Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

By: Tianhao Shi · Yang Zhang · Xiaoyan Zhao · Fengbin Zhu · Chenyi Lei · Han Li · Wenwu Ou · Yang Song · Yongdong Zhang · Fuli Feng

3 April 2026 at 12:00

arXiv:2604.01690v1 Announce Type: new Abstract: The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC. Deeper analysis uncovered the ability of the algorithmic content distribution mechanism in moderating these competing interests regarding AIGC. These findings advocated for the implementation of AIGC-sensitive distribution algorithms and precise governance frameworks to ensure the long-term health of the online content platforms.

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.

ReFlow: Self-correction Motion Learning for Dynamic Scene Reconstruction

cs.AI, q-bio.NC updates on arXiv.org

By: Yanzhe Liang · Ruijie Zhu · Hanzhi Chang · Zhuoyuan Li · Jiahao Lu · Tianzhu Zhang

3 April 2026 at 12:00

arXiv:2604.01561v1 Announce Type: cross Abstract: We present ReFlow, a unified framework for monocular dynamic scene reconstruction that learns 3D motion in a novel self-correction manner from raw video. Existing methods often suffer from incomplete scene initialization for dynamic regions, leading to unstable reconstruction and motion estimation, which often resorts to external dense motion guidance such as pre-computed optical flow to further stabilize and constrain the reconstruction of dynamic components. However, this introduces additional complexity and potential error propagation. To address these issues, ReFlow integrates a Complete Canonical Space Construction module for enhanced initialization of both static and dynamic regions, and a Separation-Based Dynamic Scene Modeling module that decouples static and dynamic components for targeted motion supervision. The core of ReFlow is a novel self-correction flow matching mechanism, consisting of Full Flow Matching to align 3D scene flow with time-varying 2D observations, and Camera Flow Matching to enforce multi-view consistency for static objects. Together, these modules enable robust and accurate dynamic scene reconstruction. Extensive experiments across diverse scenarios demonstrate that ReFlow achieves superior reconstruction quality and robustness, establishing a novel self-correction paradigm for monocular 4D reconstruction.

DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72

cs.AI, q-bio.NC updates on arXiv.org

By: Wanqian Li · Jintao Peng · Zongfei Jing · Tianyu Zhang · Ze Long · Xianjie Qiao · Xiaoming Chen · Dongxu Yang · Kefeng Duan · June Yang

3 April 2026 at 12:00

arXiv:2604.01621v1 Announce Type: cross Abstract: Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution while offloading MoE weights across peer GPUs and fetching missing experts on demand. By removing collective inter-rank synchronization, DWDP allows each GPU to progress independently. We further address the practical overheads of this design with two optimizations for split-weight management and asynchronous remote-weight prefetch. Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72, DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user serving range under 8K input sequence length and 1K output sequence length.

GPA: Learning GUI Process Automation from Demonstrations

cs.AI, q-bio.NC updates on arXiv.org

By: Zirui Zhao · Jun Hao Liew · Yan Yang · Wenzhuo Yang · Ziyang Luo · Doyen Sahoo · Silvio Savarese · Junnan Li

3 April 2026 at 12:00

arXiv:2604.01676v1 Announce Type: cross Abstract: GUI Process Automation (GPA) is a lightweight but general vision-based Robotic Process Automation (RPA), which enables fast and stable process replay with only a single demo. Addressing the fragility of traditional RPA and the non-deterministic risks of current vision language model-based GUI agents, GPA introduces three core benefits: (1) Robustness via Sequential Monte Carlo-based localization to handle rescaling and detection uncertainty; (2) Deterministic and Reliability safeguarded by readiness calibration; and (3) Privacy through fast, fully local execution. This approach delivers the adaptability, robustness, and security required for enterprise workflows. It can also be used as an MCP/CLI tool by other agents with coding capabilities so that the agent only reasons and orchestrates while GPA handles the GUI execution. We conducted a pilot experiment to compare GPA with Gemini 3 Pro (with CUA tools) and found that GPA achieves higher success rate with 10 times faster execution speed in finishing long-horizon GUI tasks.

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.

Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution

cs.AI, q-bio.NC updates on arXiv.org

By: Beidan Liu · Zhengqiu Zhu · Chen Gao · Tianle Pu · Yong Zhao · Wei Qi · Quanjun Yin

3 April 2026 at 12:00

arXiv:2509.12643v3 Announce Type: replace Abstract: Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather than proactive strategy designers, limiting their effectiveness on complex Constraint Optimization Problems (COPs). To address this, we present AutoCO, an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. A core innovation is a unified triple-representation that binds relaxation strategies, algorithmic principles, and executable codes. This design enables the LLM to synthesize, justify, and instantiate relaxation strategies that are both principled and executable. To navigate fragmented solution spaces, AutoCO employs a bidirectional global-local coevolution mechanism, synergistically coupling Monte Carlo Tree Search (MCTS) for global relaxation-trajectory exploration with Evolutionary Algorithms (EAs) for local solution intensification. This continuous exchange of priors and feedback explicitly balances diversification and intensification, thus preventing premature convergence. Extensive experiments on three challenging COP benchmarks validate AutoCO's consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. Results highlight AutoCO as a principled and effective path toward proactive, verifiable LLM-driven optimization.

Stabilizing Rubric Integration Training via Decoupled Advantage Normalization

cs.AI, q-bio.NC updates on arXiv.org

By: Zelin Tan · Zhouliang Yu · Bohan Lin · Zijie Geng · Hejia Geng · Yudong Zhang · Mulei Zhang · Yang Chen · Shuyue Hu · Zhenfei Yin · Chen Zhang · Lei Bai

3 April 2026 at 12:00

arXiv:2603.26535v2 Announce Type: replace Abstract: We propose Process-Aware Policy Optimization (PAPO), a method that integrates process-level evaluation into Group Relative Policy Optimization (GRPO) through decoupled advantage normalization, to address two limitations of existing reward designs. Outcome reward models (ORM) evaluate only final-answer correctness, treating all correct responses identically regardless of reasoning quality, and gradually lose the advantage signal as groups become uniformly correct. Process reward models (PRM) offer richer supervision, but directly using PRM scores causes reward hacking, where models exploit verbosity to inflate scores while accuracy collapses. PAPO resolves both by composing the advantage from an outcome component Aout, derived from ORM and normalized over all responses, and a process component Aproc, derived from a rubric-based PRM and normalized exclusively among correct responses. This decoupled design ensures that Aout anchors training on correctness while Aproc differentiates reasoning quality without distorting the outcome signal. Experiments across multiple model scales and six benchmarks demonstrate that PAPO consistently outperforms ORM, reaching 51.3% vs.\ 46.3% on OlympiadBench while continuing to improve as ORM plateaus and declines.

NCCL EP: Towards a Unified Expert Parallel Communication API for NCCL

cs.AI, q-bio.NC updates on arXiv.org

3 April 2026 at 12:00

arXiv:2603.13606v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures have become essential for scaling large language models, driving the development of specialized device-initiated communication libraries such as DeepEP, Hybrid-EP, and others. These libraries demonstrate the performance benefits of GPU-initiated RDMA for MoE dispatch and combine operations. This paper presents NCCL EP (Expert Parallelism), a ground-up MoE communication library built entirely on NCCL's Device API. NCCL EP provides unified ncclEpDispatch and ncclEpCombine primitives with both C and Python interfaces, supporting Low-Latency (LL) mode for inference decoding and High-Throughput (HT) mode for training and inference prefill. LL targets small batch sizes (1-128 tokens) using direct all-to-all RDMA+NVLink mesh connectivity with double-buffered communication for overlapping dispatch and combine phases. HT targets large batches (4096+ tokens) using hierarchical communication that aggregates tokens within NVLink domains before inter-node RDMA transmission. Both modes leverage Device API for both intra- and inter-node communications, taking advantage of its topology awareness and optimized GPU-initiated implementation. We evaluate NCCL EP on an H100-based cluster across multi-node configurations, demonstrating competitive LL kernel performance and presenting end-to-end results with vLLM integration. By building MoE communication natively within NCCL, NCCL EP provides a supported path for expert parallelism on current and emerging NVIDIA platforms.

Generation Is Compression: Zero-Shot Video Coding via Stochastic Rectified Flow

cs.AI, q-bio.NC updates on arXiv.org

By: Ziyue Zeng · Xun Su · Haoyuan Liu · Bingyu Lu · Yui Tatsumi · Hiroshi Watanabe

3 April 2026 at 12:00

arXiv:2603.26571v2 Announce Type: replace-cross Abstract: Recent advances in generative modeling have enabled perceptual video compression at ultra-low bitrates, yet existing methods predominantly treat the generative model as a refinement or reconstruction module attached to a separately designed codec backbone. We propose \emph{Generative Video Codebook Codec} (GVCC), a zero-shot framework that turns a pretrained video generative model into the codec itself: the transmitted bitstream directly specifies the generative decoding trajectory, with no retraining required. To enable this, we convert the deterministic rectified-flow ODE of modern video foundation models into an equivalent SDE at inference time, unlocking per-step stochastic injection points for codebook-driven compression. Building on this unified backbone, we instantiate three complementary conditioning strategies -- \emph{Image-to-Video} (I2V) with autoregressive GOP chaining, tail latent residual correction, and adaptive atom allocation, \emph{Text-to-Video} (T2V) operating at near-zero side information as a pure generative prior, and \emph{First-Last-Frame-to-Video} (FLF2V) with boundary-sharing GOP chaining for dual-anchor temporal control. Together, these variants span a principled trade-off space between spatial fidelity, temporal coherence, and compression efficiency. Experiments on standard benchmarks show that GVCC achieves high-quality reconstruction below 0.002\,bpp while supporting flexible bitrate control through a single hyperparameter.

UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems

cs.AI, q-bio.NC updates on arXiv.org

By: Mingming Ha · Guanchen Wang · Linxun Chen · Xuan Rao · Yuexin Shi · Tianbao Ma · Zhaojie Liu · Yunqian Fan · Zilong Lu · Yanan Niu · Han Li · Kun Gai

3 April 2026 at 12:00

arXiv:2604.00590v2 Announce Type: replace-cross Abstract: In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommenders. Currently, there are three mainstream architectures for achieving scaling in recommendation models, namely attention-based, TokenMixer-based, and factorization-machine-based methods, which exhibit fundamental differences in both design philosophy and architectural structure. In this paper, we propose a unified scaling architecture for recommendation systems, namely \textbf{UniMixer}, to improve scaling efficiency and establish a unified theoretical framework that unifies the mainstream scaling blocks. By transforming the rule-based TokenMixer to an equivalent parameterized structure, we construct a generalized parameterized feature mixing module that allows the token mixing patterns to be optimized and learned during model training. Meanwhile, the generalized parameterized token mixing removes the constraint in TokenMixer that requires the number of heads to be equal to the number of tokens. Furthermore, we establish a unified scaling module design framework for recommender systems, which bridges the connections among attention-based, TokenMixer-based, and factorization-machine-based methods. To further boost scaling ROI, a lightweight UniMixing module is designed, \textbf{UniMixing-Lite}, which further compresses the model parameters and computational cost while significantly improve the model performance. The scaling curves are shown in the following figure. Extensive offline and online experiments are conducted to verify the superior scaling abilities of \textbf{UniMixer}.

A high-throughput selection system for fast-acting covalent protein drugs

AAAS: Table of Contents

By: Qiongxuan Fan · Jiahao Mei · Tian Li · Chuanlong Zang · Mengjiao Li · Jing Tang · You Xu · Ge Yu · Dandan Liu · Kai Chen · Bing Yang · Jing Huang · Ting Zhou · Bobo Dang

2 April 2026 at 13:59

Science, Ahead of Print.

Divergent and programmable skeletal remodeling of complex macrocycles with a small method set

AAAS: Table of Contents

By: Ali Nikbakht · Xinghan Li · Jing Wan · Can Qin · Amir H. Hoveyda

2 April 2026 at 13:59

Science, Ahead of Print.

Functional RNA splitting drove the evolutionary emergence of type V CRISPR-Cas systems from transposons

Cell

By: Shuai Jin · Zixu Zhu · Yunjia Li · Shouyue Zhang · Yijing Liu · Danyuan Li · Yuanqing Li · Yingfeng Luo · Zhiheng Cheng · Kevin Tianmeng Zhao · Qiang Gao · Guanglei Yang · Hongchao Li · Ronghong Liang · Rui Zhang · Jin-Long Qiu · Yong E. Zhang · Jun-Jie Gogo Liu · Caixia Gao

17 March 2026 at 08:00

(Cell 188, 6283–6300.e1–e10; October 30, 2025)

Ferritin aggregation cell engager for CAR T avidity engineering against refractory leukemias

Cell

By: Feng Li · Yuxing Hu · Yan Wang · Xiao Zhang · Shaowei Qiu · Xiaoming Hu · Wenjing Wang · Shuang Wang · Peilin Guo · Cui Song · Huaiji Wang · Changhao He · Jie Sun · Di Yu · Shuhua Yi · Jun Wei · Feifan Liu · Caopei Zheng · Jianxiang Wang · Yuhua Li · Guanghui Ma · Wei Wei

9 March 2026 at 08:00

Li et al. developed a ferritin aggregation cell engager that helps CAR T cells better recognize and attack leukemia cells without re-engineering the CAR itself. This versatile platform overcomes antigen modulation and enables combination with chemotherapy.

Genetically encoded fluorescent reporters to visualize α-synuclein pathology in live brain

Cell

By: Li Zhang · Minhui Yu · Guoqing Chen · Siyuan Ge · Mengdi Wang · Xianying Zhang · Miao Zhao · Huating Gu · Meizhu Huang · Aixue Liu · Gengxin Ran · Zeyuan Liu · Tiepeng Liao · Qi Chen · Chenjian Miao · Yao Lu · Yibing Wang · Fengchao Wang · Zhihui Liu · Hongying Zhu · Qian Wu · Zhuan Zhou · Wei Xiong · Xiaoqun Wang · Peng Cao

4 March 2026 at 08:00

The development of genetically encoded fluorescent reporters, along with their corresponding knock-in mouse lines for labeling α-Syn inclusions, enables diverse applications in studying the propagation and pathological effects of α-Syn inclusions in the live brain.

Pyruvate is a natural suppressor of interferon signaling by inducing STAT1 protein pyruvylation

Cell

By: Yibo Zuo · Qin Wang · Wanying Tian · Xinhe Wang · Zhijin Zheng · Wei He · Renxia Zhang · Qian Zhao · Ying Miao · Yukang Yuan · Tingting Zhang · Qun Cui · Yuerong Zhang · Chunyan Liu · Haiyan Zhou · Hui Zheng

27 February 2026 at 08:00

Yibo et al. identify protein pyruvylation as a post-translational modification that can modulate immune signaling and host antiviral response.

The neonatal lung microbiome: a dynamic determinant of respiratory health, disease, and novel therapeutics

Omics In Lung

By: Wanwei Zheng · Yan Liang · Jiayao Li

1 April 2026 at 18:00

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

The neonatal lung microbiome: a dynamic determinant of respiratory health, disease, and novel therapeutics

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Wanwei Zheng · Yan Liang · Jiayao Li

1 April 2026 at 18:00

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent

cs.AI, q-bio.NC updates on arXiv.org

By: Hongyi Nie · Xunyuan Liu · Yudong Bai · Yaqing Wang · Yang Liu · Quanming Yao · Zhen Wang

1 April 2026 at 12:00

arXiv:2603.29318v1 Announce Type: new Abstract: Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use is highly personalized: users adopt diverse workflows and preferences, challenging agents to deliver customized assistance rather than generic solutions. Existing GUI agent benchmarks cannot adequately capture this personalization dimension due to sparse user-specific data and the lack of fine-grained evaluation metrics. To address this gap, we present PSPA-Bench, the benchmark dedicated to evaluating personalization in smartphone GUI agents. PSPA-Bench comprises over 12,855 personalized instructions aligned with real-world user behaviors across 10 representative daily-use scenarios and 22 mobile apps, and introduces a structure-aware process evaluation method that measures agents' personalized capabilities at a fine-grained level. Through PSPA-Bench, we benchmark 11 state-of-the-art GUI agents. Results reveal that current methods perform poorly under personalized settings, with even the strongest agent achieving limited success. Our analysis further highlights three directions for advancing personalized GUI agents: (1) reasoning-oriented models consistently outperform general LLMs, (2) perception remains a simple yet critical capability, and (3) reflection and long-term memory mechanisms are key to improving adaptation. Together, these findings establish PSPA-Bench as a foundation for systematic study and future progress in personalized GUI agents.

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