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Received — 10 September 2026 ⏭

HiRAD: A Flexible Large-Scale AGV Routing System

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunjie Huang · Ruizhong Wu · Mengxuan Zhang · Frodo Kin Sun Chan · Yan Nei Law · Lei Li
10 September 2026 at 12:00
arXiv:2609.09752v1 Announce Type: cross Abstract: Automatic Guided Vehicles (AGVs) substantially boost warehouse throughput, but routing large-scale AGV fleets remains challenging. Classical Multi-Agent Pathfinding solvers suffer from exploding combinatorial complexity and super-quadratic runtime, while relying on idealized grid or piecewise-linear motion models that mismatch real-world kinematics. Recent Reinforcement Learning (RL) solutions improve flexibility via decentralized agent policies but depend on discretized spatiotemporal representations, require millions of episodes to converge, and incur full-map observation at every step, which leads to large models, slow convergence, and high inference latency that violates real-time industrial control constraints. To address these bottlenecks, we propose HiRAD, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees: (1) a step-level spatiotemporal representation that translates continuous motion into a differentiable RL problem, (2) a hierarchical strategy that splits heading choice from velocity control to reduce the action space, and (3) an asynchronous event-driven decision pipeline that lowers inference complexity from O(n^2) to O(n) and cuts per-step latency by as much as 71 percent. Across random graphs and two warehouse maps, HiRAD reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.
Received — 27 May 2026 ⏭

CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fangtai Wu · Hailong Guo · Shijie Huang · Jiayi Song · Yubo Huang · Mushui Liu · Zhao Wang · Yunlong Yu · Jiaming Liu · Ruihua Huang
26 May 2026 at 12:00
arXiv:2605.25378v1 Announce Type: cross Abstract: Customized image editing aims to equip pre-trained diffusion models with specific visual effects using limited paired data, typically via Low-Rank Adaptation (LoRA). As the number of desired effects grows, storing and dynamically loading numerous these effect LoRAs significantly increases deployment overhead. Furthermore, current pipelines typically cascade these effect LoRAs with acceleration modules for fast generation, which triggers severe parameter interference and results in concept bleeding and style degradation. We propose CollectionLoRA, a multi-teacher on-policy distillation framework capable of distilling the concepts of up to 50 different effect LoRAs along with few-step generation capabilities into a single LoRA. This fundamentally resolves the feature interference issue and significantly reduces deployment costs. Specifically, the method introduces (i) a Probabilistic Dual-Stream Routing mechanism that enables the model to randomly switch between data sources during training, effectively enhancing its generalization in unseen scenarios; (ii) an Asymmetric Orthogonal Prompting strategy to achieve concept isolation within the prompt space; (iii) a Coarse-to-Fine Distillation Objective to mitigate the distribution gap between the teacher and student models. Extensive evaluations show that CollectionLoRA distills all customized effects and few-step generation into a single LoRA, reducing deployment overhead while achieving concept fidelity comparable to or better than independently trained teacher models.
Received — 13 April 2026 ⏭

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇Omics In Lung
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Epigenome-wide Mendelian randomization with multi-omics validation identifies epigenetic drivers of idiopathic pulmonary fibrosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhaoqi Li · Luna Hong · Xingyu Lv · Shuangyu Yang · Fengzhan Ye · Linjie Huang · Shanping Jiang · Huiying Zhao
11 April 2026 at 18:00

Commun Biol. 2026 Apr 11. doi: 10.1038/s42003-026-10033-1. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a complex disease without clear etiology or effective therapy. While DNA methylation has been implicated in IPF pathogenesis, the tissue-specific causal effects of the epigenetic factors on IPF remain undetermined. Here, we perform epigenome-wide Mendelian randomization using blood-based methylation quantitative trait loci of 420,509 CpG sites and genome-wide association study for IPF to elucidate the causal effects of the CpG sites on IPF. Totally, 452 CpG sites has shown putative causal effects on IPF risk after Bonferroni correction. Among them, 13 CpG sites have shown strong colocalization evidence with genetic factors associated with IPF. Specifically, DNA methylation at CpG sites within MAN2A2 and TRIM27 shows significant differences between IPF lungs and controls, correlating with altered mRNA expressions of these genes in lung tissues. The CpG site in MAN2A2 is a binding site of ZNF384 according to transcription factor databases. RNA sequencing in the TGFβ1-induced alveolar epithelia confirms significantly reduced expression of MAN2A2 and ZNF384 comparing to the controls. Collectively, our study suggests a putative causal link between DNA methylation within MAN2A2 and IPF risk, wherein lung-specific DNA methylation in MAN2A2 may perturb the interaction between ZNF384 and MAN2A2, revealing novel roles for these genes in IPF pathogenesis.

PMID:41965819 | DOI:10.1038/s42003-026-10033-1

Received — 4 April 2026 ⏭

RAE-AR: Taming Autoregressive Models with Representation Autoencoders

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hu Yu · Hang Xu · Jie Huang · Zeyue Xue · Haoyang Huang · Nan Duan · Feng Zhao
3 April 2026 at 12:00
arXiv:2604.01545v1 Announce Type: new Abstract: The latent space of generative modeling is long dominated by the VAE encoder. The latents from the pretrained representation encoders (e.g., DINO, SigLIP, MAE) are previously considered inappropriate for generative modeling. Recently, RAE method lights the hope and reveals that the representation autoencoder can also achieve competitive performance as the VAE encoder. However, the integration of representation autoencoder into continuous autoregressive (AR) models, remains largely unexplored. In this work, we investigate the challenges of employing high-dimensional representation autoencoders within the AR paradigm, denoted as \textit{RAE-AR}. We focus on the unique properties of AR models and identify two primary hurdles: complex token-wise distribution modeling and the high-dimensionality amplified training-inference gap (exposure bias). To address these, we introduce token simplification via distribution normalization to ease modeling difficulty and improve convergence. Furthermore, we enhance prediction robustness by incorporating Gaussian noise injection during training to mitigate exposure bias. Our empirical results demonstrate that these modifications substantially bridge the performance gap, enabling representation autoencoder to achieve results comparable to traditional VAEs on AR models. This work paves the way for a more unified architecture across visual understanding and generative modeling.

AeroTherm-GPT: A Verification-Centered LLM Framework for Thermal Protection System Engineering Workflows

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chuhan Qiao · Jinglai Zheng · Jie Huang · Buyue Zhao · Fan Li · Haiming Huang
3 April 2026 at 12:00
arXiv:2604.01738v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into hypersonic thermal protection system (TPS) design is bottlenecked by cascading constraint violations when generating executable simulation artifacts. General-purpose LLMs, treating generation as single-pass text completion, fail to satisfy the sequential, multi-gate constraints inherent in safety-critical engineering workflows. To address this, we propose AeroTherm-GPT, the first TPS-specialized LLM Agent, instantiated through a Constraint-Closed-Loop Generation (CCLG) framework. CCLG organizes TPS artifact generation as an iterative workflow comprising generation, validation, CDG-guided repair, execution, and audit. The Constraint Dependency Graph (CDG) encodes empirical co-resolution structure among constraint categories, directing repair toward upstream fault candidates based on lifecycle ordering priors and empirical co-resolution probabilities. This upstream-priority mechanism resolves multiple downstream violations per action, achieving a Root-Cause Fix Efficiency of 4.16 versus 1.76 for flat-checklist repair. Evaluated on HyTPS-Bench and validated against external benchmarks, AeroTherm-GPT achieves 88.7% End-to-End Success Rate (95% CI: 87.5-89.9), a gain of +12.5 pp over the matched non-CDG ablation baseline, without catastrophic forgetting on scientific reasoning and code generation tasks.

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.
Received — 5 March 2026 ⏭

MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siqi Ma · Jiajie Huang · Fan Zhang · Yue Shen · Jinlin Wu · Guohui Fan · Zhu Zhang · Zelin Zang
4 March 2026 at 13:00
arXiv:2509.23725v3 Announce Type: replace Abstract: Answering complex medical questions requires not only domain expertise and patient-specific information, but also structured and multi-perspective reasoning. Existing multi-agent approaches often rely on fixed roles or shallow interaction prompts, limiting their ability to detect and resolve fine-grained logical inconsistencies. To address this, we propose \textsc{MedLA}, a logic-driven multi-agent framework built on large language models. Each agent organizes its reasoning process into an explicit logical tree based on syllogistic triads (major premise, minor premise, and conclusion), enabling transparent inference and premise-level alignment. Agents engage in a multi-round, graph-guided discussion to compare and iteratively refine their logic trees, achieving consensus through error correction and contradiction resolution. We demonstrate that \textsc{MedLA} consistently outperforms both static role-based systems and single-agent baselines on challenging benchmarks such as MedDDx and standard medical QA tasks. Furthermore, \textsc{MedLA} scales effectively across both open-source and commercial LLM backbones, achieving state-of-the-art performance and offering a generalizable paradigm for trustworthy medical reasoning.
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