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Received — 15 September 2026 ⏭

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenyu Han · Ansheng You · Haibo Wang · Kui Luo · Guang Yang · Wenqi Shi · Menglong Chen · Sicheng Zhang · Zeshun Lan · Chunshi Deng · Huazhong Ji · Wenjie Liu · Yu Huang · Yixiang Zhang · Chenyi Pan · Jing Wang · Xin Huang · Chunsheng Li · Jianping Wu
14 September 2026 at 12:00
arXiv:2507.01663v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a pivotal technology in the post-training phase of large language models (LLMs). Traditional task-collocated RL frameworks suffer from significant scalability bottlenecks, while task-separated RL frameworks face challenges in managing complex dataflows and resolving resource idling. Furthermore, most existing frameworks are tightly coupled with LLM training or inference engines, making them difficult to support custom-designed engines. To address these challenges, we propose AsyncFlow, an asynchronous streaming RL framework tailored for efficient post-training. Specifically, we introduce a distributed data storage and transfer module that provides panoramic data management and fine-grained scheduling capabilities in a fully streamed manner. This architecture inherently enables automated pipeline overlapping among RL tasks and dynamic load-balancing. Moreover, we propose an asynchronous producer-consumer workflow, which is engineered to minimize computational idleness by strategically deferring the parameter update process within staleness thresholds. Finally, the core capabilities of AsyncFlow are architecturally decoupled from underlying training and inference engines and encapsulated by service-oriented user interfaces, offering a modular and customizable user experience. Extensive experiments demonstrate an average throughput of 1.59x compared to the state-of-the-art baseline. The architecture presented in this work provides actionable insights for designing next-generation RL training systems.
Received — 2 April 2026 ⏭

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haozhen Gong · Xiaozhong Ji · Yuansen Liu · Wenbin Wu · Xiaoxiao Yan · Jingjing Liu · Kai Wu · Jiazhen Pan · Bailiang Jian · Jiangning Zhang · Xiaobin Hu · Hongwei Bran Li
1 April 2026 at 12:00
arXiv:2512.00818v2 Announce Type: replace Abstract: MLLMs MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Complex Multimodal Reasoning benchmark. Med-CMR distinguishes from existing counterparts by three core features: 1) Systematic capability decomposition, splitting medical multimodal reasoning into fine-grained visual understanding and multi-step reasoning to enable targeted evaluation; 2) Challenging task design, with visual understanding across three key dimensions (small-object detection, fine-detail discrimination, spatial understanding) and reasoning covering four clinically relevant scenarios (temporal prediction, causal reasoning, long-tail generalization, multi-source integration); 3) Broad, high-quality data coverage, comprising 20,653 Visual Question Answering (VQA) pairs spanning 11 organ systems and 12 imaging modalities, validated via a rigorous two-stage (human expert + model-assisted) review to ensure clinical authenticity. We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model: 57.81 accuracy on multiple-choice questions (MCQs) and a 48.70 open-ended score, outperforming Gemini 2.5 Pro (49.87 MCQ accuracy, 45.98 open-ended score) and leading open-source model Qwen3-VL-235B-A22B (49.34 MCQ accuracy, 42.62 open-ended score). However, specialized medical MLLMs do not reliably outperform strong general models, and long-tail generalization emerges as the dominant failure mode. Med-CMR thus provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.
Received — 17 March 2026 ⏭

FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingren Liu · Shuning Xu · Qirui Yang · Yun Wang · Xiangyu Chen · Zhong Ji
16 March 2026 at 12:00
arXiv:2511.14099v3 Announce Type: replace-cross Abstract: All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Execution framework for image restoration. It uses a frozen Multimodal Large Language Model (MLLM) as a planner to analyze degraded images and generate concise, frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module within a diffusion-based executor, which dynamically selects high- or low-frequency experts, complemented by frequency features of the input image. To further improve restoration quality and reduce artifacts, we introduce adversarial training and a frequency regularization loss. By coupling semantic planning with frequency-based restoration, FAPE-IR offers a unified and interpretable solution for all-in-one image restoration. Extensive experiments show that FAPE-IR achieves state-of-the-art performance across seven restoration tasks and exhibits strong zero-shot generalization under mixed degradations.
Received — 14 March 2026 ⏭

Lung cancer as a global health challenge: Multidimensional biomarker research and therapeutic advances

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Dezhong Jin · Liangwang Zhong · Lai Chen
13 March 2026 at 18:00

Int J Cancer. 2026 Mar 13. doi: 10.1002/ijc.70419. Online ahead of print.

ABSTRACT

Lung cancer, the leading cause of global cancer-related mortality, is categorized into small-cell and non-small-cell subtypes. The heterogeneous non-small-cell lung cancer group is further subcategorized primarily into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, each underpinned by distinct molecular alterations. Although traditional serum biomarkers aid in subtype differentiation and treatment monitoring, their utility is limited by challenges such as poor specificity due to inflammatory confounders and the difficulty of dynamically tracking therapeutic resistance. Recent advances have identified emergent subtype-specific biomarkers that reflect metabolic reprogramming, epigenetic dysregulation, stemness signatures, and interactions within the immune microenvironment. By integrating analytes such as ctDNA, exosomal RNAs, and urinary DNA with multi-analyte panels and advanced imaging, liquid biopsies offer a promising avenue to enhance early detection accuracy, prognostication, and dynamic therapy monitoring. Nevertheless, the clinical adoption is hindered by several challenges, including incomplete validation, the need for technical standardization, intratumoral heterogeneity, and inter-ethnic variability. The convergence of artificial intelligence (AI)-enhanced multi-omics with biomarker-guided therapeutics represents a transformative strategy with the potential to overcome resistance, mitigate ethnic disparities, and ultimately transform lung cancer into a chronic, manageable disease. Therefore, prioritizing clinically validated AI-integrated platforms is pivotal to achieve precision oncology.

PMID:41826059 | DOI:10.1002/ijc.70419

Lung cancer as a global health challenge: Multidimensional biomarker research and therapeutic advances

✇Omics In Lung
By: Dezhong Jin · Liangwang Zhong · Lai Chen
13 March 2026 at 18:00

Int J Cancer. 2026 Mar 13. doi: 10.1002/ijc.70419. Online ahead of print.

ABSTRACT

Lung cancer, the leading cause of global cancer-related mortality, is categorized into small-cell and non-small-cell subtypes. The heterogeneous non-small-cell lung cancer group is further subcategorized primarily into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, each underpinned by distinct molecular alterations. Although traditional serum biomarkers aid in subtype differentiation and treatment monitoring, their utility is limited by challenges such as poor specificity due to inflammatory confounders and the difficulty of dynamically tracking therapeutic resistance. Recent advances have identified emergent subtype-specific biomarkers that reflect metabolic reprogramming, epigenetic dysregulation, stemness signatures, and interactions within the immune microenvironment. By integrating analytes such as ctDNA, exosomal RNAs, and urinary DNA with multi-analyte panels and advanced imaging, liquid biopsies offer a promising avenue to enhance early detection accuracy, prognostication, and dynamic therapy monitoring. Nevertheless, the clinical adoption is hindered by several challenges, including incomplete validation, the need for technical standardization, intratumoral heterogeneity, and inter-ethnic variability. The convergence of artificial intelligence (AI)-enhanced multi-omics with biomarker-guided therapeutics represents a transformative strategy with the potential to overcome resistance, mitigate ethnic disparities, and ultimately transform lung cancer into a chronic, manageable disease. Therefore, prioritizing clinically validated AI-integrated platforms is pivotal to achieve precision oncology.

PMID:41826059 | DOI:10.1002/ijc.70419

Received — 5 March 2026 ⏭

MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Baorong Shi · Bo Cui · Boyuan Jiang · Deli Yu · Fang Qian · Haihua Yang · Huichao Wang · Jiale Chen · Jianfei Pan · Jieqiong Cao · Jinghao Lin · Kai Wu · Lin Yang · Shengsheng Yao · Tao Chen · Xiaojun Xiao · Xiaozhong Ji · Xu Wang · Yijun He · Zhixiong Yang
4 March 2026 at 13:00
arXiv:2602.12705v3 Announce Type: replace-cross Abstract: We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.
Received — 17 February 2026 ⏭

MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Baorong Shi · Bo Cui · Boyuan Jiang · Deli Yu · Fang Qian · Haihua Yang · Huichao Wang · Jiale Chen · Jianfei Pan · Jieqiong Cao · Jinghao Lin · Kai Wu · Lin Yang · Shengsheng Yao · Tao Chen · Xiaojun Xiao · Xiaozhong Ji · Xu Wang · Yijun He · Zhixiong Yang
17 February 2026 at 13:00
arXiv:2602.12705v2 Announce Type: replace-cross Abstract: We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world clinical applications. MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities. To achieve this, we propose an entity-aware continual pretraining framework that organizes heterogeneous medical corpora to broaden knowledge coverage and reduce long-tail gaps (e.g., rare diseases). For medical expert-level reasoning and interaction, MedXIAOHE incorporates diverse medical reasoning patterns via reinforcement learning and tool-augmented agentic training, enabling multi-step diagnostic reasoning with verifiable decision traces. To improve reliability in real-world use, MedXIAOHE integrates user-preference rubrics, evidence-grounded reasoning, and low-hallucination long-form report generation, with improved adherence to medical instructions. We release this report to document our practical design choices, scaling insights, and evaluation framework, hoping to inspire further research.
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