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Received — 12 December 2025 ⏭ Favourites

Mind the Gap! Pathways Towards Unifying AI Safety and Ethics Research

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dani Roytburg · Beck Miller
12 December 2025 at 13:00
arXiv:2512.10058v1 Announce Type: new Abstract: While much research in artificial intelligence (AI) has focused on scaling capabilities, the accelerating pace of development makes countervailing work on producing harmless, "aligned" systems increasingly urgent. Yet research on alignment has diverged along two largely parallel tracks: safety--centered on scaled intelligence, deceptive or scheming behaviors, and existential risk--and ethics--focused on present harms, the reproduction of social bias, and flaws in production pipelines. Although both communities warn of insufficient investment in alignment, they disagree on what alignment means or ought to mean. As a result, their efforts have evolved in relative isolation, shaped by distinct methodologies, institutional homes, and disciplinary genealogies. We present a large-scale, quantitative study showing the structural split between AI safety and AI ethics. Using a bibliometric and co-authorship network analysis of 6,442 papers from twelve major ML and NLP conferences (2020-2025), we find that over 80% of collaborations occur within either the safety or ethics communities, and cross-field connectivity is highly concentrated: roughly 5% of papers account for more than 85% of bridging links. Removing a small number of these brokers sharply increases segregation, indicating that cross-disciplinary exchange depends on a handful of actors rather than broad, distributed collaboration. These results show that the safety-ethics divide is not only conceptual but institutional, with implications for research agendas, policy, and venues. We argue that integrating technical safety work with normative ethics--via shared benchmarks, cross-institutional venues, and mixed-method methodologies--is essential for building AI systems that are both robust and just.

IoTEdu: Access Control, Detection, and Automatic Incident Response in Academic IoT Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Joner Assolin · Diego Kreutz · Leandro Bertholdo
12 December 2025 at 13:00
arXiv:2512.09934v1 Announce Type: cross Abstract: The growing presence of IoT devices in academic environments has increased operational complexity and exposed security weaknesses, especially in academic institutions without unified policies for registration, monitoring, and incident response involving IoT. This work presents IoTEdu, an integrated platform that combines access control, incident detection, and automatic blocking of IoT devices. The solution was evaluated in a controlled environment with simulated attacks, achieving an average time of 28.6 seconds between detection and blocking. The results show a reduction in manual intervention, standardization of responses, and unification of the processes of registration, monitoring, and incident response.

MedXAI: A Retrieval-Augmented and Self-Verifying Framework for Knowledge-Guided Medical Image Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Midhat Urooj · Ayan Banerjee · Farhat Shaikh · Kuntal Thakur · Sandeep Gupta
12 December 2025 at 13:00
arXiv:2512.10098v1 Announce Type: cross Abstract: Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly un- der domain shifts and rare-class conditions. Deep learning mod- els often struggle with real-world distribution changes, exhibit bias against infrequent pathologies, and lack the transparency required for deployment in safety-critical clinical environments. We introduce MedXAI (An Explainable Framework for Med- ical Imaging Classification), a unified expert knowledge based framework that integrates deep vision models with clinician- derived expert knowledge to improve generalization, reduce rare- class bias, and provide human-understandable explanations by localizing the relevant diagnostic features rather than relying on technical post-hoc methods (e.g., Saliency Maps, LIME). We evaluate MedXAI across heterogeneous modalities on two challenging tasks: (i) Seizure Onset Zone localization from resting-state fMRI, and (ii) Diabetic Retinopathy grading. Ex periments on ten multicenter datasets show consistent gains, including a 3% improvement in cross-domain generalization and a 10% improvmnet in F1 score of rare class, substantially outperforming strong deep learning baselines. Ablations confirm that the symbolic components act as effective clinical priors and regularizers, improving robustness under distribution shift. MedXAI delivers clinically aligned explanations while achieving superior in-domain and cross-domain performance, particularly for rare diseases in multimodal medical AI.

Macrophage-targeted immunocytokine leverages myeloid, T, and NK cell synergy for cancer immunotherapy

✇Cell
By: Michelle von Locquenghien · Pascale Zwicky · Ken Xie · Diego Adhemar Jaitin · Fadi Sheban · Adam Yalin · Florian Uhlitz · Chamutal Gur · Reut Sharet Eshed · Eyal David · Kfir Mazuz · Caroline Jennings Marin · Ankita Sankar · Devin Mediratta · Roberto Avellino · Assaf Weiner · Ido Amit
19 November 2025 at 08:00
MiTEs are myeloid-targeted immunocytokine prodrugs that block TREM2+ tumor-associated macrophages while activating cytotoxic lymphocytes via TME-specific IL-2 activity, eliciting strong anti-tumor efficacy in preclinical models with minimal systemic toxicity.
Received — 11 December 2025 ⏭ Favourites

Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching

✇cs.AI, q-bio.NC updates on arXiv.org
By: Caroline N. Leach · Mitchell A. Klusty · Samuel E. Armstrong · Justine C. Pickarski · Kristen L. Hankins · Emily B. Collier · Maya Shah · Aaron D. Mullen · V. K. Cody Bumgardner
10 December 2025 at 13:00
arXiv:2512.08026v1 Announce Type: new Abstract: Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.

Multi-Agent Intelligence for Multidisciplinary Decision-Making in Gastrointestinal Oncology

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rongzhao Zhang · Junqiao Wang · Shuyun Yang · Mouxiao Bian · Chao Ding · Yuwei Bai · Chihao Zhang · Yuguang Shen · Lei Wang · Lei Zheng · Qiujuan Yan · Yun Zhong · Meiling Liu · Jiwei Yu · Zheng Wang · Jie Xu · Meng Luo
10 December 2025 at 13:00
arXiv:2512.08674v1 Announce Type: new Abstract: Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Multi state neurons Robert Worden
    arXiv:2512.08815v1 Announce Type: new Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to c
     
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Multi state neurons

✇cs.AI, q-bio.NC updates on arXiv.org
By: Robert Worden
10 December 2025 at 13:00
arXiv:2512.08815v1 Announce Type: new Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to change its state over intervals of seconds or minutes. Based on its state, a single neuron can selectively de-activate some of its synapses, sculpting a dynamic neural net from the static neural connections of the brain. Without this dynamic selection, the static neural networks in brains are too amorphous and dilute to do the computations of neural cognitive models. The use of multi-state neurons in animal brains is illustrated in hierarchical Bayesian object recognition. Multi-state neurons may support a design which is more efficient than two-state neurons, and scales better as object complexity increases. Brains could have evolved to use multi-state neurons. Multi-state neurons could be used in artificial neural networks, to use a kind of non-Hebbian learning which is faster and more focused and controllable than traditional neural net learning. This possibility has not yet been explored in computational models.

Multi-domain performance analysis with scores tailored to user preferences

✇cs.AI, q-bio.NC updates on arXiv.org
By: S\'ebastien Pi\'erard · Adrien Deli\`ege · Marc Van Droogenbroeck
10 December 2025 at 13:00
arXiv:2512.08715v1 Announce Type: cross Abstract: The performance of algorithms, methods, and models tends to depend heavily on the distribution of cases on which they are applied, this distribution being specific to the applicative domain. After performing an evaluation in several domains, it is highly informative to compute a (weighted) mean performance and, as shown in this paper, to scrutinize what happens during this averaging. To achieve this goal, we adopt a probabilistic framework and consider a performance as a probability measure (e.g., a normalized confusion matrix for a classification task). It appears that the corresponding weighted mean is known to be the summarization, and that only some remarkable scores assign to the summarized performance a value equal to a weighted arithmetic mean of the values assigned to the domain-specific performances. These scores include the family of ranking scores, a continuum parameterized by user preferences, and that the weights to consider in the arithmetic mean depend on the user preferences. Based on this, we rigorously define four domains, named easiest, most difficult, preponderant, and bottleneck domains, as functions of user preferences. After establishing the theory in a general setting, regardless of the task, we develop new visual tools for two-class classification.

AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Johann Wenckstern · Eeshaan Jain · Yexiang Cheng · Benedikt von Querfurth · Kiril Vasilev · Matteo Pariset · Phil F. Cheng · Petros Liakopoulos · Olivier Michielin · Andreas Wicki · Gabriele Gut · Charlotte Bunne
10 December 2025 at 13:00
arXiv:2501.06039v2 Announce Type: replace-cross Abstract: Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell typing and niche annotation, spatial biomarker discovery, and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response and stratify disease-free survival in an independent cohort, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.

Somatic evolution following cancer treatment in normal tissue

✇Nature - Issue - nature.com science feeds
By: Oriol Pich · Sophia Ward · Andrew Rowan · Cristina Naceur-Lombardelli · Oliver Shutkever · Carlos Martinez-Ruiz · Siân Harries · Sonya Hessey · Babu Naidu · James D. Brenton · John Le Quesne · Anne Thomas · Cathy Richards · Matthew G. Krebs · Samra Turajlic · Sanjay Jogai · Simone Zaccaria · David Moore · Crispin T. Hiley · Kai-Keen Shiu · John Bridgewater · Daniel Hochhauser · Martin Forster · Siow Ming Lee · Tanya Ahmad · Dionysis Papadatos-Pastos · Sam Janes · Peter Van Loo · Katey Enfield · Ariana Huebner · Sergio Quezada · Stephan Beck · Tariq Enver · David R. Pearce · Mary Falzon · Ron Sinclair · Zoe Rhodes · Teresa Marafioti · Miriam Mitchison · Mark Linch · Sebastian Brandner · Selvaraju Veeriah · Heather Shaw · Gerhardt Attard · Faye Gishen · Maise Al-Bakir · Nnenna Kanu · Francisco Gimeno-Valiente · Emilia L. Lim · James Reading · Benny Chain · Adrienne Flanagan · Emma Colliver · Mihaela Angelova · James Black · Olivia Lucas · William Hill · Wing Kin Liu · Alexander Frankell · Roberto Salgado · Kristiana Grigoriadis · Takahiro Karasaki · Abigail Bunkum · Sara
10 December 2025 at 08:00

Nature, Published online: 10 December 2025; doi:10.1038/s41586-025-09792-4

High-depth sequencing of non-cancerous tissue from patients with metastatic cancer reveals single-base mutational signatures of alcohol, smoking and cancer treatments, and reveals how exogenous factors, including cancer therapies, affect somatic cell evolution.

Foundation models in oncology win benchmarks but miss the clinic

✇Nature Cancer
By: Zhiyun Duan · Qihao Duan · Benjamin Wild · Roland Eils
10 December 2025 at 08:00

Nature Cancer, Published online: 10 December 2025; doi:10.1038/s43018-025-01071-5

Foundation models hold transformative promise for oncology, yet their clinical implementation remains limited, largely owing to their current model design as narrow specialists optimized for static tasks, whereas clinical oncology requires generalist systems capable of integrating multimodal data, capturing disease evolution over time and considering patient perspectives. Design along these requirements is essential to integrating foundation models as trusted partners in cancer care.

Exploring the association between DNA methylation and pancreatic cancer susceptibility through epigenome-wide Mendelian randomization and multi-omics data integration

✇(Multiomics OR Omics) AND (Pancreatic)
By: Pengxu Wang · Feng Rong · Fubao Liu
10 December 2025 at 19:00

Epigenetics. 2025 Dec;20(1):2599682. doi: 10.1080/15592294.2025.2599682. Epub 2025 Dec 9.

ABSTRACT

Investigating the role of DNA methylation in the development of pancreatic cancer (PC) may facilitate identification of potential targets for both diagnosis and treatment. We carried out a comprehensive epigenome-wide Mendelian randomization (EWMR) analysis to investigate the correlation of genetically predicted blood CpG sites with PC. Following this, we conducted various sensitivity analyses and repeated analyses using different selection criteria for instrumental variables and conditional Bayesian colocalization to guarantee the reliability of the results. External validation and a meta-analysis were then performed to further validate these results. Next, we conducted CpG site enrichment analysis, overlap with phenome-wide association studies (PheWAS) catalog analysis, overlap with epigenome-wide association studies (EWAS) Toolkit analysis, and drug target analysis to explore the enrichment, biological functions, and potential therapeutic targets associated with these sites. Finally, we used the SMR-IVW software to perform mediation analysis, aiming to uncover potential tumorigenesis pathways of PC at the transcriptional level from three distinct perspectives. Results showed 253 CpG sites passing sensitivity analysis were significantly associated with PC and 159 CpG sites were validated in at least one replication. After meta-analysis, 38 CpG sites were retained, and all 253 CpG sites were classified into three tiers. Among these, cg26373071 (CLPTM1L), cg14271713, cg11652496 (PSTPIP1), and cg20575191 (PSTPIP1) were placed in tier 1 with strong support. Finally, this study identified genetic susceptibility linked to 253 PC-related CpG sites. This study provides insights into the disease's origins and underscores potential targets for future research.

PMID:41368819 | PMC:PMC12694910 | DOI:10.1080/15592294.2025.2599682

Received — 10 December 2025 ⏭ Favourites

KidSpeak: A General Multi-purpose LLM for Kids' Speech Recognition and Screening

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rohan Sharma · Dancheng Liu · Jingchen Sun · Shijie Zhou · Jiayu Qin · Jinjun Xiong · Changyou Chen
9 December 2025 at 13:00
arXiv:2512.05994v1 Announce Type: cross Abstract: With the rapid advancement of conversational and diffusion-based AI, there is a growing adoption of AI in educational services, ranging from grading and assessment tools to personalized learning systems that provide targeted support for students. However, this adaptability has yet to fully extend to the domain of children's speech, where existing models often fail due to their reliance on datasets designed for clear, articulate adult speech. Children, particularly those in early developmental stages or with speech and language pathologies, present unique challenges that current AI models and datasets are ill-equipped to handle. To address this, we introduce KidSpeak, a multi-task speech-enhanced Foundation Model capable of both generative and discriminative tasks specifically tailored to children's speech patterns. Our framework employs a two-stage training process that incorporates phonetic knowledge into the speech encoder, achieving an average accuracy of 87% across four separate tasks. Furthermore, recognizing the limitations of scalable human annotation and existing speech alignment tools, we propose the Flexible and Automatic Speech Aligner (FASA) and leverage the method to construct high quality datasets for training and evaluation. This novel alignment tool significantly improves the quality of aligned children's speech from noisy data, enhancing data quality by 13.6x compared to human annotations, as demonstrated on the CHILDES dataset. To the best of our knowledge, KidSpeak and FASA represent the first comprehensive solution designed for speech and language therapy in children, offering both a multi-purpose speech LLM and a robust alignment tool.

Digital Health Technologies: Learnings and Perspectives From a Patient Engagement Stakeholder Expectations Matrix Study

✇Journal of Medical Internet Research
By: Leanne West · Derick Mitchell · Stuart D Faulkner · Birgit Bauer · Nicholas Brooke · Elizabeth Priest
10 December 2025 at 00:45
As digital health technologies become increasingly integrated into health care systems worldwide, there is growing recognition that their full potential can be realized only when development is rooted in patient engagement (PE). Despite its proven value in clinical research and health care delivery, PE remains insufficiently embedded in digital health design and implementation. This perspective paper explores the current state of PE in digital health through findings from the Stakeholder Expectations Matrix program developed by Patient Focused Medicines Development. Drawing from 37 in-depth interviews across 6 key stakeholder groups, complemented by insights gathered during a multisession cocreation track at the Patient Engagement Open Forum, this paper highlights differing perspectives on digital health, the barriers to meaningful engagement, and the fragmented nature of data governance and technology adoption. Findings point not only to significant gaps in shared understanding, infrastructure, and policy but also to clear opportunities for collaboration, including early recommendations for building a more inclusive and patient-centered digital health ecosystem, one that supports sustainable innovation, trust, and systemwide impact.

Transferring Clinical Knowledge into ECGs Representation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jose Geraldo Fernandes · Luiz Facury de Souza · Pedro Robles Dutenhefner · Gisele L. Pappa · Wagner Meira Jr
9 December 2025 at 13:00
arXiv:2512.07021v1 Announce Type: cross Abstract: Deep learning models have shown high accuracy in classifying electrocardiograms (ECGs), but their black box nature hinders clinical adoption due to a lack of trust and interpretability. To address this, we propose a novel three-stage training paradigm that transfers knowledge from multimodal clinical data (laboratory exams, vitals, biometrics) into a powerful, yet unimodal, ECG encoder. We employ a self-supervised, joint-embedding pre-training stage to create an ECG representation that is enriched with contextual clinical information, while only requiring the ECG signal at inference time. Furthermore, as an indirect way to explain the model's output we train it to also predict associated laboratory abnormalities directly from the ECG embedding. Evaluated on the MIMIC-IV-ECG dataset, our model outperforms a standard signal-only baseline in multi-label diagnosis classification and successfully bridges a substantial portion of the performance gap to a fully multimodal model that requires all data at inference. Our work demonstrates a practical and effective method for creating more accurate and trustworthy ECG classification models. By converting abstract predictions into physiologically grounded \emph{explanations}, our approach offers a promising path toward the safer integration of AI into clinical workflows.

A Field Guide to Deploying AI Agents in Clinical Practice

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jack Gallifant · Katherine C. Kellogg · Matt Butler · Amanda Centi · Shan Chen · Patrick F. Doyle · Sayon Dutta · Joyce Guo · Matthew J. Hadfield · Esther H. Kim · David E. Kozono · Hugo JWL Aerts · Adam B. Landman · Raymond H. Mak · Rebecca G. Mishuris · Tanna L. Nelson · Guergana K. Savova · Elad Sharon · Benjamin C. Silverman · Umit Topaloglu · Jeremy L. Warner · Danielle S. Bitterman
9 December 2025 at 13:00
arXiv:2509.26153v3 Announce Type: replace Abstract: Large language models (LLMs) integrated into agent-driven workflows hold immense promise for healthcare, yet a significant gap exists between their potential and practical implementation within clinical settings. To address this, we present a practitioner-oriented field manual for deploying generative agents that use electronic health record (EHR) data. This guide is informed by our experience deploying the "irAE-Agent", an automated system to detect immune-related adverse events from clinical notes at Mass General Brigham, and by structured interviews with 21 clinicians, engineers, and informatics leaders involved in the project. Our analysis reveals a critical misalignment in clinical AI development: less than 20% of our effort was dedicated to prompt engineering and model development, while over 80% was consumed by the sociotechnical work of implementation. We distill this effort into five "heavy lifts": data integration, model validation, ensuring economic value, managing system drift, and governance. By providing actionable solutions for each of these challenges, this field manual shifts the focus from algorithmic development to the essential infrastructure and implementation work required to bridge the "valley of death" and successfully translate generative AI from pilot projects into routine clinical care.

Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rachel Poonsiriwong · Chayapatr Archiwaranguprok · Constanze Albrecht · Peggy Yin · Nattavudh Powdthavee · Hal Hershfield · Monchai Lertsutthiwong · Kavin Winson · Pat Pataranutaporn
9 December 2025 at 13:00
arXiv:2512.05397v2 Announce Type: replace-cross Abstract: Major life transitions demand high-stakes decisions, yet people often struggle to imagine how their future selves will live with the consequences. To support this limited capacity for mental time travel, we introduce AI-enabled digital twins that have ``lived through'' simulated life scenarios. Rather than predicting optimal outcomes, these simulations extend prospective cognition by making alternative futures vivid enough to support deliberation without assuming which path is best. We evaluate this idea in a randomized controlled study (N=192) using multimodal synthesis - facial age progression, voice cloning, and large language model dialogue - to create personalized avatars representing participants 30 years forward. Young adults 18 to 28 years old described pending binary decisions and were assigned to guided imagination or one of four avatar conditions: single-option, balanced dual-option, or expanded three-option with a system-generated novel alternative. Results showed asymmetric effects: single-sided avatars increased shifts toward the presented option, while balanced presentation produced movement toward both. Introducing a system-generated third option increased adoption of this new alternative compared to control, suggesting that AI-generated future selves can expand choice by surfacing paths that might otherwise go unnoticed. Participants rated evaluative reasoning and eudaimonic meaning-making as more important than emotional or visual vividness. Perceived persuasiveness and baseline agency predicted decision change. These findings advance understanding of AI-mediated episodic prospection and raise questions about autonomy in AI-augmented decisions.
Received — 9 December 2025 ⏭ Favourites

The Seeds of Scheming: Weakness of Will in the Building Blocks of Agentic Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Robert Yang
8 December 2025 at 13:00
arXiv:2512.05449v1 Announce Type: new Abstract: Large language models display a peculiar form of inconsistency: they "know" the correct answer but fail to act on it. In human philosophy, this tension between global judgment and local impulse is called akrasia, or weakness of will. We propose akrasia as a foundational concept for analyzing inconsistency and goal drift in agentic AI systems. To operationalize it, we introduce a preliminary version of the Akrasia Benchmark, currently a structured set of prompting conditions (Baseline [B], Synonym [S], Temporal [T], and Temptation [X]) that measures when a model's local response contradicts its own prior commitments. The benchmark enables quantitative comparison of "self-control" across model families, decoding strategies, and temptation types. Beyond single-model evaluation, we outline how micro-level akrasia may compound into macro-level instability in multi-agent systems that may be interpreted as "scheming" or deliberate misalignment. By reframing inconsistency as weakness of will, this work connects agentic behavior to classical theories of agency and provides an empirical bridge between philosophy, psychology, and the emerging science of agentic AI.
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