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Received — 18 September 2026 ⏭

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qiao-Sheng Zhang · Jun-Jie Xu · Zhen-Yu Sun · Zhao-Man Zhong · Jie Liu · Yan-Li Wu · Wan-Qin Li · Meng-Jie Hu · Hong-Peng Li
17 September 2026 at 18:00

Yi Chuan. 2026 Sep;48(9):931-945. doi: 10.16288/j.yczz.25-275.

ABSTRACT

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

PMID:42751828 | DOI:10.16288/j.yczz.25-275

Received — 10 September 2026 ⏭

Intranasal delivery of a vasoactive intestinal peptide-based circRNA vaccine induces systemic and mucosal immunity against RSV in mice

✇Molecular Therapy
By: Junqi Xiang · Jie Xu · Jing Zhu · Bo Yang · Mingling Wan · Jiangyu Cai · Shuying Chen · Yuncheng Wang · Shanrong Yang · Yaokai Wang · Li Lin · Qiqi Zhang · Na Zang · Enmei Liu · Yushun Wan
19 August 2026 at 08:00
A vasoactive intestinal peptide (VIP)-based protein carrier self-assembles with respiratory syncytial virus circular RNA vaccines for intranasal delivery, inducing systemic antibodies, mucosal IgA, and Th1-biased protection in mice. This platform offers a protein-guided strategy for respiratory mucosal RNA vaccination and broadens the application of VIP in vaccine delivery.

Reconstituting human primitive streak formation through extra-embryonic cell coordination

✇Cell
By: Qiaoyan Shen · Xin Zhang · Naixin Chen · Xiaodi Yan · Hongan Ren · Xizhuang Sun · Linjie Xu · Jianchao Zhao · Rong Li · Sirui Lin · Xiaojie Jia · Leqian Yu · Yulei Wei
24 June 2026 at 08:00
Tissue patterning events of early human embryo development, including an embryo-disc with primitive streak-like structures, are captured using an in vitro model where embryonic stem cells are cultured with extra-embryonic cell types.
Received — 27 May 2026 ⏭

Stop Comparing LLM Agents Without Disclosing the Harness

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunbei Zhang · Janet Wang · Yingqiang Ge · Weijie Xu · Jihun Hamm · Chandan K. Reddy
26 May 2026 at 12:00
arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely the infrastructure layer that governs context construction, tool interaction, orchestration, and verification around a language model, is often a stronger determinant of agent performance than the model it wraps. We formalize and defend the Binding Constraint Thesis: in this regime, performance variance is governed more by harness configuration than by model choice, and current evaluation protocols therefore systematically misattribute harness-level gains to model improvements. We support this thesis along three lines. First, a control-theoretic formalization treats the harness as the controller of a closed-loop dynamical system and the LLM as the stochastic policy it governs, which explains why small harness changes can produce performance shifts that exceed those obtained by substituting one model for another. Second, published benchmarks, industry deployments, and a controlled variance decomposition show that harness-induced variance can substantially exceed model-induced variance, including cases of model ranking reversal. Third, we propose a harness-aware evaluation framework with a disclosure standard and a variance decomposition protocol. Until harness specifications are disclosed, leaderboard comparisons for long-horizon agents should be treated as incomplete and potentially misleading.

AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinyi Luo · Yiqiao Jin · Weichen Yu · Mengqi Zhang · Srijan Kumar · Xiaoxiao Li · Weijie Xu · Xin Chen · Jindong Wang
26 May 2026 at 12:00
arXiv:2602.03955v3 Announce Type: replace Abstract: While large language model (LLM) multi-agent systems achieve superior reasoning performance through iterative debate, practical deployment is limited by their high computational cost and error propagation. This paper proposes AgentArk, a novel framework to distill multi-agent dynamics into the weights of a single model, effectively transforming explicit test-time interactions into implicit model capabilities. This equips a single agent with the intelligence of multi-agent systems while remaining computationally efficient. Specifically, we investigate three hierarchical distillation strategies across various models, tasks, scaling, and scenarios: reasoning-enhanced fine-tuning; trajectory-based augmentation; and process-aware distillation. By shifting the burden of computation from inference to training, the distilled models preserve the efficiency of one agent while exhibiting strong reasoning and self-correction performance of multiple agents. They further demonstrate enhanced robustness and generalization across diverse reasoning tasks. We hope this work can shed light on future research on efficient and robust multi-agent development. Our code is at https://github.com/AIFrontierLab/AgentArk.
Received — 13 April 2026 ⏭

Applications and challenges of multi-omics approaches in lung cancer research and precision treatment

✇Omics In Lung
By: Shouyu Zhang · Renjie Xu · Qian Zheng · Bojiang Chen · Weimin Li
8 April 2026 at 18:00

Front Genet. 2026 Mar 23;16:1722368. doi: 10.3389/fgene.2025.1722368. eCollection 2025.

ABSTRACT

Lung cancer is one of the most common cancers worldwide and one of the leading causes of cancer death, with a heavy disease burden and severe public health challenges. Multi-omics techniques, such as genomics, proteomics, metabolomics, and radiomics, play a crucial role in the early diagnosis and treatment of lung cancer, revealing the molecular characteristics and mechanisms of lung cancer, and have significant clinical application value. However, it also faces numerous challenges, such as data issues, "black box" problems, and ethical and legal concerns. How to leverage strengths while mitigating weaknesses, achieve clinical translation of technology, and serve patients more effectively deserves our deep reflection. This article reviews the specific applications and challenges of multi-omics methods in lung cancer research and personalized treatment.

PMID:41948518 | PMC:PMC13050793 | DOI:10.3389/fgene.2025.1722368

Received — 8 April 2026 ⏭

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaojie Xu · Zongyuan Li · Chang Lu · Runnan Qi · Yanan Ni · Lumin Jiang · Xiangbei Liu · Xuebo Zhang · Yongchun Fang · Kuihua Huang · Xian Guo · Zhanghua Wu · Zhenya Li
7 April 2026 at 12:00
arXiv:2502.13388v3 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness.

ATLAS: A Layered Constraint-Guided Framework for Structured Artifact Generation in LLM-Assisted MDE

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tong Ma · Hui Lai · Hui Wang · Zhenhu Tian · Chaochao Li · Fengjie Xu · Ling Fang
7 April 2026 at 12:00
arXiv:2510.25890v3 Announce Type: replace-cross Abstract: ATLAS is a constraint-guided generation framework for structured engineering artifacts whose outputs must satisfy explicit schemas, domain rules, and audit requirements. Rather than treating a large language model as a standalone generator, ATLAS places generation inside a model-driven workflow that separates domain representation, constraint compilation, and post-generation validation. ATLAS combines three components. A metamodel-integration stage builds a typed representation of domain entities and relations; in this study, it operates over authoritative AUTOSAR meta-model assets. An Integrated Constraint Model (ICM) compiles heterogeneous requirements into two operational layers: generation-time structural constraints and post-generation semantic/logical obligations. Constraint-Guided, Validation-Backed Generation (CVG) then combines Layer~1 constrained decoding, Layer~2 backend validation, and audit-guided repair. In the AUTOSAR instantiation, these Layer~2 obligations are realized through SHACL/SMT-style checks, illustrating how the same ICM can be connected to domain-specific validation backends. We evaluate ATLAS on AUTOSAR artifact generation at both single-file and multi-file scales. In the evaluated AUTOSAR setting, ATLAS consistently produces schema-valid single-file outputs and preserves perfect file completeness and XSD validity at multi-file scale, while SHACL/SMT checks and result analysis continue to expose residual system-level defects. The empirical picture is therefore one of bounded automation: ATLAS secures structural validity and turns higher-level failures into explicit, diagnosable objects within the generation workflow.
Received — 4 April 2026 ⏭

The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinlei Yu · Zhangquan Chen · Yongbo He · Tianyu Fu · Cheng Yang · Chengming Xu · Yue Ma · Xiaobin Hu · Zhe Cao · Jie Xu · Guibin Zhang · Jiale Tao · Jiayi Zhang · Siyuan Ma · Kaituo Feng · Haojie Huang · Youxing Li · Ronghao Chen · Huacan Wang · Chenglin Wu · Zikun Su · Xiaogang Xu · Kelu Yao · Kun Wang · Chen Gao · Yue Liao · Ruqi Huang · Tao Jin · Cheng Tan · Jiangning Zhang · Wenqi Ren · Yanwei Fu · Yong Liu · Yu Wang · Xiangyu Yue · Yu-Gang Jiang · Shuicheng Yan
3 April 2026 at 12:00
arXiv:2604.02029v1 Announce Type: new Abstract: Latent space is rapidly emerging as a native substrate for language-based models. While modern systems are still commonly understood through explicit token-level generation, an increasing body of work shows that many critical internal processes are more naturally carried out in continuous latent space than in human-readable verbal traces. This shift is driven by the structural limitations of explicit-space computation, including linguistic redundancy, discretization bottlenecks, sequential inefficiency, and semantic loss. This survey aims to provide a unified and up-to-date landscape of latent space in language-based models. We organize the survey into five sequential perspectives: Foundation, Evolution, Mechanism, Ability, and Outlook. We begin by delineating the scope of latent space, distinguishing it from explicit or verbal space and from the latent spaces commonly studied in generative visual models. We then trace the field's evolution from early exploratory efforts to the current large-scale expansion. To organize the technical landscape, we examine existing work through the complementary lenses of mechanism and ability. From the perspective of Mechanism, we identify four major lines of development: Architecture, Representation, Computation, and Optimization. From the perspective of Ability, we show how latent space supports a broad capability spectrum spanning Reasoning, Planning, Modeling, Perception, Memory, Collaboration, and Embodiment. Beyond consolidation, we discuss the key open challenges, and outline promising directions for future research. We hope this survey serves not only as a reference for existing work, but also as a foundation for understanding latent space as a general computational and systems paradigm for next-generation intelligence.

Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaojie Xu · Zongyuan Li · Chang Lu · Runnan Qi · Yanan Ni · Lumin Jiang · Xiangbei Liu · Xuebo Zhang · Yongchun Fang · Kuihua Huang · Xian Guo · Zhanghua Wu · Zhenya Li
3 April 2026 at 12:00
arXiv:2502.13388v2 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness.
Received — 2 April 2026 ⏭

SkillTester: Benchmarking Utility and Security of Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Leye Wang · Zixing Wang · Anjie Xu
1 April 2026 at 12:00
arXiv:2603.28815v1 Announce Type: cross Abstract: This technical report presents SkillTester, a tool for evaluating the utility and security of agent skills. Its evaluation framework combines paired baseline and with-skill execution conditions with a separate security probe suite. Grounded in a comparative utility principle and a user-facing simplicity principle, the framework normalizes raw execution artifacts into a utility score, a security score, and a three-level security status label. More broadly, it can be understood as a comparative quality-assurance harness for agent skills in an agent-first world. The public service is deployed at https://skilltester.ai, and the broader project is maintained at https://github.com/skilltester-ai/skilltester.

TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiucheng Yu · Ruijie Xu · Mingang Chen · Xuequan Lu · Jianfeng Dong · Chaochao Lu · Xin Tan
1 April 2026 at 12:00
arXiv:2603.29759v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental limitations: (1) heavy reliance on synthetic datasets constructed via simulation software, creating a significant domain gap with real-world environments; (2) oversimplified safety tasks with artificial constraints on hazard and scene types, thereby limiting model generalization; and (3) absence of rigorous evaluation protocols to thoroughly assess model capabilities in complex home safety scenarios. To address these challenges, we introduce TSHA (\textbf{T}rustworthy \textbf{S}afety \textbf{H}azards \textbf{A}ssessment), a comprehensive benchmark comprising 81,809 carefully curated training samples drawn from four complementary sources: existing indoor datasets, internet images, AIGC images, and newly captured images. This benchmark set also includes a highly challenging test set with 1707 samples, comprising not only a carefully selected subset from the training distribution but also newly added videos and panoramic images containing multiple safety hazards, used to evaluate the model's robustness in complex safety scenarios. Extensive experiments on 23 popular VLMs demonstrate that current VLMs lack robust capabilities for safety hazard assessment. Importantly, models trained on the TSHA training set not only achieve a significant performance improvement of up to +18.3 points on the TSHA test set but also exhibit enhanced generalizability across other benchmarks, underscoring the substantial contribution and importance of the TSHA benchmark.
Received — 11 March 2026 ⏭

Rethinking SNN Online Training and Deployment: Gradient-Coherent Learning via Hybrid-Driven LIF Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zecheng Hao · Yifan Huang · Zijie Xu · Wenxuan Liu · Yuanhong Tang · Zhaofei Yu · Tiejun Huang
10 March 2026 at 12:00
arXiv:2410.07547v3 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence due to their brain-inspired and energy-efficient properties. Compared to vanilla Spatial-Temporal Back-propagation (STBP) training methods, online training can effectively avoid the risk of GPU memory explosion. However, current online learning frameworks cannot tackle the gradient discrepancy problem between the forward and backward process, merely aiming to optimize the GPU memory, resulting in no performance advantages compared to the STBP-based models in the inference stage. To address the aforementioned challenges, we propose Hybrid-Driven Leaky Integrate-and-Fire (HD-LIF) model family for efficient online learning, which respectively adopt different spiking calculation mechanism in the upper-region and lower-region of the firing threshold. We theoretically point out that our learning framework can effectively separate temporal gradients and address the misalignment problem of surrogate gradients, as well as achieving full-stage optimization towards learning precision, memory complexity and power consumption. Experimental results have demonstrated that our scheme is enable to achieve state-of-the-art performance for multiple evaluation metrics, breaking through the traditional paradigm of SNN online training and deployment. Code is available at \href{https://github.com/hzc1208/HD_LIF}{here}.
Received — 25 February 2026 ⏭

RDBLearn: Simple In-Context Prediction Over Relational Databases

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanlin Zhang · Linjie Xu · Quan Gan · David Wipf · Minjie Wang
24 February 2026 at 13:00
arXiv:2602.18495v1 Announce Type: cross Abstract: Recent advances in tabular in-context learning (ICL) show that a single pretrained model can adapt to new prediction tasks from a small set of labeled examples, avoiding per-task training and heavy tuning. However, many real-world tasks live in relational databases, where predictive signal is spread across multiple linked tables rather than a single flat table. We show that tabular ICL can be extended to relational prediction with a simple recipe: automatically featurize each target row using relational aggregations over its linked records, materialize the resulting augmented table, and run an off-the-shelf tabular foundation model on it. We package this approach in \textit{RDBLearn} (https://github.com/HKUSHXLab/rdblearn), an easy-to-use toolkit with a scikit-learn-style estimator interface that makes it straightforward to swap different tabular ICL backends; a complementary agent-specific interface is provided as well. Across a broad collection of RelBench and 4DBInfer datasets, RDBLearn is the best-performing foundation model approach we evaluate, at times even outperforming strong supervised baselines trained or fine-tuned on each dataset.
Received — 17 February 2026 ⏭

Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiran Guo · Zhongjian Qiao · Yingqi Xie · Jie Liu · Dan Ye · Ruiqing Zhang · Shuang Qiu · Lijie Xu
17 February 2026 at 13:00
arXiv:2602.14169v1 Announce Type: cross Abstract: Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space. Existing methods face notable limitations: GRPO samples exclusively from the root, saturating high-probability trajectories while leaving deep, error-prone states under-explored. Tree-based methods blindly disperse budgets across trivial or unrecoverable states, causing sampling dilution that fails to uncover rare correct suffixes and destabilizes local baselines. To address this, we propose Deep Dense Exploration (DDE), a strategy that focuses exploration on $\textit{pivots}$-deep, recoverable states within unsuccessful trajectories. We instantiate DDE with DEEP-GRPO, which introduces three key innovations: (1) a lightweight data-driven utility function that automatically balances recoverability and depth bias to identify pivot states; (2) local dense resampling at each pivot to increase the probability of discovering correct subsequent trajectories; and (3) a dual-stream optimization objective that decouples global policy learning from local corrective updates. Experiments on mathematical reasoning benchmarks demonstrate that our method consistently outperforms GRPO, tree-based methods, and other strong baselines.
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