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Received — 15 September 2026 ⏭

Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Benchmark Construction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Baoyang Jiang · Fengchun Zhang · Leyuan Wang · Haotian Li · Yida Wang · Zhe Ji · Jinshan Lai · Xi Ren · Danyang Li · Zheng Yang · Jianwei Hu · Qiang Ma
14 September 2026 at 12:00
arXiv:2609.13082v1 Announce Type: new Abstract: Agentic systems offer a promising way to automate embodied benchmark construction, but existing approaches typically cover isolated stages or remain specialized to predefined environments and task families. More importantly, multi-step construction produces dependent intermediate artifacts that are often passed downstream without artifact-specific verification, allowing local defects to propagate into the final benchmark. We present Embodied-BenchForge, an agentic framework that transforms user-specified evaluation intents into complete embodied benchmark artifacts. It formulates construction as Closed-Loop Benchmark Synthesis, integrating forward artifact synthesis with backward verification and repair. Skill-Orchestrated Artifact Synthesis composes typed and reusable skills into executable workflows, while an artifact dependency graph records intermediate outputs and their dependencies. Requirement-Guided Verification and Repair applies artifact-specific contracts throughout construction and uses provenance to trigger local re-execution or upstream rollback when verification fails. Embodied-BenchForge constructs six benchmarks covering diverse embodied scenarios in the Offline EQA Track, together with one interactive benchmark containing 220 executable tasks in the Interactive Embodied Track. Evaluations of representative MLLMs and embodied agents show that the benchmarks distinguish model capabilities in both observation-based understanding and closed-loop execution. Quality assessment and ablations validate benchmark quality and the effectiveness of verification and repair, while repair and skill-reuse analyses demonstrate efficient localized recovery and cross-benchmark reusability.

Integrated Metabolomic and Transcriptomic Analysis Suggests Potential Therapeutic Mechanism of Shengxian Decoction in Hypobaric Hypoxia-Induced Pulmonary Hypertension in SD Rats

✇Omics In Lung
By: Yue Lin · Junjun Han · Guiqin Liu · Kang'an Cheng · Xiangyang Li · Ming Ren
10 September 2026 at 18:00

Drug Des Devel Ther. 2026 Sep 5;20:603123. doi: 10.2147/DDDT.S603123. eCollection 2026.

ABSTRACT

BACKGROUND: High-altitude hypoxia can trigger maladaptive cardiopulmonary responses, with hypoxia-induced pulmonary hypertension (HPH) representing a major clinical challenge with limited therapeutic options. Shengxian Decoction (SXT), a classical traditional Chinese medicine formula for treating "qi deficiency and sinking", has shown clinical benefits, but the molecular pathways associated with its effects remain incompletely understood.

METHODS: Male Sprague-Dawley rats were exposed to simulated high altitude (5000 m; 404 mmHg, 10.8% O2) for 28 days and treated with SXT at three doses (1.8, 3.6, or 7.2 g/kg/day; n = 6/group). Integrated serum metabolomics (UHPLC-Q-TOF-MS) and lung transcriptomics (RNA-seq) were applied. Multivariate analysis, pathway enrichment, weighted gene co-expression network analysis, and cross-omics correlation were used for data integration. After randomization, allocation concealment and blinding were strictly implemented throughout all experimental procedures, with all interventions and outcome assessments performed by personnel blinded to group assignment until completion of data analysis.

RESULTS: Chronic hypoxia induced HPH with elevated mPAP, RVHI, RVWI and pulmonary vascular remodeling (increased WT% and WA%), while SXT dose-dependently ameliorated these abnormalities and restored hypoxia-disrupted metabolomic and transcriptomic profiles, with the high-dose group showing the most pronounced effect. Chronic hypoxia induced pronounced metabolic and transcriptional remodeling, with model animals clearly separated from controls in principal component analysis. Most differentially expressed genes exhibited downregulated expression, indicating global transcriptional suppression. SXT treatment dose-dependently restored both metabolomic and transcriptomic profiles, with the high-dose group most closely resembling controls. These pyruvate-proximal nodes may represent potential points of convergence through which SXT-associated metabolic and transcriptional alterations are coordinated. The relationships reported here are based on cross-omics associations, and causal inference will require further functional validation.

CONCLUSION: These findings suggest that SXT may ameliorate HPH partly through coordinated regulation of metabolic pathways and gene networks, particularly those related to energy metabolism, rather than fully explaining disease pathogenesis. The study provides multi-omics evidence supporting the traditional concept of "replenishing qi and elevating sunken qi" and identifies candidate metabolic biomarkers for further investigation. However, the results should be interpreted cautiously because of the relatively small sample size, the lack of functional validation experiments, and the exploratory nature of the biomarker findings. Further mechanistic and clinical studies are required to confirm these observations.

PMID:42719424 | PMC:PMC13557172 | DOI:10.2147/DDDT.S603123

Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies

✇Omics in Gastric
By: Ke Su · Zunyuan Tan · Han Li · Kai You · Susu Xiao · Haiqing Chen · Danyang Li · Xin Liu · Xinrui Du · Jiaxin Hu · Kun He · Yantian Xu · Jian Chen · Yunwei Han
10 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70150. doi: 10.1002/biof.70150.

ABSTRACT

Gastrointestinal malignancies, including gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of hematologic malignancies, its efficacy in gastrointestinal solid tumors remains limited by antigen heterogeneity, insufficient trafficking and infiltration, immunosuppressive tumor microenvironments, on-target off-tumor toxicity, and adaptive resistance. In this review, we summarize the current landscape of CAR-T therapy in gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic cancer, with a focus on representative target antigens and emerging biomarker strategies. We further discuss two major categories of biomarkers: target antigen-related biomarkers and conventional dynamic biomarkers, including serum tumor markers, cytokine changes, CAR-T expansion kinetics, and antigen-loss monitoring. In addition, we highlight how single-cell ribonucleic acid sequencing and spatial transcriptomics provide complementary insights into cellular states, immune exhaustion, stromal barriers, and spatially restricted immune exclusion. By integrating these multi-omics approaches with biomarker-guided patient stratification and next-generation CAR-T engineering, gastrointestinal solid tumor CAR-T therapy may evolve from empirical optimization toward mechanism-driven and precision-guided clinical translation.

PMID:42717494 | PMC:PMC13558850 | DOI:10.1002/biof.70150

Received — 13 September 2026 ⏭

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma

✇Omics in Hepatocellular
By: Nanbin Liu · Kun Li · Junqiao Feng · Shan Wang · Ji Li · Wenbin Yang · Senyang Li · Keping Feng
11 September 2026 at 18:00

Clin Exp Med. 2026 Jul 26;26(1):329. doi: 10.1007/s10238-026-02264-7.

ABSTRACT

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

PMID:42726304 | PMC:PMC13569553 | DOI:10.1007/s10238-026-02264-7

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Nanbin Liu · Kun Li · Junqiao Feng · Shan Wang · Ji Li · Wenbin Yang · Senyang Li · Keping Feng
11 September 2026 at 18:00

Clin Exp Med. 2026 Jul 26;26(1):329. doi: 10.1007/s10238-026-02264-7.

ABSTRACT

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

PMID:42726304 | PMC:PMC13569553 | DOI:10.1007/s10238-026-02264-7

Key Experimental Therapeutics and Knowledge Gaps in Metabolic Dysfunction-Associated Steatohepatitis (MASH)

✇Omics in Hepatocellular
By: Hanyang Liu · Mingyuan Zhang · Frank Tacke
10 September 2026 at 18:00

Drug Des Devel Ther. 2026 Sep 5;20:543657. doi: 10.2147/DDDT.S543657. eCollection 2026.

ABSTRACT

Metabolic dysfunction-associated steatohepatitis (MASH) is not solely a disorder of hepatocellular lipid accumulation, but a multicellular disease driven by coordinated metabolic stress, sterile inflammation, fibrogenesis, and niche remodeling. Recent therapeutic progress with the provisional approval of resmetirom and semaglutide has validated MASH as a tractable clinical target. However, many experimental agents have shown limited or inconsistent efficacy, particularly for regression of hepatic fibrosis or cirrhosis, reflecting the biological heterogeneity and dynamic cellular architecture of the disease. Distinct from conventional pathway- or drug class-based reviews, we summarize emerging therapeutics through a liver cell-centered framework, integrating hepatocyte-directed metabolic therapies, immune-cell modulation, hepatic stellate cell-targeted antifibrotic strategies, niche-directed approaches involving liver sinusoidal endothelial cells and cholangiocytes, systemic multi-cell modulators, and precision-delivery technologies. We further compare how these interventions reshape pathogenic communication among hepatic and extrahepatic compartments, while emphasizing unresolved challenges in drug target selection, cellular specificity, disease-stage dependency, safety, and patient stratification. This perspective emphasizes the need to move from isolated pathway targeting toward cell- and network-informed therapeutic strategies supported by spatial multi-omics, human-relevant models, and precision delivery.

PMID:42719321 | PMC:PMC13557022 | DOI:10.2147/DDDT.S543657

Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies

✇Omics in Hepatocellular
By: Ke Su · Zunyuan Tan · Han Li · Kai You · Susu Xiao · Haiqing Chen · Danyang Li · Xin Liu · Xinrui Du · Jiaxin Hu · Kun He · Yantian Xu · Jian Chen · Yunwei Han
10 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70150. doi: 10.1002/biof.70150.

ABSTRACT

Gastrointestinal malignancies, including gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of hematologic malignancies, its efficacy in gastrointestinal solid tumors remains limited by antigen heterogeneity, insufficient trafficking and infiltration, immunosuppressive tumor microenvironments, on-target off-tumor toxicity, and adaptive resistance. In this review, we summarize the current landscape of CAR-T therapy in gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic cancer, with a focus on representative target antigens and emerging biomarker strategies. We further discuss two major categories of biomarkers: target antigen-related biomarkers and conventional dynamic biomarkers, including serum tumor markers, cytokine changes, CAR-T expansion kinetics, and antigen-loss monitoring. In addition, we highlight how single-cell ribonucleic acid sequencing and spatial transcriptomics provide complementary insights into cellular states, immune exhaustion, stromal barriers, and spatially restricted immune exclusion. By integrating these multi-omics approaches with biomarker-guided patient stratification and next-generation CAR-T engineering, gastrointestinal solid tumor CAR-T therapy may evolve from empirical optimization toward mechanism-driven and precision-guided clinical translation.

PMID:42717494 | PMC:PMC13558850 | DOI:10.1002/biof.70150

Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ke Su · Zunyuan Tan · Han Li · Kai You · Susu Xiao · Haiqing Chen · Danyang Li · Xin Liu · Xinrui Du · Jiaxin Hu · Kun He · Yantian Xu · Jian Chen · Yunwei Han
10 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70150. doi: 10.1002/biof.70150.

ABSTRACT

Gastrointestinal malignancies, including gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of hematologic malignancies, its efficacy in gastrointestinal solid tumors remains limited by antigen heterogeneity, insufficient trafficking and infiltration, immunosuppressive tumor microenvironments, on-target off-tumor toxicity, and adaptive resistance. In this review, we summarize the current landscape of CAR-T therapy in gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic cancer, with a focus on representative target antigens and emerging biomarker strategies. We further discuss two major categories of biomarkers: target antigen-related biomarkers and conventional dynamic biomarkers, including serum tumor markers, cytokine changes, CAR-T expansion kinetics, and antigen-loss monitoring. In addition, we highlight how single-cell ribonucleic acid sequencing and spatial transcriptomics provide complementary insights into cellular states, immune exhaustion, stromal barriers, and spatially restricted immune exclusion. By integrating these multi-omics approaches with biomarker-guided patient stratification and next-generation CAR-T engineering, gastrointestinal solid tumor CAR-T therapy may evolve from empirical optimization toward mechanism-driven and precision-guided clinical translation.

PMID:42717494 | PMC:PMC13558850 | DOI:10.1002/biof.70150

Received — 10 September 2026 ⏭

Inhaled nanosilica orchestrates a pulmonary macrophage-NK cell axis for memory-like NK programming toward synergistic cancer immunotherapy

✇Molecular Therapy
By: Feng Yuan · Qi Li · Yingnan Liu · Fangfang Liu · Wenhe Luo · Qingle Song · Yanqiao Zeng · Jiacheng Bi · Guofang Zhang · Yang Li
2 September 2026 at 08:00
Yuan and colleagues demonstrate that inhaled biodegradable nanosilica activates an alveolar macrophage–NK axis, triggering an IL-12/15/18 triad that programs memory-like NK cells. This non-fibrotic, cell-free strategy suppresses melanoma growth, prevents postsurgical recurrence, and synergizes with anti-PD-1, establishing a robust framework for in vivo NK cell immunotherapy.

ACC1 inhibition enhances BCG-induced trained immunity by reprogramming acetyl-CoA metabolism

✇Molecular Therapy
By: Ilayda Baydemir · Fabian Nikolka · Özlem Bulut · Anaísa V. Ferreira · Gizem Kilic · Büsra Geckin · Robbin Kramer · Jorge Cuenca-Escalona · Evelien Floor · Marisol Báez-Magaña · Vasiliki Matzaraki · Jeroen Deckers · Tom Anbergen · Yuri van Elsas · Thijs Beldman · Simone JCFM. Moorlag · L. Charlotte J de Bree · Victoria Spanou · Theano Andriopoulou · Yang Li · Evangelia Karagianni · Boris Novakovic · Yahya Sohrabi · Leo A.B. Joosten · Karsten Hiller · Jorge Domínguez-Andrés
25 August 2026 at 08:00
The efficacy of vaccines remains suboptimal in many settings, underscoring the need for new strategies. Baydemir and colleagues show that modulation of acetyl-CoA metabolism reshapes metabolic and epigenetic programs underlying Bacille Calmette-Guérin-induced trained immunity, enhancing cellular innate immune responses and identifying immunometabolic targeting as a promising approach to improve vaccine efficacy.

Developmental deviations of association-network structural connectivity in youths with ADHD predict symptom and treatment outcomes

✇Nature Biomedical Engineering
By: Xiaoyu Xu · Zhao Fu · Haoshu Xu · Kangfuxi Zhang · Jing Cong · Hang Yang · Yang Li · Yilu Zhao · Xiangyu Zheng · Arielle S. Keller · Ting Xu · Runsen Chen · Weidong Cai · Qingjiu Cao · Li Yang · Zaixu Cui
31 August 2026 at 08:00

Nature Biomedical Engineering, Published online: 31 August 2026; doi:10.1038/s41551-026-01779-4

This large-scale study of white matter structural connectivity during development reveals biomarkers associated with attention deficit hyperactivity disorder in youth that track symptom trajectories and predict differential treatment response.

Structural Process Supervision for Latent Chain-of-Thought Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiqi Li · Xu Chen · Chen Ju · Jiangchao Yao · Zhaoyang Li · Jinsong Lan · Xiaoyong Zhu · Bo Zheng · Yu Wang
10 September 2026 at 12:00
arXiv:2609.09928v1 Announce Type: new Abstract: Latent reasoning approaches enhance token-level efficiency and robustness by replacing verbose, explicit chain-of-thought (CoT) tokens with compact continuous-space embeddings. However, existing methods lack direct process supervision over these latent embeddings, which often leads to representation collapse and uneven information distribution. To address this, we propose Prototype-Mediated Process Supervision (PMPS), which introduces learnable reasoning prototypes as semantic anchors to provide structural process-level supervision for latent reasoning. PMPS projects latent embeddings and explicit CoT embeddings into a shared prototype space, achieving many-to-many soft alignment between unequal-length representations through prototype assignment. Meanwhile, we introduce a Progressive Sequential Alignment (PSA) module to further guide training: positional priors initially encourage sequential alignment structure, then gradually relax to permit adaptive matching. Experimental results show that PMPS compresses output token length to under 50% of explicit CoT on GSM8K-Aug. Compared to leading baseline SIM-CoT, our method achieves average accuracy gains of 2.08% across different model families. On GPT-2, PMPS even surpasses CoT-SFT. On larger models and a more challenging task, PMPS consistently attains the highest accuracy among all latent reasoning methods with comparable output length.

CS-Guard: Benchmarking LLM Guardrails for Code Generation Security

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinyang Li · Mingyu Guo · Hung X. Nguyen
10 September 2026 at 12:00
arXiv:2609.09798v1 Announce Type: cross Abstract: Large language models (LLMs) have been ex- ploited to generate malware, but the effective- ness of guardrails for code generation secu- rity remains unclear. We introduce CS-Guard, the first benchmark to systematically evalu- ate guardrails for code generation security. It covers 1) text-to-code generation with 1000 high-quality malware-generation prompts, 7 jailbreak attacks, and a novel fictional scenario attack (FSA) that embeds malicious intent in a legitimate fictional software-development sce- nario; and 2) code-to-code generation with 331 code prompts spanning code infilling, code completion, and code translation. We empiri- cally evaluate 9 guardrails across seven LLMs. We find that current guardrails perform poorly against malicious code-generation re- quests: for text-to-code, the average attack success rate (ASR) after jailbreaks reaches about 50% for many guardrails; for code-to- code, average ASR approaches 100% on base LLMs and remains high across many guardrails (14.4% to nearly 100%). Our FSA also achieves ASR close to 100% across many guardrails, raising major reliability concerns for real-world software development. To sup- port future research, CS-Guard uses a modular three-layer guardrail taxonomy that lets devel- opers register guardrails for evaluation. We release the benchmark and data to enable fur- ther community evaluation.

A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chunxu Zhang · Bo Li · Wenliang Wang · Yang Liu · Di Jiang · Yuan Huang · Yo-ichi Nabeshima · Akinori Yamamura · Bo Yang · Qiang Yang
10 September 2026 at 12:00
arXiv:2609.10108v1 Announce Type: cross Abstract: Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questions requires learning from large molecular datasets distributed across medical centers, where privacy constraints prevent centralized data sharing. Federated learning offers a natural solution but faces four challenges in this setting: limited local sample sizes, sparse and directional inter-center trust, the need to retain discriminative age prediction while supporting interpretation, and model drift and forgetting under heterogeneous cross-center data. We propose TNFL, a trust-network-based federated learning framework that progressively propagates models along directed pairwise trust relations without centralized aggregation. TNFL combines an age-aware mixture-of-experts model with generative replay to preserve previously learned information and reduce forgetting and drift. Experiments across multiple molecular datasets show that TNFL enables effective aging-clock prediction with limited local data, provides interpretable age-dependent prediction patterns, and maintains stable performance across interaction orders. To investigate the biological questions, we analyze TNFL-identified pairwise protein interactions and their higher-order organization through functional and network analyses. The identified interactions repeatedly form coordinated higher-order subnetworks spanning multiple aging-related biological systems, with several proteins recurring across subnetworks. These findings suggest that TNFL captures molecular relationships beyond isolated pairwise associations and reveals coherent higher-order biological organization associated with aging.

LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaoan Wang · Aocheng Luo · Fei Huang · Jingyi Xu · Xiaoyang Wang · Yueyu Wang · Qianli Ma · Fan Yang · Ran Mei · Jia Wei · Jiangpeng Hu · Xuhao Liu · Hongming Chen · Yuanbin Shao · Yiyang Lin · Ziliang Li · Liang Pan · Xinhang Liu · Yuntao Ma · Tingxiang Fan
10 September 2026 at 12:00
arXiv:2608.30935v2 Announce Type: replace-cross Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.

Galanin impairs tumor immunity in glioblastoma by promoting infiltration and ferroptosis resistance of myeloid-derived suppressor cells

✇Nature Cancer
By: Lizhi Pang · Yang Liu · Fei Zhou · Songlin Guo · Fatima Khan · Heba Ali · Hardik Shah · Sara Huber · Barbara Kofler · Craig M. Horbinski · Peiwen Chen
25 August 2026 at 08:00

Nature Cancer, Published online: 25 August 2026; doi:10.1038/s43018-026-01221-3

Chen and colleagues report that the neuropeptide galanin impairs antitumor immunity in glioblastoma by interacting with its receptor GALR3 on monocytic myeloid-derived suppressor cells, promoting their infiltration and ferroptosis resistance.
Received — 27 May 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • BODHI: Precise OS Kernel Specification Inference Zhiming Chang · Ziyang Li
    arXiv:2605.23931v1 Announce Type: new Abstract: The formal verification of operating system kernels requires precise specifications that capture the intended behavior of system calls. Writing these specifications manually demands deep domain expertise, motivating the use of large language models (LLMs) to automate the process. However, in OSV-Bench, a benchmark of 245 specification generation tasks derived from the Hyperkernel OS kernel, the best reported Pass@1 is 55.10%. We propose a domain k
     
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BODHI: Precise OS Kernel Specification Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhiming Chang · Ziyang Li
26 May 2026 at 12:00
arXiv:2605.23931v1 Announce Type: new Abstract: The formal verification of operating system kernels requires precise specifications that capture the intended behavior of system calls. Writing these specifications manually demands deep domain expertise, motivating the use of large language models (LLMs) to automate the process. However, in OSV-Bench, a benchmark of 245 specification generation tasks derived from the Hyperkernel OS kernel, the best reported Pass@1 is 55.10%. We propose a domain knowledge prompting method (BODHI), which augments the standard few-shot prompt with a structured C-to-Python translation guide covering 15 categories of domain-specific translation patterns. Inspired by Structured Chain-of-Thought (SCoT) prompting, the guide organizes translation by separation of concerns, addressing pre-condition extraction and post-condition generation as distinct categories. Evaluated on nine models from six providers (Anthropic, Mistral, Amazon, DeepSeek, Meta, Alibaba), covering dense, mixture-of-experts and reasoning architectures, BODHI improves every model tested, with gains ranging from +11% to +32%. The best configuration (Claude Opus 4.6 + BODHI) reaches 96.73% Pass@1. BODHI reduces both syntax and semantic errors, with the strongest effect on models that have sufficient instruction-following capability to utilize structured reference material. These results demonstrate that domain knowledge injection is a model-agnostic technique that substantially bridges the gap between general-purpose code generation and formal specification synthesis.

From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruizhe Zhou · Xiaoyang Liu · Gaoyuan Du · Yi Zheng · Shouxi Ren · Deepayan Chakrabarti · Dengdu Jiang
26 May 2026 at 12:00
arXiv:2605.23955v1 Announce Type: new Abstract: Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reproducibility. While early financial ML addressed statistical challenges such as backtest overfitting, deep neural networks and Generative AI have introduced mechanical nondeterminism rooted in hardware and architecture. This survey provides a systems perspective on reproducibility failures across three modalities now dominant in financial AI: tabular models (post-hoc explanation variance), graph networks (stochastic sampling and temporal asynchrony), and LLM-based agentic workflows (batch-dependent divergence and trajectory drift). We supplement the literature analysis with first-party experiments on public financial datasets -- quantifying explanation rank instability in credit scoring, prediction flip rates in GNN-based fraud detection, and tensor-parallel-induced output divergence in LLM entity extraction. We propose a layered evaluation framework linking modality-specific metrics (RBO, D_cos, TDI, PSD) to audit readiness, and empirically validate the complementarity of logit-level and semantic-level determinism measures.
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