❌

Normal view

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.

Fetal monitoring for high-risk pregnancies using a wearable ultrasound patch

✇Nature Biotechnology - Issue - nature.com science feeds
By: Geonho Park · Yizhou Bian · Hao Huang · Sai Zhou · Siyu Qin · Muyang Lin · Xinyi Yang · Aaron Lee · Anand Ramkumar · Mariana Tome · Jayne Lander · Xiangjun Chen · Shenghan Wang · Pranavi Bheemreddy · Liam Stanton · Ren Sheng · Guihuan Guo · Mabel Shehada · Ruotao Wang · Alexa Roa · Chengchangfeng Lu · Wentong Yue · Ray S. Wu · Xiaoxiang Gao · Hongjie Hu · Amer Yaghi · Mark Liu · Lawrence Impey · Sally L. Collins · Aris T. Papageorghiou · Louise C. Laurent · Keith A. Wear · Antoniya Georgieva · Sheng Xu
26 May 2026 at 08:00

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03140-1

A wearable ultrasound device is optimized for continuous monitoring of pregnancies.
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.

Predicting LLM Output Length via Entropy-Guided Representations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Huanyi Xie · Yubin Chen · Liangyu Wang · Lijie Hu · Di Wang
3 April 2026 at 12:00
arXiv:2602.11812v2 Announce Type: replace Abstract: The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched inference. Existing methods rely on auxiliary models for static length prediction, but they incur high overhead, generalize poorly, and fail in stochastic "one-to-many" sampling scenarios. We introduce a lightweight framework that reuses the main model's internal hidden states for efficient length prediction. Our framework features two core components: 1) Entropy-Guided Token Pooling (EGTP), which uses on-the-fly activations and token entropy for highly accurate static prediction with negligible cost, and 2) Progressive Length Prediction (PLP), which dynamically estimates the remaining length at each decoding step to handle stochastic generation. To validate our approach, we build and release ForeLen, a comprehensive benchmark with long-sequence, Chain-of-Thought, and RL data. On ForeLen, EGTP achieves state-of-the-art accuracy, reducing MAE by 29.16\% over the best baseline. Integrating our methods with a length-aware scheduler yields significant end-to-end throughput gains. Our work provides a new technical and evaluation baseline for efficient LLM inference.

Editing strigolactone hormone receptor for robust antiviral silencing in rice

✇Cell
By: Guoyi Yang · Ming Wu · Shuai Zhang · Yucen Huang · Yixiao Liu · Xiyuan Yu · Jiayang Hu · Le Mi · Peng Gan · Yuansheng Wu · Jing Zou · Baogang Zhang · Qun Hu · Jie Hu · Ruifeng Yao · Bojian Zhong · Xianbo Huang · Huiting Xie · Yinghua Ji · Yi Li · Jie Zhang · Liming Yan · Shou-wei Ding · Shanshan Zhao · Jianguo Wu
24 February 2026 at 08:00
Precise genome editing of the rice strigolactone receptor DWARF14 confers robust, transgene-free antiviral resistance by blocking viral suppression of endogenous RNA silencing, offering a promising strategy for durable disease protection without a yield penalty.
Received — 26 March 2026 ⏭

KDFlow: A User-Friendly and Efficient Knowledge Distillation Framework for Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songming Zhang · Xue Zhang · Tong Zhang · Bojie Hu · Yufeng Chen · Jinan Xu
25 March 2026 at 12:00
arXiv:2603.01875v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is an essential technique to compress large language models (LLMs) into smaller ones. However, despite the distinct roles of the student model and the teacher model in KD, most existing frameworks still use a homogeneous training backend (e.g., FSDP and DeepSpeed) for both models, leading to suboptimal training efficiency. In this paper, we present a novel framework for LLM distillation, termed \textbf{KDFlow}, which features a decoupled architecture and employs SGLang for teacher inference. By bridging the training efficiency of FSDP2 and the inference efficiency of SGLang, KDFlow achieves full utilization of both advantages in a unified system. Moreover, instead of transferring full logits across different processes, our framework only transmits the teacher's hidden states using zero-copy data transfer and recomputes the logits on the student side, effectively balancing the communication cost and KD performance. Furthermore, our framework supports both off-policy and on-policy distillation and incorporates KD algorithms for cross-tokenizer KD through highly extensible and user-friendly APIs. Experiments show that KDFlow can achieve \textbf{1.44$\times$ to 6.36$\times$} speedup compared to current KD frameworks, enabling researchers to rapidly prototype and scale LLM distillation with minimal engineering overhead. Code is available at: https://github.com/songmzhang/KDFlow
Received — 17 March 2026 ⏭

Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinyan Jiang · Wenjing Yu · Di Wang · Lijie Hu
16 March 2026 at 12:00
arXiv:2603.12298v1 Announce Type: cross Abstract: Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from static activation differences are susceptible to high-dimensional noise and layer-wise semantic drift, often capturing spurious correlations rather than the target intent. To address this, we propose Global Evolutionary Refined Steering (GER-steer), a training-free framework that grounded in the geometric stability of the network's representation evolution. GER-steer exploits this global signal to rectify raw steering vectors, effectively decoupling robust semantic intent from orthogonal artifacts. Extensive evaluations confirm that GER-steer consistently outperforms baselines, delivering superior efficacy and generalization without layer-specific tuning, establishing a universal solution for reliable model alignment.
Received — 11 March 2026 ⏭

Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qishun Yang · Shu Yang · Lijie Hu · Di Wang
10 March 2026 at 12:00
arXiv:2603.08486v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or contrastive data; yet, threat-related concepts are concrete and visually depictable, while safety concepts, like helpfulness, are abstract and lack visual referents. Inspired by the Self-Fulfilling mechanism underlying emergent misalignment, we propose Visual Self-Fulfilling Alignment (VSFA). VSFA fine-tunes vision-language models (VLMs) on neutral VQA tasks constructed around threat-related images, without any safety labels. Through repeated exposure to threat-related visual content, models internalize the implicit semantics of vigilance and caution, shaping safety-oriented personas. Experiments across multiple VLMs and safety benchmarks demonstrate that VSFA reduces the attack success rate, improves response quality, and mitigates over-refusal while preserving general capabilities. Our work extends the self-fulfilling mechanism from text to visual modalities, offering a label-free approach to VLMs alignment.

ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaobin Ling · Yun Wang · Chenyou Fan · Tin Lun Lam · Junjie Hu
10 March 2026 at 12:00
arXiv:2509.24230v2 Announce Type: replace Abstract: Large Language Models (LLMs) enable intelligent multi-robot collaboration but face fundamental trade-offs: open-loop methods that compile tasks into formal representations for external executors produce sound plans but lack adaptability in partially observable environments, while iterative methods incur prohibitive computational costs that scale poorly with team size and task complexity. In this paper, we propose Efficient Long-Horizon Planning (ELHPlan), a novel framework that introduces Action Chains, sequences of actions explicitly bound to sub-goal intentions, as the fundamental planning primitive. ELHPlan operates via a cyclical process: 1) constructing intention-bound action sequences, 2) proactively validating for conflicts and feasibility, 3) refining issues through targeted mechanisms, and 4) executing validated actions. This design balances adaptability and efficiency by providing intention-bound action sequences with longer lookahead while avoiding expensive full re-planning. We further advocate comprehensive efficiency metrics, including token consumption and planning time, to more holistically evaluate multi-agent collaboration. Our experiments on benchmarks TDW-MAT and C-WAH demonstrate that ELHPlan achieves comparable task success rates while consuming only 30-40% of the tokens required by state-of-the-art methods. Our research establishes a new efficiency-effectiveness frontier for LLM-based multi-agent planning systems.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • In-Run Data Shapley for Adam Optimizer Meng Ding · Zeqing Zhang · Di Wang · Lijie Hu
    arXiv:2602.00329v3 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypass the prohibitive cost of retraining by estimating contributions dynamically, they heavily rely on the linear structure of Stochastic Gradient Descent (SGD) and fail to capture the complex dynamics of adaptive optimizers
     
  • 🏷️
  • ↗️

In-Run Data Shapley for Adam Optimizer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Meng Ding · Zeqing Zhang · Di Wang · Lijie Hu
10 March 2026 at 12:00
arXiv:2602.00329v3 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypass the prohibitive cost of retraining by estimating contributions dynamically, they heavily rely on the linear structure of Stochastic Gradient Descent (SGD) and fail to capture the complex dynamics of adaptive optimizers like Adam. In this work, we demonstrate that data attribution is inherently optimizer-dependent: we show that SGD-based proxies diverge significantly from true contributions under Adam (Pearson $R \approx 0.11$), rendering them ineffective for modern training pipelines. To bridge this gap, we propose Adam-Aware In-Run Data Shapley. We derive a closed-form approximation that restores additivity by redefining utility under a fixed-state assumption and enable scalable computation via a novel Linearized Ghost Approximation. This technique linearizes the variance-dependent scaling term, allowing us to compute pairwise gradient dot-products without materializing per-sample gradients. Extensive experiments show that our method achieves near-perfect fidelity to ground-truth marginal contributions ($R > 0.99$) while retaining $\sim$95\% of standard training throughput. Furthermore, our Adam-aware attribution significantly outperforms SGD-based baselines in data attribution downstream tasks.
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.
Received — 25 February 2026 ⏭

TAG: Thinking with Action Unit Grounding for Facial Expression Recognition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haobo Lin · Tianyi Bai · Jiajun Zhang · Xuanhao Chang · Sheng Lu · Fangming Gu · Zengjie Hu · Wentao Zhang
24 February 2026 at 13:00
arXiv:2602.18763v1 Announce Type: cross Abstract: Facial Expression Recognition (FER) is a fine-grained visual understanding task where reliable predictions require reasoning over localized and meaningful facial cues. Recent vision--language models (VLMs) enable natural language explanations for FER, but their reasoning is often ungrounded, producing fluent yet unverifiable rationales that are weakly tied to visual evidence and prone to hallucination, leading to poor robustness across different datasets. We propose TAG (Thinking with Action Unit Grounding), a vision--language framework that explicitly constrains multimodal reasoning to be supported by facial Action Units (AUs). TAG requires intermediate reasoning steps to be grounded in AU-related facial regions, yielding predictions accompanied by verifiable visual evidence. The model is trained via supervised fine-tuning on AU-grounded reasoning traces followed by reinforcement learning with an AU-aware reward that aligns predicted regions with external AU detectors. Evaluated on RAF-DB, FERPlus, and AffectNet, TAG consistently outperforms strong open-source and closed-source VLM baselines while simultaneously improving visual faithfulness. Ablation and preference studies further show that AU-grounded rewards stabilize reasoning and mitigate hallucination, demonstrating the importance of structured grounded intermediate representations for trustworthy multimodal reasoning in FER. The code will be available at https://github.com/would1920/FER_TAG .

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.
There are no more articles
❌