SlideChat is a multimodal generative artificial intelligence assistant for whole-slide computational pathology across cancer types

By: Ying Chen · Chenglong Ma · Qiongqiong Li · Fang Yan · Yirong Chen · Tianbin Li · Jin Ye · Ming Hu · Yuxiang Lin · Yanjun Li · Guoan Wang · Huihui Xu · Hui Dong · Xiang Wang · Xiaoxiao Xu · Yanyan Zhou · Xia Zhu · Sen Yang · Xiyue Wang · Lu Zhang · Yu Qiao · Rongshan Yu · Junjun He · Yuanfeng Ji

1 September 2026 at 08:00

Nature Cancer, Published online: 01 September 2026; doi:10.1038/s43018-026-01220-4

Chen et al. have developed SlideChat, a multimodal generative artificial intelligence assistant, which they benchmark on 27 pathology tasks across 33 cancer types. Expert pathologists rated the assistant as clinically relevant and accurate.

Explainable multi-omics modeling for risk stratification in pancreatic ductal adenocarcinoma

(Multiomics OR Omics) AND (Pancreatic)

By: Bingrong Chen · Jianuo Chen · Zhen Cao · Qiyao Zhang · Shangcheng Yan · Hongtao Cao · Jia Yu · Fang Wang · Ziwen Liu

21 May 2026 at 18:00

Gland Surg. 2026 Apr 30;15(4):91. doi: 10.21037/gs-2025-396. Epub 2026 Mar 27.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies due to a lack of reliable tools for individualized risk stratification. A comprehensive understanding of the multi-omics landscape may uncover clinically applicable biomarkers and inform precision prognostic assessment. This study aims to establish a prognostic model directly from the complete omics landscape and extract biomarkers.

METHODS: We developed prognostic models using multi-omics data from a PDAC proteogenomic cohort comprising 75 deceased tumor samples. An independent cohort of 63 deceased PDAC cases from The Cancer Genome Atlas (TCGA)-pancreatic adenocarcinoma (PAAD) was used for external validation. Logistic regression models with least absolute shrinkage and selection operator (LASSO) regularization were constructed, and SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance and identify signature genes. Model selection was based on the average area under the receiver operating characteristic curve (AUROC) across cross-validation folds. Functional validation was performed in PANC-1 cells by knockdown (KD) or overexpression (OE) of representative microRNA-, RNA-, and proteomics-derived signature genes, followed by Cell Counting Kit-8 (CCK-8) proliferation and Transwell migration assays.

RESULTS: Systematic evaluation of 120 multi-omics combinations identified a top-performing prognostic model integrating RNA, microRNA, proteomics, and mutation features. This model achieved a mean AUROC of 0.92±0.11 and accuracy of 0.87±0.01 on internal validation, and 0.99±0.00 and 0.98±0.01 on the TCGA test set. The sensitivity, specificity, precision, recall and F1 scores on the TCGA test set were 0.98±0.01, 0.97±0.02, 0.98±0.02, 0.98±0.01, 0.98±0.01, respectively. SHAP analysis revealed interpretable and clinically relevant prognostic biomarkers, many of which are implicated in immune signaling, metabolic regulation, and cell cycle control. Importantly, modulation of representative signature genes in PANC-1 cells significantly altered proliferation and migration in directions consistent with model-predicted risk associations.

CONCLUSIONS: Our findings demonstrate that explainable multi-omics machine learning frameworks can identify robust prognostic biomarkers and achieve highly accurate survival prediction in PDAC. Functional validation further supports the biological relevance of these signatures, underscoring their translational potential for personalized risk assessment.

PMID:42164702 | PMC:PMC13184197 | DOI:10.21037/gs-2025-396

Spatial multi-omics defines cancer-associated fibroblasts subtype gradients driving metabolic support and immune remodeling in pancreatic ductal adenocarcinoma

(Multiomics OR Omics) AND (Pancreatic)

By: Qiyao Zhang · Zhen Cao · Shangcheng Yan · Bingrong Chen · Bohan Huang · Ziwen Liu · Yupei Zhao

17 May 2026 at 18:00

Cancer Lett. 2026 May 16;653:218585. doi: 10.1016/j.canlet.2026.218585. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma is characterized by a fibrotic and metabolically active tumor microenvironment where cancer-associated fibroblasts (CAFs) mediate metabolic crosstalk, extracellular matrix (ECM) remodeling, and immune regulation. However, the metabolic and spatial heterogeneity of CAFs remains incompletely understood. We integrated spatial transcriptomics and spatial metabolomics data from PDAC tissues and performed SpatialGlue-based multimodal clustering to define CAF subtypes. To characterize metabolic communication, we developed an optimal transport (OT)-based metabolic inference framework to quantitatively model metabolite association between CAFs and tumor cells. Subtype-specific features were independently validated using an independent spatial metabolomics cohort and multiplex immunofluorescence (mIHC) staining. Furthermore, these features were correlated with clinical outcomes via TCGA-PAAD deconvolution. Spatial multi-omics integration identified three robust CAF subtypes with distinct signatures. OT analysis revealed differential metabolic interactions: CAF_C0 mediated amino acid/peptide transfer, CAF_C1 was the primary source of lipids, while CAF_C2 exhibited limited metabolic association but stronger immune and ECM signaling activity. Deconvolution confirmed that CAF composition was strongly associated with prognosis; CAF_C2 enrichment predicted poorer survival and gemcitabine resistance, whereas a higher CAF_C0/CAF_C1 balance correlated with improved outcomes. By combining spatial multi-omics with OT-based modeling, this study delineates metabolically and spatially distinct CAF states with clinical relevance. Our findings suggest CAFs act as both metabolic donors and immune-ECM regulators, providing new insights into stromal reprogramming and potential subtype-specific therapeutic targets in PDAC.

PMID:42144098 | DOI:10.1016/j.canlet.2026.218585

Hierarchical Memory Orchestration for Personalized Persistent Agents

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

By: Junming Liu · Yifei Sun · Weihua Cheng · Haodong Lei · Yuqi Li · Yirong Chen · Ding Wang

3 April 2026 at 12:00

arXiv:2604.01670v1 Announce Type: new Abstract: While long-term memory is essential for intelligent agents to maintain consistent historical awareness, the accumulation of extensive interaction data often leads to performance bottlenecks. Naive storage expansion increases retrieval noise and computational latency, overwhelming the reasoning capacity of models deployed on constrained personal devices. To address this, we propose Hierarchical Memory Orchestration (HMO), a framework that organizes interaction history into a three-tiered directory driven by user-centric contextual relevance. Our system maintains a compact primary cache, coupling recent and pivotal memories with an evolving user profile to ensure agent reasoning remains aligned with individual behavioral traits. This primary cache is complemented by a high-priority secondary layer, both of which are managed within a global archive of the full interaction history. Crucially, the user persona dictates memory redistribution across this hierarchy, promoting records mapped to long-term patterns toward more active tiers while relegating less relevant information. This targeted orchestration surfaces historical knowledge precisely when needed while maintaining a lean and efficient active search space. Evaluations on multiple benchmarks achieve state-of-the-art performance. Real-world deployments in ecosystems like OpenClaw demonstrate that HMO significantly enhances agent fluidity and personalization.

Strand-asymmetric G-runs and G4s downstream of TSS modulate tumor suppressor gene transcription

Oncogene - Issue - nature.com science feeds

By: Dan Huang · Xiaoting Zhang · Xiansong Wang · Ziheng Huang · Judeng Zeng · Xiaodong Liu · Likai Tan · Lin Zhang · Zhenxing Guo · Tony Gin · Jun Yu · Kwok M. Ho · Matthew Tak Vai Chan · Huarong Chen · William Ka Kei Wu

2 April 2026 at 08:00

Oncogene, Published online: 02 April 2026; doi:10.1038/s41388-026-03761-5

Strand-asymmetric G-runs and G4s downstream of TSS modulate tumor suppressor gene transcription

Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance

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

By: Xinrong Chen · Xu Chu · Yingmin Qiu · Hengyuan Zhang · Jing Xiong · Shiyu Tang · Shuai Liu · Shaokang Yang · Cheng Yang · Hayden Kwok-Hay So · Ngai Wong

25 March 2026 at 12:00

arXiv:2602.01047v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training-free method that uses historical information to aid decoding. The method relies on the internal implicit reasoning mechanism and token logits evolution mechanism of LVLMs to correct biases. Extensive experiments demonstrate that ResDec effectively suppresses hallucinations induced by language priors, significantly improves visual grounding, and reduces object hallucinations. In addition to mitigating hallucinations, ResDec also performs exceptionally well on comprehensive LVLM benchmarks, highlighting its broad applicability.

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

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

By: Jiahao Wang · Jinbo Han · Xingda Wei · Sijie Shen · Dingyan Zhang · Chenguang Fang · Rong Chen · Wenyuan Yu · Haibo Chen

17 February 2026 at 13:00

arXiv:2506.02634v5 Announce Type: replace-cross Abstract: Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV\$) after processing each request substantially improves serving throughput and latency. However, there is limited understanding of how LLM serving benefits from KV\$ caching, where system design decisions like cache eviction policies are highly workload-dependent. In this paper, we present the first systematic characterization of the KV\$ workload patterns from one of the leading LLM service providers. We draw observations that were not covered by previous studies focusing on synthetic workloads, including: KV\$ reuses are skewed across requests, where reuses between single-turn requests are equally important as multi-turn requests; the reuse time and probability are diverse considering all requests, but for a specific request category, the pattern tends to be predictable; and the overall cache size required for an ideal cache hit ratio is moderate. Based on the characterization, we further propose a workload-aware cache eviction policy that improves the serving performance under real-world traces, especially with limited cache capacity.

Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings

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

By: Yidong Jiang · Junrong Chen · Eftychia Makri · Jialin Chen · Peiwen Li · Ali Maatouk · Leandros Tassiulas · Eliot Brenner · Bing Xiang · Rex Ying

17 February 2026 at 13:00

arXiv:2602.07294v3 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures. However, existing benchmarks often focus on isolated details, failing to reflect the complexity of professional analysis that requires synthesizing information across multiple documents, reporting periods, and corporate entities. Furthermore, these benchmarks do not disentangle whether errors arise from retrieval failures, generation inaccuracies, domain-specific reasoning mistakes, or misinterpretation of the query or context, making it difficult to precisely diagnose performance bottlenecks. To bridge these gaps, we introduce Fin-RATE, a benchmark built on U.S. Securities and Exchange Commission (SEC) filings and mirroring financial analyst workflows through three pathways: detail-oriented reasoning within individual disclosures, cross-entity comparison under shared topics, and longitudinal tracking of the same firm across reporting periods. We benchmark 17 leading LLMs, spanning open-source, closed-source, and finance-specialized models, under both ground-truth context and retrieval-augmented settings. Results show substantial performance degradation, with accuracy dropping by 18.60\% and 14.35\% as tasks shift from single-document reasoning to longitudinal and cross-entity analysis. This degradation is driven by increased comparison hallucinations, temporal and entity mismatches, and is further reflected in declines in reasoning quality and factual consistency--limitations that existing benchmarks have yet to formally categorize or quantify.

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