high-value samples. In addition, an adversarial evolutionary curriculum learning mechanism is designed to enable adaptive training with progressively increasing difficulty. Extensive experimental results demonstrate that the proposed method outperforms MAPPO and other baseline algorithms in terms of training stability, convergence speed, and win rate, validating its effectiveness in multi-aircraft cooperative air combat scenarios.

Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qingyuan Zeng · Pengxiang Cai · Zixin Guan · Ziyang Chen · Anglin Liu · Lang Qin · Xinyao Lai · Jintai Chen
26 May 2026 at 12:00
arXiv:2605.25681v1 Announce Type: cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation because one candidate must satisfy two binding requirements while preserving drug-likeness and synthesizability. Existing dual-target generative methods typically introduce dual-target capability by either retraining the generator or intervening in the diffusion process during sampling. The former can be costly and difficult to stabilize when dual-target supervision is sparse, while the latter may be sensitive to denoising-time target balancing and competing update directions. These limitations motivate a generator-preserving alternative that keeps the pretrained prior intact: can dual-target candidates instead be recovered from the input space of a frozen single-target diffusion model, without modifying its parameters or denoising dynamics? We formulate this task as a constrained multi-objective optimization problem and propose REUSE, a hierarchical evolutionary input-space search framework that combines pair-conditioned exploration with structured multi-stage selection to enforce dual-target affinity, chemical quality, and diversity. Experiments show that, compared with methods that modify the diffusion process, REUSE consistently improves dual-target affinity and balance, achieving a 20.9-percentage-point gain in Dual High Affinity over the strongest prior baseline while maintaining competitive molecular quality.

Self-supervised Hierarchical Visual Reasoning with World Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuanfei Xu · Lin Liu · Wengang Zhou · Mingxiao Feng · Houqiang Li
26 May 2026 at 12:00
arXiv:2605.17537v2 Announce Type: replace Abstract: 3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings. While existing self-supervised visual foresight reasoning approaches often suffer from multi-step error accumulation, many recent studies resort to injecting domain-specific knowledge for more stable guidance. Our key insight is that the photorealistic fidelity of visual reasoning representations is secondary; what truly matters is providing informative, task-relevant signals. To this end, we propose ResDreamer, a hierarchical world model in which each higher-level layer is trained to reconstruct the residuals of the layer below. This design enables progressive abstraction of increasingly sophisticated world dynamics and fosters the emergence of richer latent representations. Drawing inspiration from the "Bitter Lesson", ResDreamer trains its reasoning representations in a purely self-supervised manner. The higher-level residual representations are used to modulate lower-level predictions, allowing the world model to scale effectively with only linearly increasing cross-layer communication costs. Experiments show that ResDreamer achieves state-of-the-art sample efficiency and parameter efficiency. This scalable hierarchical visual foresight reasoning architecture paves the way for more capable online RL agents in open-ended, dynamic environments. The code is accessible at https://github.com/XuYuanFei01/ResDreamer.
Received — 13 April 2026 ⏭

Large Language Model–Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study

✇Journal of Medical Internet Research
By: Siru Liu · Jialin Liu
11 April 2026 at 04:00
Background: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. Objective: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)–based analysis of Reddit discussions. Methods: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. Results: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. Conclusions: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.
Received — 2 April 2026 ⏭

Expressive Power of Implicit Models: Rich Equilibria and Test-Time Scaling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jialin Liu · Lisang Ding · Stanley Osher · Wotao Yin
1 April 2026 at 12:00
arXiv:2510.03638v4 Announce Type: replace-cross Abstract: Implicit models, an emerging model class, compute outputs by iterating a single parameter block to a fixed point. This architecture realizes an infinite-depth, weight-tied network that trains with constant memory, significantly reducing memory needs for the same level of performance compared to explicit models. While it is empirically known that these compact models can often match or even exceed the accuracy of larger explicit networks by allocating more test-time compute, the underlying mechanism remains poorly understood. We study this gap through a nonparametric analysis of expressive power. We provide a strict mathematical characterization, showing that a simple and regular implicit operator can, through iteration, progressively express more complex mappings. We prove that for a broad class of implicit models, this process lets the model's expressive power scale with test-time compute, ultimately matching a much richer function class. The theory is validated across four domains: image reconstruction, scientific computing, operations research, and LLM reasoning, demonstrating that as test-time iterations increase, the complexity of the learned mapping rises, while the solution quality simultaneously improves and stabilizes.

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 ⏭

Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: Umbrella Review

✇Journal of Medical Internet Research
By: Sijia Liu · Xianghong Zhang · Lijun Zhou · Jiashan Zhang · Jialin Liu · Chengqi He
25 March 2026 at 22:45
Background: Stroke is a leading cause of long-term upper limb disability, severely impacting patients’ independence and quality of life. Robot-assisted therapy (RAT) has emerged as a promising, high-intensity rehabilitation alternative. However, conclusions from existing systematic reviews on its efficacy are inconsistent and often lack a holistic framework, limiting their use for guiding personalized clinical decisions. Objective: This study aims to systematically synthesize recent evidence on RAT for upper limb rehabilitation after stroke. Guided by the International Classification of Functioning, Disability and Health framework, it moves beyond singular outcomes to provide a multidimensional evaluation across body function, activity, and participation levels. The review aims to provide stratified guidance for clinical decision-making based on patient- and intervention-specific characteristics, thereby supporting evidence-based practice and informing future research. Methods: This study included systematic reviews and meta-analyses published from January 1, 2019, to December 26, 2025, comparing RAT with conventional therapy for upper limb rehabilitation after stroke. Overall, 6 databases, including PubMed, Web of Science, and Embase, were searched. Two reviewers (XZ and LZ) independently performed study selection, data extraction, and quality assessment using the AMSTAR 2 tool. The synthesis integrated outcome measures and subgroup analyses derived from the included studies. Results: This umbrella review included 21 meta-analyses encompassing 535 randomized controlled trials and 27,598 patients across acute, subacute, and chronic stroke stages. According to AMSTAR 2, 17 reviews were high quality, 3 moderate, and 1 critically low. The synthesis demonstrated that RAT was superior in improving upper limb motor function, but no statistically significant advantages were observed in activities of daily living compared to conventional therapy. Subgroup analyses revealed that treatment effects were influenced by stroke stage, upper limb motor impairment level, and robot type. Conclusions: RAT is an effective intervention for improving upper limb motor function after stroke. However, its benefits are primarily observed at the level of body function, with limited evidence for long-term maintenance. The current evidence is constrained by significant outcome heterogeneity and methodological limitations inherent to umbrella reviews. Future research should validate these findings in broader clinical practice, focus on translating functional gains into sustained improvements in daily activities and participation, and include cost-effectiveness evaluations. Trial Registration: PROSPERO CRD42024497183; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024497183

19-Hydroxybufalin Inhibits Gastric Cancer Cell Proliferation by Modulating Metabolic Reprogramming

✇Omics in Gastric
By: Xiao Liang · Yilin Liu · Xiaoting Ma · Shuang Jiang · Zhenjiang Sun · Xia Sun · Ya Qin · Dan Wu · Simin Wang · Liting Lv · Weidong Zhong · Qiong Wang
17 March 2026 at 18:00

J Proteome Res. 2026 Apr 3;25(4):2014-2023. doi: 10.1021/acs.jproteome.5c00983. Epub 2026 Mar 17.

ABSTRACT

OBJECTIVE: 19-Hydroxybufalin (19-H) is a natural bioactive compound with anticancer potential, but its molecular target and mechanism of action remain unclear. This study aimed to systematically evaluate its antigastric cancer activity and identify potential molecular targets.

METHODS: The antitumor effect of 19-H was evaluated in both in vitro and in vivo models. Multiomics analysis, thermal proteome profiling, molecular docking, and molecular dynamics simulations were employed to elucidate the mechanism of action. Functional assays were further conducted to validate the key target.

RESULTS: 19-H exhibited nanomolar-level inhibitory activity against various gastric cancer cell lines, significantly suppressing tumor growth in subcutaneous xenograft and patient-derived xenograft models. Multiomics analysis revealed that 19-H reshaped metabolic pathways in gastric cancer. TPP screening identified PLPP2 as a potential target with significantly increased thermal stability upon 19-H treatment. Molecular simulations further revealed that 19-H binds stably to the α-helical region of PLPP2.

CONCLUSIONS: 19-H exerts its antigastric cancer effect by targeting PLPP2 and remodeling the metabolic network. PLPP2 may represent a novel therapeutic target for gastric cancer.

PMID:41842934 | DOI:10.1021/acs.jproteome.5c00983

Received — 17 March 2026 ⏭

Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiqun Zhang · Zexi Tan · Xiaopeng Luo · Yunlin Liu
16 March 2026 at 12:00
arXiv:2603.12847v1 Announce Type: cross Abstract: Most real-world IoT data analysis tasks, such as clustering and anomaly event detection, are unsupervised and highly susceptible to the presence of outliers. In addition to sporadic scattered outliers caused by factors such as faulty sensor readings, IoT systems often exhibit clustered outliers. These occur when multiple devices or nodes produce similar anomalous measurements, for instance, owing to localized interference, emerging security threats, or regional false alarms, forming micro-clusters. These clustered outliers can be easily mistaken for normal behavior because of their relatively high local density, thereby obscuring the detection of both scattered and contextual anomalies. To address this, we propose a novel outlier detection paradigm that leverages the natural neighboring relationships using graph structures. This facilitates multi-perspective anomaly evaluation by incorporating reference sets at both local and global scales derived from the graph. Our approach enables the effective recognition of scattered outliers without interference from clustered anomalies, whereas the graph structure simultaneously helps reflect and isolate clustered outlier groups. Extensive experiments, including comparative performance analysis, ablation studies, validation on downstream clustering tasks, and evaluation of hyperparameter sensitivity, demonstrate the efficacy of the proposed method. The source code is available at https://github.com/gordonlok/DROD.

OpenVision 3: A Family of Unified Visual Encoder for Both Understanding and Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Letian Zhang · Sucheng Ren · Yanqing Liu · Xianhang Li · Zeyu Wang · Yuyin Zhou · Huaxiu Yao · Zeyu Zheng · Weili Nie · Guilin Liu · Zhiding Yu · Cihang Xie
16 March 2026 at 12:00
arXiv:2601.15369v2 Announce Type: replace-cross Abstract: This paper presents a family of advanced vision encoder, named OpenVision 3, that learns a single, unified visual representation that can serve both image understanding and image generation. Our core architecture is simple: we feed VAE-compressed image latents to a ViT encoder and train its output to support two complementary roles. First, the encoder output is passed to the ViT-VAE decoder to reconstruct the original image, encouraging the representation to capture generative structure. Second, the same representation is optimized with contrastive learning and image-captioning objectives, strengthening semantic features. By jointly optimizing reconstruction- and semantics-driven signals in a shared latent space, the encoder learns representations that synergize and generalize well across both regimes. We validate this unified design through extensive downstream evaluations with the encoder frozen. For generation, we test it under the RAE framework: ours substantially surpasses the standard CLIP-based encoder (e.g., gFID: 1.87 vs. 2.54 on ImageNet). For multimodal understanding, we plug the encoder into the LLaVA-1.5 and LLaVA-NeXT framework: it performs comparably with a standard CLIP vision encoder (e.g., 63.3 vs. 61.2 on SeedBench, and 59.2 vs. 58.1 on GQA). We provide empirical evidence that generation and understanding are mutually beneficial in our architecture, while further underscoring the critical role of the VAE latent space. We hope this work can spur future research on unified modeling.
Received — 14 March 2026 ⏭

FNDC1 Competitively Binds Gbeta2 to Suppress the beta-Catenin-Destruction Complex and Promote Gastric Cancer Malignancy

✇Omics in Gastric
By: Wenyu Gao · Hao Chen · Fangyu Lin · Jialin Liu · Tingxuan Huang · Han Wang · Xiaojiao Weng · Xinli Wang · Xiaoyan Lin · Tao Jiang
11 March 2026 at 18:00

FASEB J. 2026 Mar 31;40(6):e71634. doi: 10.1096/fj.202503587R.

ABSTRACT

Gastric cancer (GC) is a leading cause of cancer-related deaths and has high recurrence rate. Although fibronectin domain-containing protein 1 (FNDC1) is implicated in GC progression, its molecular mechanisms remain unclear. Multi-omics analyses (TCGA, GEO datasets) were used to assess FNDC1 expression and clinical correlation. In vitro (cell proliferation, invasion, EMT markers) and in vivo (xenograft) experiments, combined with molecular assays (Co-IP, WB, ChIP), explored FNDC1's function and mechanism. FNDC1 was significantly upregulated in GC, correlating with advanced clinicopathological features and poor prognosis. Knockdown of FNDC1 suppressed GC cell proliferation, invasion, and metastasis by inhibiting EMT and Wnt/β-catenin signaling. Mechanistically, FNDC1 competitively bound the WD5 domain (residues 224-254) of Gβ2, disrupting Gβγ-Dvl1 interaction. This prevented Dvl1 degradation, promoted Axin1 ubiquitination, and destabilized the β-catenin-destruction complex (GSK3 β-APC-Axin1), leading to β-catenin accumulation and Wnt pathway activation. FNDC1 drives GC malignancy by targeting the Gβ2-Dvl1 axis to activate Wnt/β-catenin signaling, suggesting FNDC1 as a novel prognostic biomarker and therapeutic target.

PMID:41808415 | PMC:PMC12976582 | DOI:10.1096/fj.202503587R

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