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AgenticGen: Reward-Guided Agentic Video Generation for Advertising

arXiv:2609.09187v1 Announce Type: cross Abstract: Advertising video generation is not only a video synthesis task, but also a product-conditioned reasoning problem whose success is measured by online business metrics. Recent video foundation models can generate realistic clips from multimodal conditions, yet they do not optimize how a product should be transformed into an effective advertisement or how future generation should be improved from online business feedback. To close this loop, we propose AgenticGen, a reward-guided agentic framework that decomposes advertising video generation into two trainable reasoning stages, strategy selection and draft generation, thereby exposing optimization targets that online business feedback can supervise. AgenticGen learns a performance-based reward from accumulated online feedback and a complementary rubric-based reward aligned with human quality standards, then uses them to supervise policy optimization. DPO first moves the agentic policies toward online preferences, and GRPO further refines both stages with process and outcome rewards. Offline experiments validate the reward models and successive policy optimization. Online A/B experiments in the TikTok advertising system show that AgenticGen after DPO and GRPO improves CTR by 2.72%, CVR by 2.63%, and Advv by 9.61% over the SFT baseline.

A bivalent molecular glue linking lysine acetyltransferases to oncogene-induced cell death

Chemically induced proximity of lysine acetyltransferases (KATs) with BCL6 reprograms epigenetic signaling to eliminate lymphoma tumors. Structural and mechanistic studies demonstrate that fortuitous protein-protein contacts convert proximity induction into targeted changes in chromatin, revealing a key mechanism by which small molecules can co-opt oncogenic transcriptional regulators to elicit malignant cell death.

Human pancreatic progenitor organoids define genetic and epigenetic barriers to early PDAC transformation

Dev Cell. 2026 May 19:S1534-5807(26)00159-0. doi: 10.1016/j.devcel.2026.04.012. Online ahead of print.

ABSTRACT

The lack of accurate human models that recapitulate pancreatic ductal adenocarcinoma (PDAC) initiation has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectories of tumor initiation and progression, validated against clinical datasets and tumor histopathology. We demonstrate that CDKN2A loss, which is nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, whereas SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of the pancreatic lineage program during PDAC initiation, alongside AP-1-driven chromatin remodeling. We identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and hypermethylation of essential pancreatic transcription factors. This model captures genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a potential early intervention strategy.

PMID:42161274 | PMC:PMC13196429 | DOI:10.1016/j.devcel.2026.04.012

A Genetically Engineered Human Organoid Model Reveals Distinct Genetic and Epigenetic Barriers of Lineage Plasticity in Early PDAC Transformation

bioRxiv [Preprint]. 2026 Mar 11:2026.03.09.710586. doi: 10.64898/2026.03.09.710586.

ABSTRACT

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro , validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

PMID:41959451 | PMC:PMC13060829 | DOI:10.64898/2026.03.09.710586

Owl-AuraID 1.0: An Intelligent System for Autonomous Scientific Instrumentation and Scientific Data Analysis

arXiv:2603.29828v1 Announce Type: new Abstract: Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based systems. We present Owl-AuraID, a software-hardware collaborative embodied agent system that adopts a GUI-native paradigm to operate instruments through the same interfaces as human experts. Its skill-centric framework integrates Type-1 (GUI operation) and Type-2 (data analysis) skills into end-to-end workflows, connecting physical sample handling with scientific interpretation. Owl-AuraID demonstrates broad coverage across ten categories of precision instruments and diverse workflows, including multimodal spectral analysis, microscopic imaging, and crystallographic analysis, supporting modalities such as FTIR, NMR, AFM, and TGA. Overall, Owl-AuraID provides a practical, extensible foundation for autonomous laboratories and illustrates a path toward evolving laboratory intelligence through reusable operational and analytical skills. The code are available at https://github.com/OpenOwlab/AuraID.

Robust transcriptomic hallmarks targeting intratumor heterogeneity in intrahepatic cholangiocarcinoma

Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.

ABSTRACT

Intratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8% of tumors by existing subtyping systems. To overcome this, we identify a low-intratumor-heterogeneity/high-intertumor-variability (LIHV) gene set and develop an ITH-insensitive classification system defining five subgroups: inflammatory (SI), metabolic (SII), atypical (SIII-1), immune-silent (SIII-2), and neurodegenerative (SIII-3). These subgroups exhibit distinct clinical outcomes, molecular features, immune landscapes, and therapeutic vulnerabilities. GPRC5A and VTCN1 serve as robust immunohistochemical biomarkers for SI and SIII tumors, while serum CEA and CA19-9 identify inflammatory iCCA. Therapeutically, HSP90 inhibition synergizes with anti-PD1 in inflammatory iCCA, whereas combined anti-PD1 and anti-TIM3 suppresses neurodegenerative iCCA. Collectively, our study provides a robust molecular framework and actionable therapeutic strategies for iCCA.

PMID:41916296 | DOI:10.1016/j.xcrm.2026.102708

Elevation of Liver Elastic Value Following Radiofrequency Ablation Reflected Neutrophils Mediated Abscopal Effect in Liver Cancer

JHEP Rep. 2026 Mar 23:101824. doi: 10.1016/j.jhepr.2026.101824. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally. Radiofrequency ablation (RFA) is a widely used treatment for HCC, but its efficacy is often limited by tumor relapse. Neutrophils, serve as a double-edged sword in tumor immunology, have recently been implicated in anti-tumor immunity post-RFA. Shear wave elastography (SWE) is a non-invasive examination for liver tissue, and associated with immune response. This study investigates the correlation between dynamic change of SWE values and neutrophils response following RFA, and explores potential adjuvant strategies for RFA.

METHODS: We conducted a comprehensive analysis using both clinical data from patients undergoing RFA (n=102) and experimental studies in mouse models (n=4-6 per group). Single-cell RNA sequencing (scRNA-seq) and multi-omics analyses including multiplex immunofluorescence staining and flow cytometric analysis were performed to identify neutrophil subsets. To assess the therapeutic potential of neutrophils-activating therapy for enhancing anti-tumor immunity post-RFA, we tested CD40 agonist in combination with RFA in preclinical models.

RESULTS: We noticed that rising liver SWE values following RFA were significantly associated with reduce relapse (n=102, p<0.001), and demonstrated that this phenomenon was linked to the inflammatory environment induced by the infiltration of neutrophils (2.5-fold increase, p<0.001). scRNA-seq analysis identified neutrophil subsets characterized by high expression of interferon-stimulated genes, which exhibited potent anti-tumor activity via nitric oxide. Importantly, treatment with CD40 agonist significantly augmented this immune response, leading to reduced tumor growth in mice (149.6±38.12 mm3 vs 23.92±4.43 mm3, p=0.008).

CONCLUSIONS: We linked clinical features to neutrophil-mediated immunity post-RFA. Neutrophil-activating therapy like CD40 agonists may prevent HCC relapse after RFA.

PMID:41881314 | DOI:10.1016/j.jhepr.2026.101824

Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making

arXiv:2511.12876v3 Announce Type: replace Abstract: Economic decision-making depends not only on structured signals such as prices and taxes, but also on unstructured language, including peer dialogue and media narratives. While multi-agent reinforcement learning (MARL) has shown promise in optimizing economic decisions, it struggles with the semantic ambiguity and contextual richness of language. We propose LAMP (Language-Augmented Multi-Agent Policy), a framework that integrates language into economic decision-making and narrows the gap to real-world settings. LAMP follows a Think-Speak-Decide pipeline: (1) Think interprets numerical observations to extract short-term shocks and long-term trends, caching high-value reasoning trajectories; (2) Speak crafts and exchanges strategic messages based on reasoning, updating beliefs by parsing peer communications; and (3) Decide fuses numerical data, reasoning, and reflections into a MARL policy to optimize language-augmented decision-making. Experiments in economic simulation show that LAMP outperforms both MARL and LLM-only baselines in cumulative return (+63.5%, +34.0%), robustness (+18.8%, +59.4%), and interpretability. These results demonstrate the potential of language-augmented policies to deliver more effective and robust economic strategies.
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