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Received — 15 September 2026 ⏭

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Miguel Zabaleta · Baihan Lin
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.

Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sicheng Mao · Baihan Li · Mathieu Fontaine · Anthony Larcher · Roland Badeau
14 September 2026 at 12:00
arXiv:2609.12154v1 Announce Type: cross Abstract: Distant speaker diarization remains challenging due to difficult acoustic environments, varying numbers of speakers and overlapping speech. Model-driven methods are proposed to exploit the speech source features in multi-channel recordings that help diarization. This paper generalizes a neural model that jointly learns to perform blind source separation and diarization over speech mixtures (neural FCASA) with heavy-tailed models. The popular Gaussian distribution has been applied for variance modeling in the original source separation model, which we replace with two families of heavy-tailed models (Leptokurtic Generalized Gaussian distribution and Student's t distribution) to better capture the heavy-tailedness in speech signals. Thanks to the Gaussian scale mixture model, we are able to unify the proposed method and the original one under the same form of learning objective. Our experiments show consistent large improvements in Diarization Error Rate (DER) and Jaccard Error Rate (JER) compared to the baseline on various corpora.

SCOPE-OPSD: Fisher-Conditioned Privileged Subspaces for On-Policy Self-Distillation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunmeng Chen (Chongqing Ant Consumer Finance Co. · Ltd) · Kunyu Wang (Alibaba Cloud Computing Co. · Ltd) · Peihan Li (Chongqing Ant Consumer Finance Co. · Ltd) · Yi Wang (Chongqing Ant Consumer Finance Co. · Ltd) · Shuyin Xia (Chongqing University of Posts and Telecommunications) · Yi Liu (Chongqing Ant Consumer Finance Co. · Ltd) · Xinyong Cheng (Alibaba Cloud Computing Co. · Ltd) · Dehui Wang (Alibaba Cloud Computing Co. · Ltd) · Xiangyong Zhai (Alibaba Cloud Computing Co. · Ltd) · Yanxing Liu (Chongqing Ant Consumer Finance Co. · Ltd) · Song Liu (Chongqing Ant Consumer Finance Co. · Ltd)
14 September 2026 at 12:00
arXiv:2609.12579v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) scores student-generated prefixes with a solution-conditioned self-teacher, yet transfers supervision only through next-token probabilities. We ask whether the aligned final-layer discrepancy offers a useful second channel, and how to test that channel without confusing its geometry with auxiliary strength. SCOPE-OPSD projects the privileged teacher-student residual onto a frozen rank-64 factor estimated from residual covariance and language-model-head Fisher sensitivity. It reuses the forwards already required by OPSD and adds neither rollouts nor inference-time modules. A matched Random control preserves the structured factor's rank and nonzero spectrum and uses per-arm gradient-RMS calibration, isolating the effect of the data-dependent orientation. Across the complete 25/50/75/100-step trajectories for Qwen3-1.7B, 4B, and 8B, Structured is never below Pure OPSD, with strict gains in 11 of the 12 model-checkpoint combinations and an exact tie at 4B step 25. Structured also exceeds matched Random in 10 of the 12 combinations. At step 75 on Qwen3-1.7B, Structured exceeds matched Random by 1.39 Macro Avg@12 points in each of two independent training reruns. A cross-fitted diagnostic also shows 4.40 times greater held-out privileged-gap capture than the matched random orientation. The results support a compact, Fisher-conditioned privileged subspace for short-budget OPSD.

Hepatic Usp2 orchestrates de novo lipogenesis through G3bp2 stabilization and β-catenin activation

✇Cell Death Discovery nature.com science feeds
By: Wei Wang · Hui Wang · Yihan Liu · Siyuan Cui · Chuanxi Tang · Mengxue Wang · Chunyan Mu · Jingyu Li · Xianlong Ye · Xinmiao Liang · Shenglong Zhu
14 September 2026 at 08:00

Cell Death Discovery, Published online: 14 September 2026; doi:10.1038/s41420-026-03333-2

Hepatic Usp2 orchestrates de novo lipogenesis through G3bp2 stabilization and β-catenin activation

Multi-Omics-Enabled Precision Strategies for Overcoming CAR-T Therapy Limitations in Gastrointestinal Malignancies

✇Omics in Gastric
By: Ke Su · Zunyuan Tan · Han Li · Kai You · Susu Xiao · Haiqing Chen · Danyang Li · Xin Liu · Xinrui Du · Jiaxin Hu · Kun He · Yantian Xu · Jian Chen · Yunwei Han
10 September 2026 at 18:00

Biofactors. 2026 Sep-Oct;52(5):e70150. doi: 10.1002/biof.70150.

ABSTRACT

Gastrointestinal malignancies, including gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic ductal adenocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized the treatment of hematologic malignancies, its efficacy in gastrointestinal solid tumors remains limited by antigen heterogeneity, insufficient trafficking and infiltration, immunosuppressive tumor microenvironments, on-target off-tumor toxicity, and adaptive resistance. In this review, we summarize the current landscape of CAR-T therapy in gastric cancer, colorectal cancer, hepatocellular carcinoma, and pancreatic cancer, with a focus on representative target antigens and emerging biomarker strategies. We further discuss two major categories of biomarkers: target antigen-related biomarkers and conventional dynamic biomarkers, including serum tumor markers, cytokine changes, CAR-T expansion kinetics, and antigen-loss monitoring. In addition, we highlight how single-cell ribonucleic acid sequencing and spatial transcriptomics provide complementary insights into cellular states, immune exhaustion, stromal barriers, and spatially restricted immune exclusion. By integrating these multi-omics approaches with biomarker-guided patient stratification and next-generation CAR-T engineering, gastrointestinal solid tumor CAR-T therapy may evolve from empirical optimization toward mechanism-driven and precision-guided clinical translation.

PMID:42717494 | PMC:PMC13558850 | DOI:10.1002/biof.70150

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