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Received — 27 May 2026 ⏭

Dynamic Dual-Granularity Skill Bank for Agentic RL

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songjun Tu · Chengdong Xu · Qichao Zhang · Yaocheng Zhang · Xiangyuan Lan · Linjing Li · Dong Li · Dongbin Zhao
26 May 2026 at 12:00
arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Built entirely from training-time experience, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Experiments on ALFWorld, WebShop, and Search-Augmented QA tasks show that D2Skill substantially improves performance over skill-free baselines across models of different scales. Further ablations and analyses show that both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains, while the learned skills exhibit higher utility, transfer across evaluation settings, and introduce only modest training overhead.

CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhangyi Hu · Chenhui Liu · Tian Huang · Jindong Li · Yang Yang · Jiemin Wu · Zining Zhong · Menglin Yang · Yutao Yue
26 May 2026 at 12:00
arXiv:2605.23491v2 Announce Type: replace-cross Abstract: Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit Tests (GT UTs) remain a bottleneck: SOTA RLVR methods require them for costly training, while existing TTS methods lose competitiveness without them. This motivates GT-free TTS, where existing methods directly use self-generated UTs to refine and select code candidates. Yet such UTs are often noisy or spuriously coupled with wrong code, and UT quality in turn cannot be validated without reliable code. The key challenge is therefore to jointly improve both. To this end, we present CoSPlay, a GT-free, training-free framework that jointly improves codes and UTs through cooperative self-play. It first explores diverse solution ideas and identifies their potential failure modes to produce discriminative UT ideas. It then uses bidirectional pass-count signals from the Code-UT execution matrix to iteratively prune or fix weak codes and refresh or replace unreliable UTs, letting the two pools co-evolve. Finally, when multiple codes remain tied at the highest pass count, it picks the final code from the largest output-consensus cluster, since correct codes agree on the same inputs while wrong codes diverge. Experiments on four challenging benchmarks show that CoSPlay on Qwen2.5-7B-Instruct improves average BoN from 22.1% to 33.2% and UT accuracy from 14.6% to 78.3%, matching or surpassing the RLVR model CURE-7B. When applied to CURE-7B, it further improves BoN by 5.7%. CoSPlay also generalizes across diverse backbones and outperforms GT-free TTS baselines under comparable token budgets, with continued gains as the budget scales up. These results suggest a scalable inference strategy for competitive code generation without any GT data.

Machine learning-based identification of key genes underlying sex differences in hepatocellular carcinoma and targeted drug screening

✇Omics in Gastric
By: Zhen Wang · Jinghua Ning · Hongyuan Zhang · Qingfen Ruan · Yanhong Zhao · Run Qu · Congcong Lv · Yutong Wu · Weidong Liu · Xiaoyin Yang · Ziming Li · Yi Liang · Yuzhe Zhang
13 May 2026 at 18:00

Biomed Rep. 2026 Apr 24;24(6):74. doi: 10.3892/br.2026.2147. eCollection 2026 Jun.

ABSTRACT

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study leveraged multi-omics data and machine learning algorithms to identify key genes associated with sex-specific differences in HCC and to screen for putative candidate compounds, aiming to provide new insights for sex-specific therapy. The mRNA expression data of male and female patients with HCC and paracancerous tissues were obtained from the GEO and TCGA databases. To mitigate overfitting, data were partitioned into independent training and testing sets. Candidate genes were screened by differential expression analysis and weighted gene co-expression network analysis. A total of four complementary algorithms, random forest, support vector machines, generalized linear models and extreme gradient boosting were used to identify key genes with high predictive capability. CYP17A1 and IRX3 were identified as the top differentially expressed core genes associated with HCC in men. Pan-cancer analysis showed that CYP17A1 was lowly expressed in the majority of tumors, but significantly highly expressed in HCC, rectal adenocarcinoma and gastric cancer (P<0.001). Functional cell-based assays showed that knockout of CYP17A1 inhibited the proliferation, migration and invasion ability of HCC cells (P<0.001). Immunohistochemistry showed that CYP17A1 protein expression was significantly increased in HCC tissues from male patients when compared with that in paracancerous tissues (P<0.001), whereas there was no significant difference in female patient tissues (P>0.05). Notably, while IRX3 was identified computationally, its functional role remains to be experimentally validated. Molecular docking predicted a potential interaction between the natural compound Saikosaponin A and the CYP17A1 protein, and cellular assays revealed that it dose-dependently inhibits HCC cell malignant phenotypes. The present study suggests that CYP17A1 is associated with sex differences in HCC, potentially via the androgen signaling axis. Furthermore, IRX3 emerges as a novel hypothesis-generating candidate gene. Finally, the findings of the present study highlight Saikosaponin A as a putative therapeutic candidate for male patients with HCC, warranting further target-dependency investigations.

PMID:42125766 | PMC:PMC13158723 | DOI:10.3892/br.2026.2147

Received — 13 April 2026 ⏭

Effect of the Maxing Huoqiao granule on nonsevere community-acquired pneumonia: A multicenter, double-blind, placebo-controlled randomized trial

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qingming Ke · Caiping Zhao · Shengda Luo · Yuntao Liu · Ye Ye · Xingdong Lin · Jing Zeng · Xinghua Liu · Yang Liao · Hongtao Li · Shu Chen · Hong Ren · Liang Liu · Hudan Pan
12 April 2026 at 18:00

Pharmacol Res. 2026 Apr 9:108186. doi: 10.1016/j.phrs.2026.108186. Online ahead of print.

ABSTRACT

Community-acquired pneumonia (CAP) remains a major global public health challenge with substantial morbidity and mortality. Although preclinical studies suggest that Maxing Huoqiao (MXHQ) granule may have therapeutic potential for pneumonia, high-quality clinical evidence is still limited. We conducted a multicenter, double-blind, randomized, placebo-controlled trial at two tertiary hospitals in China to evaluate the clinical efficacy of MXHQ as adjunctive therapy and to explore its potential mechanisms in adults with nonsevere CAP receiving standard moxifloxacin treatment. A total of 96 patients were enrolled and randomized (1:1:1) to receive standard-dose MXHQ, low-dose MXHQ, or placebo in addition to moxifloxacin for 7 days, with a 14-day follow-up. The primary endpoint was clinical cure, defined as composite recovery of major respiratory symptoms, lung rales, and fever; secondary endpoints included symptom relief, radiographic improvement, and safety. Compared with placebo, standard-dose MXHQ was associated with a higher day-14 clinical cure rate (30.78% vs. 68.97%; RR = 0.45, 95% CI = 0.24-0.83; P < 0.01). Furthermore, the standard-dose intervention was correlated with a shorter time to relief and recovery of cough and sputum (P < 0.05), as well as improvements in symptom scores (P < 0.05) and promoting lesion absorption on chest CT (P < 0.05). Low-dose MXHQ showed no significant clinical benefit, whereas safety profiles were comparable across all groups. Transcriptomic analyses of peripheral blood mononuclear cells, complemented by a Streptococcus pneumonia animal model, indicated that the clinical benefits of MXHQ are linked to the modulation of inflammation and innate immunity. These omics and in vivo observations suggest a potential mechanism underlying the protective effects of MXHQ against inflammatory injury and promotion of tissue repair, involving the regulation of anti-inflammatory mediators and tissue repair-related factors. (Chictr.org.cn, ID Number: ChiCTR2400082095).

PMID:41966499 | DOI:10.1016/j.phrs.2026.108186

Received — 8 April 2026 ⏭

Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: XinYu Zhao · ChengYou Li · XiangBao Meng · Kai Zhang · XiaoDong Liu
7 April 2026 at 12:00
arXiv:2604.03656v1 Announce Type: new Abstract: Generative Engine Optimization (GEO) is rapidly reshaping digital marketing paradigms in the era of Large Language Models (LLMs). However, current GEO strategies predominantly rely on Retrieval-Augmented Generation (RAG), which inherently suffers from probabilistic hallucinations and the "zero-click" paradox, failing to establish sustainable commercial trust. In this paper, we systematically deconstruct the probabilistic flaws of existing RAG-based GEO and propose a paradigm shift towards deterministic multi-agent intent routing. First, we mathematically formulate Semantic Entropy Drift (SED) to model the dynamic decay of confidence curves in LLMs over continuous temporal and contextual perturbations. To rigorously quantify optimization value in black-box commercial engines, we introduce the Isomorphic Attribution Regression (IAR) model, leveraging a Multi-Agent System (MAS) probe with strict human-in-the-loop physical isolation to enforce hallucination penalties. Furthermore, we architect the Deterministic Agent Handoff (DAH) protocol, conceptualizing an Agentic Trust Brokerage (ATB) ecosystem where LLMs function solely as intent routers rather than final answer generators. We empirically validate this architecture using EasyNote, an industrial AI meeting minutes product by Yishu Technology. By routing the intent of "knowledge graph mapping on an infinite canvas" directly to its specialized proprietary agent via DAH, we demonstrate the reduction of vertical task hallucination rates to near zero. This work establishes a foundational theoretical framework for next-generation GEO and paves the way for a well-ordered, deterministic human-AI collaboration ecosystem.

3D-IDE: 3D Implicit Depth Emergent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chushan Zhang · Ruihan Lu · Jinguang Tong · Yikai Wang · Hongdong Li
7 April 2026 at 12:00
arXiv:2604.03296v1 Announce Type: cross Abstract: Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground-truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade-off in 2D-3D representation fusion, leading to suboptimal deployment. To this end, we propose 3D-Implicit Depth Emergence, a method that reframes 3D perception as an emergent property derived from geometric self-supervision rather than explicit encoding. Our core insight is the Implicit Geometric Emergence Principle: by strategically leveraging privileged geometric supervision through mechanisms like a fine-grained geometry validator and global representation constraints, we construct an information bottleneck. This bottleneck forces the model to maximize the mutual information between visual features and 3D structures, allowing 3D awareness to emerge naturally within a unified visual representation. Unlike existing approaches, our method enables 3D perception to emerge implicitly, disentangling features in dense regions and, crucially, eliminating depth and pose dependencies during inference with zero latency overhead. This paradigm shift from external grafting to implicit emergence represents a fundamental rethinking of 3D knowledge integration in visual-language models. Extensive experiments demonstrate that our method surpasses SOTA on multiple 3D scene understanding benchmarks. Our approach achieves a 55% reduction in inference latency while maintaining strong performance across diverse downstream tasks, underscoring the effectiveness of meticulously designed auxiliary objectives for dependency-free 3D understanding. Source code can be found at github.com/ChushanZhang/3D-IDE.

StoryBlender: Inter-Shot Consistent and Editable 3D Storyboard with Spatial-temporal Dynamics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bingliang Li · Zhenhong Sun · Jiaming Bian · Yuehao Wu · Yifu Wang · Hongdong Li · Yatao Bian · Huadong Mo · Daoyi Dong
7 April 2026 at 12:00
arXiv:2604.03315v1 Announce Type: cross Abstract: Storyboarding is a core skill in visual storytelling for film, animation, and games. However, automating this process requires a system to achieve two properties that current approaches rarely satisfy simultaneously: inter-shot consistency and explicit editability. While 2D diffusion-based generators produce vivid imagery, they often suffer from identity drift along with limited geometric control; conversely, traditional 3D animation workflows are consistent and editable but require expert-heavy, labor-intensive authoring. We present StoryBlender, a grounded 3D storyboard generation framework governed by a Story-centric Reflection Scheme. At its core, we propose the StoryBlender system, which is built on a three-stage pipeline: (1) Semantic-Spatial Grounding, to construct a continuity memory graph to decouple global assets from shot-specific variables for long-horizon consistency; (2) Canonical Asset Materialization, to instantiate entities in a unified coordinate space to maintain visual identity; and (3) Spatial-Temporal Dynamics, to achieve layout design and cinematic evolution through visual metrics. By orchestrating multiple agents in a hierarchical manner within a verification loop, StoryBlender iteratively self-corrects spatial hallucinations via engine-verified feedback. The resulting native 3D scenes support direct, precise editing of cameras and visual assets while preserving unwavering multi-shot continuity. Experiments demonstrate that StoryBlender significantly improves consistency and editability over both diffusion-based and 3D-grounded baselines. Code, data, and demonstration video will be available on https://engineeringai-lab.github.io/StoryBlender/

Lightweight liquid neural networks decipher salivary metabolic fingerprinting for high-risk periodontitis screening in diabetes

✇npj Digital Medicine
By: Yizhou Liu · Ya Zhao · Zhenhe Chen · Qin Liang · Yajuan Lei · Lilei Zhu · Xiaodong Li · Chao Yuan
7 April 2026 at 08:00

npj Digital Medicine, Published online: 07 April 2026; doi:10.1038/s41746-026-02593-7

Lightweight liquid neural networks decipher salivary metabolic fingerprinting for high-risk periodontitis screening in diabetes
Received — 6 April 2026 ⏭

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction

✇npj Digital Medicine
By: Qiao Zhou · Yao Liu · Chenfei Wang · Liang Wang · Zhenghan Chen · Yiyi Chen · Chi Yang · Dong Liu · Yuxin Zhang · Jisi Zheng · Xingmei Feng
4 April 2026 at 08:00

npj Digital Medicine, Published online: 04 April 2026; doi:10.1038/s41746-026-02573-x

HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction
Received — 4 April 2026 ⏭
Received — 2 April 2026 ⏭

Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhiqian Zhang · Xu Zhao · Xiaoqing Xu · Guangdong Liang · Weijia Wang · Xiaolei Lv · Bo Li · Jun Gao
1 April 2026 at 12:00
arXiv:2603.29211v1 Announce Type: new Abstract: In recent years, multimodal large models have continued to improve on general benchmarks. However, in real-world content moderation and adversarial settings, mainstream models still suffer from degraded generalization and catastrophic forgetting because of limited fine-grained visual perception and insufficient modeling of long-tail noise. In this paper, we present Xuanwu VL-2B as a case study of how general multimodal models can be developed into an industrial-grade foundation model for content ecosystems. The model adopts a compact InternViT-300M + MLP + Qwen3 1.7B architecture, balancing fine-grained visual perception, language-semantic alignment, and deployment cost within an approximately 2B-parameter budget. To balance business specialization with the retention of general capabilities, we developed a data iteration and curation mechanism and trained the model through a progressive three-stage pipeline: pre-training, mid-training, and post-training. Ablation studies and offline business evaluations show that Xuanwu VL-2B achieves an average score of 67.90 across seven OpenCompass multimodal metrics (vs. 64.27 for InternVL 3.5 2B), an average recall of 94.38% over seven independent business moderation tasks, and a weighted overall recall of 82.82% on policy-violating text in challenging adversarial OCR scenarios, outperforming Gemini-2.5-Pro (76.72%). These results show that, under a limited parameter budget, Xuanwu VL-2B achieves a practical balance among business alignment, visual perception, general capability retention, and deployment cost.

Incorporating LLM Embeddings for Variation Across the Human Genome

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongqian Niu · Jordan Bryan · Jacob Williams · Hufeng Zhou · Haoyu Zhang · Xihao Li · Didong Li
1 April 2026 at 12:00
arXiv:2509.20702v2 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to date focus on gene-level information. We present one of the first systematic frameworks to generate genetic variant-level embeddings across the entire human genome. Using curated annotations from FAVOR, ClinVar, and the GWAS Catalog, we construct functional text descriptions for 8.9 billion possible variants and generated embeddings at three scales: 1.5 million HapMap3/MEGA variants, 90 million imputed UK Biobank (UKB) variants, and 9 billion all possible variants. Embeddings were produced using general purpose models including both OpenAI's text-embedding-3-large and the open-source Qwen3-Embedding-0.6B models. Baseline quality control experiments demonstrate high predictive accuracy for variant-level properties, validating the embeddings as structured representations of genomic variation. We further apply them to real-world embedding-augmented genetic risk predictions that demonstrate the performance of using LLM embeddings in polygenic risk score (PRS) style predictions over the UK Biobank cohort data. These resources, publicly available on Hugging Face, provide a foundation for advancing large-scale genomic discovery and precision medicine.

DNA damage burden causes selective CUX2 neuron loss in neuroinflammation

✇Nature - Issue - nature.com science feeds
By: Laura Morcom · Wenlong Xia · Zhaoyang Xu · Yashika Awasthi · Celine Geywitz · Matthew O. Ellis · Tomas Noli · Amel Zulji · Daniel Yamamoto · Gemma C. Girdler · Li Kai · Keying Zhu · Mingming Wei · Xiao-Yan Tang · Kimberly K. Hoi · Julio Gonzalez-Maya · Greg J. Duncan · Adrien M. Vaquie · Diana Gold Diaz · Riki Kawaguchi · Erdong Liu · Yu Sun · Denny Yang · Gregory D. Jordan · I-Ling Lu · Staffan Holmqvist · Theresa Bartels · Katherine Ridley · Jennifer Ja-Yoon Choi · Santos J. Franco · Eric J. Huang · Ben Emery · Daniel Geschwind · Lucas Schirmer · Gabriel Balmus · Brian Popko · Stephen P. J. Fancy · David H. Rowitch
1 April 2026 at 08:00

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10310-3

DNA damage burden and inadequate repair in CUX2+ cortical layer 2/3 excitatory neurons contributes to selective vulnerability in neuroinflammatory injury.
Received — 26 March 2026 ⏭

MKA: Memory-Keyed Attention for Efficient Long-Context Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dong Liu · Yanxuan Yu · Ben Lengerich · Ying Nian Wu
25 March 2026 at 12:00
arXiv:2603.20586v2 Announce Type: replace-cross Abstract: As long-context language modeling becomes increasingly important, the cost of maintaining and attending to large Key/Value (KV) caches grows rapidly, becoming a major bottleneck in both training and inference. While prior works such as Multi-Query Attention (MQA) and Multi-Latent Attention (MLA) reduce memory by sharing or compressing KV features, they often trade off representation quality or incur runtime overhead. We propose Memory-Keyed Attention (MKA), a hierarchical attention mechanism that integrates multi-level KV caches (local, session, and long-term) and learns to route attention across them dynamically. We further introduce Route-Fused MKA (FastMKA), a broadcast-routed variant that fuses memory sources before attention computation for improved efficiency. Experiments on different sequence lengths show that FastMKA achieves a favorable accuracy-efficiency trade-off: comparable perplexity to MLA while achieving up to 5x faster training throughput and 1.8x lower evaluation latency. These results highlight MKA as a practical and extensible framework for efficient long-context attention.

tRF-3005a regulates exon skipping of SPAG4 by interacting with RALY to drive gastric cancer progression

✇Cell Death Discovery nature.com science feeds
By: Huaiping Cui · Yancong Yuan · Yizhe Yin · Ruihong Gao · Zhaodong Liu · Lipan Peng · Jinshen Wang · Zhu Wang · Tingting Song · Jinglei Liu
24 March 2026 at 08:00

Cell Death Discovery, Published online: 24 March 2026; doi:10.1038/s41420-026-03049-3

tRF-3005a regulates exon skipping of SPAG4 by interacting with RALY to drive gastric cancer progression
Received — 17 March 2026 ⏭

Thinking in Streaming Video

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zikang Liu · Longteng Guo · Handong Li · Ru Zhen · Xingjian He · Ruyi Ji · Xiaoming Ren · Yanhao Zhang · Haonan Lu · Jing Liu
16 March 2026 at 12:00
arXiv:2603.12938v1 Announce Type: cross Abstract: Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video reasoning approaches follow a batch paradigm that defers reasoning until the full video context is observed, resulting in high latency and growing computational cost that are incompatible with streaming scenarios. In this paper, we introduce ThinkStream, a framework for streaming video reasoning based on a Watch--Think--Speak paradigm that enables models to incrementally update their understanding as new video observations arrive. At each step, the model performs a short reasoning update and decides whether sufficient evidence has accumulated to produce a response. To support long-horizon streaming, we propose Reasoning-Compressed Streaming Memory (RCSM), which treats intermediate reasoning traces as compact semantic memory that replaces outdated visual tokens while preserving essential context. We further train the model using a Streaming Reinforcement Learning with Verifiable Rewards scheme that aligns incremental reasoning and response timing with the requirements of streaming interaction. Experiments on multiple streaming video benchmarks show that ThinkStream significantly outperforms existing online video models while maintaining low latency and memory usage. Code, models and data will be released at https://github.com/johncaged/ThinkStream
Received — 11 March 2026 ⏭

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haodong Li · Chunmei Qing · Huanyu Zhang · Dongzhi Jiang · Yihang Zou · Hongbo Peng · Dingming Li · Yuhong Dai · ZePeng Lin · Juanxi Tian · Yi Zhou · Siqi Dai · Jingwei Wu
10 March 2026 at 12:00
arXiv:2603.08652v1 Announce Type: new Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning. However, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spatial layouts, structured visual elements, and dense textual content. In this work, we propose CoCo (Code-as-CoT), a code-driven reasoning framework that represents the reasoning process as executable code, enabling explicit and verifiable intermediate planning for image generation. Given a text prompt, CoCo first generates executable code that specifies the structural layout of the scene, which is then executed in a sandboxed environment to render a deterministic draft image. The model subsequently refines this draft through fine-grained image editing to produce the final high-fidelity result. To support this training paradigm, we construct CoCo-10K, a curated dataset containing structured draft-final image pairs designed to teach both structured draft construction and corrective visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of +68.83%, +54.8%, and +41.23% over direct generation, while also outperforming other generation methods empowered by CoT. These results demonstrate that executable code is an effective and reliable reasoning paradigm for precise, controllable, and structured text-to-image generation. The code is available at: https://github.com/micky-li-hd/CoCo

Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiang Zhang · Hongming Xu · Le Zhou · Wei Zhou · Xuanhe Zhou · Guoliang Li · Yuyu Luo · Changdong Liu · Guorun Chen · Jiang Liao · Fan Wu
10 March 2026 at 12:00
arXiv:2603.07449v1 Announce Type: cross Abstract: Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in functions, and execution constraints. However, most existing NL2SQL methods assume a single dialect (e.g., SQLite) and struggle to produce queries that are both semantically correct and executable on target engines. Prompt-based approaches tightly couple intent reasoning with dialect syntax, rule-based translators often degrade native operators into generic constructs, and multi-dialect fine-tuning suffers from cross-dialect interference. In this paper, we present Dial, a knowledge-grounded framework for dialect-specific NL2SQL. Dial introduces: (1) a Dialect-Aware Logical Query Planning module that converts natural language into a dialect-aware logical query plan via operator-level intent decomposition and divergence-aware specification; (2) HINT-KB, a hierarchical intent-aware knowledge base that organizes dialect knowledge into (i) a canonical syntax reference, (ii) a declarative function repository, and (iii) a procedural constraint repository; and (3) an execution-driven debugging and semantic verification loop that separates syntactic recovery from logic auditing to prevent semantic drift. We construct DS-NL2SQL, a benchmark covering six major database systems with 2,218 dialect-specific test cases. Experimental results show that Dial consistently improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over state-of-the-art baselines. The code is at https://github.com/weAIDB/Dial.

Input-Adaptive Generative Dynamics in Diffusion Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yucheng Xing · Xiaodong Liu · Xin Wang
10 March 2026 at 12:00
arXiv:2411.15199v2 Announce Type: replace-cross Abstract: Diffusion models typically generate data through a fixed denoising trajectory that is shared across all samples. However, generation targets can differ in complexity, suggesting that a single pre-defined diffusion process may not be optimal for every input. In this work, we investigate input-adaptive generative dynamics for diffusion models, where the generation process itself adapts to the conditions of each sample. Instead of relying on a fixed diffusion trajectory, the proposed framework allows the generative dynamics to adjust across inputs according to their generation requirements. To enable this behavior, we train the diffusion backbone under varying horizons and noise schedules, so that it can operate consistently under different input-adaptive trajectories. Experiments on conditional image generation show that diffusion trajectories can vary across inputs while maintaining generation quality and reducing the average number of sampling steps. These results provide a proof of the concept that diffusion processes can benefit from input-adaptive generative dynamics rather than relying on a single fixed trajectory.

Effects of daily multivitamin–multimineral and cocoa extract supplementation on epigenetic aging clocks in the COSMOS randomized clinical trial

✇Nature Medicine
By: Sidong Li · Rikuta Hamaya · Haidong Zhu · Brian H. Chen · Alexandre C. Pereira · Kerry L. Ivey · Pamela M. Rist · JoAnn E. Manson · Yanbin Dong · Howard D. Sesso
9 March 2026 at 08:00

Nature Medicine, Published online: 09 March 2026; doi:10.1038/s41591-026-04239-3

In a prespecified ancillary analysis of the COSMOS randomized trial, supplementation with daily multivitamins, but not with cocoa extract, over the course of 2 years decreased biological aging, as measured by epigenetic aging clocks.
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