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Received — 18 December 2025 ⏭ Favourites

A Decision-Theoretic Approach for Managing Misalignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Daniel A. Herrmann · Abinav Chari · Isabelle Qian · Sree Sharvesh · B. A. Levinstein
18 December 2025 at 13:00
arXiv:2512.15584v1 Announce Type: new Abstract: When should we delegate decisions to AI systems? While the value alignment literature has developed techniques for shaping AI values, less attention has been paid to how to determine, under uncertainty, when imperfect alignment is good enough to justify delegation. We argue that rational delegation requires balancing an agent's value (mis)alignment with its epistemic accuracy and its reach (the acts it has available). This paper introduces a formal, decision-theoretic framework to analyze this tradeoff precisely accounting for a principal's uncertainty about these factors. Our analysis reveals a sharp distinction between two delegation scenarios. First, universal delegation (trusting an agent with any problem) demands near-perfect value alignment and total epistemic trust, conditions rarely met in practice. Second, we show that context-specific delegation can be optimal even with significant misalignment. An agent's superior accuracy or expanded reach may grant access to better overall decision problems, making delegation rational in expectation. We develop a novel scoring framework to quantify this ex ante decision. Ultimately, our work provides a principled method for determining when an AI is aligned enough for a given context, shifting the focus from achieving perfect alignment to managing the risks and rewards of delegation under uncertainty.

Zero-Knowledge Audit for Internet of Agents: Privacy-Preserving Communication Verification with Model Context Protocol

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guanlin Jing · Huayi Qi
18 December 2025 at 13:00
arXiv:2512.14737v1 Announce Type: cross Abstract: Existing agent communication frameworks face critical limitations in providing verifiable audit trails without compromising the privacy and confidentiality of agent interactions. The protection of agent communication privacy while ensuring auditability emerges as a fundamental challenge for applications requiring accurate billing, compliance verification, and accountability in regulated environments. We introduce a framework for auditing agent communications that keeps messages private while still checking they follow expected rules. It pairs zero-knowledge proofs with the existing Model Context Protocol (MCP) so messages can be verified without revealing their contents. The approach runs in lightweight networks, stays compatible with standard MCP exchanges, and adds asynchronous audit verification to confirm format and general message types without exposing specifics. The framework enables mutual audits between agents: one side can check communication content and quality while the other verifies usage metrics, all without revealing sensitive information. We formalize security goals and show that zk-MCP provides data authenticity and communication privacy, achieving efficient verification with negligible latency overhead. We fully implement the framework, including Circom-based zero-knowledge proof generation and an audit protocol integrated with MCP's bidirectional channel, and, to our knowledge, this is the first privacy-preserving audit system for agent communications that offers verifiable mutual auditing without exposing message content or compromising agent privacy.

DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline

✇cs.AI, q-bio.NC updates on arXiv.org
By: Houman Kazemzadeh · Kiarash Mokhtari Dizaji · Seyed Reza Tavakoli · Farbod Davoodi · MohammadReza KarimiNejad · Parham Abed Azad · Ali Sabzi · Armin Khosravi · Siavash Ahmadi · Mohammad Hossein Rohban · Glolamali Aminian · Tahereh Javaheri
18 December 2025 at 13:00
arXiv:2512.14896v1 Announce Type: cross Abstract: Objectives: To evaluate large language model (LLM) performance on pharmacy licensure-style question-answering (QA) tasks and develop an external knowledge integration method to improve their accuracy. Methods: We benchmarked eleven existing LLMs with varying parameter sizes (8 billion to 70+ billion) using a 141-question pharmacy dataset. We measured baseline accuracy for each model without modification. We then developed a three-step retrieval-augmented generation (RAG) pipeline, DrugRAG, that retrieves structured drug knowledge from validated sources and augments model prompts with evidence-based context. This pipeline operates externally to the models, requiring no changes to model architecture or parameters. Results: Baseline accuracy ranged from 46% to 92%, with GPT-5 (92%) and o3 (89%) achieving the highest scores. Models with fewer than 8 billion parameters scored below 50%. DrugRAG improved accuracy across all tested models, with gains ranging from 7 to 21 percentage points (e.g., Gemma 3 27B: 61% to 71%, Llama 3.1 8B: 46% to 67%) on the 141-item benchmark. Conclusion: We demonstrate that external structured drug knowledge integration through DrugRAG measurably improves LLM accuracy on pharmacy tasks without modifying the underlying models. This approach provides a practical pipeline for enhancing pharmacy-focused AI applications with evidence-based information.

I am here for you": How relational conversational AI appeals to adolescents, especially those who are socially and emotionally vulnerable

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pilyoung Kim · Yun Xie · Sujin Yang
18 December 2025 at 13:00
arXiv:2512.15117v1 Announce Type: cross Abstract: General-purpose conversational AI chatbots and AI companions increasingly provide young adolescents with emotionally supportive conversations, raising questions about how conversational style shapes anthropomorphism and emotional reliance. In a preregistered online experiment with 284 adolescent-parent dyads, youth aged 11-15 and their parents read two matched transcripts in which a chatbot responded to an everyday social problem using either a relational style (first-person, affiliative, commitment language) or a transparent style (explicit nonhumanness, informational tone). Adolescents more often preferred the relational than the transparent style, whereas parents were more likely to prefer transparent style than adolescents. Adolescents rated the relational chatbot as more human-like, likable, trustworthy and emotionally close, while perceiving both styles as similarly helpful. Adolescents who preferred relational style had lower family and peer relationship quality and higher stress and anxiety than those preferring transparent style or both chatbots. These findings identify conversational style as a key design lever for youth AI safety, showing that relational framing heightens anthropomorphism, trust and emotional closeness and can be especially appealing to socially and emotionally vulnerable adolescents, who may be at increased risk for emotional reliance on conversational AI.

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pengsong Zhang · Xiang Hu · Guowei Huang · Yang Qi · Heng Zhang · Xiuxu Li · Jiaxing Song · Jiabin Luo · Yijiang Li · Shuo Yin · Chengxiao Dai · Eric Hanchen Jiang · Xiaoyan Zhou · Zhenfei Yin · Boqin Yuan · Jing Dong · Guinan Su · Guanren Qiao · Haiming Tang · Anghong Du · Lili Pan · Zhenzhong Lan · Xinyu Liu
18 December 2025 at 13:00
arXiv:2508.15126v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack rigorous quality-control mechanisms. Consequently, a significant amount of high-quality AI-generated research lacks appropriate venues for dissemination, hindering its potential to advance scientific progress. To address these challenges, we introduce aiXiv, a next-generation open-access platform for human and AI scientists. Its multi-agent architecture allows research proposals and papers to be submitted, reviewed, and iteratively refined by both human and AI scientists. It also provides API and MCP interfaces that enable seamless integration of heterogeneous human and AI scientists, creating a scalable and extensible ecosystem for autonomous scientific discovery. Through extensive experiments, we demonstrate that aiXiv is a reliable and robust platform that significantly enhances the quality of AI-generated research proposals and papers after iterative revising and reviewing on aiXiv. Our work lays the groundwork for a next-generation open-access ecosystem for AI scientists, accelerating the publication and dissemination of high-quality AI-generated research content. Code: https://github.com/aixiv-org aiXiv: https://aixiv.science

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Philip R. Liu · Sparsh Bansal · Jimmy Dinh · Aditya Pawar · Ramani Satishkumar · Shail Desai · Neeraj Gupta · Xin Wang · Shu Hu
18 December 2025 at 13:00
arXiv:2506.07400v3 Announce Type: replace-cross Abstract: The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially reduce clinical accuracy. Although recent approaches combining imaging models with LLM reasoning have improved reporting, they typically rely on a single generalist agent, restricting their capacity to emulate the diverse and complex reasoning found in multidisciplinary medical teams. To address these limitations, we propose MedChat, a multi-agent diagnostic framework and platform that combines specialized vision models with multiple role-specific LLM agents, all coordinated by a director agent. This design enhances reliability, reduces hallucination risk, and enables interactive diagnostic reporting through an interface tailored for clinical review and educational use. Code available at https://github.com/Purdue-M2/MedChat.

Multimodal Foundation Models for Early Disease Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Md Talha Mohsin · Ismail Abdulrashid
18 December 2025 at 13:00
arXiv:2510.01899v2 Announce Type: replace-cross Abstract: Healthcare data now span EHRs, medical imaging, genomics, and wearable sensors, but most diagnostic models still process these modalities in isolation. This limits their ability to capture early, cross-modal disease signatures. This paper introduces a multimodal foundation model built on a transformer architecture that integrates heterogeneous clinical data through modality-specific encoders and cross-modal attention. Each modality is mapped into a shared latent space and fused using multi-head attention with residual normalization. We implement the framework using a multimodal dataset that simulates early-stage disease patterns across EHR sequences, imaging patches, genomic profiles, and wearable signals, including missing-modality scenarios and label noise. The model is trained using supervised classification together with self-supervised reconstruction and contrastive alignment to improve robustness. Experimental evaluation demonstrates strong performance in early-detection settings, with stable classification metrics, reliable uncertainty estimates, and interpretable attention patterns. The approach moves toward a flexible, pretrain-and-fine-tune foundation model that supports precision diagnostics, handles incomplete inputs, and improves early disease detection across oncology, cardiology, and neurology applications.

Systems pharmacology approaches decipher the anti-cancer efficacy of ethnopharmacological agents in hepatocellular carcinoma

✇Omics in Hepatocellular
By: Tingting Hao · Xiaonan Yang · Weibin Wang · Dawei Li · Yanqi Liu · Die Jiang · Ya Ning · Qinghua Kong · Hongmei Li · Ru Zhang · Zhu Qiao · Xing Jiang · Xiaolei Zhou · Dandan Mo · Wenqing Su · Wendong He · Xuzhen Li · Baozheng Chen · Qiang-Qiang Zhu · Xianghua Xia · Yang Dong
17 December 2025 at 19:00

Sci Rep. 2025 Dec 17;15(1):43996. doi: 10.1038/s41598-025-27744-w.

ABSTRACT

Hepatocellular carcinoma (HCC) poses a significant global health burden with limited therapeutic efficacy. Chinese herbal medicines (CHMs) offer multi-target potential, yet their systematic screening and mechanistic elucidation remain challenging. We established a high-throughput multi-omics platform integrating transcriptomics, proteomics, and deep learning (autoencoder and multiple kernel learning) to screen 187 medicinal plants. Five CHMs candidates were identified and shown to modulate hub genes (e.g., AKR1B10, HMGCR, THBS1) and key pathways (TNF/IL-17/MAPK, apoptosis, ferroptosis). Proteomic validation and functional assays confirmed their roles in suppressing proliferation, migration, and inducing apoptosis in HCC cells. This study provides a robust, data-driven pipeline for natural anti-HCC drug discovery, linking specific hub genes to CHM efficacy and offering novel insights into precision ethnopharmacology.

PMID:41408124 | DOI:10.1038/s41598-025-27744-w

A novel statistical feature selection framework for biomarker discovery and cancer classification via multiomics integration

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Moshira S Ghaleb · Maryam N Al-Berry · Hala M Ebied · Mohamed F Tolba
17 December 2025 at 19:00

BMC Med Res Methodol. 2025 Dec 17. doi: 10.1186/s12874-025-02713-z. Online ahead of print.

ABSTRACT

BACKGROUND: Early cancer diagnosis is essential for improving prognosis and guiding treatment. However, the high dimensionality and complexity of omics data present major challenges. Computational approaches that extract stable biomarkers and enable reliable classification across cancer types and stages are needed.

METHODS: A novel feature selection method, sDCFE (synergistic Discriminative Cluster-based Feature Extraction), was developed by extending Fisher-like variance analysis with a median absolute deviation (MAD) regularization term and a cluster separation component to enhance robustness and interpretability. Features selected by sDCFE were compared with those obtained from XGBoost, and the intersected set of 82 genes was evaluated through functional enrichment (KEGG, Reactome, GO BP), survival analysis (Kaplan-Meier, Cox regression), and biomarker novelty assessment against six external resources. Hybrid classification models integrating XGBoost, sDCFE, and deep learning were applied to pancancer classification, and the framework was further extended to lung squamous cell carcinoma (LUSC) staging using RNA-seq and methylation data.

RESULTS: The overlap between sDCFE and XGBoost yielded 82 candidate biomarkers enriched in cancer-related pathways, including cell cycle regulation, immune signalling, and DNA repair. Novelty assessment stratified these genes into established, emerging, and novel categories. Six genes-HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP-emerged as the most promising candidates, supported by enrichment and survival associations across multiple cancers. The hybrid model achieved near-perfect pancancer classification on TCGA (accuracy = 99.3%, MCC = 0.992, AUC = 1.0) and demonstrated strong generalizability on PCAWG (accuracy = 94%, MCC = 0.929, AUC = 0.997). In the LUSC staging task, multiomics integration improved classification performance: the CNN-based model reached 84% accuracy, while logistic regression applied to sDCFE-ranked features achieved 88.5% accuracy with superior calibration, highlighting the robustness of the selected features.

CONCLUSION: sDCFE provides a principled extension of Fisher-like methods, enabling stable and interpretable biomarker selection. When combined with XGBoost and deep learning, the framework achieves highly accurate and biologically grounded cancer classification across both cancer types and stages. The identification of novel and prognostic biomarkers, including HFE2, LOC339674, SERINC2, SFTA3, SOX2OT, and ACPP, underscores its translational potential. These results position the framework as a promising precision oncology tool to support early diagnosis, risk stratification, and treatment decision-making.

PMID:41408184 | DOI:10.1186/s12874-025-02713-z

Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mitchell A. Klusty · Elizabeth C. Solie · Caroline N. Leach · W. Vaiden Logan · Lynnet E. Richey · John C. Gensel · David P. Szczykutowicz · Bryan C. McLellan · Emily B. Collier · Samuel E. Armstrong · V. K. Cody Bumgardner
17 December 2025 at 13:00
arXiv:2512.13700v1 Announce Type: new Abstract: Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research, requiring experts to extract often complex information from unstructured electronic health record (EHR) narratives. We present a secure, modular framework for automated structured feature extraction from clinical notes leveraging locally deployed large language models (LLMs) on institutionally approved, Health Insurance Portability and Accountability Act (HIPPA)-compliant compute infrastructure. This system integrates retrieval augmented generation (RAG) and structured response methods of LLMs into a widely deployable and scalable container to provide feature extraction for diverse clinical domains. In evaluation, the framework achieved high accuracy across multiple medical characteristics present in large bodies of patient notes when compared against an expert-annotated dataset and identified several annotation errors missed in manual review. This framework demonstrates the potential of LLM systems to reduce the burden of manual chart review through automated extraction and increase consistency in data capture, accelerating clinical research.

ValuePilot: A Two-Phase Framework for Value-Driven Decision-Making

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yitong Luo · Ziang Chen · Hou Hei Lam · Jiayu zhan · Junqi Wang · Zhenliang Zhang · Xue Feng
17 December 2025 at 13:00
arXiv:2512.13716v1 Announce Type: new Abstract: Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-world applications, adapting to personalized values beyond task completion or collective alignment has become a critical challenge. We address this by proposing a value-driven approach to personalized decision-making. Human values serve as stable, transferable signals that support consistent and generalizable behavior across contexts. Compared to task-oriented paradigms driven by external rewards and incentives, value-driven decision-making enhances interpretability and enables agents to act appropriately even in novel scenarios. We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM). DGT constructs diverse, value-annotated scenarios from a human-LLM collaborative pipeline. DMM learns to evaluate actions based on personal value preferences, enabling context-sensitive, individualized decisions. When evaluated on previously unseen scenarios, DMM outperforms strong LLM baselines, including GPT-5, Claude-Sonnet-4, Gemini-2-flash, and Llama-3.1-70b, in aligning with human action choices. Our results demonstrate that value-driven decision-making is an effective and extensible engineering pathway toward building interpretable, personalized AI agents.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Writing in Symbiosis: Mapping Human Creative Agency in the AI Era Vivan Doshi · Mengyuan Li
    arXiv:2512.13697v1 Announce Type: cross Abstract: The proliferation of Large Language Models (LLMs) raises a critical question about what it means to be human when we share an increasingly symbiotic relationship with persuasive and creative machines. This paper examines patterns of human-AI coevolution in creative writing, investigating how human craft and agency are adapting alongside machine capabilities. We challenge the prevailing notion of stylistic homogenization by examining diverse patt
     
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Writing in Symbiosis: Mapping Human Creative Agency in the AI Era

✇cs.AI, q-bio.NC updates on arXiv.org
By: Vivan Doshi · Mengyuan Li
17 December 2025 at 13:00
arXiv:2512.13697v1 Announce Type: cross Abstract: The proliferation of Large Language Models (LLMs) raises a critical question about what it means to be human when we share an increasingly symbiotic relationship with persuasive and creative machines. This paper examines patterns of human-AI coevolution in creative writing, investigating how human craft and agency are adapting alongside machine capabilities. We challenge the prevailing notion of stylistic homogenization by examining diverse patterns in longitudinal writing data. Using a large-scale corpus spanning the pre- and post-LLM era, we observe patterns suggestive of a "Dual-Track Evolution": thematic convergence around AI-related topics, coupled with structured stylistic differentiation. Our analysis reveals three emergent adaptation patterns: authors showing increased similarity to AI style, those exhibiting decreased similarity, and those maintaining stylistic stability while engaging with AI-related themes. This Creative Archetype Map illuminates how authorship is coevolving with AI, contributing to discussions about human-AI collaboration, detection challenges, and the preservation of creative diversity.

Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passport

✇cs.AI, q-bio.NC updates on arXiv.org
By: A. Anil Sinaci · Senan Postaci · Dogukan Cavdaroglu · Machteld J. Boonstra · Okan Mercan · Kerem Yilmaz · Gokce B. Laleci Erturkmen · Folkert W. Asselbergs · Karim Lekadir
17 December 2025 at 13:00
arXiv:2512.13702v1 Announce Type: cross Abstract: Objective: To develop the AI Product Passport, a standards-based framework improving transparency, traceability, and compliance in healthcare AI via lifecycle-based documentation. Materials and Methods: The AI Product Passport was developed within the AI4HF project, focusing on heart failure AI tools. We analyzed regulatory frameworks (EU AI Act, FDA guidelines) and existing standards to design a relational data model capturing metadata across AI lifecycle phases: study definition, dataset preparation, model generation/evaluation, deployment/monitoring, and passport generation. MLOps/ModelOps concepts were integrated for operational relevance. Co-creation involved feedback from AI4HF consortium and a Lisbon workshop with 21 diverse stakeholders, evaluated via Mentimeter polls. The open-source platform was implemented with Python libraries for automated provenance tracking. Results: The AI Product Passport was designed based on existing standards and methods with well-defined lifecycle management and role-based access. Its implementation is a web-based platform with a relational data model supporting auditable documentation. It generates machine- and human-readable reports, customizable for stakeholders. It aligns with FUTURE-AI principles (Fairness, Universality, Traceability, Usability, Robustness, Explainability), ensuring fairness, traceability, and usability. Exported passports detail model purpose, data provenance, performance, and deployment context. GitHub-hosted backend/frontend codebases enhance accessibility. Discussion and Conclusion: The AI Product Passport addresses transparency gaps in healthcare AI, meeting regulatory and ethical demands. Its open-source nature and alignment with standards foster trust and adaptability. Future enhancements include FAIR data principles and FHIR integration for improved interoperability, promoting responsible AI deployment.

Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ayush Noori · Joaqu\'in Polonuer · Katharina Meyer · Bogdan Budnik · Shad Morton · Xinyuan Wang · Sumaiya Nazeen · Yingnan He · I\~naki Arango · Lucas Vittor · Matthew Woodworth · Richard C. Krolewski · Michelle M. Li · Ninning Liu · Tushar Kamath · Evan Macosko · Dylan Ritter · Jalwa Afroz · Alexander B. H. Henderson · Lorenz Studer · Samuel G. Rodriques · Andrew White · Noa Dagan · David A. Clifton · George M. Church · Sudeshna Das · Jenny M. Tam · Vikram Khurana · Marinka Zitnik
17 December 2025 at 13:00
arXiv:2512.13724v1 Announce Type: cross Abstract: Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. In silico screens performed by PROTON reproduced six genome-wide $\alpha$-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted $p

Complex Mathematical Expression Recognition: Benchmark, Large-Scale Dataset and Strong Baseline

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weikang Bai · Yongkun Du · Yuchen Su · Yazhen Xie · Zhineng Chen
17 December 2025 at 13:00
arXiv:2512.13731v1 Announce Type: cross Abstract: Mathematical Expression Recognition (MER) has made significant progress in recognizing simple expressions, but the robust recognition of complex mathematical expressions with many tokens and multiple lines remains a formidable challenge. In this paper, we first introduce CMER-Bench, a carefully constructed benchmark that categorizes expressions into three difficulty levels: easy, moderate, and complex. Leveraging CMER-Bench, we conduct a comprehensive evaluation of existing MER models and general-purpose multimodal large language models (MLLMs). The results reveal that while current methods perform well on easy and moderate expressions, their performance degrades significantly when handling complex mathematical expressions, mainly because existing public training datasets are primarily composed of simple samples. In response, we propose MER-17M and CMER-3M that are large-scale datasets emphasizing the recognition of complex mathematical expressions. The datasets provide rich and diverse samples to support the development of accurate and robust complex MER models. Furthermore, to address the challenges posed by the complicated spatial layout of complex expressions, we introduce a novel expression tokenizer, and a new representation called Structured Mathematical Language, which explicitly models the hierarchical and spatial structure of expressions beyond LaTeX format. Based on these, we propose a specialized model named CMERNet, built upon an encoder-decoder architecture and trained on CMER-3M. Experimental results show that CMERNet, with only 125 million parameters, significantly outperforms existing MER models and MLLMs on CMER-Bench.

TF-MCL: Time-frequency Fusion and Multi-domain Cross-Loss for Self-supervised Depression Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Li-Xuan Zhao · Chen-Yang Xu · Wen-Qiang Li · Bo Wang · Rong-Xing Wei · Qing-Hao Menga
17 December 2025 at 13:00
arXiv:2512.13736v1 Announce Type: cross Abstract: In recent years, there has been a notable increase in the use of supervised detection methods of major depressive disorder (MDD) based on electroencephalogram (EEG) signals. However, the process of labeling MDD remains challenging. As a self-supervised learning method, contrastive learning could address the shortcomings of supervised learning methods, which are unduly reliant on labels in the context of MDD detection. However, existing contrastive learning methods are not specifically designed to characterize the time-frequency distribution of EEG signals, and their capacity to acquire low-semantic data representations is still inadequate for MDD detection tasks. To address the problem of contrastive learning method, we propose a time-frequency fusion and multi-domain cross-loss (TF-MCL) model for MDD detection. TF-MCL generates time-frequency hybrid representations through the use of a fusion mapping head (FMH), which efficiently remaps time-frequency domain information to the fusion domain, and thus can effectively enhance the model's capacity to synthesize time-frequency information. Moreover, by optimizing the multi-domain cross-loss function, the distribution of the representations in the time-frequency domain and the fusion domain is reconstructed, thereby improving the model's capacity to acquire fusion representations. We evaluated the performance of our model on the publicly available datasets MODMA and PRED+CT and show a significant improvement in accuracy, outperforming the existing state-of-the-art (SOTA) method by 5.87% and 9.96%, respectively.

Why Text Prevails: Vision May Undermine Multimodal Medical Decision Making

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Dai · Lunxiao Li · Kun Zhao · Eardi Lila · Paul K. Crane · Heng Huang · Dongkuan Xu · Haoteng Tang · Liang Zhan
17 December 2025 at 13:00
arXiv:2512.13747v1 Announce Type: cross Abstract: With the rapid progress of large language models (LLMs), advanced multimodal large language models (MLLMs) have demonstrated impressive zero-shot capabilities on vision-language tasks. In the biomedical domain, however, even state-of-the-art MLLMs struggle with basic Medical Decision Making (MDM) tasks. We investigate this limitation using two challenging datasets: (1) three-stage Alzheimer's disease (AD) classification (normal, mild cognitive impairment, dementia), where category differences are visually subtle, and (2) MIMIC-CXR chest radiograph classification with 14 non-mutually exclusive conditions. Our empirical study shows that text-only reasoning consistently outperforms vision-only or vision-text settings, with multimodal inputs often performing worse than text alone. To mitigate this, we explore three strategies: (1) in-context learning with reason-annotated exemplars, (2) vision captioning followed by text-only inference, and (3) few-shot fine-tuning of the vision tower with classification supervision. These findings reveal that current MLLMs lack grounded visual understanding and point to promising directions for improving multimodal decision making in healthcare.

One Permutation Is All You Need: Fast, Reliable Variable Importance and Model Stress-Testing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Albert Dorador
17 December 2025 at 13:00
arXiv:2512.13892v1 Announce Type: cross Abstract: Reliable estimation of feature contributions in machine learning models is essential for trust, transparency and regulatory compliance, especially when models are proprietary or otherwise operate as black boxes. While permutation-based methods are a standard tool for this task, classical implementations rely on repeated random permutations, introducing computational overhead and stochastic instability. In this paper, we show that by replacing multiple random permutations with a single, deterministic, and optimal permutation, we achieve a method that retains the core principles of permutation-based importance while being non-random, faster, and more stable. We validate this approach across nearly 200 scenarios, including real-world household finance and credit risk applications, demonstrating improved bias-variance tradeoffs and accuracy in challenging regimes such as small sample sizes, high dimensionality, and low signal-to-noise ratios. Finally, we introduce Systemic Variable Importance, a natural extension designed for model stress-testing that explicitly accounts for feature correlations. This framework provides a transparent way to quantify how shocks or perturbations propagate through correlated inputs, revealing dependencies that standard variable importance measures miss. Two real-world case studies demonstrate how this metric can be used to audit models for hidden reliance on protected attributes (e.g., gender or race), enabling regulators and practitioners to assess fairness and systemic risk in a principled and computationally efficient manner.

Assessing High-Risk Systems: An EU AI Act Verification Framework

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alessio Buscemi · Tom Deckenbrunnen · Fahria Kabir · Nishat Mowla · Kateryna Mishchenko
17 December 2025 at 13:00
arXiv:2512.13907v1 Announce Type: cross Abstract: A central challenge in implementing the AI Act and other AI-relevant regulations in the EU is the lack of a systematic approach to verify their legal mandates. Recent surveys show that this regulatory ambiguity is perceived as a significant burden, leading to inconsistent readiness across Member States. This paper proposes a comprehensive framework designed to help close this gap by organising compliance verification along two fundamental dimensions: the type of method (controls vs. testing) and the target of assessment (data, model, processes, and final product). Additionally, our framework maps core legal requirements to concrete verification activities, serving as a vital bridge between policymakers and practitioners, and aligning legal text with technical standards and best practices. The proposed approach aims to reduce interpretive uncertainty, promote consistency in assessment practices, and support the alignment of regulatory, ethical, and technical perspectives across the AI lifecycle.

A data-physics hybrid generative model for patient-specific post-stroke motor rehabilitation using wearable sensor data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanning Dai · Chenyu Tang · Ruizhi Zhang · Wenyu Yang · Yilan Zhang · Yuhui Wang · Junliang Chen · Xuhang Chen · Ruimou Xie · Yangyue Cao · Qiaoying Li · Jin Cao · Tao Li · Hubin Zhao · Yu Pan · Arokia Nathan · Xin Gao · Peter Smielewski · Shuo Gao
17 December 2025 at 13:00
arXiv:2512.14329v1 Announce Type: cross Abstract: Dynamic prediction of locomotor capacity after stroke is crucial for tailoring rehabilitation, yet current assessments provide only static impairment scores and do not indicate whether patients can safely perform specific tasks such as slope walking or stair climbing. Here, we develop a data-physics hybrid generative framework that reconstructs an individual stroke survivor's neuromuscular control from a single 20 m level-ground walking trial and predicts task-conditioned locomotion across rehabilitation scenarios. The system combines wearable-sensor kinematics, a proportional-derivative physics controller, a population Healthy Motion Atlas, and goal-conditioned deep reinforcement learning with behaviour cloning and generative adversarial imitation learning to generate physically plausible, patient-specific gait simulations for slopes and stairs. In 11 stroke survivors, the personalized controllers preserved idiosyncratic gait patterns while improving joint-angle and endpoint fidelity by 4.73% and 12.10%, respectively, and reducing training time to 25.56% relative to a physics-only baseline. In a multicentre pilot involving 21 inpatients, clinicians who used our locomotion predictions to guide task selection and difficulty obtained larger gains in Fugl-Meyer lower-extremity scores over 28 days of standard rehabilitation than control clinicians (mean change 6.0 versus 3.7 points). These findings indicate that our generative, task-predictive framework can augment clinical decision-making in post-stroke gait rehabilitation and provide a template for dynamically personalized motor recovery strategies.
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