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Received — 15 September 2026 ⏭

Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuxin Xiao · Sheng Zhang · Chandan Singh · Tristan Naumann · Hoifung Poon · Jianfeng Gao · Xiaodong Liu
14 September 2026 at 12:00
arXiv:2609.12277v1 Announce Type: cross Abstract: Electronic health record (EHR) foundation models trained on longitudinal patient trajectories have demonstrated strong performance across diverse clinical prediction tasks. However, their clinical reasoning capabilities remain constrained by next-token prediction on limited and incomplete EHR data. To address this, we propose a reinforcement learning (RL) fine-tuning framework that treats EHR foundation models as generative policies over patient trajectories. We formulate common clinical prediction problems (e.g., hospital readmission) as event-conditioned, time-windowed reasoning tasks. We then design time-aware, rollout-sensitive rewards to account for finite rollout lengths and temporally inconclusive outcomes. We find that RL fine-tuning consistently improves over pre-trained backbones and strong baselines. Notably, it enables smaller models to surpass larger pre-trained models in data-limited regimes and induces positive transfer across tasks. Further analysis shows that RL fine-tuned models generate trajectories with stronger structural and semantic alignment to ground truth and greater downstream utility.
Received — 10 September 2026 ⏭

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhuodong Liu · Xiangyu Li · Chunhong Yuan · Hongyang Du · Bodong Shang · Qingqing Wu · Tony Q. S. Quek · Mohsen Guizani
10 September 2026 at 12:00
arXiv:2609.09591v1 Announce Type: cross Abstract: Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and distributed sensing. Vision-language-action (VLA) models offer a foundation by integrating visual perception, language understanding, and action generation into a unified closed-loop policy. However, training and adapting VLA models to distributed robotic agents introduce challenges in privacy protection, communication efficiency, and model heterogeneity. Existing federated learning (FL) methods overlook the intrinsic differences among vision, language, and action pathways in parameter scale, privacy exposure, update dynamics, and tolerance to compression or perturbation. To address this issue, this article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks. FedMVLA incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-sliced transport design that routes the precision-critical action stream through a protected ultra-reliable low-latency slice. A case study on federated robotic manipulation over the Third Generation Partnership Project (3GPP)-based wireless substrate, covering fading, co-channel interference, and malicious jamming, shows that FedMVLA achieves an 84.8% task success rate, exceeds FedAvg by 22.2 percentage points, sustains a widening margin when scaling to 128 clients across eight cells, and reduces the schedule-averaged per-client uplink model-update payload by 95.6% (approximately 96%), while keeping the 95th percentile (p95) of the round-critical uplink completion time near 1.5s.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Query Brand Entity Linking in E-Commerce Search Dong Liu · Sreyashi Nag
    arXiv:2502.01555v3 Announce Type: replace-cross Abstract: Associating user search queries with the correct brand entity is critical for e-commerce product retrieval, yet remains challenging due to the brevity of queries (three to four words on average), their lack of grammatical structure, and a catalog of hundreds of thousands of distinct brands. We formulate this as a brand entity linking task and develop two complementary solutions deployed at scale: (1) a cascaded pipeline that first detect
     
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Query Brand Entity Linking in E-Commerce Search

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dong Liu · Sreyashi Nag
10 September 2026 at 12:00
arXiv:2502.01555v3 Announce Type: replace-cross Abstract: Associating user search queries with the correct brand entity is critical for e-commerce product retrieval, yet remains challenging due to the brevity of queries (three to four words on average), their lack of grammatical structure, and a catalog of hundreds of thousands of distinct brands. We formulate this as a brand entity linking task and develop two complementary solutions deployed at scale: (1) a cascaded pipeline that first detects brand mentions via sequence labeling and then disambiguates against a brand knowledge base, and (2) a single-stage approach that frames linking as extreme multiclass classification, directly mapping queries to brand identifiers. Through extensive multilingual evaluation (11 languages) and a controlled online experiment, we demonstrate that the proposed methods substantially improve brand recall while maintaining high precision, leading to measurable gains in customer engagement.
Received — 27 May 2026 ⏭

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 — 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.
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 ⏭

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 — 11 March 2026 ⏭

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.
Received — 5 March 2026 ⏭

ConEQsA: Concurrent and Asynchronous Embodied Questions Scheduling and Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haisheng Wang · Dong Liu · Weiming Zhi
4 March 2026 at 13:00
arXiv:2509.11663v2 Announce Type: replace-cross Abstract: This paper formulates the Embodied Questions Answering (EQsA) problem, introduces a corresponding benchmark, and proposes an agentic system to tackle the problem. Classical Embodied Question Answering (EQA) is typically formulated as answering one single question by actively exploring a 3D environment. Real deployments, however, often demand handling multiple questions that may arrive asynchronously and carry different urgencies. We formalize this setting as Embodied Questions Answering (EQsA) and present ConEQsA, an agentic framework for concurrent, urgency-aware scheduling and answering. ConEQsA leverages shared group memory to reduce redundant exploration, and a priority-planning method to dynamically schedule questions. To evaluate the EQsA setting fairly, we contribute the Concurrent Asynchronous Embodied Questions (CAEQs) benchmark containing 40 indoor scenes and five questions per scene (200 in total), featuring asynchronous follow-up questions and human-annotated urgency labels. We further propose metrics for EQsA performance: Direct Answer Rate (DAR), and Normalized Urgency-Weighted Latency (NUWL), which serve as a fair evaluation protocol for EQsA. Empirical evaluations demonstrate that ConEQsA consistently outperforms strong sequential baselines, and show that urgency-aware, concurrent scheduling is key to making embodied agents responsive and efficient under realistic, multi-question workloads. Code is available on https://anonymous.4open.science/r/ConEQsA.
Received — 17 February 2026 ⏭

AdaCorrection: Adaptive Offset Cache Correction for Accurate Diffusion Transformers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dong Liu · Yanxuan Yu · Ben Lengerich · Ying Nian Wu
17 February 2026 at 13:00
arXiv:2602.13357v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive inference due to their iterative denoising structure. While prior methods accelerate sampling by caching intermediate features, they rely on static reuse schedules or coarse-grained heuristics, which often lead to temporal drift and cache misalignment that significantly degrade generation quality. We introduce \textbf{AdaCorrection}, an adaptive offset cache correction framework that maintains high generation fidelity while enabling efficient cache reuse across Transformer layers during diffusion inference. At each timestep, AdaCorrection estimates cache validity with lightweight spatio-temporal signals and adaptively blends cached and fresh activations. This correction is computed on-the-fly without additional supervision or retraining. Our approach achieves strong generation quality with minimal computational overhead, maintaining near-original FID while providing moderate acceleration. Experiments on image and video diffusion benchmarks show that AdaCorrection consistently improves generation performance.

FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Huajian Zeng · Lingyun Chen · Jiaqi Yang · Yuantai Zhang · Fan Shi · Peidong Liu · Xingxing Zuo
17 February 2026 at 13:00
arXiv:2602.13444v1 Announce Type: cross Abstract: Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tasks because the underlying hand-object interaction (HOI) structure is not explicitly represented. An embodiment-agnostic interaction representation that captures this structure would make manipulation behaviors easier to validate and transfer across robots. We propose FlowHOI, a two-stage flow-matching framework that generates semantically grounded, temporally coherent HOI sequences, comprising hand poses, object poses, and hand-object contact states, conditioned on an egocentric observation, a language instruction, and a 3D Gaussian splatting (3DGS) scene reconstruction. We decouple geometry-centric grasping from semantics-centric manipulation, conditioning the latter on compact 3D scene tokens and employing a motion-text alignment loss to semantically ground the generated interactions in both the physical scene layout and the language instruction. To address the scarcity of high-fidelity HOI supervision, we introduce a reconstruction pipeline that recovers aligned hand-object trajectories and meshes from large-scale egocentric videos, yielding an HOI prior for robust generation. Across the GRAB and HOT3D benchmarks, FlowHOI achieves the highest action recognition accuracy and a 1.7$\times$ higher physics simulation success rate than the strongest diffusion-based baseline, while delivering a 40$\times$ inference speedup. We further demonstrate real-robot execution on four dexterous manipulation tasks, illustrating the feasibility of retargeting generated HOI representations to real-robot execution pipelines.
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