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Received — 10 September 2026 ⏭

Viral gene replication enhances AAV vector quality and reduces manufacturing costs

✇Molecular Therapy
By: Hao Liu · Ailing Du · Nan Liu · Mayank Kapadia · Xiupeng Chen · Jialing Liang · Ji Sun Lee · Jun Xie · Guangping Gao · Dan Wang
11 August 2026 at 08:00
Liu and colleagues developed a robust in cellulo plasmid DNA replication system in human cells for replicating plasmid-borne adeno-associated virus (AAV) Rep/Cap genes during recombinant AAV (rAAV) production. This new approach not only enables a 10- to 20-fold plasmid reduction to significantly lower manufacturing costs but also substantially enhances rAAV potency, titer, and purity.

Lineage-specific pulmonary transcriptome landscape of coronavirus infection unveils universal immunotherapy for viral pneumonia

✇Molecular Therapy
By: Ming Zhou · Xuan Liu · Haiqing Xiao · Zhaojie Su · Kun Wu · Longmei Zhang · Xinglin Li · Wanyun Li · Chang Zhang · Hao Li · Baojie Shi · Chaoyang Chen · Nan Xu · Jiang Li · Jinxin Miao · Yifan Hu · Huachen Zhu · Yi Guan · Tong Cheng · Ningshao Xia · Lunzhi Yuan
10 August 2026 at 08:00
In the infection courses of different SARS-CoV-2 variants, disease outcomes and signatures were delineated by physiological changes, viral load, pathology, and pulmonary transcriptome analysis. This multi-dimensional landscape of disease outcomes and underlying mechanisms might provide important clues for immunotherapy of SARS-CoV-2 infection and pneumonia caused by other respiratory viruses.

UnitBoost: Managing Compound LLM Systems with a Merge Operator, Not a Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xing Zhang · Guanghui Wang · Yanwei Cui · Mengdie Flora Wang · Peiyang He
10 September 2026 at 12:00
arXiv:2609.09815v1 Announce Type: new Abstract: Compound LLM systems often solve a coordination problem by adding a higher-level LLM. The resulting meta-agent reads workers' outputs, writes the final answer, allocates later calls, and decides when to stop. It is expressive, but it also concentrates three control decisions in an opaque, order-sensitive model call. We ask whether the manager needs to be generative at all. UnitBoost replaces that model with a defined meta-level operator: a task-given unit map turns worker outputs into slot-value proposals, a constrained argmax assembles the output, and the slots left unfilled or unsupported become an explicit residual for the next round. The operator is order-free, records unit provenance, and gives a simple guarantee: without coupling constraints, unit-wise maximization under the same admission score dominates selection of any complete candidate. On three held-out benchmarks, it exceeds the best single candidate chosen with gold labels by 0.060-0.195 absolute task-score points and input-matched generative managers by 0.048-0.076. Replacing only the management step improves six compound-system configurations by 0.013-0.182. Residual-directed rounds raise FanOutQA cell F1 from 0.4778 to 0.5524; matched controls show that the true residual outperforms random targets and ordinary rereading, while a label-free supply signal flags exhaustion after one unproductive round. The same analysis measures three conditions in which no such gain is available (one indivisible unit, unavailable unit identity, and an endpoint that charges for every emitted unit) and quantifies cross-unit coupling as a repair cost. The manager gives up semantic freedom and gains order invariance, unit provenance, and testable failure conditions.

BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RL

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guanqun Zhao · Zijun Xie · Binbin Zheng · Jiafeng Lu · Enlei Gong · Zeyu Chen
10 September 2026 at 12:00
arXiv:2609.09783v1 Announce Type: cross Abstract: Asynchronous reinforcement learning has become the standard way to scale training for language models, but the resulting policy lag biases the critic toward the stale behavior policy. Existing work on asynchronous LLM training corrects the actor and leaves this bias unaddressed, while the off-policy value correction of classical RL does not carry over to long-horizon agentic tasks, since a short correction horizon leaves the regression target free of the reward and a long one lets the product of importance ratios drift exponentially with the trajectory length. We propose BRACE, an anchored Bellman-residual correction for stale value models. BRACE bounds the correction horizon to a prefix of policy tokens and anchors a constant-weight Monte-Carlo tail beyond it, which separates policy correction from reward propagation. BRACE improves mean@1 on BrowseComp-Plus by $2.4\%$ over the strongest baseline, runs $2.46\times$ faster per step than synchronous training, and remains stable $50$ updates off-policy.

Active Adaptation, Not Static Defense: Temporal Dynamics of Preventative Steering in Adversarial Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jing Guan · Yachao Yang · Zhaoliang Liu · Yuyao Zhang · Fanyu Meng · Junlan Feng
10 September 2026 at 12:00
arXiv:2609.10142v1 Announce Type: cross Abstract: Large language models remain fragile against malicious fine-tuning, motivating training-time defenses against harmful persona drift. Preventative Steering injects undesirable-trait persona vectors during fine-tuning and removes them at evaluation time, yet the mechanism behind its lasting protection remains unclear. Analyzing its temporal optimization dynamics, we find that the defense emerges from an early compensatory adaptation phase followed by a steady-state phase where the corrective signal decays; in parameter space, attention output projections emerge as the dominant residual-write route for defensive updates. Through Intervention Delta Preservation (IDP) and IDP Continuation experiments, we further show that preserving or reinjecting the weight offset fails to maintain protection, indicating that preventative steering relies on active adaptation rather than a static defense. Motivated by this finding, we propose Progressive Intensity Scheduling (PIS), which starts with a moderate injection strength and increases it after static-strength alignment begins to decay. Across the evaluated Qwen2.5 and Gemma-3 models, PIS improves safety robustness over static-strength steering while reducing harmful trait expression.

KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Peiyu Zang · Jian Tao · Jialing Zhang · Yichen Yuan · Wentao Zhang · Guang Liu · Yonghua Lin
10 September 2026 at 12:00
arXiv:2607.27231v3 Announce Type: replace Abstract: Modern AI systems depend on specialized accelerator kernels, whose development is complicated by increasingly diverse operators and hardware. LLMs and agentic systems promise to automate this work, but existing evaluations do not show whether their performance transfers across operator sources and hardware platforms, or what such transfer costs. We present KernelGenBench, the first unified multi-source and multi-chip infrastructure for evaluating LLM- and agent-generated Triton kernels. With a common Triton target spanning six hardware platforms, it provides the broadest cross-vendor hardware coverage among existing kernel-generation benchmarks. We report two controlled analytical views: KernelGenBench-MS (Multi-Source) covers 210 operators from PyTorch ATen, production vLLM operators, and proprietary cuBLAS routines, while KernelGenBench-MC (Multi-Chip) evaluates a semantically stable 110-operator subset across six hardware platforms. Our evaluation consumed over 15 billion tokens. Agentic execution improved correctness, but no method dominated across sources and platforms: vLLM posed the strongest correctness challenge, cuBLAS set the highest performance ceiling, and AutoKernel accuracy fell from 87% on NVIDIA to 25% on Iluvatar CoreX. These improvements were costly: specialized agents averaged 4.99 million tokens per successful operator, rising to 6.25 million for CUDA Optimized Skill. The results establish operator source, hardware platform, and agentic scaffold as distinct dimensions of kernel-generation capability, and show that success in a familiar source-hardware setting is not a reliable proxy for deployment readiness.

Efficient Diversity-based Experience Replay for Deep Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Kaiyan Zhao · Yiming Wang · Yuyang Chen · Yan Li · Leong Hou U · Xiaoguang Niu
10 September 2026 at 12:00
arXiv:2410.20487v5 Announce Type: replace-cross Abstract: Experience replay is widely used to improve learning efficiency in reinforcement learning by leveraging past experiences. However, existing experience replay methods, whether based on uniform or prioritized sampling, often suffer from low efficiency, particularly in real-world scenarios with high-dimensional state spaces. To address this limitation, we propose a novel approach, Efficient Diversity-based Experience Replay (EDER). EDER employs a determinantal point process to model the diversity between samples and prioritizes replay based on the diversity between samples. To further enhance learning efficiency, we incorporate Cholesky decomposition for handling large state spaces in realistic environments. Additionally, rejection sampling is applied to select samples with higher diversity, thereby improving overall learning efficacy. Extensive experiments are conducted on robotic manipulation tasks in MuJoCo, Atari games, and realistic indoor environments in Habitat. The results demonstrate that our approach not only significantly improves learning efficiency but also achieves superior performance in high-dimensional, realistic environments.

MADS: Multi-Agent Dialogue Simulation for Diverse Persuasion Data Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mingjin Li · Yu Liu · Huayi Liu · Xiang Ye · Chao Jiang · Hongguang Zhang · Yu Ruan
10 September 2026 at 12:00
arXiv:2510.05124v3 Announce Type: replace-cross Abstract: We propose MADS (Multi-Agent Dialogue Simulation), a scalable framework for generating persuasive multi-turn dialogues via agent self-play. MADS employs three coordinated agents: User Agents designed to simulate diverse persona-driven behaviors by leveraging personality signifiers such as Zodiac Signs and MBTI types, a Dialog Agent executing task-oriented persuasion strategies and an Optimization Agent evaluating and refining dialogue outcomes. We further validate its effectiveness through users' Chain-of-Attitude (CoA) modeling and dedicated LLMs' persuasion assessment. This approach enables low-cost generation of training data without human annotation, addressing key industry challenges such as lack of user data, cold-start evaluation difficulties, and prompt inefficiency. Applied to a real-world marketing scenario, MADS significantly improved the persuasion capacity of small LLMs, increasing the organic traffic conversion rate by 22.4% (from 1.83% to 2.24%) , demonstrating clear business value.

Revisiting the Shape Convention of Transformer Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Feng-Ting Liao · Guan-Ting Yi · Tzu-Quan Lin · Meng-Hsi Chen · Da-shan Shiu
10 September 2026 at 12:00
arXiv:2602.06471v2 Announce Type: replace-cross Abstract: The architectural shape of dense Transformers has remained remarkably stable: narrow-wide-narrow feed-forward networks (FFNs) consume most non-embedding parameters. Motivated by theoretical and empirical evidences that residual wide-narrow-wide (hourglass) MLPs remain expressive despite bottlenecks, we revisit whether this architectural convention is necessary for dense language models. We study Hourglass Transformers, which replace the conventional FFN with residual stacks of hourglass sub-MLPs and use hourglass attention to decouple residual-stream width from attention width. This exposes a practical depth-width trade-off: compressing the FFN intermediate dimension allows wider hidden states and fewer layers at matched parameter budgets. Across model scales from 113M to 8B parameters, Hourglass Transformers achieve language-modeling and downstream performance comparable to conventional Transformers, while improving training compute efficiency by $8.7\%$ at matched average downstream accuracy across the 906M, 3B, and 8B scales. After long-context extension, the 8B Hourglass model also outperforms its matched conventional baseline across 4k-64k context lengths. At 64k context, the reduced attention layer count lowers both computation and KV-cache requirements, yielding up to $1.93\times$ faster token decoding and $50\%$ lower KV-cache memory at the 1B scale. These results identify hourglass structures as a practical architecture-efficiency alternative for compute- and latency-conscious Transformer design.

PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuchen Sun · Qian Yang · Jun Wang · Detai Xin · Guoqiao Yu · Guanglu Wan · Qi Jia
10 September 2026 at 12:00
arXiv:2609.04867v2 Announce Type: replace-cross Abstract: Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality. Existing benchmarks either treat audio as an auxiliary component of video quality or assess it in isolation from audiovisual grounding, making it difficult to diagnose where current systems truly succeed or fail in audio generation. We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation. Built from a rigorously curated dataset of 900 human-verified samples, PRISM-Bench factorizes audio evaluation along two orthogonal axes: audio type (Speech, Music, and Sound) and sound-source visibility (On-screen vs. Off-screen). It evaluates generated content across four perceptual dimensions (Audio-Visual Coherence, Audio Quality, Audio Expressiveness, and Prompt Following) with 35 fine-grained criteria. To ensure reliable assessment, we adopt an enhanced MLLM-as-a-Judge protocol based on blind, side-by-side comparison against ground-truth references, demonstrating strong alignment (over 70% mean agreement) with human raters. Our evaluation of recent T2AV systems highlights a significant performance gap between frontier and open-source models. Furthermore, we demonstrate that current generation paradigms overfit to perceptual fidelity while struggling with complex grounding and control tasks, particularly in generating music and synchronized On-screen audio.

Effectiveness, Safety, and Workflow Burden of Large Language Model–Based Medical Report Generation: Systematic Review

✇Journal of Medical Internet Research
By: Jie-Lin Huang · Ji-Qing Zhu · Xiao-Guang Ni
10 September 2026 at 04:00
Background: Systems based on large language models (LLMs), multimodal LLMs, and vision-language foundation models are increasingly being evaluated for medical report generation in imaging and related clinical workflows. Existing reviews have summarized technical architectures, radiology applications, readability, and benchmark performance, but clinical readiness remains uncertain because safety, human oversight, and workflow outcomes are sparsely and inconsistently reported. Objective: The aim of this study is to assess the effectiveness (expert acceptance and blinded preference), safety (clinically significant, omission, and commission errors), and workflow burden (reporting time, corrections, edit distance, and editing burden) of LLM-based medical report generation. Methods: We searched PubMed/MEDLINE, Embase, Web of Science Core Collection, Scopus, and the Cochrane Library for studies published from January 1, 2016, through May 15, 2026. Eligible studies evaluated LLMs, multimodal LLMs, or vision-language foundation models for image-to-report generation, impression generation from findings, report drafting, or structured reporting in imaging workflows. Two reviewers performed screening, extraction, risk-of-bias assessment, and Grading of Recommendations Assessment, Development, and Evaluation–informed narrative certainty assessment. Outcomes were clinically significant error rate, omission error rate, commission error rate, reporting time, edit burden, expert acceptance, and blinded expert preference. Meta-analysis was not performed because no comparable outcome had at least 2 studies with compatible task structure and analyzable data. Results: A total of 101 studies were included. Chest x-ray was the largest modality group (36 studies), followed by computed tomography, magnetic resonance imaging (MRI), ultrasound, endoscopy, pathology, ophthalmic, electrocardiographic, dental, and mixed-modality contexts. No study was judged at low risk of bias; 15 were moderate, 72 high, and 14 serious. Safety and workflow evidence remained heterogeneous and largely nonpoolable. In a chest x-ray study, AI report acceptance was similar to that of radiologist reports (6047/8580, 70.5% vs 6288/8580, 73.3%), but false-negative findings were slightly higher (1584/8580, 18.5% vs 1527/8580, 17.8%). In a clinician-collaboration chest x-ray study, AI reports were equivalent or preferred in 233 of 300 (77.7%) and 170 of 303 (56.1%) cases across 2 datasets; yet, clinically significant errors persisted. In a brain MRI study, AI assistance reduced reading time from 61 to 53 seconds, whereas impression drafting increased editing time and edit distance. Conclusions: This review shifts the synthesis from plausible report generation to clinically interpretable effectiveness, safety, and workflow effects. Expert acceptance and preference suggested assistive value in selected supervised settings, but these signals were limited by inconsistent reporting of clinically significant errors, omissions, commissions, and failed generations. Workflow effects were mixed, with some studies reporting shorter reading time or drafting support, and others reporting greater editing time or edit distance. The evidence remains too heterogeneous, biased, and sparse on case-level end points to support a pooled meta-analysis or autonomous clinical-readiness claims. Adoption should remain locally validated, clinician-supervised, and accompanied by standardized reporting of acceptance, preference, omissions, commissions, failed generations, reporting time, corrections, and editing burden. Trial Registration: PROSPERO CRD420261302844; https://www.crd.york.ac.uk/PROSPERO/view/CRD420261302844

An operational perturbation proteomics-based virtual cell model

✇Nature - Issue - nature.com science feeds
By: Rui Sun · Liujia Qian · Yongge Li · Tong Liu · Honghan Cheng · Xuedong Zhang · Xueya Zhou · Yuecheng Zhan · Guangmei Zhang · Zhengchao Luo · Kunpeng Ma · Chunlong Wu · Dongchen Ji · Zhangzhi Xue · Hongxue Meng · Yuhang Xiang · Dingwei Lei · Qianhe Zhou · Wenbin Hu · Yuhan Deng · Lingling Tan · Qi Xiao · Zhiwei Liu · Lei Zeng · Liqin Qian · Xuan Zheng · Qiong Hu · Nianzi Luo · Weinan E · Peijie Zhou · Han Wen · Yi Zhu · Tiannan Guo
9 September 2026 at 08:00

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-11001-9

Temporal protein-abundance measurements from systematically perturbed breast cancer cell lines were generated to develop ProteinTalks, a virtual cell model that functions as an operational tool for diverse drug discovery tasks.

Advances in understanding the mechanisms underlying acquired resistance to third-generation tyrosine kinase inhibitors in non-small cell lung cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Gu-Ha A-Lai · Lian Li · Guangzhi Ma · Yong-Sheng Zhao · Peng Yao · Yi-Dan Lin
8 September 2026 at 18:00

Front Cell Dev Biol. 2026 Aug 24;14:1867246. doi: 10.3389/fcell.2026.1867246. eCollection 2026.

ABSTRACT

Acquired resistance to third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) presents a formidable challenge in the treatment of non-small cell lung cancer (NSCLC). Despite the remarkable efficacy of these agents, resistance inevitably develops, typically within approximately 10 months of treatment initiation. This review elucidates the multifaceted mechanisms driving this resistance, broadly categorized into on-target EGFR-dependent alterations and off-target EGFR-independent bypass pathway activations. On-target mechanisms include the emergence of tertiary EGFR mutations, most notably C797S, which disrupts TKI binding. Off-target mechanisms encompass the activation of alternative signaling pathways such as MET and HER2/HER3 amplification, as well as histological transformations and complex changes within the tumor microenvironment. Furthermore, recent discoveries highlight the role of epigenetic dysregulation and metabolic reprogramming in fostering resistance. To counter this pervasive adaptability, advanced diagnostic methodologies, including liquid biopsy and high-resolution omics technologies, are crucial for real-time molecular profiling. The field is actively exploring emerging combination therapeutic strategies to circumvent these diverse resistance pathways, aiming to prolong clinical benefits and improve patient outcomes. The persistent emergence of resistance underscores that current targeted therapies, while revolutionary, are primarily disease-modifying rather than curative, necessitating continuous innovation to overcome the inherent biological challenge of tumor adaptability and heterogeneity.

PMID:42707604 | PMC:PMC13547778 | DOI:10.3389/fcell.2026.1867246

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

✇Cell Death Discovery nature.com science feeds
By: Mingrui Xue · Qi Zhu · Shuli Zhang · Guangzong Zhao · Xiaoqi Hong · Di Pang · Yue Xu · Xiaolu Li · Yuxin Liu · Ying Li · Qiucheng Jia · Sisi Xu · Hongshan Ge
8 September 2026 at 08:00

Cell Death Discovery, Published online: 08 September 2026; doi:10.1038/s41420-026-03339-w

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

Pulmonary nodule prediction in the multi-omics era: Integrating radiomics, AI, liquid biopsy, and airway classifiers

✇Pulmonary nodule
By: Yan Li · Guanyu Lu · Xiangliang Liu · Jin Lu · Wei Song · Jiuwei Cui
10 July 2026 at 18:00

Crit Rev Oncol Hematol. 2026 Sep;225:105483. doi: 10.1016/j.critrevonc.2026.105483. Epub 2026 Jul 10.

ABSTRACT

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of these nodules are benign, leading to a high false-positive rate that triggers unnecessary invasive procedures and patient anxiety, underscoring the need for more precise noninvasive diagnostic tools. Critically, single-modality liquid biopsy biomarkers, including circulating tumor cells, cell-free DNA mutations, or individual microRNAs, have demonstrated insufficient sensitivity or specificity for independent clinical deployment when used in isolation. This necessitates a paradigm shift toward multimodal molecular integration, wherein complementary biomarker classes are combined to overcome the inherent limitations of any single analyte. Traditional clinical prediction models (Mayo, VA, Brock, Herder) assist in estimating malignancy risk, yet their accuracy remains modest. Emerging approaches harness radiomics and artificial intelligence (AI) to extract high-dimensional imaging features from chest CT scans, improving risk stratification beyond human assessment alone. In parallel, minimally invasive liquid biopsy biomarkers offer complementary avenues to detect occult malignancy signals. Additionally, bronchial airway gene expression classifiers leverage the "field-of-injury" effect in normal respiratory epithelium to help identify lung cancer even when the nodule itself cannot be directly sampled via biopsy. Integrating these radiologic and molecular data streams into a multi-omics framework has the potential to enhance diagnostic precision for indeterminate pulmonary nodules, enabling more confident discrimination between benign and malignant lesions. However, most of these emerging tools have not yet been validated in large prospective trials and face technological barriers as well as challenges in real-world implementation. This review focuses primarily on LDCT screening detected pulmonary nodules, while incorporating evidence from incidentally detected and other indeterminate nodule cohorts when relevant to broader CT based management. By synthesizing advances in radiomics, AI, liquid biopsy, airway classifiers, and multi-omics integration, we highlight the need for prospective validation and multidisciplinary collaboration to translate these approaches into clinically useful pathways that improve early lung cancer detection, reduce unnecessary interventions, and enhance patient outcomes.

PMID:42431477 | DOI:10.1016/j.critrevonc.2026.105483

Recurrent patterns of TOP1-mediated neuronal genomic damage shared by major neurodegenerative disorders

✇Cell
By: Zinan Zhou · Lovelace J. Luquette · Guanlan Dong · Junho Kim · Jayoung Ku · Kisong Kim · Nandini Ramesh · Mingyun Bae · Ann Caplin · Diane D. Shao · Bezawit Sahile · Kow Essuman · Eitan Goodman · Michael B. Miller · August Yue Huang · William J. Nathan · Andre Nussenzweig · Peter J. Park · Clotilde Lagier-Tourenne · Eunjung Alice Lee · Christopher A. Walsh
1 July 2026 at 08:00
Single-cell whole-genome sequencing reveals a shared pattern of excessive somatic mutations across C9ORF72 ALS, C9ORF72 FTD, and Alzheimer’s disease, dominated by 2-bp deletions. These mutations arise from aberrant topoisomerase 1 (TOP1)-mediated mutagenesis linked to oxidative DNA damage, identifying a unifying mechanism of neuronal genomic instability in neurodegeneration.

Complete biosynthesis of the anticancer cephalotaxinone and homoerythratine

✇Cell
By: Runze Tian · Feifan Lin · Nianxin Guo · Chendi Liu · Kaiqi Chen · Yunxi Han · Ruiyun Lan · Qiang Li · Jianbin Yan · Xiaoguang Lei
11 June 2026 at 08:00
Complete biosynthetic pathways for cephalotaxinone and homoerythratine were elucidated from the endangered plant Cephalotaxus fortunei. Thirteen key enzymes were identified, including two homologous cytochrome P450 enzymes that catalyze a rare divergent oxidation process governing alkaloid scaffold diversification. Full pathway reconstitution in Nicotiana benthamiana establishes a foundation for the sustainable production of the anticancer agent homoharringtonine.
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