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Received — 15 September 2026 ⏭

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghang Liu · Yuanzhuo Wang · Qiang Qiu · Huawei Shen · Xueqi Cheng
14 September 2026 at 12:00
arXiv:2609.12403v1 Announce Type: new Abstract: Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to represent students, exercises, and concepts. This paradigm diverges from the nature of learner cognition, where knowledge is not stored and retrieved as isolated symbols. As a result, CDMs suffer from semantic limitations when new exercises or concepts appear. In this paper, we propose a Process-aware Language Cognitive Diagnosis (PLCD) framework that uses language-derived structures as cognitive priors and response records to calibrate student posterior states. PLCD leverages large language models (LLMs) to construct concept schemas and cognitive process graphs, and uses target-conditioned semantic memory to retrieve historical responses that are relevant to each target exercise. A process-grounded Language-to-Cognition Mapper with DA-MoE experts and process-level contrastive learning then maps the textual evidence into a unified cognitive space. Experimental results show that PLCD not only outperforms traditional baselines in predicting student performance but also exhibits strong cognitive transfer capabilities. These results connect the computational power of LLMs with the psychometric goal of measuring latent knowledge states, suggesting that structured language priors calibrated by response records can improve cold-start robustness and cognitive grounding.

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenghang Song · Tang Qian · Lu Chen · Yushuai Li · Zhengke Hu · Bingbing Fang · Yumeng Song · Junbo Zhao · Sheng Zhang · Tianyi Li
14 September 2026 at 12:00
arXiv:2603.16513v4 Announce Type: replace-cross Abstract: Structured data is widely used in domains such as healthcare, finance, and scientific data management. Recent studies on structured data foundation models (SFMs) aim to support data analysis and mining tasks over such data, but still face scalability and generalization challenges when applied to real-world enterprise databases. First, many SFMs rely on full self-attention, which introduces an O(N^2) computational bottleneck and limits the number of tuples that can be processed jointly. Second, directly replacing attention with linear-complexity sequence models may conflict with the permutation-invariant nature of structured data, introducing artificial order bias and degrading representation quality. Moreover, models trained only on synthetic data may struggle to generalize to the heavy-tailed and heterogeneous distributions commonly found in real-world databases. To address these challenges, we propose FEAT, a linear-complexity foundation model for extremely large structured data. FEAT replaces quadratic attention with a multi-layer dual-axis encoding architecture. It integrates an adaptive-fusion bidirectional state-space model (AFBM) with convolutional gated linear attention (Conv-GLA), enabling cross-tuple contextualization in O(N) time while supporting permutation-invariant representation learning. To improve robustness under real-world data skewness, FEAT further adopts a hybrid structural causal pre-training pipeline with a robust reconstruction objective. Experiments on 12 real-world database benchmarks show that FEAT consistently outperforms representative SFMs on zero-shot tasks and scales linearly with structured-data sample length, achieving up to 50x faster inference latency.
Received — 13 September 2026 ⏭

Unveiling the Diagnostic Value and Potential Therapeutic Targets of Phenylalanine Metabolism in Pancreatic Cancer via Integrated Multi-Omics and Machine Learning

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xing Liu · Yibin Li · Fangfang Qin · Jianghong Ou
12 September 2026 at 18:00

FASEB J. 2026 Sep 30;40(18):e72296. doi: 10.1096/fj.202603069R.

ABSTRACT

Pancreatic cancer (PC) presents a significant global health challenge because of its high mortality rate, highlighting the urgent requirement for effective early diagnostic and therapeutic strategies. This study examined the function of phenylalanine metabolism in PC and developed a high-accuracy diagnostic model by integrating metabolomics, Mendelian randomization (MR), and machine learning (ML) algorithms. Initially, MR analysis was conducted on 55 plasma metabolites, revealing a significant causal link between phenylalanine and PC. Utilizing GeneCards and public transcriptomic databases, we determined eight differentially expressed genes (DEGs) in PC associated with phenylalanine. Based on these genes, we utilized 12 ML algorithms, totaling 113 combinations, to select the optimal diagnostic model. We applied Shapley Additive exPlanations (SHAP) for feature interpretation and constructed a prognostic nomogram with strong predictive performance by incorporating clinical variables. Furthermore, immune infiltration analysis demonstrated strong connections between these key genes and specific immune cell populations. Based on the SHAP value, we conducted single-cell RNA sequencing (scRNA-seq) data and simulated gene knockout analyses using SLC6A14 as the key gene. Drug target prediction-guided molecular docking and molecular dynamics simulations, focusing on the core gene SLC6A14, confirmed the high binding stability of candidate compounds. Finally, in vitro cell experiments quantitative real-time PCR (RT-qPCR) verified the expression trends of the key genes in PC cell lines. In conclusion, this study successfully developed an ML diagnostic model with high biological interpretability. This analysis aims to identify biomarkers related to phenylalanine metabolism and potential therapeutic drugs for PC, offering new strategies for personalized targeted therapy of PC.

PMID:42730913 | PMC:PMC13570651 | DOI:10.1096/fj.202603069R

Deep learning combined habitat radiomics analysis of central lymph node metastasis in papillary thyroid carcinoma

✇npj Digital Medicine
By: Tianhan Zhou · Yang Zhou · Zhongkai Ni · Hao Fang · Fan Wu · Yangyang Xie · Xinyang Song · Sihan Sun · Xianfeng Wang · Xiangfeng Lin · Dong Guo · Lihua Hong · Xiaogang Qi · Chang Su · Jie Wang · Yu Chen · Quanbao Zhang · Jinwang Ding · Lei Xu · Hai Huang
12 September 2026 at 08:00

npj Digital Medicine, Published online: 12 September 2026; doi:10.1038/s41746-026-03203-2

Deep learning combined habitat radiomics analysis of central lymph node metastasis in papillary thyroid carcinoma
Received — 10 September 2026 ⏭

XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Qiao · Junjie Wu · Deqiang Qiu · James J. Lah · Liang Zhao
10 September 2026 at 12:00
arXiv:2609.09388v1 Announce Type: cross Abstract: Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported mechanisms. Post-hoc explanation methods can expose these mechanisms, but existing workflows typically stop at diagnosis or require correction targets to be specified before model analysis. We propose XAI-Refine, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity. At each iteration, XAI-Refine consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations. It converts each reliable explanation into a neutral neurobiological question, retrieves and verifies relevant literature, and compiles the verified evidence into an admissible set in the same typed explanation space. The target for refinement is defined as the minimal projection of the current model explanation onto the admissible set induced by applicable verified knowledge. This revised explanation is then translated into a differentiable constraint while preserving the originating model variable, measurement operator, and applicable scope. Candidate updates are promoted only when multi-seed validation confirms target-directed explanatory movement, predictive performance remains within a prespecified guardrail, and non-target explanatory drift remains bounded. Experiments on functional-connectivity-based brain-age prediction evaluate predictive performance, explanation reliability, literature alignment, and target-specific model revision, illustrating a structured route from post-hoc analysis to evidence-guided model refinement.

Can AI Agents Detect and Repair Artifact Drift in Network Experiments?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianzhu Zhang · Weichen Tao · Changgang Zheng · Yusheng Zheng · Long Chen · Xiaoyi Fan · Meikang Qiu
10 September 2026 at 12:00
arXiv:2609.09849v1 Announce Type: cross Abstract: In recent years, AI agents have evolved into capable assistants that carry out multi-step tasks in digital environments. The network systems community is beginning to explore these capabilities in operational and experimental settings. However, an agent operating in network systems should not be judged solely by whether it completes the immediate task. The experiment record it modifies must also remain trustworthy. We call this property artifact integrity: the record's claims must remain supported by the available evidence, confined to the scope established by that evidence, and traceable through the artifacts that encode their support. To make this property measurable, we introduce NetArtifactBench, which tests whether AI agents can repair inconsistent records derived from public network-system artifacts while preserving claims that remain supported. The benchmark contains 52 instances with injected inconsistencies ranging from direct contradictions to unstated relations spread across several artifacts. We evaluate 23 agent configurations across three general-purpose AI agent runtimes using deterministic scoring. The average contract pass rate is 65.3 % across 5,980 outputs, but no agent runtime exceeds 30 % when repair requires recovering implicit relations and propagating changes across artifacts. These results reveal a sharp boundary between local correction and complete record-level repair. Therefore, we argue that artifact integrity should become a first-class design and evaluation requirement for AI agents operating on network systems.

Can AI Agents Deliver Verifiable Network-Wide Outcomes Across Authority Boundaries?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianzhu Zhang · Chih-Kai Huang · Meikang Qiu
10 September 2026 at 12:00
arXiv:2609.10181v1 Announce Type: cross Abstract: AI agents are increasingly involved in network automation, where they can initiate configuration changes through mediated operational interfaces and assess the resulting state. Nonetheless, operational networks usually span many devices and administrative domains. Realizing an operator's intent requires coordinating agents with distinct authority scopes that define the resources they can access, the operations they can invoke, and the network state they can observe. This division limits the blast radius of an erroneous action but fragments the evidence needed to assess the network-wide outcome. Successful execution of a configuration action proposed by one agent does not establish that remote devices responded as intended or that routing changes reached the required devices. A valid observation may also become stale after a subsequent change. Before the coordinated operation can be declared complete, a trusted assurance layer must collect current observations from the required scopes and determine whether they collectively support the operator's intended network-wide outcome. To address the completion admission problem, we present EvidenceNet, a runtime assurance layer for deciding whether coordinated agent operations have achieved an operator's network intent. Its broker collects the post-change observations required by a completion contract, and its admission gate checks that the evidence comes from the required scopes, remains current, and satisfies the task rules. A verifier agent provides an additional assessment of the observation content. Experiments on live routing networks show that post-change state checks recognize successful outcomes that configuration-action records alone cannot establish. Controlled interventions further show that EvidenceNet rejects completion when otherwise satisfactory observations have the wrong source, have been substituted, or are stale.
Received — 27 May 2026 ⏭

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minwei Kong · Chonghe Jiang · Ao Qu · Wenbin Ouyang · Zhaoming Zeng · Xiaotong Guo · Zhekai Li · Junyi Li · Yi Fan · Xinshou Zheng · Xi Jing · Yikai Zhang · Zhiwei Liang · Seonghoo Kim · Runqing Yang · Zijian Zhou · Sirui Li · Han Zheng · Wangyang Ying · Ou Zheng · Chonghuan Wang · Jinglong Zhao · Hanzhang Qin · Cathy Wu · Paul Pu Liang · Jinhua Zhao · Hai Wang
26 May 2026 at 12:00
arXiv:2605.25246v2 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, among the first benchmarks to systematically evaluate LLM-based efficient algorithm design for realistic large-scale optimization problems. FrontierOR includes 180 tasks derived from methodologically diverse papers published in top-tier operations research venues, each with standardized instances and a hidden, expert-verified evaluation suite. We evaluate seven LLMs spanning frontier, cost-effective, and open-source models both in one-shot and test-time evolution settings. The results reveal that frontier models still struggle to move from executable formulations to efficient optimization algorithms: the strongest one-shot model outperforms Gurobi in only 31% of cases in both solution quality and computational efficiency, and even strong coding agents with test-time evolution achieve only 50% on selected hard tasks. FrontierOR establishes a practical evaluation platform for LLM-based optimization algorithm design, which enables future LLMs and agents to be systematically tested on whether they can move beyond correct formulation toward a feasible, high-quality, and efficient algorithm.

Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qingyuan Zeng · Pengxiang Cai · Zixin Guan · Ziyang Chen · Anglin Liu · Lang Qin · Xinyao Lai · Jintai Chen
26 May 2026 at 12:00
arXiv:2605.25681v1 Announce Type: cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard single-target generation because one candidate must satisfy two binding requirements while preserving drug-likeness and synthesizability. Existing dual-target generative methods typically introduce dual-target capability by either retraining the generator or intervening in the diffusion process during sampling. The former can be costly and difficult to stabilize when dual-target supervision is sparse, while the latter may be sensitive to denoising-time target balancing and competing update directions. These limitations motivate a generator-preserving alternative that keeps the pretrained prior intact: can dual-target candidates instead be recovered from the input space of a frozen single-target diffusion model, without modifying its parameters or denoising dynamics? We formulate this task as a constrained multi-objective optimization problem and propose REUSE, a hierarchical evolutionary input-space search framework that combines pair-conditioned exploration with structured multi-stage selection to enforce dual-target affinity, chemical quality, and diversity. Experiments show that, compared with methods that modify the diffusion process, REUSE consistently improves dual-target affinity and balance, achieving a 20.9-percentage-point gain in Dual High Affinity over the strongest prior baseline while maintaining competitive molecular quality.

Generative structure search for efficient and diverse discovery of molecular and crystal structures

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifang Qin · Yu Shi · Junfu Tan · Chang Liu · Ming Zhang · Ziheng Lu
26 May 2026 at 12:00
arXiv:2604.27636v2 Announce Type: replace Abstract: Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimensional energy landscapes. Deep generative models offer efficient structure sampling, yet their outputs remain shaped by training data and can underexplore minima that are rare but physically relevant. We introduce generative structure search (GSS), a unified framework that formulates diffusion-based generation and random structure search (RSS) as limiting regimes of a common sampling process driven by learned score fields and physical forces. Coupling these drivers lets GSS use data priors to accelerate sampling while retaining energy-guided exploration of local minima. Across molecular and crystalline systems, GSS recovers diverse metastable structures with more than tenfold lower sampling cost than RSS for broad coverage and remains effective for compositions outside the training distribution. The results establish a physically grounded generative search strategy for discovering structures beyond the reach of data-driven sampling alone.

Transplantation of encapsulated mitochondria alleviates dysfunction in mitochondrial and Parkinson’s disease models

✇Cell
By: Shiwei Du · Qi Long · Yanshuang Zhou · Jiangqin Fu · Hao Wu · Liang Yang · Yaohang Xie · Yingzhe Ding · Maolei Zhang · Jingyi Guo · Mengfei Wang · Jiajun Lin · Mingli Hu · Jian Zhang · Deyang Yao · Wei Li · Feixiang Bao · Ge Xiang · Yi Wu · Yile Huang · Haozhao Liang · Rui Wang · Heying Li · Baodan Chen · Chong Li · Junwei Wang · Jiwei Zhang · Dajiang Qin · Jianwei Sun · Yun Zhu · Fei Sun · Wuming Wang · Gang Lu · Wai-Yee Chan · Hui Zhao · Chenli Liu · Xingguo Liu
18 March 2026 at 08:00
A mitochondrial transplantation approach rescues mitochondrial deficiency and prevents mitochondrial DNA depletion syndrome, Leigh syndrome, and Parkinson’s disease in cellular and mouse models.
Received — 8 April 2026 ⏭

Memory Intelligence Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jingyang Qiao · Weicheng Meng · Yu Cheng · Zhihang Lin · Zhizhong Zhang · Xin Tan · Jingyu Gong · Kun Shao · Yuan Xie
7 April 2026 at 12:00
arXiv:2604.04503v2 Announce Type: new Abstract: Deep research agents (DRAs) integrate LLM reasoning with external tools. Memory systems enable DRAs to leverage historical experiences, which are essential for efficient reasoning and autonomous evolution. Existing methods rely on retrieving similar trajectories from memory to aid reasoning, while suffering from key limitations of ineffective memory evolution and increasing storage and retrieval costs. To address these problems, we propose a novel Memory Intelligence Agent (MIA) framework, consisting of a Manager-Planner-Executor architecture. Memory Manager is a non-parametric memory system that can store compressed historical search trajectories. Planner is a parametric memory agent that can produce search plans for questions. Executor is another agent that can search and analyze information guided by the search plan. To build the MIA framework, we first adopt an alternating reinforcement learning paradigm to enhance cooperation between the Planner and the Executor. Furthermore, we enable the Planner to continuously evolve during test-time learning, with updates performed on-the-fly alongside inference without interrupting the reasoning process. Additionally, we establish a bidirectional conversion loop between parametric and non-parametric memories to achieve efficient memory evolution. Finally, we incorporate a reflection and an unsupervised judgment mechanisms to boost reasoning and self-evolution in the open world. Extensive experiments across eleven benchmarks demonstrate the superiority of MIA.

SkillX: Automatically Constructing Skill Knowledge Bases for Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenxi Wang · Zhuoyun Yu · Xin Xie · Wuguannan Yao · Runnan Fang · Shuofei Qiao · Kexin Cao · Guozhou Zheng · Xiang Qi · Peng Zhang · Shumin Deng
7 April 2026 at 12:00
arXiv:2604.04804v1 Announce Type: cross Abstract: Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this problem, we propose SkillX, a fully automated framework for constructing a \textbf{plug-and-play skill knowledge base} that can be reused across agents and environments. SkillX operates through a fully automated pipeline built on three synergistic innovations: \textit{(i) Multi-Level Skills Design}, which distills raw trajectories into three-tiered hierarchy of strategic plans, functional skills, and atomic skills; \textit{(ii) Iterative Skills Refinement}, which automatically revises skills based on execution feedback to continuously improve library quality; and \textit{(iii) Exploratory Skills Expansion}, which proactively generates and validates novel skills to expand coverage beyond seed training data. Using a strong backbone agent (GLM-4.6), we automatically build a reusable skill library and evaluate its transferability on challenging long-horizon, user-interactive benchmarks, including AppWorld, BFCL-v3, and $\tau^2$-Bench. Experiments show that SkillKB consistently improves task success and execution efficiency when plugged into weaker base agents, highlighting the importance of structured, hierarchical experience representations for generalizable agent learning. Our code will be publicly available soon at https://github.com/zjunlp/SkillX.
Received — 4 April 2026 ⏭

RIFT: A RubrIc Failure Mode Taxonomy and Automated Diagnostics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhengyang Qi · Charles Dickens · Derek Pham · Amanda Dsouza · Armin Parchami · Frederic Sala · Paroma Varma
3 April 2026 at 12:00
arXiv:2604.01375v1 Announce Type: new Abstract: Rubric-based evaluation is widely used in LLM benchmarks and training pipelines for open-ended, less verifiable tasks. While prior work has demonstrated the effectiveness of rubrics using downstream signals such as reinforcement learning outcomes, there remains no principled way to diagnose rubric quality issues from such aggregated or downstream signals alone. To address this gap, we introduce RIFT: RubrIc Failure mode Taxonomy, a taxonomy for systematically characterizing failure modes in rubric composition and design. RIFT consists of eight failure modes organized into three high-level categories: Reliability Failures, Content Validity Failures, and Consequential Validity Failures. RIFT is developed using grounded theory by iteratively annotating rubrics drawn from five diverse benchmarks spanning general instruction following, code generation, creative writing, and expert-level deep research, until no new failure modes are identified. We evaluate the consistency of the taxonomy by measuring agreement among independent human annotators, observing fair agreement overall (87% pairwise agreement and 0.64 average Cohen's kappa). Finally, to support scalable diagnosis, we propose automated rubric quality metrics and show that they align with human failure-mode annotations, achieving up to 0.86 F1.
Received — 26 March 2026 ⏭

Energy-Aware Reinforcement Learning for Robotic Manipulation of Articulated Components in Infrastructure Operation and Maintenance

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaowen Tao · Yinuo Wang · Haitao Ding · Yuanyang Qi · Ziyu Song
25 March 2026 at 12:00
arXiv:2602.12288v3 Announce Type: replace-cross Abstract: With the growth of intelligent civil infrastructure and smart cities, operation and maintenance (O&M) increasingly requires safe, efficient, and energy-conscious robotic manipulation of articulated components, including access doors, service drawers, and pipeline valves. However, existing robotic approaches either focus primarily on grasping or target object-specific articulated manipulation, and they rarely incorporate explicit actuation energy into multi-objective optimisation, which limits their scalability and suitability for long-term deployment in real O&M settings. Therefore, this paper proposes an articulation-agnostic and energy-aware reinforcement learning framework for robotic manipulation in intelligent infrastructure O&M. The method combines part-guided 3D perception, weighted point sampling, and PointNet-based encoding to obtain a compact geometric representation that generalises across heterogeneous articulated objects. Manipulation is formulated as a Constrained Markov Decision Process (CMDP), in which actuation energy is explicitly modelled and regulated via a Lagrangian-based constrained Soft Actor-Critic scheme. The policy is trained end-to-end under this CMDP formulation, enabling effective articulated-object operation while satisfying a long-horizon energy budget. Experiments on representative O&M tasks demonstrate 16%-30% reductions in energy consumption, 16%-32% fewer steps to success, and consistently high success rates, indicating a scalable and sustainable solution for infrastructure O&M manipulation.

PAK4 functions as an immune suppressor by reprogramming the phosphatidylcholine metabolism of CD8 + T cells within the glioblastoma tumor microenvironment

✇Oncogene - Issue - nature.com science feeds
By: Lin Yao · Heyang Gao · Zuopeng Su · Guozheng Zhao · Liyan Tang · Shuo Feng · Yuting Ma · Xiao Zhang · Ming Feng · Jialiang Qian · Yanyan Li · Ting Sun · Jiangang Liu · Hao Wang · Youxin Zhou
23 March 2026 at 08:00

Oncogene, Published online: 23 March 2026; doi:10.1038/s41388-026-03734-8

PAK4 functions as an immune suppressor by reprogramming the phosphatidylcholine metabolism of CD8 + T cells within the glioblastoma tumor microenvironment

Mechanisms of Xinwei Tang in stress-induced gastric dysmotility: evidence from rat and In Vitro models

✇Omics in Gastric
By: Man Tian · Honghui Xiao · Yingbing Mei · Xinyu Zhang · Zhengliang Qi
19 March 2026 at 18:00

In Vitro Cell Dev Biol Anim. 2026 Mar 18. doi: 10.1007/s11626-026-01151-5. Online ahead of print.

ABSTRACT

Stress is a key trigger of gastric dysmotility, partly via mitochondrial dysfunction and disordered gut-brain hormonal signaling. Xinwei Tang (XWT) is a multi-herb formula used empirically for upper gastrointestinal symptoms, but its mechanisms remain unclear. This study aimed to determine whether XWT alleviates water-immersion restraint stress (WIRS)-induced gastric dysmotility and to delineate underlying mitochondrial and metabolic pathways using integrated in vivo, in vitro and multi-omics approaches. Male rats underwent 7-d WIRS and received vehicle, domperidone (3 mg/kg) or XWT (3, 6, 12 g/kg). Gastric emptying, serum motilin/gastrin, oxidative stress indices and PINK1/Parkin-LC3/p62 proteins were assessed, and H₂O₂-injured GES-1 cells were treated with XWT-medicated serum. Gastric antra from MOD and XWT-H rats were analyzed by RNA-seq and DIA proteomics (n = 3/group). WIRS reduced gastric emptying by roughly half and lowered motilin/gastrin, increased ROS/MDA and disrupted PINK1/Parkin-LC3/p62 profiles; XWT dose-dependently reversed these changes, with XWT-H approximating domperidone. Omics revealed XWT-associated downregulation of inflammatory/protease and acute-phase genes/proteins and enrichment of oxidative phosphorylation, tricarboxylic-acid cycle and other metabolic pathways, without global activation of canonical autophagy/mitophagy gene sets. These preclinical data indicate that XWT ameliorates stress-induced gastric dysmotility via mitochondria- and metabolism-centred protection with selective tuning of mitophagy-related proteins.

PMID:41851413 | DOI:10.1007/s11626-026-01151-5

Received — 17 March 2026 ⏭

When Drafts Evolve: Speculative Decoding Meets Online Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu-Yang Qian · Hao-Cong Wu · Yichao Fu · Hao Zhang · Peng Zhao
16 March 2026 at 12:00
arXiv:2603.12617v1 Announce Type: cross Abstract: Speculative decoding has emerged as a widely adopted paradigm for accelerating large language model inference, where a lightweight draft model rapidly generates candidate tokens that are then verified in parallel by a larger target model. However, due to limited model capacity, drafts often struggle to approximate the target distribution, resulting in shorter acceptance lengths and diminished speedup. A key yet under-explored observation is that speculative decoding inherently provides verification feedback that quantifies the deviation between the draft and target models at no additional cost. This process naturally forms an iterative "draft commits-feedback provides-draft adapts" evolving loop, which precisely matches the online learning paradigm. Motivated by this connection, we propose OnlineSpec, a unified framework that systematically leverages interactive feedback to continuously evolve draft models. Grounded in dynamic regret minimization, we establish a formal link between online learning performance and speculative system's acceleration rate, and develop novel algorithms via modern online learning techniques, including optimistic online learning that adaptively reuses historical gradients as predictive update hints, and online ensemble learning that dynamically maintains multiple draft models. Our algorithms are equipped with theoretical justifications and improved acceleration rates, achieving up to 24% speedup over seven benchmarks and three foundation models.
Received — 11 March 2026 ⏭

FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiajun Xu · Jiageng Mao · Ang Qi · Weiduo Yuan · Alexander Romanus · Helen Xia · Vitor Campagnolo Guizilini · Yue Wang
10 March 2026 at 12:00
arXiv:2603.06600v1 Announce Type: cross Abstract: Vision Language Models (VLMs) are prone to errors, and identifying where these errors occur is critical for ensuring the reliability and safety of AI systems. In this paper, we propose an approach that automatically generates questions designed to deliberately induce incorrect responses from VLMs, thereby revealing their vulnerabilities. The core of this approach lies in fuzz testing and reinforcement finetuning: we transform a single input query into a large set of diverse variants through vision and language fuzzing. Based on the fuzzing outcomes, the question generator is further instructed by adversarial reinforcement fine-tuning to produce increasingly challenging queries that trigger model failures. With this approach, we can consistently drive down a target VLM's answer accuracy -- for example, the accuracy of Qwen2.5-VL-32B on our generated questions drops from 86.58\% to 65.53\% in four RL iterations. Moreover, a fuzzing policy trained against a single target VLM transfers to multiple other VLMs, producing challenging queries that degrade their performance as well.
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