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Received — 27 May 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Fundamental Limitation in Explaining AI Atsushi Suzuki · Jing Wang
    arXiv:2605.24727v1 Announce Type: new Abstract: While large-scale models such as LLMs and diffusion models have achieved practical success, public institutions have emphasized the importance of explainability in AI. Existing methods for explaining AI, however, are not designed to provide completely faithful explanations of the behavior of large-scale AI systems. Although a completely faithful and interpretable explanation of the behavior of an AI system might be useful for AI governance, it has
     
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Fundamental Limitation in Explaining AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Atsushi Suzuki · Jing Wang
26 May 2026 at 12:00
arXiv:2605.24727v1 Announce Type: new Abstract: While large-scale models such as LLMs and diffusion models have achieved practical success, public institutions have emphasized the importance of explainability in AI. Existing methods for explaining AI, however, are not designed to provide completely faithful explanations of the behavior of large-scale AI systems. Although a completely faithful and interpretable explanation of the behavior of an AI system might be useful for AI governance, it has not been known whether providing such an explanation is theoretically possible. In this paper, we mathematically prove a fundamental quadrilemma in explaining AI, stating that AI and its explanation cannot satisfy the following four conditions simultaneously: 1) the complexity of the operation environment, 2) the goodness of the AI's performance, 3) the interpretability of the AI's explanation, and 4) the complete faithfulness of the AI's explanation. This quadrilemma suggests that, in most applications where we cannot change the environment or sacrifice good AI performance and an interpretable explanation, we should give up complete faithfulness of explanations and should instead aim to explain only the parts that are important for applications. As a consequence, the quadrilemma implies that AI governance should be designed on the premise that the faithfulness of AI explanations is always incomplete.

D3S2: Diffusion-Guided Dataset Distillation for Semantic Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenjie Zheng · Haoji Hu · Jiali Lu · Xingze Zou · Jing Wang
26 May 2026 at 12:00
arXiv:2605.25022v1 Announce Type: cross Abstract: Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing studies mainly focus on image classification, leaving dense prediction tasks such as semantic segmentation largely underexplored. In this work, we identify three key challenges for segmentation DD: (i) long-tailed class imbalance, (ii) the need for strict pixel-wise alignment between images and dense labels, and (iii) the high computational cost of optimizing high-resolution data with complex models. To address these challenges, we propose D3S2, a Diffusion-guided Dataset Distillation framework for Semantic Segmentation. Our method adopts a two-stage design. In Class-Balanced Mask Selection, we construct a representative mask set via a greedy strategy that prioritizes underrepresented classes. In Diffusion-Guided Image Synthesis, we employ a pretrained layout-to-image diffusion model to generate images conditioned on the selected masks, naturally ensuring spatial alignment. To further enhance the training utility of synthesized data, we introduce guided diffusion sampling with two complementary objectives: a segmentation-consistency loss for pixel-level alignment, and a class-wise feature matching loss for aligning per-class feature statistics across layers. Extensive experiments demonstrate the superiority of D3S2. Notably, at an extremely compression rate of 1%, our method achieves 24.99% and 35.49% mIoU on ADE20K and COCO-Stuff with Mask2Former (Swin-S), outperforming random selection by 9.34% and 5.70%, respectively.

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

✇Nature - Issue - nature.com science feeds
By: Min He · Jia Su · Xiaogang Zhou · Tuo Qi · Jiazheng Wang · Tianxin Zhang · Jinhua Chen · Mawsheng Chern · Youpin Xu · Xiang Lu · Qingqing Hou · Hongrui Liu · Wenfang Huang · Jiawei Liu · Shiying Li · Yunfei Zhu · Xue Chen · Senyu Ran · Han Li · Renju Liu · Mingliang Lei · Guosong Shu · Haicheng Liao · Junjie Yin · Xiaobo Zhu · Yongyan Tang · Li Song · Long Wang · Qing Xiong · Jiali Liu · Yu Bi · Yihua Yang · Hai Qing · Bingtian Ma · Yan Li · Jing Fan · Peng Qin · Yuping Wang · Jianming Wu · Wenming Wang · Shigui Li · Weitao Li · Jing Wang · Xuewei Chen
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

Neural representation of action symbols in primate frontal cortex

✇Nature - Issue - nature.com science feeds
By: Lucas Y. Tian · Kedar Garzón Gupta · Daniel J. Hanuska · Adam G. Rouse · Mark A. G. Eldridge · Marc H. Schieber · Xiao-Jing Wang · Joshua B. Tenenbaum · Winrich A. Freiwald
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10297-x

A drawing-like task designed to study compositional generalization identifies a specific neural population in the ventral premotor cortex in primates that encodes action symbols.

Characterization of dysbiosis patterns in gut microbiota of digestive system cancers: an umbrella review

✇Omics in Gastric
By: Jing Wang · Yingkai Liu · Yan Yu · Xiaoli Chen · Genghang Chen · Yang Chen · Xueyin Chen · Shaonan Liu
14 May 2026 at 18:00

Front Microbiol. 2026 Apr 28;17:1782471. doi: 10.3389/fmicb.2026.1782471. eCollection 2026.

ABSTRACT

Digestive system cancers (DSCs) represent a substantial global health burden. In recent years, the role of gut microbiota in the DSCs has garnered considerable attention, but its change pattern during tumor progression and the specific mechanisms are still not fully understood. We conducted a comprehensive systematic review to characterize patterns of gut microbiota dysbiosis across different DSC types and assess their clinical significance. We systematically searched four English and three Chinese databases up to January 2025 to identify systematic reviews focused on the dynamic characteristics of the gut microbiota during gastrointestinal tumorigenesis. Microbiota biodiversity and taxonomic composition were extracted to identify specific signatures associated with DSCs. The ROBIS tool was used to evaluate the methodological quality of the included studies. Ultimately, 59 studies involving six distinct DSC types were included. Data synthesis and comparison revealed distinct microbiota profiles across DSCs. At the phylum level, Bacillota was decreased in esophageal cancer (EC) and pancreatic ductal adenocarcinoma (PDAC), Pseudomonadota was augmented in EC but exhibited divergent trajectories in colorectal cancer (CRC) and PDAC. Genus-level analyses revealed Veillonella enrichment in EC and PDAC, and Fusobacterium outgrowth in EC, gastric cancer (GC) and CRC. Parvimonas and Streptococcus showed a concordant ascending trend in GC and CRC. Prevotella was overrepresented in EC and GC. This synthesis delineates a qualitative landscape of gut microbiota imbalances associated with various DSCs, highlighting the potential for these microbial shifts to serve as markers for early detection and targeted therapy. Multiomics integration and prospective cohort studies should be prioritized to accelerate clinical translation.

PMID:42131199 | PMC:PMC13161176 | DOI:10.3389/fmicb.2026.1782471

Received — 4 April 2026 ⏭

Distinctive respiratory toxicity induced by hypoxanthine metabolic disorder from polystyrene microplastics and nanoplastics at environmentally relevant doses: multi-omics insights and experimental validation

✇Omics In Lung
By: Xiaole Xu · Mengnan He · Meiqi Lan · Lan Qiu · Jiajing Wang · Chenghui Zhong · Liqiu Qiu · Wenfeng Lu · Jingwen Fang · Shaojuan Wang · Zhizun Deng · Yingxin Chen · Ping Guo · Yun Zhou · Lieyang Fan
1 April 2026 at 18:00

Environ Int. 2026 Mar 28;210:110212. doi: 10.1016/j.envint.2026.110212. Online ahead of print.

ABSTRACT

Microplastics (MPs) and nanoplastics (NPs) are pervasive environmental contaminants, raising concerns about their potential to cause inflammation, oxidative stress, and lung injury through respiratory toxicity. Due to their smaller size, larger surface area, and greater reactivity, NPs may pose a greater risk than MPs, yet size-dependent toxicity mechanisms remain unclear. This study investigates the distinct early molecular initiating events and toxicological effects of 1 μm polystyrene MPs (PS-MPs) and 20 nm polystyrene NPs (PS-NPs). Based on the internal exposure dose estimated from Py-GC/MS analysis, in vitro exposure concentrations were set at 0, 62.5, 125, 250, 500, and 1000 μg/mL. Multi-omics sequencing and integrative analysis identify specific proteomic and metabolomic alterations. Molecular dynamics simulations and co-immunoprecipitation assays elucidate binding interactions between PS-NPs-induced proteins and metabolic enzymes. In vitro and in vivo experiments reveal a greater accumulation of PS-NPs through endocytosis compared to PS-MPs; while pronounced histopathological damage with inflammatory response in mice lungs were only induced by PS-NPs, rather than PS-MPs. Compared to control group, PS-MPs partly caused proteomic or metabolomic perturbations, while PS-NPs induced significant differential expression of more extensive proteins and metabolites. PS-NPs exposure specifically upregulates insulin-like growth factor 2 receptor (IGF2R) expression and reduces Hypoxanthine levels when compared with PS-MPs. IGF2R directly interacts with Hypoxanthine-guanine phosphoribosyl transferase (HPRT), a key enzyme in Hypoxanthine metabolism, causing its disruption. This study provides important insights into the comparative toxic effects between PS-NPs with PS-MPs, especially the unique toxicological mechanisms of PS-NPs, thereby advancing the understanding of airborne plastic pollutant risks and supporting future regulatory assessments.

PMID:41921402 | DOI:10.1016/j.envint.2026.110212

Ferritin aggregation cell engager for CAR T avidity engineering against refractory leukemias

✇Cell
By: Feng Li · Yuxing Hu · Yan Wang · Xiao Zhang · Shaowei Qiu · Xiaoming Hu · Wenjing Wang · Shuang Wang · Peilin Guo · Cui Song · Huaiji Wang · Changhao He · Jie Sun · Di Yu · Shuhua Yi · Jun Wei · Feifan Liu · Caopei Zheng · Jianxiang Wang · Yuhua Li · Guanghui Ma · Wei Wei
9 March 2026 at 08:00
Li et al. developed a ferritin aggregation cell engager that helps CAR T cells better recognize and attack leukemia cells without re-engineering the CAR itself. This versatile platform overcomes antigen modulation and enables combination with chemotherapy.
Received — 2 April 2026 ⏭

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease

✇npj Digital Medicine
By: Jing Wang · Aocheng Zhong · Qian Xu · Haolin Huang · Yuhua Zhu · Jiaying Lu · Min Wang · Jiehui Jiang · Chengyang Li · Ming Ni · Kaicong Sun · Yihui Guan · Jie Lu · Mei Tian · Dinggang Shen · Huiwei Zhang · Qian Wang · Chuantao Zuo
30 March 2026 at 08:00

npj Digital Medicine, Published online: 30 March 2026; doi:10.1038/s41746-026-02570-0

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease

A multicenter randomized clinical trial of portable transcranial alternating current stimulation for major depressive disorder

✇npj Digital Medicine
By: Yunhao Wu · Chuanbin Ning · Lei Shi · Jijun Wang · Kuanghao Ye · Ziyu Shen · Yuhan Wang · Duanwei Wang · Huihui Guo · Xian Qiu · Yixin Pan · Halimureti Paerhati · Debo Li · Yu Mo · Jing Wan · Huilin Hu · Yu Cao · Bomin Sun · Xin Lv
28 March 2026 at 08:00

npj Digital Medicine, Published online: 28 March 2026; doi:10.1038/s41746-026-02575-9

A multicenter randomized clinical trial of portable transcranial alternating current stimulation for major depressive disorder
Received — 26 March 2026 ⏭

Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanjing Wang · S. Mostafa Mousavi · Patrick Robertson · Richard M. Allen · Alexie Barski · Robert Bosch · Nivetha Thiruverahan · Youngmin Cho · Tajinder Gadh · Steve Malkos · Boone Spooner · Greg Wimpey · Marc Stogaitis
25 March 2026 at 12:00
arXiv:2603.23322v1 Announce Type: cross Abstract: Android's Earthquake Alert (AEA) system provided timely early warnings to millions during the Mw 6.2 Marmara Ereglisi, T\"urkiye earthquake on April 23, 2025. This event, the largest in the region in 25 years, served as a critical real-world test for smartphone-based Earthquake Early Warning (EEW) systems. The AEA system successfully delivered alerts to users with high precision, offering over a minute of warning before the strongest shaking reached urban areas. This study leveraged Large Language Models (LLMs) to analyze more than 500 public social media posts from the X platform, extracting 42 distinct attributes related to user experience and behavior. Statistical analyses revealed significant relationships, notably a strong correlation between user trust and alert timeliness. Our results indicate a distinction between engineering and the user-centric definition of system accuracy. We found that timeliness is accuracy in the user's mind. Overall, this study provides actionable insights for optimizing alert design, public education campaigns, and future behavioral research to improve the effectiveness of such systems in seismically active regions.

Tuning the sensitivity of mechanosensory receptors through histidine scanning

✇Cell
By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao
18 February 2026 at 08:00
Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.
Received — 5 March 2026 ⏭

AriadneMem: Threading the Maze of Lifelong Memory for LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenhui Zhu · Xiwen Chen · Zhipeng Wang · Jingjing Wang · Xuanzhao Dong · Minzhou Huang · Rui Cai · Hejian Sang · Hao Wang · Peijie Qiu · Yueyue Deng · Prayag Tiwari · Brendan Hogan Rappazzo · Yalin Wang
5 March 2026 at 13:00
arXiv:2603.03290v1 Announce Type: cross Abstract: Long-horizon LLM agents require memory systems that remain accurate under fixed context budgets. However, existing systems struggle with two persistent challenges in long-term dialogue: (i) \textbf{disconnected evidence}, where multi-hop answers require linking facts distributed across time, and (ii) \textbf{state updates}, where evolving information (e.g., schedule changes) creates conflicts with older static logs. We propose AriadneMem, a structured memory system that addresses these failure modes via a decoupled two-phase pipeline. In the \textbf{offline construction phase}, AriadneMem employs \emph{entropy-aware gating} to filter noise and low-information message before LLM extraction and applies \emph{conflict-aware coarsening} to merge static duplicates while preserving state transitions as temporal edges. In the \textbf{online reasoning phase}, rather than relying on expensive iterative planning, AriadneMem executes \emph{algorithmic bridge discovery} to reconstruct missing logical paths between retrieved facts, followed by \emph{single-call topology-aware synthesis}. On LoCoMo experiments with GPT-4o, AriadneMem improves \textbf{Multi-Hop F1 by 15.2\%} and \textbf{Average F1 by 9.0\%} over strong baselines. Crucially, by offloading reasoning to the graph layer, AriadneMem reduces \textbf{total runtime by 77.8\%} using only \textbf{497} context tokens. The code is available at https://github.com/LLM-VLM-GSL/AriadneMem.
Received — 25 February 2026 ⏭

DICArt: Advancing Category-level Articulated Object Pose Estimation in Discrete State-Spaces

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li Zhang · Mingyu Mei · Ailing Wang · Xianhui Meng · Yan Zhong · Xinyuan Song · Liu Liu · Rujing Wang · Zaixing He · Cewu Lu
24 February 2026 at 13:00
arXiv:2602.19565v1 Announce Type: cross Abstract: Articulated object pose estimation is a core task in embodied AI. Existing methods typically regress poses in a continuous space, but often struggle with 1) navigating a large, complex search space and 2) failing to incorporate intrinsic kinematic constraints. In this work, we introduce DICArt (DIsCrete Diffusion for Articulation Pose Estimation), a novel framework that formulates pose estimation as a conditional discrete diffusion process. Instead of operating in a continuous domain, DICArt progressively denoises a noisy pose representation through a learned reverse diffusion procedure to recover the GT pose. To improve modeling fidelity, we propose a flexible flow decider that dynamically determines whether each token should be denoised or reset, effectively balancing the real and noise distributions during diffusion. Additionally, we incorporate a hierarchical kinematic coupling strategy, estimating the pose of each rigid part hierarchically to respect the object's kinematic structure. We validate DICArt on both synthetic and real-world datasets. Experimental results demonstrate its superior performance and robustness. By integrating discrete generative modeling with structural priors, DICArt offers a new paradigm for reliable category-level 6D pose estimation in complex environments.
Received — 17 February 2026 ⏭

Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rongqiang Zhao · Hengrui Hu · Yijing Wang · Mingchun Sun · Jie Liu
17 February 2026 at 13:00
arXiv:2602.14408v1 Announce Type: cross Abstract: Multimodal methods are widely used in rice deterioration detection, which exhibit limited capability in representing and extracting fine-grained abnormal features. Moreover, these methods rely on devices, such as hyperspectral cameras and mass spectrometers, increasing detection costs and prolonging data acquisition time. To address these issues, we propose a feature recalibration based olfactory-visual multimodal model for fine-grained rice deterioration detection. The fine-grained deterioration embedding constructor (FDEC) is proposed to reconstruct the labeled multimodal embedded-feature dataset, enhancing sample representation. The fine-grained deterioration recalibration attention network (FDRA-Net) is proposed to emphasize signal variations and increase sensitivity to fine-grained deterioration on the rice surface. Experiments show that the proposed method achieves a classification accuracy of 99.89%. Compared with state-of-the-art methods, the detection accuracy is improved and the procedure is simplified. Furthermore, field detection demonstrates the advantages of accuracy and operational simplicity. The proposed method can also be extended to other agrifood in agriculture and food industry.
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