Segmented poly(A) tails with microRNA target sites confer tissue-specific regulation for mRNA therapeutics

By: Rui Qi · Hua Chen · Rui Chen · Hui Bao · Ying Sun · Ruiwen Xu · Lu Han · Yingmei Xu · Juan Li · Na Li · Qiang Li · Tingting Zhang · Kai Lv · Yijie Dong · Shan Cen · Weiguo Zhang

8 September 2026 at 08:00

Zhang and colleagues engineered the poly(A) tail as a programmable regulatory element, showing that embedding cell-type-specific microRNA target sites directly within it confers robust, position-dependent silencing in off-target tissues while preserving activity in target cells. This strategy offers a new modular tool to enhance mRNA therapeutic safety and precision.

Show-Harness: Just a VLM Agent Can Play Robots

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

By: Yanzhe Chen · Zechen Bai · Zhijun Cao · Wenzheng Zeng · Kevin Qinghong Lin · Yiqi Lin · Guoqiang Liang · Kevin Yuchen Ma · Qiming Huang · Mike Zheng Shou

10 September 2026 at 12:00

arXiv:2609.10522v1 Announce Type: cross Abstract: Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots through a compact semantic interface linking intent to action. Show-Harness exposes discrete semantic action units that VLMs can naturally reason over, while embodiment-specific interpreters deterministically ground them into local robot actions, keeping the VLM directly responsible for fine-grained physical decisions. Through the same interface, Show-Harness demonstrates the feasibility of (1) directly unlocking closed-source frontier VLMs for zero-shot robot control, and (2) adapting small-scale open-source VLMs for low-cost deployment with just a few GPU-hours of fine-tuning. We further develop GUMI (GUI Manipulation Interface), which extends the same semantic action space to GUI-based demonstration collection, allowing humans and agents to "play" robots across embodiments without specialized teleoperation hardware. Extensive experiments show that Show-Harness-equipped VLM agents generalize robustly across tasks, embodiments, and environments, outperforming representative agentic and VLA paradigms. These results suggest that the right interface can unlock substantial embodied capability from foundation VLMs, without requiring additional model capacity or costly embodiment-specific pretraining.

Research Status and Prospects of <em>Helicobacter pylori</em>-associated gastritis: From Mechanisms to Traditional Chinese Medicine Treatment

Omics in Gastric

By: Zhongyao Fan · Ziling Jin · Caiyue Liu · Ruifan Hu · Weiqiang Li

8 September 2026 at 18:00

Gastroenterol Res Pract. 2026 Sep 7;2026:3413458. doi: 10.1155/grp/3413458. eCollection 2026.

ABSTRACT

Helicobacter pylori-associated gastritis (HPAG) is a chronic inflammatory condition of the gastric mucosa caused by Helicobacter pylori infection, serving as the core etiological factor for peptic ulcers and gastric precancerous lesions. Given the persistently high global infection rates and the escalating burden of antibiotic resistance, conventional eradication therapies are encountering significant challenges. This article systematically delineates the molecular pathogenic mechanisms underlying HPAG, encompassing bacterial virulence factors, host immune responses, aberrant signaling pathways, oxidative stress, epigenetic regulation, and mucosal barrier damage. Building upon this foundation and in alignment with international mainstream diagnostic and therapeutic guidelines, we summarize the research progress of traditional Chinese medicine (TCM) interventions from a novel perspective of microecological homeostasis regulation. Specifically, we clarify the multifaceted roles of TCM monomers and formulas in immunomodulation, mucosal repair, and antibacterial synergism. By systematically synthesizing existing evidence from TCM studies, we construct a whole-course TCM intervention framework for HPAG that integrates "susceptibility prevention, active treatment, and posteradication repair." Furthermore, we critically analyze the current clinical translation bottlenecks and the limitations inherent in the "black-box" research paradigm of TCM monomers and formulas, and propose future research directions driven by multiomics technologies. This work provides both theoretical support and practical references for precise integrated Chinese and Western medicine diagnosis and treatment of HPAG.

PMID:42707495 | PMC:PMC13548316 | DOI:10.1155/grp/3413458

Research Status and Prospects of <em>Helicobacter pylori</em>-associated gastritis: From Mechanisms to Traditional Chinese Medicine Treatment

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Zhongyao Fan · Ziling Jin · Caiyue Liu · Ruifan Hu · Weiqiang Li

8 September 2026 at 18:00

Gastroenterol Res Pract. 2026 Sep 7;2026:3413458. doi: 10.1155/grp/3413458. eCollection 2026.

ABSTRACT

Helicobacter pylori-associated gastritis (HPAG) is a chronic inflammatory condition of the gastric mucosa caused by Helicobacter pylori infection, serving as the core etiological factor for peptic ulcers and gastric precancerous lesions. Given the persistently high global infection rates and the escalating burden of antibiotic resistance, conventional eradication therapies are encountering significant challenges. This article systematically delineates the molecular pathogenic mechanisms underlying HPAG, encompassing bacterial virulence factors, host immune responses, aberrant signaling pathways, oxidative stress, epigenetic regulation, and mucosal barrier damage. Building upon this foundation and in alignment with international mainstream diagnostic and therapeutic guidelines, we summarize the research progress of traditional Chinese medicine (TCM) interventions from a novel perspective of microecological homeostasis regulation. Specifically, we clarify the multifaceted roles of TCM monomers and formulas in immunomodulation, mucosal repair, and antibacterial synergism. By systematically synthesizing existing evidence from TCM studies, we construct a whole-course TCM intervention framework for HPAG that integrates "susceptibility prevention, active treatment, and posteradication repair." Furthermore, we critically analyze the current clinical translation bottlenecks and the limitations inherent in the "black-box" research paradigm of TCM monomers and formulas, and propose future research directions driven by multiomics technologies. This work provides both theoretical support and practical references for precise integrated Chinese and Western medicine diagnosis and treatment of HPAG.

PMID:42707495 | PMC:PMC13548316 | DOI:10.1155/grp/3413458

Complete biosynthesis of the anticancer cephalotaxinone and homoerythratine

Cell

By: Runze Tian · Feifan Lin · Nianxin Guo · Chendi Liu · Kaiqi Chen · Yunxi Han · Ruiyun Lan · Qiang Li · Jianbin Yan · Xiaoguang Lei

11 June 2026 at 08:00

Complete biosynthetic pathways for cephalotaxinone and homoerythratine were elucidated from the endangered plant Cephalotaxus fortunei. Thirteen key enzymes were identified, including two homologous cytochrome P450 enzymes that catalyze a rare divergent oxidation process governing alkaloid scaffold diversification. Full pathway reconstitution in Nicotiana benthamiana establishes a foundation for the sustainable production of the anticancer agent homoharringtonine.

Self-supervised Hierarchical Visual Reasoning with World Model

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

By: Yuanfei Xu · Lin Liu · Wengang Zhou · Mingxiao Feng · Houqiang Li

26 May 2026 at 12:00

arXiv:2605.17537v2 Announce Type: replace Abstract: 3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings. While existing self-supervised visual foresight reasoning approaches often suffer from multi-step error accumulation, many recent studies resort to injecting domain-specific knowledge for more stable guidance. Our key insight is that the photorealistic fidelity of visual reasoning representations is secondary; what truly matters is providing informative, task-relevant signals. To this end, we propose ResDreamer, a hierarchical world model in which each higher-level layer is trained to reconstruct the residuals of the layer below. This design enables progressive abstraction of increasingly sophisticated world dynamics and fosters the emergence of richer latent representations. Drawing inspiration from the "Bitter Lesson", ResDreamer trains its reasoning representations in a purely self-supervised manner. The higher-level residual representations are used to modulate lower-level predictions, allowing the world model to scale effectively with only linearly increasing cross-layer communication costs. Experiments show that ResDreamer achieves state-of-the-art sample efficiency and parameter efficiency. This scalable hierarchical visual foresight reasoning architecture paves the way for more capable online RL agents in open-ended, dynamic environments. The code is accessible at https://github.com/XuYuanFei01/ResDreamer.

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

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

By: Shiqi Liu · Zeyu He · Guojian Zhan · Letian Tao · Zhilong Zheng · Jiang Wu · Yinuo Wang · Yang Guan · Kehua Sheng · Bo Zhang · Keqiang Li · Jingliang Duan · Shengbo Eben Li

26 May 2026 at 12:00

arXiv:2602.15620v5 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often suffer from late-stage performance collapse, leading to degraded reasoning quality and unstable training. We identify a key factor behind this instability: a small fraction of tokens, termed spurious tokens (around 0.01%), which contribute little to the reasoning outcome but receive disproportionately amplified gradient updates due to inheriting the full sequence-level reward. We present a unified framework for evaluating token-level optimization impacts across spurious risk, gradient norms, and entropy changes. Building on the analysis of token characteristics that severely disrupt optimization, we propose the Silencing Spurious Tokens (S2T) mechanism to efficiently suppress their gradient perturbations. Incorporating this mechanism into a group-based objective, we propose Spurious-Token-Aware Policy Optimization (STAPO), which promotes stable and effective large-scale model refinement. Across six mathematical reasoning benchmarks using Qwen 1.7B, 8B, and 14B base models, STAPO consistently demonstrates superior entropy stability and achieves an average performance improvement of 11.49% ($\rho_{\mathrm{T}}$=1.0, top-p=1.0) and 3.73% ($\rho_{\mathrm{T}}$=0.7, top-p=0.9) over GRPO, 20-Entropy, and JustRL.

Circulating Tumor Cells in Pancreatic Ductal Adenocarcinoma: The Systemic Execution Hub of Metastasis

MRD

By: Liang Hao · Shi-Yang Lu · Fu-Ying Zhao · Jia-Mei Wang · Bai-Qiang Li · Ting Zhang · Zhen-Xian Du · Hua-Qin Wang

18 May 2026 at 18:00

Pharmacol Res. 2026 May 17:108253. doi: 10.1016/j.phrs.2026.108253. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) exemplifies early systemic dissemination, with circulating tumor cells (CTCs) at its core. We advance a unified conceptual framework that positions CTCs as the systemic execution hub of PDAC metastasis, dynamic entity that coordinates the metastatic cascade via four cardinal functions: Seeding, Adapting, Engineering, and Signaling. Integrating eco-evolutionary dynamics, this hub actively drives phenotypic selection, niche remodeling, and immune evasion, while providing real-time biologic intelligence through liquid biopsy. Robust clinical correlation has not yet translated into routine practice because of technical variability, biological complexity, and a lack of interventional evidence. We therefore propose an evidence-driven, phased roadmap: grounded in prospective clinical cohort data, progressing from immediate multi-center technical standardization and pragmatic trials, such as minimal residual disease (MRD)-triggered salvage therapy, to mid-term biomarker-driven adjuvant trials and long-term integration into multimodal liquid biopsy ecosystems, aimed at intercepting this execution hub. By reframing CTCs from correlative indicators to actionable therapeutic targets and dynamic sentinels, this framework charts a path toward transforming the management of this recalcitrant systemic disease.

PMID:42150733 | DOI:10.1016/j.phrs.2026.108253

Engineered immunosuppressive dendritic cells protect against cardiac remodelling

Nature - Issue - nature.com science feeds

By: Xiaoying Li · Jiamin Li · Guohua Li · Lisheng Zhu · Guo Cheng · Huanqiang Li · Hao Lin · Ningqing Jia · Xiaoqian Hong · Ye Liu · Zhiwei Zhong · Yize Chen · Biqing Wang · Jing Zhao · Zhenqi Hua · Lingjun Wang · Qiming Chen · Peijie Zheng · Shuyuan Sheng · Songting Gu · Cheng Ni · Shuchang Ye · Changle Ke · Feimu Zhang · Mo Li · Shaohui Shi · Junhua He · Yan Wu · Yinghui Xu · Minjian Kong · Qi Chen · Huajun Li · Yu Zhang · Jianzhong Sun · Guanhua Hu · Chengchen Zhao · Yiping Dong · Lili Yu · Yang Xu · Xinyang Hu

8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10346-5

Lesion-targeted immune modulation is a feasible strategy to control cardiac fibrosis, and engineered dendritic cells are a promising therapeutic platform for treating cardiac remodelling and heart failure.

New approach methodologies for drug discovery

Cell

By: Wenqiang Liu · Paul D. Pang · Catherine A. Wu · Danilo Tagle · Joseph C. Wu

2 April 2026 at 08:00

New approach methodologies that are human-centered, such as stem cells, organoids, and in silico models, can span the full drug discovery pipeline—from disease modeling to efficacy testing—and hold promise as both tools for drug discovery and potential therapies.

Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities

Journal of Medical Internet Research

By: Ziwen Yu · Anthony Mulholland · Tianyan Huang · Qiang Liu

26 March 2026 at 04:30

Background: Early detection of Alzheimer disease (AD) is essential for timely intervention; yet, diagnostic performance varies widely across modalities and datasets. Recent multimodal artificial intelligence (AI) models have made significant progress, but the evidence base remains fragmented due to heterogeneous datasets, modeling frameworks, and reporting quality. Objective: This systematic review aimed to analyze studies on multimodal AI models for AD diagnosis, prognosis, and risk prediction over 5 years. We evaluated dataset characteristics, modality combinations, modeling strategies, performance metrics, and methodological limitations. We further discuss real-world implications and translational pathways. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched PubMed, IEEE Xplore, Scopus, ACM Digital Library, Cochrane, and arXiv, with the final datasets last searched on November 15, 2025. Studies applying multimodal machine learning or deep learning to AD, mild cognitive impairment, and dementia outcomes were included, whereas studies using a single modality or lacking sufficient methodological detail were excluded. QUADAS-2 (Revised Quality Assessment of Diagnostic Accuracy Studies tool) assessed risk of bias. Extracted performance results were synthesized across 4 major multimodal dataset families. Results: A total of 66 studies met the inclusion criteria. Across datasets, multimodal models consistently outperformed single-modal baselines. Alzheimer’s Disease Neuroimaging Initiative–based diagnosis achieved an average accuracy of 92.5% (SD 3.8%), while mild cognitive impairment–conversion models achieved an average area under the curve (AUC) of 0.922 (SD 0.045), and several fusion architectures reported AUCs above 0.95. In contrast, UK Biobank risk-prediction studies reported an average AUC of 0.84 (SD 0.056), and this reflects performance in large, population-based datasets. DementiaBank speech-language studies achieved an average AUC of 0.813 (SD 0.042), and cross-lingual AD detection achieved an accuracy of 77% (SD 6.5%). Self-collected multimodal datasets demonstrated average accuracies around 96% (SD 2.4%), but their generalizability is limited due to small sample sizes and single-center designs. Conclusions: This systematic review demonstrates that multimodal AI models consistently outperform single-modal models for AD diagnosis, prognosis, and risk prediction by integrating complementary biological, clinical, and behavioral information. Unlike prior reviews, this review provides a unified synthesis across heterogeneous clinical, imaging, genetic, and linguistic datasets, enabling cross-domain comparison of modeling strategies and performance. However, the generalizability of reported performance was limited due to substantial heterogeneity in dataset composition, outcome definitions, and validation, and prevalent risks of bias. By evaluating these factors, this review clarifies where current evidence is robust and where caution is warranted. The findings highlight the need for standardized multimodal benchmarks, transparent evaluation protocols, and clinically grounded model design to enable reliable real-world deployment. Overall, this work advances the field by framing multimodal AI not only as a performance-driven tool but also as a translational framework for equitable, interpretable, and scalable AD diagnosis. Trial Registration: PROSPERO CRD420251241895;

WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement

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

By: Fangyuan Li · Pengfei Li · Shijie Wang · Junqi Gao · Jianxing Liu · Biqing Qi · Yuqiang Li

25 March 2026 at 12:00

arXiv:2603.22352v1 Announce Type: cross Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely on curated data environments. We present \textbf{WIST}, a \textbf{W}eb-grounded \textbf{I}terative \textbf{S}elf-play \textbf{T}ree framework for domain-targeted reasoning improvement that learns directly from the open web without requiring any pre-arranged domain corpus. WIST incrementally expands a domain tree for exploration, and retrieves and cleans path-consistent web corpus to construct a controllable training environment. It then performs Challenger--Solver self-play with verifiable rewards, and feeds learnability signals back to update node posteriors and guide subsequent exploration through an adaptive curriculum. Across four backbones, WIST consistently improves over the base models and typically outperforms both purely endogenous self-evolution and corpus-grounded self-play baselines, with the Overall gains reaching \textbf{+9.8} (\textit{Qwen3-4B-Base}) and \textbf{+9.7} (\textit{OctoThinker-8B}). WIST is also domain-steerable, improving \textit{Qwen3-8B-Base} by \textbf{+14.79} in medicine and \textit{Qwen3-4B-Base} by \textbf{+5.28} on PhyBench. Ablations further confirm the importance of WIST's key components for stable open-web learning. Our Code is available at https://github.com/lfy-123/WIST.

Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation

16 March 2026 at 12:00

arXiv:2603.12581v1 Announce Type: cross Abstract: Although diffusion models have achieved remarkable progress in multi-modal magnetic resonance imaging (MRI) translation tasks, existing methods still tend to suffer from anatomical inconsistencies or degraded texture details when handling arbitrary missing-modality scenarios. To address these issues, we propose a latent diffusion-based multi-modal MRI translation framework, termed MSG-LDM. By leveraging the available modalities, the proposed method infers complete structural information, which preserves reliable boundary details. Specifically, we introduce a style--structure disentanglement mechanism in the latent space, which explicitly separates modality-specific style features from shared structural representations, and jointly models low-frequency anatomical layouts and high-frequency boundary details in a multi-scale feature space. During the structure disentanglement stage, high-frequency structural information is explicitly incorporated to enhance feature representations, guiding the model to focus on fine-grained structural cues while learning modality-invariant low-frequency anatomical representations. Furthermore, to reduce interference from modality-specific styles and improve the stability of structure representations, we design a style consistency loss and a structure-aware loss. Extensive experiments on the BraTS2020 and WMH datasets demonstrate that the proposed method outperforms existing MRI synthesis approaches, particularly in reconstructing complete structures. The source code is publicly available at https://github.com/ziyi-start/MSG-LDM.

Physics vs Distributions: Pareto Optimal Flow Matching with Physics Constraints

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

By: Giacomo Baldan · Qiang Liu · Alberto Guardone · Nils Thuerey

24 February 2026 at 13:00

arXiv:2506.08604v4 Announce Type: replace-cross Abstract: Physics-constrained generative modeling aims to produce high-dimensional samples that are both physically consistent and distributionally accurate, a task that remains challenging due to often conflicting optimization objectives. Recent advances in flow matching and diffusion models have enabled efficient generative modeling, but integrating physical constraints often degrades generative fidelity or requires costly inference-time corrections. Our work is the first to recognize the trade-off between distributional and physical accuracy. Based on the insight of inherently conflicting objectives, we introduce Physics-Based Flow Matching (PBFM) a method that enforces physical constraints at training time using conflict-free gradient updates and unrolling to mitigate Jensen's gap. Our approach avoids manual loss balancing and enables simultaneous optimization of generative and physical objectives. As a consequence, physics constraints do not impede inference performance. We benchmark our method across three representative PDE benchmarks. PBFM achieves a Pareto-optimal trade-off, competitive inference speed, and generalizes to a wide range of physics-constrained generative tasks, providing a practical tool for scientific machine learning. Code and datasets available at https://github.com/tum-pbs/PBFM.

Cross-Modal Purification and Fusion for Small-Object RGB-D Transmission-Line Defect Detection

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

By: Jiaming Cui · Wenqiang Li · Shuai Zhou · Ruifeng Qin · Feng Shen

24 February 2026 at 13:00

arXiv:2602.01696v5 Announce Type: replace-cross Abstract: Transmission line defect detection remains challenging for automated UAV inspection due to the dominance of small-scale defects, complex backgrounds, and illumination variations. Existing RGB-based detectors, despite recent progress, struggle to distinguish geometrically subtle defects from visually similar background structures under limited chromatic contrast. This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled purify-then-fuse paradigm. CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification via a learned codebook to suppress modality-specific noise while preserving defect-discriminative information, and a Contextual Semantic Integration Framework that captures global spatial dependencies using partial-channel attention to enhance structural semantic reasoning. Position-wise normalization within the purification stage enforces explicit reconstruction-driven cross-modal alignment, ensuring statistical compatibility between heterogeneous features prior to fusion. Extensive experiments on the TLRGBD benchmark, where 94.5% of instances are small objects, demonstrate that CMAFNet achieves 32.2% mAP@50 and 12.5% APs, outperforming the strongest baseline by 9.8 and 4.0 percentage points, respectively. A lightweight variant reaches 24.8% mAP50 at 228 FPS with only 4.9M parameters, surpassing all YOLO-based detectors while matching transformer-based methods at substantially lower computational cost.

STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens

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

By: Shiqi Liu · Zeyu He · Guojian Zhan · Letian Tao · Zhilong Zheng · Jiang Wu · Yinuo Wang · Yang Guan · Kehua Sheng · Bo Zhang · Keqiang Li · Jingliang Duan · Shengbo Eben Li

24 February 2026 at 13:00

arXiv:2602.15620v3 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often suffer from late-stage performance collapse, leading to degraded reasoning quality and unstable training. Our analysis shows that the magnitude of token-wise policy gradients in RL is negatively correlated with token probability and local policy entropy. We find that training instability can be caused by a tiny fraction of tokens, approximately 0.01%, which we term spurious tokens. When such tokens appear in correct responses, they contribute little to the reasoning outcome but inherit the full sequence-level reward, leading to abnormally amplified gradient updates. To mitigate this instability, we design an S2T (silencing spurious tokens) mechanism to efficiently identify spurious tokens through characteristic signals with low probability, low entropy, and positive advantage, and then suppress their gradient perturbations during optimization. Incorporating this mechanism into a group-based objective, we propose Spurious-Token-Aware Policy Optimization (STAPO), which promotes stable and effective large-scale model refinement. Across six mathematical reasoning benchmarks using Qwen 1.7B, 8B, and 14B base models, STAPO consistently demonstrates superior entropy stability and achieves an average performance improvement of 7.13% ($\rho_{\mathrm{T}}$=1.0, top-p=1.0) and 3.69% ($\rho_{\mathrm{T}}$=0.7, top-p=0.9) over GRPO, 20-Entropy, and JustRL.

Cautious Optimizers: Improving Training with One Line of Code

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

By: Kaizhao Liang · Lizhang Chen · Bo Liu · Qiang Liu

17 February 2026 at 13:00

arXiv:2411.16085v4 Announce Type: replace-cross Abstract: AdamW has been the default optimizer for transformer pretraining. For many years, our community searched for faster and more stable optimizers with only constrained positive outcomes. In this work, we propose a \textbf{one-line modification in Pytorch} to any momentum-based optimizer, which we rename cautious optimizer, e.g. C-AdamW and C-Lion. Our theoretical result shows that this modification preserves Adam's Hamiltonian function and it does not break the convergence guarantee under the Lyapunov analysis. In addition, a whole new family of optimizers is revealed by our theoretical insight. Among them, we pick the simplest one for empirical experiments, showing not only consistent speed-up on LLM pretraining, but also image classification, with minimum extra tuning on hyperparameters. Code is available at https://github.com/kyleliang919/C-Optim.

Cross-Modal Purification and Fusion for Small-Object RGB-D Transmission-Line Defect Detection

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

By: Jiaming Cui · Wenqiang Li · Shuai Zhou · Ruifeng Qin · Feng Shen

17 February 2026 at 13:00

arXiv:2602.01696v3 Announce Type: replace-cross Abstract: Transmission line defect detection remains challenging for automated UAV inspection due to the dominance of small-scale defects, complex backgrounds, and illumination variations. Existing RGB-based detectors, despite recent progress, struggle to distinguish geometrically subtle defects from visually similar background structures under limited chromatic contrast. This paper proposes CMAFNet, a Cross-Modal Alignment and Fusion Network that integrates RGB appearance and depth geometry through a principled purify-then-fuse paradigm. CMAFNet consists of a Semantic Recomposition Module that performs dictionary-based feature purification via a learned codebook to suppress modality-specific noise while preserving defect-discriminative information, and a Contextual Semantic Integration Framework that captures global spatial dependencies using partial-channel attention to enhance structural semantic reasoning. Position-wise normalization within the purification stage enforces explicit reconstruction-driven cross-modal alignment, ensuring statistical compatibility between heterogeneous features prior to fusion. Extensive experiments on the TLRGBD benchmark, where 94.5% of instances are small objects, demonstrate that CMAFNet achieves 32.2% mAP@50 and 12.5% APs, outperforming the strongest baseline by 9.8 and 4.0 percentage points, respectively. A lightweight variant reaches 24.8% mAP50 at 228 FPS with only 4.9M parameters, surpassing all YOLO-based detectors while matching transformer-based methods at substantially lower computational cost.

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