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Received — 27 May 2026 ⏭

NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhongtao Miao · Kaiyan Zhao · Masaaki Nagata · Yoshimasa Tsuruoka
26 May 2026 at 12:00
arXiv:2601.03790v4 Announce Type: replace-cross Abstract: Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages. This field remains underexplored compared with general machine translation (MT). In this paper, we propose an agentic framework, NeoAMT, for neologism-aware machine translation equipped with a Wiktionary-based search toolkit. Specifically, we first construct a dedicated dataset for neologism-aware machine translation and build a search toolkit grounded in Wiktionary. The dataset covers 16 languages and 75 translation directions in total, derived from approximately 10 million records of an English Wiktionary dump. The retrieval corpus of the search toolkit is also constructed from around 3 million cleaned records of the same dump. We then leverage the dataset and toolkit to train a translation agent via reinforcement learning (RL) and to evaluate the accuracy of neologism-aware machine translation. Furthermore, we propose an RL training framework featuring a novel reward design and an adaptive rollout generation strategy that exploits translation difficulty to further improve the translation quality of translation agents using our search toolkit.

Kaempferol functionally reprograms CD47 signaling to promote cytoprotection and attenuate oxeiptosis in severe acute pancreatitis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yu Tian · Yan Zhang · Qiang Gao · Peng Zhang · Lanlan You · Rongzhong Huang · An Zhang · E Wen
25 May 2026 at 18:00

Phytomedicine. 2026 May 15;157:158305. doi: 10.1016/j.phymed.2026.158305. Online ahead of print.

ABSTRACT

BACKGROUND: Severe acute pancreatitis (SAP) lacks targeted therapies, and massive loss of functional pancreatic acinar cells (PAC) drives mortality. Kaempferol (KA) possesses well-established anti-inflammatory and cytoprotective activities and is derived from herbal medicinal plants, but its direct molecular targets and mechanism of action in SAP remain undefined.

PURPOSE: To evaluate the protective effects of KA against SAP and to elucidate its molecular mechanism of specific action, with a focus on identifying the direct cellular target through which KA exerts its cytoprotective effects.

STUDY DESIGN: Gain‑/loss‑of‑function in vitro and PAC‑specific CD47 SAP mouse models, combined with multi‑omics screening and biophysical assays.

METHODS: CD47 manipulation (siRNA/overexpression) was performed in primary PACs and cell lines, combined with WT/CD47-/-/Mist1‑CD47‑iOE (PAC‑specific) mouse models. Network pharmacology, transcriptomics and proteomics were integrated to screen and validate KA's protective effects. Computational‑experimental approaches (molecular docking/dynamics, CETSA, SPR, co‑IP, pharmacological epistasis) characterized KA's allosteric modulation of CD47 signaling.

RESULTS: CD47 was upregulated in SAP; its knockout reduced PAC death via KEAP1/PGAM5/AIFM1-driven oxeiptosis. KA reduced PAC death across genotypes, afforded no extra benefit in CD47-KO, and was not overridden by CD47‑OE. Mechanistically, KA allosterically binds CD47 ectodomain, stabilizes the CD47‑ UBQLN1 complex, and redirects signaling from Gαi‑mediated death to Gβγ/ ERK/NRF2‑mediated survival. ERK inhibition attenuated KA's protection. KA's action was CD47‑dependent.

CONCLUSION: This study identifies anti-oxeiptosis as a novel pharmacological activity of KA in SAP. This is achieved through allosteric modulation of CD47, redirecting its signaling from death‑promoting to a protective axis via activating Gβγ/ERK/NRF2 to suppress oxeiptosis. These findings reveal the CD47‑oxeiptosis axis as a therapeutic target and position KA as a promising candidate for SAP therapy, adding a new mechanistic dimension to KA's known pharmacological profile.

PMID:42184499 | DOI:10.1016/j.phymed.2026.158305

CD300ld on pathologically activated neutrophils promotes tumor immune suppression by binding phosphatidylserine on CD8<sup>+</sup> T cells

✇Nature Cancer
By: Chaoxiong Wang · Peixuan Zheng · Anhui Wang · Xinrui Fan · Tianxiang Li · Jingjing Hong · Runze Wang · Chunyan Zhang · Chaogui Luo · Weiwei Yang · Dong Gao · Adili Salai · Min Luo · Zhigang Lu · Dianfan Li · Moubin Lin · Jiayin Peng · Guohui Li · Shuo Han · Yun Zhao
15 May 2026 at 08:00

Nature Cancer, Published online: 15 May 2026; doi:10.1038/s43018-026-01169-4

Zhao and colleagues show that CD300ld, upregulated in pathologically activated neutrophils, mediates contact-dependent suppression of cytotoxic CD8+ T cells by binding to phosphatidylserine, inhibiting antitumor immune responses.

Induction of broadly neutralizing HIV antibodies by a two-step mechanism informs vaccine design

✇AAAS: Table of Contents
By: Ashwin N. Skelly · Harry B. Gristick · Hui Li · Edem Gavor · Andrew J. Connell · Edward F. Kreider · Lorie Marchitto · Michael P. Hogarty · Maddy L. Newby · Joel D. Allen · Weimin Liu · Anthony P. West · Kasirajan Ayyanathan · Mary S. Campion · Kaitlyn Winters · Colette G. Gordon · Rebecca A. Osbaldeston · Macy J. Akeley · Emily Lewis · Yingying Li · Ajay Singh · Kendra Cruickshank · Younghoon Park · Chengyan Zhao · Xuduo Li · Khaled Amereh · Elizabeth Van Itallie · John W. Carey · Amie Albertus · Andrew T. DeLaitsch · Jennifer R. Keeffe · Melinda G. Lituchy · Agnes A. Walsh · Daniel J. Morris · Rumi Habib · Frederic Bibollet-Ruche · Nitesh Mishra · Gabriel Avillion · Nicholas S. Koranda · Samantha J. Plante · Christian L. Martella · Jinery Lora · Eric J. D. Wang · Mark G. Lewis · Malcolm A. Martin · Michel C. Nussenzweig · Michael S. Seaman · Darrell J. Irvine · Kevin J. Wiehe · Barton F. Haynes · Kshitij Wagh · Bette Korber · Raiees Andrabi · Max Crispin · Drew Weissman · Pamela J. Bjorkman · Beatrice H. Hahn · George M. Shaw
7 May 2026 at 13:59
Science, Ahead of Print.
Received — 8 April 2026 ⏭

ActionNex: A Virtual Outage Manager for Cloud

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenfeng Lin · Haoji Hu · Ming Hao · Xuchao Zhang · Ryan Zhang · Junhao Li · Ze Li · Oleg Kulygin · Chetan Bansal · Hatay Tuna · Murali Chintalapati · Sheila Jiang · Salman Zafar · Angie Anderson
7 April 2026 at 12:00
arXiv:2604.03512v1 Announce Type: new Abstract: Outage management in large-scale cloud operations remains heavily manual, requiring rapid triage, cross-team coordination, and experience-driven decisions under partial observability. We present \textbf{ActionNex}, a production-grade agentic system that supports end-to-end outage assistance, including real-time updates, knowledge distillation, and role- and stage-conditioned next-best action recommendations. ActionNex ingests multimodal operational signals (e.g., outage content, telemetry, and human communications) and compresses them into critical events that represent meaningful state transitions. It couples this perception layer with a hierarchical memory subsystem: long-term Key-Condition-Action (KCA) knowledge distilled from playbooks and historical executions, episodic memory of prior outages, and working memory of the live context. A reasoning agent aligns current critical events to preconditions, retrieves relevant memories, and generates actionable recommendations; executed human actions serve as an implicit feedback signal to enable continual self-evolution in a human-agent hybrid system. We evaluate ActionNex on eight real Azure outages (8M tokens, 4,000 critical events) using two complementary ground-truth action sets, achieving 71.4\% precision and 52.8-54.8\% recall. The system has been piloted in production and has received positive early feedback.
Received — 4 April 2026 ⏭

Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianhao Shi · Yang Zhang · Xiaoyan Zhao · Fengbin Zhu · Chenyi Lei · Han Li · Wenwu Ou · Yang Song · Yongdong Zhang · Fuli Feng
3 April 2026 at 12:00
arXiv:2604.01690v1 Announce Type: new Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.
Received — 2 April 2026 ⏭

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization

✇Oncogene - Issue - nature.com science feeds
By: Yan Zhang · Zhiwei Xue · Naibin Zhang · Yuehua Zhu · YanZhao Wu · Meilin Lv · Zhihan Zhang · Feiyu Mu · Wenchen Xing · Ziyi Tang · Chunjie Wang · Zhiyi Xue · Wenjing Zhou · Xiaofei Liu · Xingang Li · Rolf Bjerkvig · Bin Huang · Mingzhi Han · Jian Wang · Donghai Wang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03756-2

Correction: The multifunctional RNA helicase DDX39A drives glioblastoma progression by modulating WISP1 alternative splicing that induces an immunosuppressive macrophage polarization
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 — 11 March 2026 ⏭

Investigating the Effect of Hospital Infection Control Informatization on Optimizing Microbiological Specimen Submission Before Antibiotic Therapy: Failure Mode and Effects Analysis

✇Journal of Medical Internet Research
By: Jianxiong Wu · Weijiang Zhan · Jun Hua · Qinling Ge · Yuexian Zhu · Huixian Yu · Min Zhao · Xiaoyan Zhan · Bingwei Zhu · Tongqi Xiang · Longxi Lu · Tieying Dai
11 March 2026 at 04:15
Background: Antimicrobial resistance (AMR) poses a critical global health threat, with inappropriate antibiotic use being a major driver. Timely microbiological specimen submission before initiating antibiotic therapy is a cornerstone of antimicrobial stewardship (AMS), enabling pathogen-directed therapy and reducing unnecessary broad-spectrum exposure. However, suboptimal compliance remains common due to workflow interruptions, technological barriers, and behavioral factors. Failure Mode and Effects Analysis (FMEA), a proactive risk-assessment method widely used in health care quality improvement, provides a systematic framework to identify process vulnerabilities and prioritize corrective actions. Despite its increasing application, few studies have integrated FMEA with hospital informatization to optimize microbiological specimen submission workflows in routine AMS practice. Objective: This study aimed to systematically identify workflow risks affecting preantibiotic microbiological specimen submission and to design, implement, and evaluate informatization-enabled interventions using an FMEA-based framework. Methods: FMEA was conducted at a tertiary hospital in China. A multidisciplinary team identified potential failure modes across 4 domains: health information systems, personnel, administration, and external support. Risk Priority Numbers (RPNs) and Action Priority (AP) indices were calculated for each failure mode. Targeted interventions were implemented, including dual-verification barcode scanning, artificial intelligence-driven clinical decision support alerts, EHR-integrated training modules, and automated compliance dashboards. Pre- and postintervention specimen submission rates (January 2024-December 2024) were analyzed using the Mann-Kendall trend test. Results: The top 5 failure modes included PDA barcode scanning failures (RPN=175), inadequate clinical decision support (RPN=140), insufficient clinician awareness (RPN=56), suboptimal oversight mechanisms, and patient-related barriers. Postintervention, significant upward trends were observed in overall specimen submission rates (
Received — 5 March 2026 ⏭

GeoSeg: Training-Free Reasoning-Driven Segmentation in Remote Sensing Imagery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lifan Jiang · Yuhang Pei · oxi Wu · Yan Zhao · Tianrun Wu · Shulong Yu · Lihui Zhang · Deng Cai
5 March 2026 at 13:00
arXiv:2603.03983v1 Announce Type: cross Abstract: Recent advances in MLLMs are reframing segmentation from fixed-category prediction to instruction-grounded localization. While reasoning based segmentation has progressed rapidly in natural scenes, remote sensing lacks a generalizable solution due to the prohibitive cost of reasoning-oriented data and domain-specific challenges like overhead viewpoints. We present GeoSeg, a zero-shot, training-free framework that bypasses the supervision bottleneck for reasoning-driven remote sensing segmentation. GeoSeg couples MLLM reasoning with precise localization via: (i) bias-aware coordinate refinement to correct systematic grounding shifts and (ii) a dual-route prompting mechanism to fuse semantic intent with fine-grained spatial cues. We also introduce GeoSeg-Bench, a diagnostic benchmark of 810 image--query pairs with hierarchical difficulty levels. Experiments show that GeoSeg consistently outperforms all baselines, with extensive ablations confirming the effectiveness and necessity of each component.
Received — 25 February 2026 ⏭

HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziyan Zhang · Changxin Wan · Peng Hao · Kanok Boriboonsomsin · Matthew J. Barth · Yongkang Liu · Seyhan Ucar · Guoyuan Wu
24 February 2026 at 13:00
arXiv:2602.18740v1 Announce Type: cross Abstract: This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and Automated Vehicles (CAVs). The framework jointly optimizes vehicle-level eco-driving behaviors and intersection-level traffic signal control to enhance overall network efficiency and decrease energy consumption. A decentralized Multi-Agent Reinforcement Learning (MARL) approach by Value Decomposition Network (VDN) manages cycle-based traffic signal control (TSC) at intersections, while an innovative Signal Phase and Timing (SPaT) prediction method integrates a Machine Learning-based Trajectory Planning Algorithm (MLTPA) to guide CAVs in executing Eco-Approach and Departure (EAD) maneuvers. The framework is evaluated across varying CAV proportions and powertrain types to assess its effects on mobility and energy performance. Experimental results conducted in a 4*4 real-world network demonstrate that the MARL-based TSC method outperforms the baseline model (i.e., Webster method) in speed, fuel consumption, and idling time. In addition, with MLTPA, HONEST-CAV benefits the traffic system further in energy consumption and idling time. With a 60% CAV proportion, vehicle average speed, fuel consumption, and idling time can be improved/saved by 7.67%, 10.23%, and 45.83% compared with the baseline. Furthermore, discussions on CAV proportions and powertrain types are conducted to quantify the performance of the proposed method with the impact of automation and electrification.

AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li Xiao · Liqing Liu · Hongjun Wu · Jiayi Zhong · Xixia Li · Yan Zhang · Junjie Hu · Sun Fei · Ge Yang · Tao Xu
24 February 2026 at 13:00
arXiv:2511.02860v2 Announce Type: replace-cross Abstract: The distribution and interactions of cellular organelles play a critical role in mediating cellular physiology and pathology. Large-scale electron microscopy enables visualization of organelle distribution and interactions at the tissue level with nanometer resolution, but robust and efficient computational analysis tools are lacking. Here, we present a deep learning tool for universal large-scale 2D/3D electron microscopy analysis, DeepOrganelle. This new tool enables high-throughput, cell-resolved spatiotemporal mapping and digitization of organelle distribution and interactions. When applied to spermatogenesis across 12 stages and 22 differentiation status of the germ cells, DeepOrganelle uncovered previously unrecognized, stage-dependent dynamics of mitochondria-endoplasmic reticulum contact sites within one subphase of prophase I during meiosis. It also revealed coordinated organelle redistribution in Sertoli cells towards the blood-testis barrier, digitizing the remodeling dynamics of the tissue. This study demonstrates that DeepOrganelle provides a powerful framework that captures subcellular dynamics at the whole-tissue level.
Received — 17 February 2026 ⏭

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.

C^2ROPE: Causal Continuous Rotary Positional Encoding for 3D Large Multimodal-Models Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guanting Ye · Qiyan Zhao · Wenhao Yu · Xiaofeng Zhang · Jianmin Ji · Yanyong Zhang · Ka-Veng Yuen
17 February 2026 at 13:00
arXiv:2602.10551v2 Announce Type: replace-cross Abstract: Recent advances in 3D Large Multimodal Models (LMMs) built on Large Language Models (LLMs) have established the alignment of 3D visual features with LLM representations as the dominant paradigm. However, the inherited Rotary Position Embedding (RoPE) introduces limitations for multimodal processing. Specifically, applying 1D temporal positional indices disrupts the continuity of visual features along the column dimension, resulting in spatial locality loss. Moreover, RoPE follows the prior that temporally closer image tokens are more causally related, leading to long-term decay in attention allocation and causing the model to progressively neglect earlier visual tokens as the sequence length increases. To address these issues, we propose C^2RoPE, an improved RoPE that explicitly models local spatial Continuity and spatial Causal relationships for visual processing. C^2RoPE introduces a spatio-temporal continuous positional embedding mechanism for visual tokens. It first integrates 1D temporal positions with Cartesian-based spatial coordinates to construct a triplet hybrid positional index, and then employs a frequency allocation strategy to encode spatio-temporal positional information across the three index components. Additionally, we introduce Chebyshev Causal Masking, which determines causal dependencies by computing the Chebyshev distance of image tokens in 2D space. Evaluation results across various benchmarks, including 3D scene reasoning and 3D visual question answering, demonstrate C^2RoPE's effectiveness. The code is be available at https://github.com/ErikZ719/C2RoPE.
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