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Received — 15 September 2026 ⏭

Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dawei Liu · Zongxia Li · Hongyang Du · Xiyang Wu · Shihang Gui · Yongbei Kuang · Lichao Sun
14 September 2026 at 12:00
arXiv:2604.05333v4 Announce Type: replace Abstract: As LLM agents act across personal applications, web browsers, and other interfaces, their reusable skill libraries can scale to thousands of skills. This scale introduces two challenges. First, loading the full library saturates the context window, driving up token costs, hallucination, and latency. Second, semantic retrieval surfaces topically relevant skills but can miss upstream and downstream prerequisite skills, creating a prerequisite gap that leaves the retrieved bundle insufficient for execution. We present Graph-of-Skills (GoS), an inference-time structural retrieval layer for large skill libraries. GoS constructs an executable skill graph offline from skill packages, then retrieves a bounded, dependency-aware bundle through hybrid semantic-lexical seeding, reverse-aware Personalized PageRank, and context-budgeted hydration. Across SkillsBench and ALFWorld, with three model families (Claude Sonnet 4.5, MiniMax M2.7, and GPT-5.2 Codex), GoS attains the highest average reward in all six model-benchmark blocks, at a fraction of the token cost of loading the full library. On SkillsBench with GPT-5.2 Codex it raises average reward by 7.0 absolute points over full skill loading, a 25.6% relative gain, while cutting total tokens by 56.7%. Ablations isolate the mechanism: replacing reverse traversal with forward propagation costs 9.1 reward points, a larger loss than removing the graph altogether. The gain thus comes from traversing dependencies backwards, not from graph diffusion as such. A budget-matched retrieval study holding seeding, reranking, hydration, and context budget fixed reproduces the same ordering, with dependency-pair co-recovery falling from 0.654 to 0.362. Code is available at https://github.com/davidliuk/graph-of-skills
Received — 27 May 2026 ⏭

CODESKILL: Learning Self-Evolving Skills for Coding Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanzhou Li · Yiran Zhang · Xiaoyu Zhang · Xiaoxia Liu · Yang Liu
26 May 2026 at 12:00
arXiv:2605.25430v1 Announce Type: new Abstract: Coding agents produce rich trajectories while solving software-engineering tasks. To enable agent self-evolution, these trajectories can be distilled into reusable procedural skills that compactly encode experience to guide future behavior. However, existing skill construction and maintenance methods often rely on fixed prompts and heuristic update rules, leaving it unclear how knowledge should be selected, abstracted, and maintained to best serve downstream agents. We propose CODESKILL, an LLM-based framework that reformulates skill extraction and skill-bank maintenance as a learnable management policy. CODESKILL extracts multi-granularity procedural skills from coding-agent trajectories, evolves skills with new experience, and maintains a compact skill bank for future task solving. We train CODESKILL with reinforcement learning, using a hybrid reward that combines dense rubric-based skill-quality feedback with sparse verifiable execution feedback from the frozen downstream agent. Experiments on EnvBench, SWE-Bench Verified, and Terminal-Bench 2 show that CODESKILL improves average pass rate by 9.69 over the no-skill baseline and by 4.01 over the strongest prompt-based or memory baseline, while maintaining the skill bank at a stable size during iterative construction.

Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xia Li · Xinran Liu · Lin Qi · Junyu Dong
26 May 2026 at 12:00
arXiv:2605.25385v1 Announce Type: cross Abstract: Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections by utilizing initial masks generated by our custom-designed Cascaded Mask Decoder (CMD) to guide the segmentation process and enhance edge predictions. We introduce a Context Enhancement Module(CEM) to reduce the missing detection, and a Mask-guided Feature Aggregation Module (MFAM) for effective feature aggregation. For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels. By employing a redundant processing strategy, high quality pixel-level pseudo-labels are provided for training MGNet. Extensive experiments demonstrate that our method delivers competitive performance against current state-of-the-art methods.

ANP32E drives lung adenocarcinoma progression via GSK3beta-mediated glycolytic reprogramming

✇Omics In Lung
By: Zhongliang Wang · Qianxia Li · Zeguang Ye · Xi Wu · Can Fang · Wenyang Jiang · Min Zhu
14 April 2026 at 18:00

Cell Death Dis. 2026 Apr 14. doi: 10.1038/s41419-026-08712-2. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD), a leading cause of cancer mortality, involves incompletely understood epigenetic-metabolic crosstalk. We identified ANP32E as a key regulator through multi-omics (TCGA, scRNA-seq) and clinical analyses, finding its overexpression correlates with poor prognosis. Functionally, ANP32E knockdown suppressed proliferation, migration, and glycolysis in LUAD cells (A549/H1975) and attenuated xenograft growth, while overexpression promoted tumorigenesis. Mechanistically, ANP32E transcriptionally upregulates histone demethylase KDM3B, reducing repressive H3K9me2 marks at the EGFR promoter to enhance EGFR transcription. This activates PI3K/AKT signaling, inducing inhibitory GSK3β phosphorylation. Combined with ANP32E-mediated GSK3β suppression, this dual inactivation liberates oncogenic glycolysis. Crucially, KDM3B silencing or EGFR inhibition (Cetuximab) abrogated ANP32E-driven phenotypes. High-throughput screening identified Penta-O-galloyl-β-D-glucose (PGG) as an ANP32E-targeting compound, with molecular dynamics confirming binding. PGG dose-dependently inhibited the ANP32E/KDM3B/EGFR axis in vitro and suppressed tumor growth in vivo. Thus, ANP32E drives LUAD progression via KDM3B/EGFR-mediated GSK3β inactivation, representing a prognostic biomarker and therapeutic target validated by PGG.

PMID:41980942 | DOI:10.1038/s41419-026-08712-2

Received — 13 April 2026 ⏭

FCGR2B (+) Macrophages as a Critical Node Linking Ferroptosis and Immunosuppression: A Multiomics Framework for Prognosis and Therapy in High-Grade Serous Ovarian Cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jialu Zhou · Tao Zeng · Yi Liu · Mingxia Ye · Mingxia Li · Zhe Zhang · Yuanguang Meng
9 April 2026 at 18:00

Hum Mutat. 2026 Apr 6;2026:8027584. doi: 10.1155/humu/8027584. eCollection 2026.

ABSTRACT

BACKGROUND: High-grade serous ovarian cancer (HGSOC) is characterized by a complex tumor microenvironment and poor prognosis, yet the roles of specific tumor-associated macrophages (TAMs) subpopulations in driving disease progression remain elusive.

METHODS: This study evaluated the prognostic relevance of FCGR2B in HGSOC. Single-cell RNA sequencing identified FCGR2B + TAMs as a distinct macrophage subpopulation with unique transcriptional features. Integrative analyses combining single-cell and bulk differentially expressed genes, macrophage-associated modules, and ferroptosis-related gene sets identified 26 candidate prognostic genes, from which a four-gene signature (CRYAB, PLAUR, EREG, and C5AR1) was derived to construct the prognostic risk model. The model was validated in an independent cohort. Immune infiltration, single-cell trajectory, copy number variation, and drug-gene associations were analyzed to explore the molecular and therapeutic implications of risk stratification.

RESULTS: HGSOC patients classified as high risk exhibited poorer survival outcomes, increased infiltration of M2-like macrophages, elevated expression of immune checkpoints, and enrichment of immune- and ferroptosis-related pathways. Trajectory and copy number variation analyses revealed stage-specific gene expression patterns and amplification-associated regulation. Drug-gene association analyses further suggested that high-risk patients may be more responsive to targeted therapies and proteasome inhibitors, whereas low-risk patients may benefit from conventional chemotherapy.

CONCLUSION: FCGR2B + TAMs are closely linked to HGSOC progression, and the proposed prognostic model based on FCGR2B + TAMs provides predictive value and potential therapeutic insights for patient stratification.

PMID:41953398 | PMC:PMC13054137 | DOI:10.1155/humu/8027584

Received — 8 April 2026 ⏭
Received — 6 April 2026 ⏭

Unmasking FCGR2B as a high-grade serous ovarian cancer specific marker of immune suppression and tumor progression through multi-omics mining

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jialu Zhou · Tao Zeng · Mingxia Ye · Mingxia Li · Liang Wen · Xinyi Liu · Zhe Zhang · Yuanguang Meng
4 April 2026 at 18:00

Transl Oncol. 2026 Apr 3;67:102748. doi: 10.1016/j.tranon.2026.102748. Online ahead of print.

ABSTRACT

BACKGROUND: Epithelial ovarian cancer (EOC) encompasses five major histological subtypes with marked genetic, immunological, and clinical heterogeneity. While genome-wide association studies (GWAS) have identified subtype-specific risk loci, a critical gap remains in understanding how plasma proteins influence immune-cell traits and contribute to EOC pathogenesis.

METHODS: We integrated subtype-stratified GWAS data from two EOC cohorts with plasma proteomics and immune-cell traits to construct protein-immune-EOC regulatory landscapes using a three-stage Mendelian randomization framework. Single-cell RNA-seq and multiplex immunofluorescence were employed to delineate the cellular distribution and spatial context of causal proteins. Subsequent analyses characterized immune infiltration, macrophage polarization, and clinicopathological associations. Drug-gene correlations were used to identify potential therapeutic targets, and transcriptomic analyses were applied to delineate the underlying transcriptional landscape.

RESULTS: We identified 20 subtype-specific protein-immune-EOC regulatory axes, with FCGR2B emerging as a causal plasma protein in immune regulation and high-grade serous ovarian cancer (HGSOC) progression. FCGR2B was highly expressed in tumor-associated macrophages and was associated with an M2-like polarization phenotype. Functional characterization revealed that FCGR2B was associated with shorter progression-free survival and an immunosuppressive tumor microenvironment. Transcriptomic analyses revealed altered NF-κB signaling upon FCGR2B knockdown, and drug-response data suggested a potential association between high FCGR2B expression and sensitivity to NF-κB inhibitors.

CONCLUSIONS: These findings delineate subtype-specific genetically informed protein-immune regulatory landscapes in EOC and identify FCGR2B as a key immunoregulatory and prognostic biomarker in HGSOC, suggesting FCGR2B as a potential therapeutic vulnerability that warrants further investigation.

PMID:41934917 | DOI:10.1016/j.tranon.2026.102748

Received — 2 April 2026 ⏭

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

✇Omics in Hepatocellular
By: Ke Lin · Jiayao Huang · Nan Zhang · Bin Qiao · Daopeng Yang · Jianping Guo · Xiaoyan Xie · Manxia Lin · Bowen Zhuang
25 March 2026 at 18:00

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

Received — 26 March 2026 ⏭

Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zeping Li · Hongru Wang · Yiwen Zhao · Guanhua Chen · Yixia Li · Keyang Chen · Yixin Cao · Guangnan Ye · Hongfeng Chai · Zhenfei Yin
25 March 2026 at 12:00
arXiv:2602.02050v3 Announce Type: replace Abstract: Tool-using agents based on Large Language Models (LLMs) excel in tasks such as mathematical reasoning and multi-hop question answering. However, in long trajectories, agents often trigger excessive and low-quality tool calls, increasing latency and degrading inference performance, making managing tool-use behavior challenging. In this work, we conduct entropy-based pilot experiments and observe a strong positive correlation between entropy reduction and high-quality tool calls. Building on this finding, we propose using entropy reduction as a supervisory signal and design two reward strategies to address the differing needs of optimizing tool-use behavior. Sparse outcome rewards provide coarse, trajectory-level guidance to improve efficiency, while dense process rewards offer fine-grained supervision to enhance performance. Experiments across diverse domains show that both reward designs improve tool-use behavior: the former reduces tool calls by 72.07% compared to the average of baselines, while the latter improves performance by 22.27%. These results position entropy reduction as a key mechanism for enhancing tool-use behavior, enabling agents to be more adaptive in real-world applications.

LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shuaibang Peng · Juelin Zhu · Xia Li · Kun Yang · Maojun Zhang · Yu Liu · Shen Yan
25 March 2026 at 12:00
arXiv:2603.19609v2 Announce Type: replace-cross Abstract: We present LoD-Loc v3, a novel method for generalized aerial visual localization in dense urban environments. While prior work LoD-Loc v2 achieves localization through semantic building silhouette alignment with low-detail city models, it suffers from two key limitations: poor cross-scene generalization and frequent failure in dense building scenes. Our method addresses these challenges through two key innovations. First, we develop a new synthetic data generation pipeline that produces InsLoD-Loc - the largest instance segmentation dataset for aerial imagery to date, comprising 100k images with precise instance building annotations. This enables trained models to exhibit remarkable zero-shot generalization capability. Second, we reformulate the localization paradigm by shifting from semantic to instance silhouette alignment, which significantly reduces pose estimation ambiguity in dense scenes. Extensive experiments demonstrate that LoD-Loc v3 outperforms existing state-of-the-art (SOTA) baselines, achieving superior performance in both cross-scene and dense urban scenarios with a large margin. The project is available at https://nudt-sawlab.github.io/LoD-Locv3/.
Received — 14 March 2026 ⏭

Effect of a Digital-Driven Physician-Pharmacist Collaborative Model for Diabetes in Primary Health Care: Cluster Randomized Trial

✇Journal of Medical Internet Research
By: Jie Xiao · Qing Wang · Shenglan Tan · Lei Chen · Daxiong Xiang · Haiyan Yuan · Xia Li · Shuting Huang · Bingjie Tang · Yan Guo · Haiying Huang · Danhui Zhao · Yue Li · Li Wang · Qun Li · Juan Liu · Ping Xu
14 March 2026 at 03:00
Background: Evidence-based physician-pharmacist collaborative clinics have demonstrated significant short-term benefits for patients with type 2 diabetes (T2D), but their long-term effectiveness remains unclear, especially in primary health care settings. Objective: This study aimed to explore the long-term effectiveness and cost-effectiveness of a novel, digital-driven, multifaceted physician-pharmacist collaborative model for managing patients with T2D in underresourced settings. Methods: We conducted a 12-month cluster randomized controlled trial from May 2021 to December 2022 across 6 primary health care settings in China. Guided by the theory of planned behavior, the intervention involved routine therapy from physicians along with pharmaceutical interventions from pharmacists. These were delivered through a combination of face-to-face visits and mobile health care. The intervention group received 4 face-to-face visits and biweekly remote education sessions over the 12 months. We conducted intention-to-treat analyses to estimate differences in clinical and behavior indicators between the intervention and control groups. Primary outcomes included glycosylated hemoglobin and 10-year atherosclerotic cardiovascular risk. Data were analyzed using adjusted generalized estimation equations. Results: This study included 574 patients (291 in the intervention group and 283 in the control group). Over 12 months, patients in the intervention group had significant reductions in hemoglobin A1c (–2.57 vs –1.96, respectively; P<.001; 95% CI –1.027 to –0.238) and 10-year atherosclerotic cardiovascular risk (–1.35 vs 0.01, respectively; P<.001; 95% CI –1.690 to –0.630) compared with the control group. Substantial improvements were also observed in several secondary outcomes, including fasting blood glucose, 2-hour postprandial blood glucose, waist circumference, waist-to-hip ratio, blood pressure, triglyceride, and total cholesterol. Total diabetes-related costs decreased, and patient satisfaction improved significantly in the intervention group. There were no significant differences in BMI, high-density lipoprotein, or low-density lipoprotein. Conclusions: These findings suggest that the physician-pharmacist collaborative model could improve the long-term quality and efficiency of T2D management and reduce medical costs in underresourced areas globally. Patients with T2D, especially those with central obesity or high cardiovascular risk, may benefit more from collaborative clinics. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000031839; https://www.chictr.org.cn/showproj.html?proj=51910
Received — 11 March 2026 ⏭
Received — 25 February 2026 ⏭

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 ⏭

PT-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Papers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Yu · Tianyi Wang · Ruixia Liu · Yinglong Wang
17 February 2026 at 13:00
arXiv:2602.13647v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is increasingly applied to question-answering over long academic papers, where accurate evidence allocation under a fixed token budget is critical. Existing approaches typically flatten academic papers into unstructured chunks during preprocessing, which destroys the native hierarchical structure. This loss forces retrieval to operate in a disordered space, thereby producing fragmented contexts, misallocating tokens to non-evidential regions under finite token budgets, and increasing the reasoning burden for downstream language models. To address these issues, we propose PT-RAG, an RAG framework that treats the native hierarchical structure of academic papers as a low-entropy retrieval prior. PT-RAG first inherits the native hierarchy to construct a structure-fidelity PaperTree index, which prevents entropy increase at the source. It then designs a path-guided retrieval mechanism that aligns query semantics to relevant sections and selects high relevance root-to-leaf paths under a fixed token budget, yielding compact, coherent, and low-entropy retrieval contexts. In contrast to existing RAG approaches, PT-RAG avoids entropy increase caused by destructive preprocessing and provides a native low-entropy structural basis for subsequent retrieval. To assess this design, we introduce entropy-based structural diagnostics that quantify retrieval fragmentation and evidence allocation accuracy. On three academic question-answering benchmarks, PT-RAG achieves consistently lower section entropy and evidence alignment cross entropy than strong baselines, indicating reduced context fragmentation and more precise allocation to evidential regions. These structural advantages directly translate into higher answer quality.
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