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Aetheria: A multimodal interpretable content safety framework based on multi-agent debate and collaboration

arXiv:2512.02530v1 Announce Type: new Abstract: The exponential growth of digital content presents significant challenges for content safety. Current moderation systems, often based on single models or fixed pipelines, exhibit limitations in identifying implicit risks and providing interpretable judgment processes. To address these issues, we propose Aetheria, a multimodal interpretable content safety framework based on multi-agent debate and collaboration.Employing a collaborative architecture of five core agents, Aetheria conducts in-depth analysis and adjudication of multimodal content through a dynamic, mutually persuasive debate mechanism, which is grounded by RAG-based knowledge retrieval.Comprehensive experiments on our proposed benchmark (AIR-Bench) validate that Aetheria not only generates detailed and traceable audit reports but also demonstrates significant advantages over baselines in overall content safety accuracy, especially in the identification of implicit risks. This framework establishes a transparent and interpretable paradigm, significantly advancing the field of trustworthy AI content moderation.

Leveraging artificial intelligence to validate traditional biomarkers and drug targets in liver cancer recovery: a mini review

Front Pharmacol. 2025 Oct 17;16:1697608. doi: 10.3389/fphar.2025.1697608. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) remains a leading cause of cancer death, and recovery after therapy is shaped by heterogeneous etiologies, genomes and microenvironments. Targeted and immunotherapy combinations have broadened first-line options; yet durable benefit is uneven, and serum/imaging anchors (AFP, AFP-L3%, PIVKA-II, LI-RADS/mRECIST) incompletely resolve residual disease or functional restoration. In this review we summarise AI-enabled radiology, digital pathology and multi-omic/liquid-biopsy analytics that test and refine traditional biomarkers and drug-target readouts, and appraise translational opportunities in composite surveillance and recovery forecasting. We also discuss enduring challenges-including assay standardisation, spectrum bias, data leakage, domain shift and limited prospective external validation-that temper implementation. By integrating established anchors (AFP/AFP-L3%, PIVKA-II, ALBI, contrast-enhanced hallmarks) with AI-derived signals (radiomics/pathomics, cfDNA methylation) and pathway contexts (VEGF-VEGFR, WNT/β-catenin), emerging strategies align predictions with clinical endpoints, individualise therapy and chart hepatic function. Our synthesis provides an appraisal of AI-traditional integration in liver cancer recovery and outlines pragmatic standards-analytical robustness, transparent reporting and prospective, guideline-conformant evaluation-required for clinical adoption. We hope these insights will aid researchers and clinicians as they implement more effective, individualised monitoring and treatment pathways.

PMID:41181592 | PMC:PMC12575369 | DOI:10.3389/fphar.2025.1697608

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases

Oncogene, Published online: 30 March 2024; doi:10.1038/s41388-024-03012-5

ScRNA-seq of gastric cancer tissues reveals differences in the immune microenvironment of primary tumors and metastases

N1-Methyladenosine modification of mRNA regulates neuronal gene expression and oxygen glucose deprivation/reoxygenation induction

Cell Death Discovery, Published online: 12 May 2023; doi:10.1038/s41420-023-01458-2

N1-Methyladenosine modification of mRNA regulates neuronal gene expression and oxygen glucose deprivation/reoxygenation induction
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