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Received — 15 September 2026 ⏭

Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Gengxian Zhou · Jian Xu · Zichen Tang · Shiming Xiang · Haihong E · Cheng-Lin Liu
14 September 2026 at 12:00
arXiv:2609.12791v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (large-scale knowledge graphs and text corpora) for complex reasoning remains a challenge. Existing methods typically employ reactive, graph-driven exploration strategies, which blindly follow graph topology without adapting to the question context or evolving exploration progress, and lack deep bidirectional synergy between graph and text. To address these limitations, we propose CoG (Cognition on Graph), a cognitive-inspired, training-free framework for adaptive knowledge exploration. Drawing inspiration from human problem-solving, CoG performs a continuous plan-explore-reflect cycle, where it proactively formulates investigation plans, performs dual-source retrieval, and dynamically reflects on progress to adjust strategies. Crucially, it establishes deep bidirectional synergy between structured graph and unstructured text, where entities extracted from text dynamically guide graph exploration to bridge knowledge gaps. Extensive experiments on seven multi-hop QA benchmarks demonstrate that CoG significantly outperforms state-of-the-art methods while achieving superior exploration efficiency. Our code and datasets are available at https://github.com/zhougengxian/CoG.

A Unified Conditional Flow for Motion Generation, Editing, and Intra-Structural Retargeting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junlin Li · Xinhao Song · Siqi Wang · Haibin Huang · Yili Zhao
14 September 2026 at 12:00
arXiv:2604.13427v3 Announce Type: replace-cross Abstract: Text-driven motion editing and intra-structural retargeting, where skeletons share topology but may differ in bone lengths and rest pose, are traditionally handled by fragmented pipelines with incompatible inputs and representations: editing relies on specialized generative steering, while retargeting is deferred to geometric post-processing. We present a unified conditional-flow framework that casts generation, semantic editing, and intra-structural retargeting as condition-modulated transport within one text- and skeleton-conditioned rectified-flow model. Under this formulation, editing changes the semantic condition while preserving skeletal structure, whereas retargeting changes the skeletal condition while preserving motion semantics. This makes FlowEdit-style transport a unified inference rule for motion manipulation rather than a task-specific editor. To instantiate this for articulated 3D motion, we develop a text- and skeleton-conditioned rectified-flow transformer. The model uses per-joint tokenization and explicit joint self-attention to capture spatial kinematic dependencies. We further inject text conditions at both joint and frame levels, while residual multi-condition classifier-free guidance balances text adherence and skeletal conformity. Experiments on SnapMoGen and a multi-character Mixamo subset show that one trained model supports text-to-motion generation, zero-shot editing, and zero-shot intra-structural retargeting without task-specific fine-tuning. This unified framework replaces separate pipelines with a single conditional motion transport model while keeping the same-topology retargeting scope explicit.
Received — 10 September 2026 ⏭

Lineage-specific pulmonary transcriptome landscape of coronavirus infection unveils universal immunotherapy for viral pneumonia

✇Molecular Therapy
By: Ming Zhou · Xuan Liu · Haiqing Xiao · Zhaojie Su · Kun Wu · Longmei Zhang · Xinglin Li · Wanyun Li · Chang Zhang · Hao Li · Baojie Shi · Chaoyang Chen · Nan Xu · Jiang Li · Jinxin Miao · Yifan Hu · Huachen Zhu · Yi Guan · Tong Cheng · Ningshao Xia · Lunzhi Yuan
10 August 2026 at 08:00
In the infection courses of different SARS-CoV-2 variants, disease outcomes and signatures were delineated by physiological changes, viral load, pathology, and pulmonary transcriptome analysis. This multi-dimensional landscape of disease outcomes and underlying mechanisms might provide important clues for immunotherapy of SARS-CoV-2 infection and pneumonia caused by other respiratory viruses.

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

✇Nature Biomedical Engineering
By: Chong Cui · Shengyi Wang · Daqi Wang · Jingjing Zhao · Bowei Huang · Biyun Zhu · Yuxin Chen · Honghai Tang · Yu Han · Cheng Ye · Dan Mu · Chengdong Zhang · Yuan Yang · Yihan Bao · Jun Lv · Shuang Han · Geng-Lin Li · Huawei Li · Yilai Shu
7 September 2026 at 08:00

Nature Biomedical Engineering, Published online: 07 September 2026; doi:10.1038/s41551-026-01794-5

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Muchen Li · Anglin Liu · Xuetian Gao · Ruijian Xu · Jintai Chen
10 September 2026 at 12:00
arXiv:2609.09728v1 Announce Type: cross Abstract: Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to arise. Beyond detecting whether an IED is present, this setting requires assigning IED-positive activity to clinically meaningful brain-region categories. This setting is challenging because source-region evidence in short electroencephalography (EEG) windows can be subtle, partial, and affected by subject variability, class imbalance, and imperfect multimodal context. We present EEGBind, an EEG-centric multimodal binding framework for five-class source-level IED classification. EEGBind treats EEG as the primary modality and binds synchronized video-context features around an EEG-centric representation. Instead of relying on early or overly strong multimodal fusion, which may perturb the source-sensitive EEG representation, EEGBind uses video context as auxiliary evidence for robust classification. A view-consistent repair stage is further used to improve hidden-set robustness while preserving the learned source-class boundary. On the NeuroMM 2026 Grand Challenge Track 3 NMM-Source-IED benchmark, EEGBind achieves 0.8395 on weighted-F1 and outperforms strong competitors. These results support EEG-centric multimodal binding as a practical strategy for source-level IED classification. The open-source code is available at https://github.com/HKUSTGZ-ML4Health-Lab/NeuroMM2026_IED_Detection.

FrontierChallenge: Evaluating Scientific Workflow Completion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liangcai Su · Zhaopeng Feng · Zhuo Chen · Zhen Zhang · Xiang Lin · Ruilin Li · Handuo Zhang · Ning Wang · Kailong Wen · Yueqi Guo · Feng Xing · Yiling Guo · Brian Wang · Chenxiong Qian · Simon Shaolei Du · Lidong Bing · Xinyu Wang
10 September 2026 at 12:00
arXiv:2608.24979v2 Announce Type: replace Abstract: Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We introduce FrontierChallenge, a cross-domain benchmark comprising 300 end-to-end scientific workflows. In this paper, we release and evaluate 97 of these tasks, spanning quantum chemistry, molecular dynamics, materials characterization, analytical chemistry, life science, and electrochemistry/environment. Each task provides fixed inputs and specifies a bundle of required scientific deliverables. We evaluate twelve frontier models with three agent scaffolds. Pass Rate measures the fraction of tasks satisfying the full-completion criterion, while Avg. Score captures partial progress. Each of the best-performing configurations completed only 20 of the 97 released tasks, yielding a Pass Rate of 20.6%. Partial progress translated especially poorly into complete delivery in analytical chemistry and electrochemistry/environment: Avg. Scores reached 87.6 and 94.9, but the highest Pass Rates were only 4% and 0%. Among non-passing Claude Code trajectories, 75.5% still ended with language claiming completion. Complementary HDS6 process scores correlate strongly with task outcomes, supporting FrontierChallenge as a benchmark of Heavy Duty Solver capabilities. These findings show that neither high partial scores nor confident claims of completion reliably indicate that a scientific task has been fully delivered, highlighting the need to evaluate end-to-end workflow execution and the completeness of scientific deliverables together.

Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haonan Wang · Jiaji Mao · Lehan Wang · Qixiang Zhang · Marawan Elbatel · Yi Qin · Huijun Hu · Baoxun Li · Wenhui Deng · Weifeng Qin · Hongrui Li · Jialin Liang · Jun Shen · Xiaomeng Li
10 September 2026 at 12:00
arXiv:2505.03380v2 Announce Type: replace-cross Abstract: Accurate delineation of tumors and surrounding organs-at-risk is essential for radiotherapy, surgery and treatment response assessment, yet remains time-consuming and expertise-intensive. Existing artificial intelligence systems often require manual spatial prompts or task-specific retraining, while generic class labels provide limited semantic grounding for heterogeneous disease targets. Here we present SyRe, a promptable segmentation foundation model based on Synergistic vision-language Reinforcement. SyRe strengthens bidirectional interaction between visual and linguistic representations to improve semantically grounded spatial understanding. To support large-scale training, we introduce the Color Region Description strategy and construct SyReData, comprising 20 million image-mask-description triplets across 9 modalities and 229 segmentation tasks. Training with diversified prompt forms further enables open-ended prompting, invalid-prompt rejection and flexible switching between single- and multi-target analysis. SyRe achieves accurate text-prompted segmentation across diverse clinical scenarios, with particularly strong performance on disease-related targets. Across 28 unseen external datasets, including 20 cancer types and multinational in-house cohorts, SyRe generalizes robustly under real-world distribution shifts. SyRe-generated masks also preserve clinically relevant quantitative information in pathology and yield radiomics features that stratify survival and improve prognostic modeling across five retrospective CT and MRI tumor cohorts. Finally, clinician-in-the-loop refinement enables efficient case-level correction when greater precision is required. These results establish SyRe as a generalizable foundation for scalable quantitative oncology and clinician-guided segmentation refinement.

Gastrointestinal motility in microgravity: a critical review of multi-level mechanisms and model-dependent effects

✇Omics in Gastric
By: Qian Niu · Tong Mu · Junhai Zhang · Yue Chen · Zhikun Guo · Yuanmin Zhu · Lin Li
4 September 2026 at 18:00

Front Physiol. 2026 Aug 20;17:1930628. doi: 10.3389/fphys.2026.1930628. eCollection 2026.

ABSTRACT

BACKGROUND: Gastrointestinal motility disturbances rank among the most frequently reported medical complications of spaceflight. Astronauts experience delayed gastric emptying, erratic small intestinal transit and reduced colonic propulsion. The underlying mechanisms are multifactorial. Microgravity alters intra-abdominal physical mechanics, disrupts autonomic and enteric neural circuits, shifts gastrointestinal hormone secretion profiles, inflicts oxidative stress upon effector cells, and perturbs gut microbial communities. Cross-model comparisons reveal substantial disagreement, suggesting that no single ground-based analog fully captures the pathophysiology of orbital flight.

AIM: To critically review how weightlessness affects gastric emptying, small intestinal transit and colonic motility; to critically evaluate contradictory findings across simulation platforms; and to delineate the neural, humoral, cellular and microbiological mechanisms involved.

METHODS: We searched PubMed, Web of Science and the NASA Technical Reports Server for articles published between January 1990 and June 2026 (last search 30 June 2026). Search terms included: "microgravity", "weightlessness", "spaceflight", "gastrointestinal motility", "gastric emptying", "intestinal transit", "gut microbiome", "interstitial cells of Cajal" and "oxidative stress". Studies using head-down bed rest, hindlimb unloading, clinorotation, parabolic flight and actual spaceflight were included. The review follows a critical narrative design; the full search strategy and the framework used to appraise the evidence are described in Section 1.1.

RESULTS: Altered-gravity studies suggest that gastrointestinal dysmotility may involve neurohumoral dysregulation, oxidative injury to interstitial cells of Cajal and smooth muscle, barrier dysfunction and altered enteric signaling; however, most mechanistic evidence derives from simulated models and has not been directly validated during human spaceflight. Direct human motility measurements remain sparse, and the evidence comprises a mixture of direct observations, model-dependent inferences and testable hypotheses. Cross-study agreement is poor: some head-down bed rest trials report accelerated small-bowel transit, whereas tail-suspension models and limited flight observations suggest motor suppression. These divergences may reflect model-specific confounding rather than a uniform effect of microgravity.

CONCLUSION: Current ground-based models each capture only partial aspects of orbital GI pathophysiology. Future work should combine multi-omics profiling with next-generation simulation platforms to develop evidence-based countermeasures for long-duration missions.

PMID:42694486 | PMC:PMC13539599 | DOI:10.3389/fphys.2026.1930628

DHCR24<sup>+</sup> tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling

✇Oncogene - Issue - nature.com science feeds
By: Yuanqiao Zhao · Zhuo Xing · Mengmeng Wang · Liangmin Fu · Ruilin Liu · Zexian Ding · Yuan Li · Yinhuai Wang · Bin Yan · Xueqiang Zhu
29 August 2026 at 08:00

Oncogene, Published online: 29 August 2026; doi:10.1038/s41388-026-03967-7

DHCR24+ tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling
Received — 27 May 2026 ⏭

Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chengwei Li · Junlin Liu · Yang Gao
26 May 2026 at 12:00
arXiv:2605.25091v1 Announce Type: new Abstract: As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aerial vehicles (UCAVs) faces significant challenges due to high-dimensional state spaces, discrete action commands, and strongly adversarial dynamic environments. To overcome the limitations of existing multi-agent reinforcement learning (MARL) methods in such settings, namely insufficient exploration efficiency, low sample utilization, and poor policy generalization, we propose Adversarial Curriculum and Evolutionary-enhanced Multi-agent Proximal Policy Optimization (ACE-MAPPO), a hybrid learning framework that integrates evolutionary algorithms with MAPPO. Specifically, a genetic soft update mechanism is introduced to enhance population diversity and mitigate convergence to local optima. An evolutionary-augmented prioritized trajectory replay strategy is further employed to improve the utilization of sparse high-value samples. In addition, an adversarial evolutionary curriculum learning mechanism is designed to enable adaptive training with progressively increasing difficulty. Extensive experimental results demonstrate that the proposed method outperforms MAPPO and other baseline algorithms in terms of training stability, convergence speed, and win rate, validating its effectiveness in multi-aircraft cooperative air combat scenarios.

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.

Self-supervised Hierarchical Visual Reasoning with World Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuanfei Xu · Lin Liu · Wengang Zhou · Mingxiao Feng · Houqiang Li
26 May 2026 at 12:00
arXiv:2605.17537v2 Announce Type: replace Abstract: 3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings. While existing self-supervised visual foresight reasoning approaches often suffer from multi-step error accumulation, many recent studies resort to injecting domain-specific knowledge for more stable guidance. Our key insight is that the photorealistic fidelity of visual reasoning representations is secondary; what truly matters is providing informative, task-relevant signals. To this end, we propose ResDreamer, a hierarchical world model in which each higher-level layer is trained to reconstruct the residuals of the layer below. This design enables progressive abstraction of increasingly sophisticated world dynamics and fosters the emergence of richer latent representations. Drawing inspiration from the "Bitter Lesson", ResDreamer trains its reasoning representations in a purely self-supervised manner. The higher-level residual representations are used to modulate lower-level predictions, allowing the world model to scale effectively with only linearly increasing cross-layer communication costs. Experiments show that ResDreamer achieves state-of-the-art sample efficiency and parameter efficiency. This scalable hierarchical visual foresight reasoning architecture paves the way for more capable online RL agents in open-ended, dynamic environments. The code is accessible at https://github.com/XuYuanFei01/ResDreamer.

Rethinking the Comparison Unit in Sequence-Level Reinforcement Learning: An Equal-Length Paired Training Framework from Loss Correction to Sample Construction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fei Ding · Yongkang Zhang · Runhao Liu · Yuhao Liao · Zijian Zeng · Huiming Yang · Sibo wang · Linglin Liao
26 May 2026 at 12:00
arXiv:2604.17328v2 Announce Type: replace-cross Abstract: This paper investigates the length problem in sequence-level relative reinforcement learning. We observe that, although existing methods partially alleviate length-related phenomena, a more fundamental issue remains insufficiently characterized: the comparison units used during training lack inherent comparability. Building on this observation, we propose a new perspective: the length problem should not be viewed merely as a loss-scaling or normalization bias, but rather as a \emph{comparison unit construction} problem. We further establish a sample-construction-based training framework that, instead of applying post-hoc corrections to unequal-length responses, proactively constructs equal-length, alignable, and comparable training segments during generation. Within this framework, we propose EqLen, a concrete method applicable to group-relative comparison algorithms such as GRPO, GSPO, and RLOO. Through dual-track synchronous generation, prefix inheritance, and segment masking, EqLen efficiently collects effective equal-length training segments and enables stable

Pulmonary-Intestinal Axis: Shared Genetic Basis and Mediating Factors Identified Through Multi-Omics Analysis

✇Omics In Lung
By: Zhenzhu Zhang · Haoyue Liu · Yihang Su · Chengwen Wang · Wei Wang · Yinglin Li · Xiaobao Zhang · Chenguang Gao · Xue Tian · Chunqing Zhao
14 April 2026 at 18:00

Int J Chron Obstruct Pulmon Dis. 2026 Apr 7;21:561645. doi: 10.2147/COPD.S561645. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a systemic condition with comorbidities beyond the lung (eg, cardiovascular and metabolic disorders), and gastrointestinal (GI) disorders are also common. The shared genetic basis of COPD-GI comorbidity and its mediating factors remain unclear. We hypothesized that COPD and GI diseases share pleiotropic genetic architecture implicating lipid-metabolic pathways, with smoking mediating part of the association.

METHODS: We analyzed publicly available European-ancestry GWAS summary statistics for COPD (Global Biobank Meta-analysis Initiative), 15 GI diseases (FinnGen), and smoking phenotypes (UK Biobank). Genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Multi-trait analysis of GWAS (MTAG) boosted COPD discovery by leveraging genetically correlated GI traits. We integrated locus-to-gene mapping with multi-tissue expression quantitative trait loci (eQTL) and plasma protein quantitative trait loci (pQTL) evidence to prioritize shared loci, genes, and proteins. Bidirectional two-sample Mendelian randomization (MR) tested causal directions, and two-step mediation MR evaluated smoking.

RESULTS: COPD showed significant genetic correlation with nine GI diseases. We identified six comorbidity-associated loci (three with CADD > 12.37) and 13 unique candidate pleiotropic genes; APOE was supported by proteomic evidence. Enrichment analyses highlighted lipid-metabolism pathways. MR suggested COPD increases risk of gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), acute appendicitis, and gastric ulcer, while diverticular disease showed reverse causality toward COPD. Smoking partially mediated the COPD effect on GERD, acute appendicitis, and gastric ulcer.

CONCLUSION: COPD and multiple GI disorders share a distributed pleiotropic genetic basis within the broader systemic comorbidity spectrum of COPD. Multi-omics evidence supports a genomic pulmonary-intestinal axis in which lipid metabolism and smoking-related mechanisms contribute to COPD and GI comorbidity, providing targets for risk stratification and potential intervention.

PMID:41978582 | PMC:PMC13070119 | DOI:10.2147/COPD.S561645

Received — 13 April 2026 ⏭

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

✇Journal of Medical Internet Research
By: Siru Liu · Jialin Liu
11 April 2026 at 04:00
Background: Statin therapy, despite proven cardiovascular benefits, remains underused. Social media platforms may capture patient perspectives that are less visible in clinical encounters. Objective: This study aimed to characterize themes, sentiment, and decision-making factors related to statin therapy through large language model (LLM)–based analysis of Reddit discussions. Methods: This cross-sectional observational study analyzed English-language Reddit posts and comments mentioning statins from January 2022 to May 2025, identified via keyword-based Reddit application programming interface searches (≤1000 posts per keyword). A total of 5328 retrieved discussions (n=1661, 31.2% posts and n=3667, 68.8% keyword-containing comments) from public subreddits were included. Themes, sentiments (positive, neutral, or negative), guideline-informed clinical relevance, information-seeking behavior, adverse effect mentions, decision factors, and adherence-related content were extracted using an LLM-based pipeline. Results: Among 5328 discussions, prominent topics included adverse effects (n=1697, 31.9%), decision-making references related to laboratory results and physician advice (n=2767, 51.9% and n=2034, 38.2%, respectively), and alternative approaches (n=2485, 46.6%). Overall sentiment was neutral in 34% (n=1812) of discussions, negative in 30.9% (n=1646), and positive in 16.9% (n=900); the remainder were mixed or unclear. Statin-directed sentiment was neutral in 44.1% (n=2350) of discussions, negative in 25.2% (n=1343), and positive in 12.5% (n=666); the remainder did not express statin-directed sentiment. High clinical relevance was identified in 12.6% (n=672) of discussions. Adherence-related issues were mentioned in 29.8% (n=1587) of discussions. Among adverse effect mentions, muscle pain (n=129, 7.6%) and fatigue (n=110, 6.5%) were common. Conclusions: LLM-enabled analysis of Reddit discourse highlights substantial negative sentiment, adherence-related concerns, and adverse effect narratives surrounding statin therapy. These findings suggest opportunities for patient-centered communication and shared decision-making strategies that address symptom attribution, uncertainty, and information needs in digital information environments.
Received — 4 April 2026 ⏭

Development and multi-center evaluation of domain-adapted speech recognition for human-AI teaming in real-world gastrointestinal endoscopy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruijie Yang · Yan Zhu · Peiyao Fu · Te Luo · Zhihua Wang · Xian Yang · Quanlin Li · Pinghong Zhou · Shuo Wang
3 April 2026 at 12:00
arXiv:2604.01705v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) is a critical interface for human-AI interaction in gastrointestinal endoscopy, yet its reliability in real-world clinical settings is limited by domain-specific terminology and complex acoustic conditions. Here, we present EndoASR, a domain-adapted ASR system designed for real-time deployment in endoscopic workflows. We develop a two-stage adaptation strategy based on synthetic endoscopy reports, targeting domain-specific language modeling and noise robustness. In retrospective evaluation across six endoscopists, EndoASR substantially improves both transcription accuracy and clinical usability, reducing character error rate (CER) from 20.52% to 14.14% and increasing medical term accuracy (Med ACC) from 54.30% to 87.59%. In a prospective multi-center study spanning five independent endoscopy centers, EndoASR demonstrates consistent generalization under heterogeneous real-world conditions. Compared with the baseline Paraformer model, CER is reduced from 16.20% to 14.97%, while Med ACC is improved from 61.63% to 84.16%, confirming its robustness in practical deployment scenarios. Notably, EndoASR achieves a real-time factor (RTF) of 0.005, significantly faster than Whisper-large-v3 (RTF 0.055), while maintaining a compact model size of 220M parameters, enabling efficient edge deployment. Furthermore, integration with large language models demonstrates that improved ASR quality directly enhances downstream structured information extraction and clinician-AI interaction. These results demonstrate that domain-adapted ASR can serve as a reliable interface for human-AI teaming in gastrointestinal endoscopy, with consistent performance validated across multi-center real-world clinical settings.

Efficient Reasoning with Balanced Thinking

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yulin Li · Tengyao Tu · Li Ding · Junjie Wang · Huiling Zhen · Yixin Chen · Yong Li · Zhuotao Tian
3 April 2026 at 12:00
arXiv:2603.12372v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose ReBalance, a training-free framework that achieves efficient reasoning with balanced thinking. ReBalance leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs' reasoning trajectories. A dynamic control function modulates this vector's strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that ReBalance effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Project page and code are available at https://rebalance-ai.github.io .

UniMark: Artificial Intelligence Generated Content Identification Toolkit

✇cs.AI, q-bio.NC updates on arXiv.org
By: Meilin Li · Ji He · Yi Yu · Jia Xu · Shanzhe Lei · Yan Teng · Yingchun Wang · Xuhong Wang
3 April 2026 at 12:00
arXiv:2512.12324v3 Announce Type: replace-cross Abstract: The rapid proliferation of Artificial Intelligence Generated Content has precipitated a crisis of trust and urgent regulatory demands. However, existing identification tools suffer from fragmentation and a lack of support for visible compliance marking. To address these gaps, we introduce the \textbf{UniMark}, an open-source, unified framework for multimodal content governance. Our system features a modular unified engine that abstracts complexities across text, image, audio, and video modalities. Crucially, we propose a novel dual-operation strategy, natively supporting both \emph{Hidden Watermarking} for copyright protection and \emph{Visible Marking} for regulatory compliance. Furthermore, we establish a standardized evaluation framework with three specialized benchmarks (Image/Video/Audio-Bench) to ensure rigorous performance assessment. This toolkit bridges the gap between advanced algorithms and engineering implementation, fostering a more transparent and secure digital ecosystem.
Received — 2 April 2026 ⏭

Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Du · Siyi Li · Shuwei Bai · Ang Li · Haolin Li · Mingqing Xiao · Yang Pan · Dongsheng Li · Weidi Xie · Yanfeng Wang · Ya Zhang · Chencheng Zhang · Jiangchao Yao
1 April 2026 at 12:00
arXiv:2603.29176v1 Announce Type: new Abstract: Parkinson's disease (PD) affects over ten million people worldwide. Although temporal interference (TI) and deep brain stimulation (DBS) are promising therapies, inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost. Previous explorations either resort to limited statistical biomarkers that are insufficient to characterize variability, or employ AI-driven methods which is prone to overfitting and opacity. We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI. Critically, a generative virtual brain foundation model, pretrained on a collective dataset (2707 subjects, 5621 sessions) to capture universal disorder patterns, was finetuned on PD cohorts receiving TI (n=51) or DBS (n=55) to yield individualized virtual brains with high fidelity to empirical functional connectivity (r=0.935). By constructing counterfactual estimations between pathological and healthy neural states within these personalized models, we predicted clinical responses (TI: AUPR=0.853; DBS: AUPR=0.915), substantially outperforming baselines. External and prospective validations (n=14, n=11) highlight the feasibility of clinical translation. Moreover, our framework provides state-dependent regional patterns linked to response, offering hypothesis-generating mechanistic insights.

LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haihong Hao · Lei Chen · Mingfei Han · Changlin Li · Dong An · Yuqiang Yang · Zhihui Li · Xiaojun Chang
1 April 2026 at 12:00
arXiv:2603.29165v1 Announce Type: cross Abstract: Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by actions. As a result, they often lack an effective understanding of the causal relationship between actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can imagine the near future by leveraging action-dynamics causality, which improves both environmental understanding and navigation choices. Inspired by this capability, we propose LatentPilot, a new paradigm that exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics, while requiring no access to future frames at inference. Concretely, we propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution, with an expert takeover triggered when the agent deviates excessively. LatentPilot further learns visual latent tokens without explicit supervision; these latent tokens attend globally in a continuous latent space and are carried across steps, serving as both the current output and the next input, thereby enabling the agent to dream ahead and reason about how actions will affect subsequent observations. Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene. Project page:https://abdd.top/latentpilot/
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