Evaluating Peer Online Forums to Support Health: Ethical and Practical Challenges

By: Fiona Lobban · Neil Caton · Zoe Glossop · Jade Haines · Gemma Hayward · Connor Heapy · Rose Johnston · Steve Jones · Chris Lodge · Karen Machin · Paul Marshall · Tamara Rakic · Paul Rayson · Heather Robinson · Elena Semino · John Vidler

25 December 2025 at 01:30

Many people use peer online forums to seek support for health-related problems. More research is needed to understand the impacts of forum use, and how these are generated. However, there are significant ethical and practical challenges with the methods available to do the required research. We examine the key challenges associated with conducting each of the most commonly used online data collection methods: surveys, interviews, forum post analysis; and triangulation of these methods. Based on our learning from the Improving Peer Online Forums (iPOF) study, an inter-disciplinary realist informed mixed methods evaluation of peer online forums, we outline strategies that can be used to address key issues pertaining to assessing important outcomes, facilitating participation, validating participants (users who consent to take part in one or more parts of the study), protecting anonymity, gaining consent, managing risk, multi-stakeholder engagement, and triangulation. We share this learning to support researchers, reviewers, and ethics committees faced with deciding how best to address these challenges. We highlight the need for ongoing open, transparent discussion to ensure the research field keeps pace with evolving technology design and societal attitudes to online data use.

Correction: Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study

Journal of Medical Internet Research

By: Imogen E Leaning · Andrea Costanzo · Raj Jagesar · Lianne M Reus · Pieter Jelle Visser · Martien J H Kas · Christian F Beckmann · Henricus G Ruhé · Andre F Marquand

24 December 2025 at 19:00

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

npj Digital Medicine

By: Andrew Wen · Qiuhao Lu · Yu-Neng Chuang · Guanchu Wang · Jiayi Yuan · Jiamu Zhang · Liwei Wang · Sunyang Fu · Kurt D. Miller · Heling Jia · Steven D. Bedrick · William R. Hersh · Kirk E. Roberts · Xia Hu · Hongfang Liu

27 December 2025 at 08:00

npj Digital Medicine, Published online: 27 December 2025; doi:10.1038/s41746-025-02253-2

Context matching is not reasoning when performing generalized clinical evaluation of generative language models

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

npj Digital Medicine

By: Shirui Wang · Zhihui Tang · Huaxia Yang · Qiuhong Gong · Tiantian Gu · Hongyang Ma · Yongxin Wang · Wubin Sun · Zeliang Lian · Kehang Mao · Yinan Jiang · Zhicheng Huang · Lingyun Ma · Wenjie Shen · Yajie Ji · Yunhui Tan · Chunbo Wang · Yunlu Gao · Qianling Ye · Rui Lin · Mingyu Chen · Lijuan Niu · Zhihao Wang · Peng Yu · Mengran Lang · Yue Liu · Huimin Zhang · Haitao Shen · Long Chen · Qiguang Zhao · Si-Xuan Liu · Lina Zhou · Hua Gao · Dongqiang Ye · Lingmin Meng · Youtao Yu · Naixin Liang · Jianxiong Wu

26 December 2025 at 08:00

npj Digital Medicine, Published online: 26 December 2025; doi:10.1038/s41746-025-02277-8

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

Comparison of liquid biopsy-based technologies for cancer screening

MRD

By: Andreas P Papanastasiou · Miyo K Chatanaka · Eleftherios P Diamandis

27 December 2025 at 19:00

Crit Rev Clin Lab Sci. 2025 Dec 27:1-12. doi: 10.1080/10408363.2025.2606357. Online ahead of print.

ABSTRACT

Circulating plasma DNA has found important applications in diverse medical fields, including prenatal testing, transplantation, and especially cancer. Many companies have developed products for detecting minimal residual disease, selecting or monitoring therapy, assessing prognosis, and confirming diagnosis. One major application is in screening asymptomatic individuals for the presence of cancer. Screening may facilitate better clinical outcomes through earlier interventions. Collectively, these technologies are widely known as "liquid biopsies". After the extraction of free DNA from the circulation, it is analyzed by various molecular techniques to explore differences between DNA originating from normal cells and cancer cells. Circulating plasma DNA originating from tumors (ctDNA) is expected to harbor the same molecular changes as tumor tissue itself. Thus, ctDNA is considered a surrogate of cancer tissue, but without the need to perform invasive biopsies to obtain it. Many new diagnostic companies have taken advantage of this new biomarker and developed technologies for screening for one or multiple cancers. We previously estimated the amount of ctDNA in circulation, which is admixed with DNA originating from normal cells. We concluded that since only a small fraction of the whole plasma (3 liters) is used for testing (3 to 4 mL), it is possible that the retrieved ctDNA may not be enough for cancer diagnosis in all patients. This problem is more acute with small tumors. Here, we mention some companies in the "liquid biopsy" arena and analyze their clinical data to establish if their tests are close to entering the clinic. We conclude from this analysis that current data do not support the use of these technologies for population screening due to many false negative and false positive results.

PMID:41454842 | DOI:10.1080/10408363.2025.2606357

The Principle of Proportional Duty: A Knowledge-Duty Framework for Ethical Equilibrium in Human and Artificial Systems

cs.AI, q-bio.NC updates on arXiv.org

By: Timothy Prescher

19 December 2025 at 13:00

arXiv:2512.15740v1 Announce Type: new Abstract: Traditional ethical frameworks often struggle to model decision-making under uncertainty, treating it as a simple constraint on action. This paper introduces the Principle of Proportional Duty (PPD), a novel framework that models how ethical responsibility scales with an agent's epistemic state. The framework reveals that moral duty is not lost to uncertainty but transforms: as uncertainty increases, Action Duty (the duty to act decisively) is proportionally converted into Repair Duty (the active duty to verify, inquire, and resolve uncertainty). This dynamic is expressed by the equation D_total = K[(1-HI) + HI * g(C_signal)], where Total Duty is a function of Knowledge (K), Humility/Uncertainty (HI), and Contextual Signal Strength (C_signal). Monte Carlo simulations demonstrate that systems maintaining a baseline humility coefficient (lambda > 0) produce more stable duty allocations and reduce the risk of overconfident decision-making. By formalizing humility as a system parameter, the PPD offers a mathematically tractable approach to moral responsibility that could inform the development of auditable AI decision systems. This paper applies the framework across four domains, clinical ethics, recipient-rights law, economic governance, and artificial intelligence, to demonstrate its cross-disciplinary validity. The findings suggest that proportional duty serves as a stabilizing principle within complex systems, preventing both overreach and omission by dynamically balancing epistemic confidence against contextual risk.

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

npj Digital Medicine

By: Konstantin Georgiev · Dimitrios Doudesis · Joanne McPeake · Nicholas L. Mills · Susan D. Shenkin · Jacques D. Fleuriot · Atul Anand

17 December 2025 at 08:00

npj Digital Medicine, Published online: 17 December 2025; doi:10.1038/s41746-025-02138-4

Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records

Data-Chain Backdoor: Do You Trust Diffusion Models as Generative Data Supplier?

cs.AI, q-bio.NC updates on arXiv.org

By: Junchi Lu · Xinke Li · Yuheng Liu · Qi Alfred Chen

19 December 2025 at 13:00

arXiv:2512.15769v1 Announce Type: cross Abstract: The increasing use of generative models such as diffusion models for synthetic data augmentation has greatly reduced the cost of data collection and labeling in downstream perception tasks. However, this new data source paradigm may introduce important security concerns. This work investigates backdoor propagation in such emerging generative data supply chains, namely Data-Chain Backdoor (DCB). Specifically, we find that open-source diffusion models can become hidden carriers of backdoors. Their strong distribution-fitting ability causes them to memorize and reproduce backdoor triggers during generation, which are subsequently inherited by downstream models, resulting in severe security risks. This threat is particularly concerning under clean-label attack scenarios, as it remains effective while having negligible impact on the utility of the synthetic data. Furthermore, we discover an Early-Stage Trigger Manifestation (ESTM) phenomenon: backdoor trigger patterns tend to surface more explicitly in the early, high-noise stages of the diffusion model's reverse generation process before being subtly integrated into the final samples. Overall, this work reveals a previously underexplored threat in generative data pipelines and provides initial insights toward mitigating backdoor risks in synthetic data generation.

AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

19 December 2025 at 13:00

arXiv:2512.16455v1 Announce Type: cross Abstract: In this paper, we describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads. Putting the effort into reproducible deployments, it delivers consistent, transparent access to a federation of physically distributed e-Infrastructures. Through a comprehensive service catalogue, the platform is able to offer an integrated user experience covering the full Machine Learning lifecycle, including model development (with dedicated interactive development environments), training (with GPU resources, annotation tools, experiment tracking, and federated learning support) and deployment (covering a wide range of deployment options all along the Cloud Continuum). The platform also provides tools for traceability and reproducibility of AI models, integrates with different Artificial Intelligence model providers, datasets and storage resources, allowing users to interact with the broader Machine Learning ecosystem. Finally, it is easily customizable to lower the adoption barrier by external communities.

XTC, A Research Platform for Optimizing AI Workload Operators

cs.AI, q-bio.NC updates on arXiv.org

By: Pompougnac Hugo · Guillon Christophe · Noiry Sylvain · Dutilleul Alban · Iooss Guillaume · Rastello Fabrice

19 December 2025 at 13:00

arXiv:2512.16512v1 Announce Type: cross Abstract: Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.

Multi-Modality Collaborative Learning for Sentiment Analysis

cs.AI, q-bio.NC updates on arXiv.org

By: Shanmin Wang · Chengguang Liu · Qingshan Liu

19 December 2025 at 13:00

arXiv:2501.12424v2 Announce Type: replace-cross Abstract: Multimodal sentiment analysis (MSA) identifies individuals' sentiment states in videos by integrating visual, audio, and text modalities. Despite progress in existing methods, the inherent modality heterogeneity limits the effective capture of interactive sentiment features across modalities. In this paper, by introducing a Multi-Modality Collaborative Learning (MMCL) framework, we facilitate cross-modal interactions and capture enhanced and complementary features from modality-common and modality-specific representations, respectively. Specifically, we design a parameter-free decoupling module and separate uni-modality into modality-common and modality-specific components through semantics assessment of cross-modal elements. For modality-specific representations, inspired by the act-reward mechanism in reinforcement learning, we design policy models to adaptively mine complementary sentiment features under the guidance of a joint reward. For modality-common representations, intra-modal attention is employed to highlight crucial components, playing enhanced roles among modalities. Experimental results, including superiority evaluations on four databases, effectiveness verification of each module, and assessment of complementary features, demonstrate that MMCL successfully learns collaborative features across modalities and significantly improves performance. The code can be available at https://github.com/smwanghhh/MMCL.

Towards Practical Alzheimer's Disease Diagnosis: A Lightweight and Interpretable Spiking Neural Model

cs.AI, q-bio.NC updates on arXiv.org

By: Changwei Wu · Yifei Chen · Yuxin Du · Jinying Zong · Jie Dong · Mingxuan Liu · Feiwei Qin · Yong Peng · Jin Fan · Changmiao Wang

19 December 2025 at 13:00

arXiv:2506.09695v3 Announce Type: replace-cross Abstract: Early diagnosis of Alzheimer's Disease (AD), particularly at the mild cognitive impairment stage, is essential for timely intervention. However, this process faces significant barriers, including reliance on subjective assessments and the high cost of advanced imaging techniques. While deep learning offers automated solutions to improve diagnostic accuracy, its widespread adoption remains constrained due to high energy requirements and computational demands, particularly in resource-limited settings. Spiking neural networks (SNNs) provide a promising alternative, as their brain-inspired design is well-suited to model the sparse and event-driven patterns characteristic of neural degeneration in AD. These networks offer the potential for developing interpretable, energy-efficient diagnostic tools. Despite their advantages, existing SNNs often suffer from limited expressiveness and challenges in stable training, which reduce their effectiveness in handling complex medical tasks. To address these shortcomings, we introduce FasterSNN, a hybrid neural architecture that combines biologically inspired Leaky Integrate-and-Fire (LIF) neurons with region-adaptive convolution and multi-scale spiking attention mechanisms. This approach facilitates efficient, sparse processing of 3D MRI data while maintaining high diagnostic accuracy. Experimental results on benchmark datasets reveal that FasterSNN delivers competitive performance with significantly enhanced efficiency and training stability, highlighting its potential for practical application in AD screening. Our source code is available at https://github.com/wuchangw/FasterSNN.

Constitutional Law and AI Governance: Constraints on Model Licensing and Research Classification

cs.AI, q-bio.NC updates on arXiv.org

By: Alex Mark · Aaron Scher

19 December 2025 at 13:00

arXiv:2509.05361v2 Announce Type: replace-cross Abstract: Transformative AI systems may pose unprecedented catastrophic risks, but the U.S. Constitution places significant constraints on the government's ability to govern this technology. This paper examines how the First Amendment, administrative law, and the Fourteenth Amendment shape the legal vulnerability of two regulatory proposals: model licensing and AI research classification. While the First Amendment may provide some degree of protection for model algorithms or outputs, this protection does not foreclose regulation. Policymakers must also consider administrative legal requirements, due to both agency review and authority. Finally, while substantive due process and equal protection pose minimal obstacles, procedural due process requires the government to clearly define when developers vest a legal interest in their models. Given this analysis, effective AI governance requires careful implementation to avoid these legal challenges.

Voice-Interactive Surgical Agent for Multimodal Patient Data Control

cs.AI, q-bio.NC updates on arXiv.org

By: Hyeryun Park · Byung Mo Gu · Jun Hee Lee · Byeong Hyeon Choi · Sekeun Kim · Hyun Koo Kim · Kyungsang Kim

19 December 2025 at 13:00

arXiv:2511.07392v3 Announce Type: replace-cross Abstract: In robotic surgery, surgeons fully engage their hands and visual attention in procedures, making it difficult to access and manipulate multimodal patient data without interrupting the workflow. To overcome this problem, we propose a Voice-Interactive Surgical Agent (VISA) built on a hierarchical multi-agent framework consisting of an orchestration agent and three task-specific agents driven by Large Language Models (LLMs). These LLM-based agents autonomously plan, refine, validate, and reason to interpret voice commands and execute tasks such as retrieving clinical information, manipulating CT scans, or navigating 3D anatomical models within surgical video. We construct a dataset of 240 user commands organized into hierarchical categories and introduce the Multi-level Orchestration Evaluation Metric (MOEM) that evaluates the performance and robustness at both the command and category levels. Experimental results demonstrate that VISA achieves high stage-level accuracy and workflow-level success rates, while also enhancing its robustness by correcting transcription errors, resolving linguistic ambiguity, and interpreting diverse free-form expressions. These findings highlight the strong potential of VISA to support robotic surgery and its scalability for integrating new functions and agents.

First, do NOHARM: towards clinically safe large language models

cs.AI, q-bio.NC updates on arXiv.org

By: David Wu · Fateme Nateghi Haredasht · Saloni Kumar Maharaj · Priyank Jain · Jessica Tran · Matthew Gwiazdon · Arjun Rustagi · Jenelle Jindal · Jacob M. Koshy · Vinay Kadiyala · Anup Agarwal · Bassman Tappuni · Brianna French · Sirus Jesudasen · Christopher V. Cosgriff · Rebanta Chakraborty · Jillian Caldwell · Susan Ziolkowski · David J. Iberri · Robert Diep · Rahul S. Dalal · Kira L. Newman · Kristin Galetta · J. Carl Pallais · Nancy Wei · Kathleen M. Buchheit · David I. Hong · Ernest Y. Lee · Allen Shih · Vartan Pahalyants · Tamara B. Kaplan · Vishnu Ravi · Sarita Khemani · April S. Liang · Daniel Shirvani · Advait Patil · Nicholas Marshall · Kanav Chopra · Joel Koh · Adi Badhwar · Liam G. McCoy · David J. H. Wu · Yingjie Weng · Sumant Ranji · Kevin Schulman · Nigam H. Shah · Jason Hom · Arnold Milstein · Adam Rodman · Jonathan H. Chen · Ethan Goh

19 December 2025 at 13:00

arXiv:2512.01241v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

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.

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

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

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.

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