PRISMA-LLM: An Empirical Reporting Framework for AI-Assisted Systematic Reviews

14 September 2026 at 12:00

arXiv:2609.11559v1 Announce Type: cross Abstract: Large language models (LLMs) and AI-enabled software increasingly participate in systematic-review decisions, yet the information needed to audit these workflows is reported inconsistently. We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations. Automation has shifted toward LLM- and software-facing workflows, including stages that can alter the evidence base. Since 2023, 38.0% of software/product papers reported no evaluation, compared with 9.3% of LLM papers. Reporting coverage increased with LLM workflow complexity, yet 52% of positive-only LLM evaluations still reported an unmet reliability or performance requirement. From these patterns, we introduce PRISMA-LLM, an empirically grounded framework separating implementation disclosure from consequence-sensitive evaluation and limitation reporting.

BRD4 Inhibition Mitigates Acute and Chronic Corneal Injury Following Topical Nitrogen Mustard Exposure

Molecular Therapy

By: Zhongqiao Lu · Shuizhen Shi · Fan Xia · Adwaid Manu Krishna Chandran · Jonathan Lin · Daniela J. Iniguez · Massoud Motamedi · Hua Liu · Wenbo Zhang

2 September 2026 at 08:00

Lu and colleagues identify BRD4 as a central epigenetic driver of vesicant-induced corneal injury. Using reproducible mouse and rabbit models, they show that short-term topical BRD4 inhibition suppresses acute inflammation and provides durable protection of corneal clarity, stromal organization, endothelial integrity, and neovascularization, supporting translational therapy for chemical eye injuries.

MITF-SCD1 Lipid Metabolic Axis Prevents Ouabain-Induced Spiral Ganglion Neuron Ferroptosis and Hearing Loss

Molecular Therapy

By: Xue Wang · Fang Chen · Ziji Zhang · Man Zhang · Man Wang · Lei Chen · Anqi Suo · Xugang Zhuang · Tenghan Ling · Renjie Chai · Haibo Wang · Bo Chu · Lei Xu · Wenwen Liu

31 August 2026 at 08:00

Ouabain triggers cochlear spiral ganglion neuron (SGN) ferroptosis and hearing loss via SCD1 downregulation. MITF directly activates Scd1 transcription, and the MITF–SCD1 axis mitigates SGN ferroptosis and hearing impairment in ototoxic ouabain and cisplatin models, revealing a lipid metabolic vulnerability and therapeutic target for sensorineural hearing loss.

Genetic predictors of GLP1 receptor agonist weight loss and side effects

Nature - Issue - nature.com science feeds

By: Qiaojuan Jane Su · James R. Ashenhurst · Wanwan Xu · Vinh Tran · R. Ryanne Wu · Catherine H. Weldon · Jingchunzi Shi · Barry Hicks · Robert K. Bell · Katelyn Kukar Bond · Zayn Cochinwala · Sayantan Das · Kahsaia de Brito · Devika Dhamija · Payambr Dibaeinia · Emily DelloRusso · Chris Eijsbouts · Sarah L. Elson · Shirin Fuller · Chris German · Julie M. Granka · Larry Hengl · David A. Hinds · Reza Jabal · Aly Khan · Matthew J. Kmiecik · Alan Kwong · Yanyu Liang · Keng-Han Lin · Matthew H. McIntyre · Alex Moran · Carrie Northover · Shubham Saini · Anjali J. Shastri · Suyash Shringarpure · Teague Sterling · Joyce Y. Tung · Noura S. Abul-Husn · Stella Aslibekyan · Michael V. Holmes · Bertram L. Koelsch · Adam Auton

8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10330-z

Identification of genetic variants associated with the efficacy and side effects of GLP1 medications could underpin development of precision medicine approaches in the treatment of obesity.

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.

Targeting the OXNAD1-PTGS2 axis with resveratrol overcomes ferroptosis Inhibition and reverses 5-FU resistance in gastric cancer

Omics in Gastric

By: Jinzhou Wang · Bishuang Xu · Xiaohan Lin · Biyu Chen · Xiaoyan Liu · Wei Wu

13 March 2026 at 18:00

Gastric Cancer. 2026 Mar 13. doi: 10.1007/s10120-026-01718-x. Online ahead of print.

ABSTRACT

BACKGROUND: 5-Fluorouracil (5-FU) remains a cornerstone of first-line chemotherapy for gastric cancer, yet the emergence of resistance severely compromises its clinical efficacy. Although ferroptosis suppression has been recognized as a pivotal mechanism of chemoresistance, the mitochondrial regulatory processes involved remain poorly understood.

METHODS: We integrated clinical specimen analysis, in vitro and in vivo functional assays, multi-omics profiling, and molecular docking to delineate the role of the mitochondrial oxidoreductase OXNAD1 in mediating 5-FU resistance in gastric cancer, and to assess the therapeutic potential of the natural polyphenol resveratrol as a chemosensitizing agent.

RESULTS: OXNAD1 was found to be significantly overexpressed in gastric cancer tissues and cell lines, correlating with unfavorable prognosis and enhanced 5-FU resistance. Mechanistically, OXNAD1 directly bound to and suppressed the ferroptosis driver PTGS2, thereby attenuating lipid peroxidation and mitochondrial damage, ultimately restraining ferroptosis and promoting drug resistance. Notably, resveratrol disrupted the OXNAD1-PTGS2 interaction by directly binding OXNAD1, reinstating ferroptotic activity, markedly enhancing the cytotoxic effect of 5-FU in resistant cells, and potentiating the antitumor efficacy of 5-FU in xenograft models.

CONCLUSION: The OXNAD1-PTGS2 axis constitutes a critical metabolic-cell death cross-regulatory pathway underlying 5-FU resistance in gastric cancer. Targeting this axis with resveratrol provides a promising combinatorial strategy to overcome chemoresistance.

PMID:41824193 | DOI:10.1007/s10120-026-01718-x

CoDAR: Continuous Diffusion Language Models are More Powerful Than You Think

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

By: Junzhe Shen · Jieru Zhao · Ziwei He · Zhouhan Lin

4 March 2026 at 13:00

arXiv:2603.02547v1 Announce Type: cross Abstract: We study why continuous diffusion language models (DLMs) have lagged behind discrete diffusion approaches despite their appealing continuous generative dynamics. Under a controlled token--recovery study, we identify token rounding, the final projection from denoised embeddings to tokens, as a primary bottleneck. Building on these insights, we propose CoDAR (Continuous Diffusion with Contextual AutoRegressive Decoder), a two--stage framework that keeps diffusion entirely continuous in an embedding space while learning a strong, context--conditional discretizer: an autoregressive Transformer decoder that cross--attends to the denoised embedding sequence and performs contextualized rounding to tokens. Experiments on LM1B and OpenWebText demonstrate that CoDAR substantially improves generation quality over latent diffusion and becomes competitive with strong discrete DLMs, while exposing a simple decoder--temperature knob to navigate the fluency--diversity trade off.

AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories

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

By: Zun Wang · Han Lin · Jaehong Yoon · Jaemin Cho · Yue Zhang · Mohit Bansal

17 February 2026 at 13:00

arXiv:2602.14941v1 Announce Type: cross Abstract: Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross-view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross-view inconsistencies. To this end, AnchorWeave performs coverage-driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi-anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi-anchor control, and coverage-driven retrieval.

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