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Received — 13 April 2026 ⏭

Large Language Model–Based Analysis of Statin Therapy Discussions and Sentiment on Social Media: Cross-Sectional Observational Study

✇Journal of Medical Internet Research
By: Siru Liu · Jialin Liu
11 April 2026 at 04:00
Background: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. Objective: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)–based analysis of Reddit discussions. Methods: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. Results: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. Conclusions: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.
Received — 8 April 2026 ⏭

CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Baicheng Chen · Yu Wang · Ziheng Zhou · Xiangru Liu · Juanru Li · Yilei Chen · Tianxing He
7 April 2026 at 12:00
arXiv:2604.03750v1 Announce Type: cross Abstract: Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports critical tasks such as vulnerability discovery and malware analysis. Despite its importance, RE remains labor-intensive and requires substantial expertise, making large language models (LLMs) a potential solution for automating the process. However, their capabilities for RE remain systematically underexplored. To address this gap, we study the cryptographic binary RE capabilities of LLMs and introduce \textbf{CREBench}, a benchmark comprising 432 challenges built from 48 standard cryptographic algorithms, 3 insecure crypto key usage scenarios, and 3 difficulty levels. Each challenge follows a Capture-the-Flag (CTF) RE challenge, requiring the model to analyze the underlying cryptographic logic and recover the correct input. We design an evaluation framework comprising four sub-tasks, from algorithm identification to correct flag recovery. We evaluate eight frontier LLMs on CREBench. GPT-5.4, the best-performing model, achieves 64.03 out of 100 and recovers the flag in 59\% of challenges. We also establish a strong human expert baseline of 92.19 points, showing that humans maintain an advantage in cryptographic RE tasks. Our code and dataset are available at https://github.com/wangyu-ovo/CREBench.

Integrative Multi-Omics and Single-Cell Analysis Reveal THOC3 and THOC7 as Oncogenic RNA Processing Regulators in Lung Adenocarcinoma

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Sachin Kumar · Chung-Che Wu · Dahlak Daniel Solomon · Meng-Chi Yen · I-Jeng Yeh · Ching-Chung Ko · Do Thi Minh Xuan · Kai-Fu Chang · Hui-Ru Lin · Hung-Yun Lin · Chia-Lung Shih · Jian-Bin Chen · Wei-Yi Lee · Chih-Yang Wang · Yung-Kuo Lee · Ngoc Uyen Nhi Nguyen
6 April 2026 at 18:00

Int J Med Sci. 2026 Mar 9;23(4):1408-1430. doi: 10.7150/ijms.128975. eCollection 2026.

ABSTRACT

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide. Although the transcription-export (TREX) complex plays a central role in RNA maturation and nuclear export, the clinical and biological relevance of individual THO Complex Subunit (including THOC1, THOC2, THOC3, THOC5, THOC6, and THOC7) in LUAD is not well defined. We performed integrative analyses combining bulk transcriptomics from TCGA/GTEx and independent GEO cohorts, survival modeling, DNA methylation profiling, protein-level annotation from public resources, protein-protein interaction network analysis, immune infiltration estimation (TIMER), and single-cell RNA sequencing (scRNA-seq) to evaluate the relevance of THOC3 and THOC7 in LUAD. Across TCGA and external GEO validation datasets, THOC3 and THOC7 were consistently upregulated in LUAD and associated with poorer overall and disease-free survival, whereas other THO complex members showed weaker or inconsistent associations. Given these comparatively consistent and reproducible signals, we therefore prioritized THOC3 and THOC7 for downstream multi-layer analyses. Epigenetic profiling and interaction network analyses placed both genes within conserved RNA processing and export programs linked to genome maintenance pathways. Single-cell transcriptomic analysis provided additional resolution, demonstrating predominant enrichment of THOC3 and THOC7 in malignant epithelial clusters, with THOC3 aligning with transcriptional programs associated with DNA replication and repair, and THOC7 with proliferative and checkpoint-related states. Notably, expression of both genes was also detectable in myeloid and neutrophil subsets, and THOC7 expression remained elevated in recurrent LUAD samples, indicating association with aggressive and treatment-resistant disease states. Collectively, by integrating bulk, single-cell, epigenetic, and immune profiling across multiple independent cohorts, this study identifies THOC3 and THOC7 as reproducible molecular correlates of aggressive LUAD phenotypes. These highlight dysregulated RNA export programs as potential biomarkers of poor prognosis and motivate future functional studies to assess RNA export dependencies in LUAD.

PMID:41938520 | PMC:PMC13048885 | DOI:10.7150/ijms.128975

Integrative Multi-Omics and Single-Cell Analysis Reveal THOC3 and THOC7 as Oncogenic RNA Processing Regulators in Lung Adenocarcinoma

✇Omics In Lung
By: Sachin Kumar · Chung-Che Wu · Dahlak Daniel Solomon · Meng-Chi Yen · I-Jeng Yeh · Ching-Chung Ko · Do Thi Minh Xuan · Kai-Fu Chang · Hui-Ru Lin · Hung-Yun Lin · Chia-Lung Shih · Jian-Bin Chen · Wei-Yi Lee · Chih-Yang Wang · Yung-Kuo Lee · Ngoc Uyen Nhi Nguyen
6 April 2026 at 18:00

Int J Med Sci. 2026 Mar 9;23(4):1408-1430. doi: 10.7150/ijms.128975. eCollection 2026.

ABSTRACT

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide. Although the transcription-export (TREX) complex plays a central role in RNA maturation and nuclear export, the clinical and biological relevance of individual THO Complex Subunit (including THOC1, THOC2, THOC3, THOC5, THOC6, and THOC7) in LUAD is not well defined. We performed integrative analyses combining bulk transcriptomics from TCGA/GTEx and independent GEO cohorts, survival modeling, DNA methylation profiling, protein-level annotation from public resources, protein-protein interaction network analysis, immune infiltration estimation (TIMER), and single-cell RNA sequencing (scRNA-seq) to evaluate the relevance of THOC3 and THOC7 in LUAD. Across TCGA and external GEO validation datasets, THOC3 and THOC7 were consistently upregulated in LUAD and associated with poorer overall and disease-free survival, whereas other THO complex members showed weaker or inconsistent associations. Given these comparatively consistent and reproducible signals, we therefore prioritized THOC3 and THOC7 for downstream multi-layer analyses. Epigenetic profiling and interaction network analyses placed both genes within conserved RNA processing and export programs linked to genome maintenance pathways. Single-cell transcriptomic analysis provided additional resolution, demonstrating predominant enrichment of THOC3 and THOC7 in malignant epithelial clusters, with THOC3 aligning with transcriptional programs associated with DNA replication and repair, and THOC7 with proliferative and checkpoint-related states. Notably, expression of both genes was also detectable in myeloid and neutrophil subsets, and THOC7 expression remained elevated in recurrent LUAD samples, indicating association with aggressive and treatment-resistant disease states. Collectively, by integrating bulk, single-cell, epigenetic, and immune profiling across multiple independent cohorts, this study identifies THOC3 and THOC7 as reproducible molecular correlates of aggressive LUAD phenotypes. These highlight dysregulated RNA export programs as potential biomarkers of poor prognosis and motivate future functional studies to assess RNA export dependencies in LUAD.

PMID:41938520 | PMC:PMC13048885 | DOI:10.7150/ijms.128975

Received — 11 March 2026 ⏭

Parallelized Planning-Acting for Efficient LLM-based Multi-Agent Systems in Minecraft

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yaoru Li · Shunyu Liu · Tongya Zheng · Li Sun · Mingli Song
10 March 2026 at 12:00
arXiv:2503.03505v2 Announce Type: replace Abstract: Recent advancements in Large Language Model~(LLM)-based Multi-Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existing frameworks inevitably rely on serialized execution paradigms, where agents must complete sequential LLM planning before taking action. This fundamental constraint severely limits real-time responsiveness and adaptation, which is crucial in dynamic environments with ever-changing scenarios like Minecraft. In this paper, we propose a novel parallelized planning-acting framework for LLM-based MAS, featuring a dual-thread architecture with interruptible execution to enable concurrent planning and acting. Specifically, our framework comprises two core threads: (1) a planning thread driven by a centralized memory system, maintaining synchronization of environmental states and agent communication to support dynamic decision-making; and (2) an acting thread equipped with a comprehensive skill library, enabling automated task execution through recursive decomposition. Extensive experiments on Minecraft demonstrate the effectiveness of the proposed framework.
Received — 5 March 2026 ⏭

Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Moru Liu · Hao Dong · Jessica Kelly · Olga Fink · Mario Trapp
5 March 2026 at 13:00
arXiv:2505.16985v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection and segmentation are crucial for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. While prior research has primarily focused on unimodal image data, real-world applications are inherently multimodal, requiring the integration of multiple modalities for improved OOD detection. A key challenge is the lack of supervision signals from unknown data, leading to overconfident predictions on OOD samples. To address this challenge, we propose Feature Mixing, an extremely simple and fast method for multimodal outlier synthesis with theoretical support, which can be further optimized to help the model better distinguish between in-distribution (ID) and OOD data. Feature Mixing is modality-agnostic and applicable to various modality combinations. Additionally, we introduce CARLA-OOD, a novel multimodal dataset for OOD segmentation, featuring synthetic OOD objects across diverse scenes and weather conditions. Extensive experiments on SemanticKITTI, nuScenes, CARLA-OOD datasets, and the MultiOOD benchmark demonstrate that Feature Mixing achieves state-of-the-art performance with a $10 \times$ to $370 \times$ speedup. Our source code and dataset will be available at https://github.com/mona4399/FeatureMixing.

Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guangjun Zhang · Hu Zhang · Yazhou Han · Yue Fan · Yuhang Shao · Ru Li · Hongye Tan
5 March 2026 at 13:00
arXiv:2603.02909v2 Announce Type: replace-cross Abstract: Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents . In the zero-shot setting, existing methods employ LLMs to generate synthetic data to address the challenge posed by the scarcity of annotated data. However, relying solely on Event-type-only prompts makes it difficult for the generated content to accurately capture the contextual and structural relationships of unseen events. Moreover, ensuring the reliability and usability of synthetic data remains a significant challenge due to the absence of quality evaluation mechanisms. To this end, we introduce a multi-agent collaboration framework for zero-shot document-level event argument extraction (ZS-DEAE), which simulates the human collaborative cognitive process of "Propose-Evaluate-Revise." Specifically, the framework comprises a generation agent and an evaluation agent. The generation agent synthesizes data for unseen events by leveraging knowledge from seen events, while the evaluation agent extracts arguments from the synthetic data and assesses their semantic consistency with the context. The evaluation results are subsequently converted into reward signals, with event structure constraints incorporated into the reward design to enable iterative optimization of both agents via reinforcement learning.In three zero-shot scenarios constructed from the RAMS and WikiEvents datasets, our method achieves improvements both in data generation quality and argument extraction performance, while the generated data also effectively enhances the zero-shot performance of other DEAE models.

Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guangjun Zhang · Hu Zhang · Yazhou Han · Yue Fan · Yuhang Shao · Ru Li · Hongye Tan
4 March 2026 at 13:00
arXiv:2603.02909v1 Announce Type: cross Abstract: Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents.In the zero-shot setting, existing methods employ LLMs to generate synthetic data to address the challenge posed by the scarcity of annotated data. However, relying solely on Event-type-only prompts makes it difficult for the generated content to accurately capture the contextual and structural relationships of unseen events. Moreover, ensuring the reliability and usability of synthetic data remains a significant challenge due to the absence of quality evaluation mechanisms. To this end, we introduce a multi-agent collaboration framework for zero-shot document-level event argument extraction (ZS-DEAE), which simulates the human collaborative cognitive process of "Propose-Evaluate-Revise." Specifically, the framework comprises a generation agent and an evaluation agent. The generation agent synthesizes data for unseen events by leveraging knowledge from seen events, while the evaluation agent extracts arguments from the synthetic data and assesses their semantic consistency with the context. The evaluation results are subsequently converted into reward signals, with event structure constraints incorporated into the reward design to enable iterative optimization of both agents via reinforcement learning.In three zero-shot scenarios constructed from the RAMS and WikiEvents datasets, our method achieves improvements both in data generation quality and argument extraction performance, while the generated data also effectively enhances the zero-shot performance of other DEAE models.

How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Toru Lin · Shuying Deng · Zhao-Heng Yin · Pieter Abbeel · Jitendra Malik
4 March 2026 at 13:00
arXiv:2603.03280v1 Announce Type: cross Abstract: Many essential manipulation tasks - such as food preparation, surgery, and craftsmanship - remain intractable for autonomous robots. These tasks are characterized not only by contact-rich, force-sensitive dynamics, but also by their "implicit" success criteria: unlike pick-and-place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward engineering difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two-stage pipeline: first, we learn a robust initial policy via force-aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference-based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human notions of task quality. Using only 50-200 peeling trajectories, our system achieves over 90% average success rates on challenging produce including cucumbers, apples, and potatoes, with performance improving by up to 40% through preference-based finetuning. Remarkably, policies trained on a single produce category exhibit strong zero-shot generalization to unseen in-category instances and to out-of-distribution produce from different categories while maintaining over 90% success rates.
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