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Received β€” 12 January 2026 ⏭ Favourites

The Evaluation Gap in Medicine, AI and LLMs: Navigating Elusive Ground Truth & Uncertainty via a Probabilistic Paradigm

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Aparna Elangovan Β· Lei Xu Β· Mahsa Elyasi Β· Ismail Akdulum Β· Mehmet Aksakal Β· Enes Gurun Β· Brian Hur Β· Saab Mansour Β· Ravid Shwartz Ziv Β· Karin Verspoor Β· Dan Roth
12 January 2026 at 13:00
arXiv:2601.05500v1 Announce Type: new Abstract: Benchmarking the relative capabilities of AI systems, including Large Language Models (LLMs) and Vision Models, typically ignores the impact of uncertainty in the underlying ground truth answers from experts. This ambiguity is particularly consequential in medicine where uncertainty is pervasive. In this paper, we introduce a probabilistic paradigm to theoretically explain how high certainty in ground truth answers is almost always necessary for even an expert to achieve high scores, whereas in datasets with high variation in ground truth answers there may be little difference between a random labeller and an expert. Therefore, ignoring uncertainty in ground truth evaluation data can result in the misleading conclusion that a non-expert has similar performance to that of an expert. Using the probabilistic paradigm, we thus bring forth the concepts of expected accuracy and expected F1 to estimate the score an expert human or system can achieve given ground truth answer variability. Our work leads to the recommendation that when establishing the capability of a system, results should be stratified by probability of the ground truth answer, typically measured by the agreement rate of ground truth experts. Stratification becomes critical when the overall performance drops below a threshold of 80%. Under stratified evaluation, performance comparison becomes more reliable in high certainty bins, mitigating the effect of the key confounding factor -- uncertainty.

A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Sahaya Jestus Lazer Β· Kshitiz Aryal Β· Maanak Gupta Β· Elisa Bertino
12 January 2026 at 13:00
arXiv:2601.05293v1 Announce Type: cross Abstract: Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating memory, tool use, and iterative decision cycles, these systems enable continuous, autonomous workflows in real-world environments. This survey examines the implications of agentic AI for cybersecurity. On the defensive side, agentic capabilities enable continuous monitoring, autonomous incident response, adaptive threat hunting, and fraud detection at scale. Conversely, the same properties amplify adversarial power by accelerating reconnaissance, exploitation, coordination, and social-engineering attacks. These dual-use dynamics expose fundamental gaps in existing governance, assurance, and accountability mechanisms, which were largely designed for non-autonomous and short-lived AI systems. To address these challenges, we survey emerging threat models, security frameworks, and evaluation pipelines tailored to agentic systems, and analyze systemic risks including agent collusion, cascading failures, oversight evasion, and memory poisoning. Finally, we present three representative use-case implementations that illustrate how agentic AI behaves in practical cybersecurity workflows, and how design choices shape reliability, safety, and operational effectiveness.

Streamlining evidence based clinical recommendations with large language models

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Dubai Li Β· Nan Jiang Β· Kangping Huang Β· Ruiqi Tu Β· Shuyu Ouyang Β· Huayu Yu Β· Lin Qiao Β· Chen Yu Β· Tianshu Zhou Β· Danyang Tong Β· Qian Wang Β· Mengtao Li Β· Xiaofeng Zeng Β· Yu Tian Β· Xinping Tian Β· Jingsong Li
12 January 2026 at 13:00
arXiv:2505.10282v2 Announce Type: replace-cross Abstract: Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Dongchen Li Β· Jitao Liang Β· Wei Li Β· Xiaoyu Wang Β· Longbing Cao Β· Kun Yu
12 January 2026 at 13:00
arXiv:2507.22533v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for improving clinical decision support and reducing physician burnout by synthesizing complex, longitudinal cancer Electronic Health Records (EHRs). However, their implementation in this critical field faces three primary challenges: the inability to effectively process the extensive length and fragmented nature of patient records for accurate temporal analysis; a heightened risk of clinical hallucination, as conventional grounding techniques such as Retrieval-Augmented Generation (RAG) do not adequately incorporate process-oriented clinical guidelines; and unreliable evaluation metrics that hinder the validation of AI systems in oncology. To address these issues, we propose CliCARE, a framework for Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records. The framework operates by transforming unstructured, longitudinal EHRs into patient-specific Temporal Knowledge Graphs (TKGs) to capture long-range dependencies, and then grounding the decision support process by aligning these real-world patient trajectories with a normative guideline knowledge graph. This approach provides oncologists with evidence-grounded decision support by generating a high-fidelity clinical summary and an actionable recommendation. We validated our framework using large-scale, longitudinal data from a private Chinese cancer dataset and the public English MIMIC-IV dataset. In these settings, CliCARE significantly outperforms baselines, including leading long-context LLMs and Knowledge Graph-enhanced RAG methods. The clinical validity of our results is supported by a robust evaluation protocol, which demonstrates a high correlation with assessments made by oncologists.

Benchmarking LLM-based Agents for Single-cell Omics Analysis

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yang Liu Β· Lu Zhou Β· Xiawei Du Β· Ruikun He Β· Rongbo Shen Β· Yixue Li
12 January 2026 at 13:00
arXiv:2508.13201v2 Announce Type: replace-cross Abstract: The surge in multimodal single-cell omics data exposes limitations in traditional, manually defined analysis workflows. AI agents offer a paradigm shift, enabling adaptive planning, executable code generation, traceable decisions, and real-time knowledge fusion. However, the lack of a comprehensive benchmark critically hinders progress. We introduce a novel benchmarking evaluation system to rigorously assess agent capabilities in single-cell omics analysis. This system comprises: a unified platform compatible with diverse agent frameworks and LLMs; multidimensional metrics assessing cognitive program synthesis, collaboration, execution efficiency, bioinformatics knowledge integration, and task completion quality; and 50 diverse real-world single-cell omics analysis tasks spanning multi-omics, species, and sequencing technologies. Our evaluation reveals that Grok-3-beta achieves state-of-the-art performance among tested agent frameworks. Multi-agent frameworks significantly enhance collaboration and execution efficiency over single-agent approaches through specialized role division. Attribution analyses of agent capabilities identify that high-quality code generation is crucial for task success, and self-reflection has the most significant overall impact, followed by retrieval-augmented generation (RAG) and planning. This work highlights persistent challenges in code generation, long-context handling, and context-aware knowledge retrieval, providing a critical empirical foundation and best practices for developing robust AI agents in computational biology.

Personalizing Treatment for Pancreatic Ductal Adenocarcinoma: The Emerging Role of Minimal Residual Disease in Perioperative Decision-Making

βœ‡MRD
By: Charalampos Theocharopoulos Β· Nikolaos Machairas Β· Ioannis A Ziogas Β· Benedetto Mungo Β· Marco Del Chiaro Β· Georgios K Glatzounis Β· Richard Schulick Β· Georgios C Sotiropoulos
10 January 2026 at 19:00

Cancers (Basel). 2025 Dec 27;18(1):94. doi: 10.3390/cancers18010094.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with poor long-term survival despite advances in surgical techniques, systemic therapies, and perioperative management. High rates of systemic recurrence following curative-intent resection suggest that many patients harbor minimal residual disease (MRD), microscopic tumor burden that persists postoperatively and remains undetectable by conventional diagnostic tools. Recent advances in liquid biopsy technologies, particularly circulating tumor DNA (ctDNA) analysis, alongside detailed characterization of the PDAC mutational landscape, offer a promising non-invasive approach for MRD detection. Emerging evidence indicates that MRD status can serve as a sensitive prognostic biomarker, identify patients at high risk of relapse, and guide personalized perioperative therapy, including optimization of adjuvant treatment. This review summarizes current knowledge on the biology and detection of MRD in PDAC, its implications for perioperative risk stratification and treatment decision-making, and discusses future directions for integrating MRD assessment into clinical practice to enable more precise, individualized patient management.

PMID:41514607 | PMC:PMC12784771 | DOI:10.3390/cancers18010094

Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Katya H BΓ©nard Β· Vanessa G P Souza Β· Greg L Stewart Β· Katey S S Enfield Β· Wan L Lam
10 January 2026 at 19:00

Int J Mol Sci. 2026 Jan 4;27(1):521. doi: 10.3390/ijms27010521.

ABSTRACT

Tobacco smoking accounts for nearly 90% of lung cancer deaths worldwide, yet the mechanisms underlying persistent cancer risk in former smokers are not fully understood. Epidemiological evidence shows that more than 40% of lung cancers develop over 15 years after cessation, demonstrating that while some smoking-induced molecular alterations resolve rapidly, others remain as long-lasting scars that promote carcinogenesis. This review synthesizes longitudinal and cross-sectional genomic, epigenomic, and transcriptomic studies of airway and lung tissues to distinguish persistent from nonpersistent smoking-induced molecular alterations. Persistent alterations include somatic mutations in TP53 and KRAS, DNA methylation at tumor suppressor loci, dysregulated noncoding RNAs, chromosomal instability, and epigenetic age acceleration. Nonpersistent changes, such as acute inflammatory responses and detoxification pathways, generally normalize within months to several years following cessation. Multi-omics profiling reveals coordinated patterns of dysregulation consistent with field cancerization in former smokers. In addition, the integration of multi-omics data with artificial intelligence may enable composite molecular signatures for stratifying high-risk former smokers, link molecular persistence to clinical outcomes, and inform chemoprevention strategies. Collectively, these observations clarify which molecular alterations sustain long-term cancer risk despite smoking cessation and highlight opportunities for precision prevention and earlier detection in high-risk populations.

PMID:41516393 | PMC:PMC12786486 | DOI:10.3390/ijms27010521

Received β€” 10 January 2026 ⏭ Favourites

BCMA-directed mRNA CAR-T cell therapy for myasthenia gravis: exploratory biomarker analysis of a placebo-controlled phase 2b trial

βœ‡Nature Medicine
By: Renee R. Fedak Β· Rachel N. Ruggerie Β· Yufei Shan Β· Elizabeth J. Curvino Β· Juliana F. de Sousa Β· Shaji Daniel Β· Minhtran Ngo-Casi Β· Hafsa Kamboh Β· Tuan Vu Β· Hacer Durmuş Β· Tahseen Mozaffar Β· James F. Howard Jr. Β· Emily P. English Β· Albina Benson Β· Matthew T. Duvernay Β· Michael S. Singer Β· Murat V. Kalayoglu Β· Carsten Brunn Β· Aaron Bodansky Β· Mark S. Anderson Β· Joseph L. DeRisi Β· Samantha T. Garcia Β· David J. L. Yu Β· Kelsey C. Zorn Β· Metin Kurtoglu Β· MiloΕ‘ D. MiljkoviΔ‡ Β· C. Andrew Stewart Β· Christopher M. Jewell Β· Charito Buensuceso Β· Lisa H. Tostanoski Β· Yi Zhang Β· Heng Xu Β· Chad A. Pope Β· Dina Schneider Β· Adam English Β· Atanas Filev Β· Jennifer Biaksangi Β· George Small Β· Michael Rivner Β· Michael A. Badruddoja Β· Bennett Myers Β· Sheetal Shroff Β· Adam Slanksy Β· Kelly Holley Β· Gregory Sahagian Β· Tara Quesnell Β· Marc H. Feinberg Β· Gabrielle DeMaria Β· Mithila Vullaganti Β· Carolina Barnett-Tapia Β· Meg Mendoza Β· Mona Irannejad Β· Zaeem Siddiqi Β· Ali A. Habib Β· Thomas Ragole Β· Mamatha Pasnoor Β· Rebecca E. Traub Β· Manisha Chopra Β· Natalie S. Grover Β· Yara A. Park Β· Matthew Ka
9 January 2026 at 08:00

Nature Medicine, Published online: 09 January 2026; doi:10.1038/s41591-025-04170-z

Analysis of a placebo-controlled trial of a BCMA-targeting CAR-T cell therapy in patients with myasthenia gravis shows that CAR-T cell infusion selectively remodels the systemic immune environment, with elimination of BCMA-high plasma cells and activated plasmacytoid dendritic cells and changes in the autoreactive B cell repertoire.

BCMA-directed mRNA CAR T cell therapy for myasthenia gravis: a randomized, double-blind, placebo-controlled phase 2b trial

βœ‡Nature Medicine
By: Tuan Vu Β· Hacer Durmus Β· Michael Rivner Β· Sheetal Shroff Β· Thomas Ragole Β· Bennett Myers Β· Mamatha Pasnoor Β· George Small Β· Chafic Karam Β· Mithila Vullaganti Β· Amanda Peltier Β· Gregory Sahagian Β· Marc H. Feinberg Β· Adam Slanksy Β· Carolina Barnett-Tapia Β· Zaeem Siddiqi Β· Kelly Gwathmey Β· Michael A. Badruddoja Β· Hafsa Kamboh Β· Rachel N. Ruggerie Β· Renee R. Fedak Β· C. Andrew Stewart Β· Metin Kurtoglu Β· Murat Kalayoglu Β· Michael Singer Β· Christopher M. Jewell Β· Milos D. Miljkovic Β· Mazen Dimachkie Β· Tahseen Mozaffar Β· James F. Howard Jr
9 January 2026 at 08:00

Nature Medicine, Published online: 09 January 2026; doi:10.1038/s41591-025-04171-y

In a randomized, double-blind, placebo-controlled trial comparing autologous mRNA-engineered BCMA-targeting CAR T cell therapy versus placebo in patients with generalized myasthenia gravis, a significantly higher percentage of patients exhibited a reduction in disease activity in the treatment arm than in the placebo arm.
Received β€” 9 January 2026 ⏭ Favourites

Formal Analysis of AGI Decision-Theoretic Models and the Confrontation Question

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Denis Saklakov
9 January 2026 at 13:00
arXiv:2601.04234v1 Announce Type: new Abstract: Artificial General Intelligence (AGI) may face a confrontation question: under what conditions would a rationally self-interested AGI choose to seize power or eliminate human control (a confrontation) rather than remain cooperative? We formalize this in a Markov decision process with a stochastic human-initiated shutdown event. Building on results on convergent instrumental incentives, we show that for almost all reward functions a misaligned agent has an incentive to avoid shutdown. We then derive closed-form thresholds for when confronting humans yields higher expected utility than compliant behavior, as a function of the discount factor $\gamma$, shutdown probability $p$, and confrontation cost $C$. For example, a far-sighted agent ($\gamma=0.99$) facing $p=0.01$ can have a strong takeover incentive unless $C$ is sufficiently large. We contrast this with aligned objectives that impose large negative utility for harming humans, which makes confrontation suboptimal. In a strategic 2-player model (human policymaker vs AGI), we prove that if the AGI's confrontation incentive satisfies $\Delta \ge 0$, no stable cooperative equilibrium exists: anticipating this, a rational human will shut down or preempt the system, leading to conflict. If $\Delta

Systems Explaining Systems: A Framework for Intelligence and Consciousness

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Sean Niklas Semmler
9 January 2026 at 13:00
arXiv:2601.04269v1 Announce Type: new Abstract: This paper proposes a conceptual framework in which intelligence and consciousness emerge from relational structure rather than from prediction or domain-specific mechanisms. Intelligence is defined as the capacity to form and integrate causal connections between signals, actions, and internal states. Through context enrichment, systems interpret incoming information using learned relational structure that provides essential context in an efficient representation that the raw input itself does not contain, enabling efficient processing under metabolic constraints. Building on this foundation, we introduce the systems-explaining-systems principle, where consciousness emerges when recursive architectures allow higher-order systems to learn and interpret the relational patterns of lower-order systems across time. These interpretations are integrated into a dynamically stabilized meta-state and fed back through context enrichment, transforming internal models from representations of the external world into models of the system's own cognitive processes. The framework reframes predictive processing as an emergent consequence of contextual interpretation rather than explicit forecasting and suggests that recursive multi-system architectures may be necessary for more human-like artificial intelligence.

An ASP-based Solution to the Medical Appointment Scheduling Problem

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Alina Vozna (University of Pisa Β· University of L'Aquila) Β· Andrea Monaldini (University of Pisa Β· University of L'Aquila) Β· Stefania Costantini (University of L'Aquila) Β· Valentina Pitoni (University of l'Aquila) Β· Dawid Pado (University of l'Aquila)
9 January 2026 at 13:00
arXiv:2601.04274v1 Announce Type: new Abstract: This paper presents an Answer Set Programming (ASP)-based framework for medical appointment scheduling, aimed at improving efficiency, reducing administrative overhead, and enhancing patient-centered care. The framework personalizes scheduling for vulnerable populations by integrating Blueprint Personas. It ensures real-time availability updates, conflict-free assignments, and seamless interoperability with existing healthcare platforms by centralizing planning operations within an ASP logic model.

Sci-Reasoning: A Dataset Decoding AI Innovation Patterns

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Jiachen Liu Β· Maestro Harmon Β· Zechen Zhang
9 January 2026 at 13:00
arXiv:2601.04577v1 Announce Type: new Abstract: While AI innovation accelerates rapidly, the intellectual process behind breakthroughs -- how researchers identify gaps, synthesize prior work, and generate insights -- remains poorly understood. The lack of structured data on scientific reasoning hinders systematic analysis and development of AI research agents. We introduce Sci-Reasoning, the first dataset capturing the intellectual synthesis behind high-quality AI research. Using community-validated quality signals and an LLM-accelerated, human-verified pipeline, we trace Oral and Spotlight papers across NeurIPS, ICML, and ICLR (2023-2025) to its key predecessors, articulating specific reasoning links in a structured format. Our analysis identifies 15 distinct thinking patterns, with three dominant strategies accounting for 52.7%: Gap-Driven Reframing (24.2%), Cross-Domain Synthesis (18.0%), and Representation Shift (10.5%). The most powerful innovation recipes combine multiple patterns: Gap-Driven Reframing + Representation Shift, Cross-Domain Synthesis + Representation Shift, and Gap-Driven Reframing + Cross-Domain Synthesis. This dataset enables quantitative studies of scientific progress and provides structured reasoning trajectories for training the next generation AI research agents.

Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Saad Alqithami
9 January 2026 at 13:00
arXiv:2601.04583v1 Announce Type: new Abstract: Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step workflows, while public blockchains have evolved into a programmable substrate for value transfer, access control, and verifiable state transitions. Their convergence introduces a high-stakes systems challenge: designing standard, interoperable, and secure interfaces that allow agents to observe on-chain state, formulate transaction intents, and authorize execution without exposing users, protocols, or organizations to unacceptable security, governance, or economic risks. This survey systematizes the emerging landscape of agent-blockchain interoperability through a systematic literature review, identifying 317 relevant works from an initial pool of over 3000 records. We contribute a five-part taxonomy of integration patterns spanning read-only analytics, simulation and intent generation, delegated execution, autonomous signing, and multi-agent workflows; a threat model tailored to agent-driven transaction pipelines that captures risks ranging from prompt injection and policy misuse to key compromise, adversarial execution dynamics, and multi-agent collusion; and a comparative capability matrix analyzing more than 20 representative systems across 13 dimensions, including custody models, permissioning, policy enforcement, observability, and recovery. Building on the gaps revealed by this analysis, we outline a research roadmap centered on two interface abstractions: a Transaction Intent Schema for portable and unambiguous goal specification, and a Policy Decision Record for auditable, verifiable policy enforcement across execution environments. We conclude by proposing a reproducible evaluation suite and benchmarks for assessing the safety, reliability, and economic robustness of agent-mediated on-chain execution.

ResMAS: Resilience Optimization in LLM-based Multi-agent Systems

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Zhilun Zhou Β· Zihan Liu Β· Jiahe Liu Β· Qingyu Shao Β· Yihan Wang Β· Kun Shao Β· Depeng Jin Β· Fengli Xu
9 January 2026 at 13:00
arXiv:2601.04694v1 Announce Type: new Abstract: Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive performance in many areas. However, MAS are typically distributed across different devices or environments, making them vulnerable to perturbations such as agent failures. While existing works have studied the adversarial attacks and corresponding defense strategies, they mainly focus on reactively detecting and mitigating attacks after they occur rather than proactively designing inherently resilient systems. In this work, we study the resilience of LLM-based MAS under perturbations and find that both the communication topology and prompt design significantly influence system resilience. Motivated by these findings, we propose ResMAS: a two-stage framework for enhancing MAS resilience. First, we train a reward model to predict the MAS's resilience, based on which we train a topology generator to automatically design resilient topology for specific tasks through reinforcement learning. Second, we introduce a topology-aware prompt optimization method that refines each agent's prompt based on its connections and interactions with other agents. Extensive experiments across a range of tasks show that our approach substantially improves MAS resilience under various constraints. Moreover, our framework demonstrates strong generalization ability to new tasks and models, highlighting its potential for building resilient MASs.

Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yiqun Chen Β· Lingyong Yan Β· Zixuan Yang Β· Erhan Zhang Β· Jiashu Zhao Β· Shuaiqiang Wang Β· Dawei Yin Β· Jiaxin Mao
9 January 2026 at 13:00
arXiv:2601.04703v1 Announce Type: new Abstract: Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use. However, prevailing systems rely on monolithic agents that suffer from structural bottlenecks, including unconstrained reasoning outputs that inflate trajectories, sparse outcome-level rewards that complicate credit assignment, and stochastic search noise that destabilizes learning. To address these challenges, we propose \textbf{M-ASK} (Multi-Agent Search and Knowledge), a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context. This decomposition allows each agent to focus on a well-defined subtask and reduces interference between search and context construction. Furthermore, to enable stable coordination, M-ASK employs turn-level rewards to provide granular supervision for both search decisions and knowledge updates. Experiments on multi-hop QA benchmarks demonstrate that M-ASK outperforms strong baselines, achieving not only superior answer accuracy but also significantly more stable training dynamics.\footnote{The source code for M-ASK is available at https://github.com/chenyiqun/M-ASK.}

Balancing Usability and Compliance in AI Smart Devices: A Privacy-by-Design Audit of Google Home, Alexa, and Siri

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Trevor De Clark Β· Yulia Bobkova Β· Ajay Kumar Shrestha
9 January 2026 at 13:00
arXiv:2601.04403v1 Announce Type: cross Abstract: This paper investigates the privacy and usability of AI-enabled smart devices commonly used by youth, focusing on Google Home Mini, Amazon Alexa, and Apple Siri. While these devices provide convenience and efficiency, they also raise privacy and transparency concerns due to their always-listening design and complex data management processes. The study proposes and applies a combined framework of Heuristic Evaluation, Personal Information Protection and Electronic Documents Act (PIPEDA) Compliance Assessment, and Youth-Centered Usability Testing to assess whether these devices align with Privacy-by-Design principles and support meaningful user control. Results show that Google Home achieved the highest usability score, while Siri scored highest in regulatory compliance, indicating a trade-off between user convenience and privacy protection. Alexa demonstrated clearer task navigation but weaker transparency in data retention. Findings suggest that although youth may feel capable of managing their data, their privacy self-efficacy remains limited by technical design, complex settings, and unclear data policies. The paper concludes that enhancing transparency, embedding privacy guidance during onboarding, and improving policy alignment are critical steps toward ensuring that smart devices are both usable and compliant with privacy standards that protect young users.

Surface-based Molecular Design with Multi-modal Flow Matching

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Fang Wu Β· Zhengyuan Zhou Β· Shuting Jin Β· Xiangxiang Zeng Β· Jure Leskovec Β· Jinbo Xu
9 January 2026 at 13:00
arXiv:2601.04506v1 Announce Type: cross Abstract: Therapeutic peptides show promise in targeting previously undruggable binding sites, with recent advancements in deep generative models enabling full-atom peptide co-design for specific protein receptors. However, the critical role of molecular surfaces in protein-protein interactions (PPIs) has been underexplored. To bridge this gap, we propose an omni-design peptides generation paradigm, called SurfFlow, a novel surface-based generative algorithm that enables comprehensive co-design of sequence, structure, and surface for peptides. SurfFlow employs a multi-modality conditional flow matching (CFM) architecture to learn distributions of surface geometries and biochemical properties, enhancing peptide binding accuracy. Evaluated on the comprehensive PepMerge benchmark, SurfFlow consistently outperforms full-atom baselines across all metrics. These results highlight the advantages of considering molecular surfaces in de novo peptide discovery and demonstrate the potential of integrating multiple protein modalities for more effective therapeutic peptide discovery.

Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Jessica Ryan Β· Alexander I. Gumilang Β· Robert Wiliam Β· Derwin Suhartono
9 January 2026 at 13:00
arXiv:2601.04531v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medical Question Answering (QA), yet they remain prone to hallucinations and ungrounded reasoning, limiting their reliability in high-stakes clinical scenarios. While Retrieval-Augmented Generation (RAG) mitigates these issues by incorporating external knowledge, conventional single-shot retrieval often fails to resolve complex biomedical queries requiring multi-step inference. To address this, we propose Self-MedRAG, a self-reflective hybrid framework designed to mimic the iterative hypothesis-verification process of clinical reasoning. Self-MedRAG integrates a hybrid retrieval strategy, combining sparse (BM25) and dense (Contriever) retrievers via Reciprocal Rank Fusion (RRF) to maximize evidence coverage. It employs a generator to produce answers with supporting rationales, which are then assessed by a lightweight self-reflection module using Natural Language Inference (NLI) or LLM-based verification. If the rationale lacks sufficient evidentiary support, the system autonomously reformulates the query and iterates to refine the context. We evaluated Self-MedRAG on the MedQA and PubMedQA benchmarks. The results demonstrate that our hybrid retrieval approach significantly outperforms single-retriever baselines. Furthermore, the inclusion of the self-reflective loop yielded substantial gains, increasing accuracy on MedQA from 80.00% to 83.33% and on PubMedQA from 69.10% to 79.82%. These findings confirm that integrating hybrid retrieval with iterative, evidence-based self-reflection effectively reduces unsupported claims and enhances the clinical reliability of LLM-based systems.
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