(MedPage Today) -- At the Consumer Electronics Show in Las Vegas, FDA Commissioner Marty Makary, MD, MPH, announced that his agency issued two new guidance documents aimed at easing regulation of digital health products. Both guidance documents...

If you have ever taken a self-driving Uber through downtown LA, you might recognise the strange sense of uncertainty that settles in when there is no driver and no conversation, just a quiet car making assumptions about the world around it. The journey feels fine until the car misreads a shadow or slows abruptly for something harmless. In that moment you see the real issue with autonomy. It does not panic when it should, and that gap between confidence and judgement is where trust is either earned or lost. Much of today’s enterprise AI feels remarkably similar. It is competent without being confident, and efficient without being empathetic, which is why the deciding factor in every successful deployment is no longer computing power but trust.

The MLQ State of AI in Business 2025 [PDF] report puts a sharp number on this. 95% of early AI pilots fail to produce measurable ROI, not because the technology is weak but because it is mismatched to the problems organisations are trying to solve. The pattern repeats itself in industries. Leaders get uneasy when they can’t tell if the output is right, teams are unsure whether dashboards can be trusted, and customers quickly lose patience when an interaction feels automated rather than supported. Anyone who has been locked out of their bank account while the automated recovery system insists their answers are wrong knows how quickly confidence evaporates.

Klarna remains the most publicised example of large-scale automation in action. The company has now halved its workforce since 2022 and says internal AI systems are performing the work of 853 full-time roles, up from 700 earlier this year. Revenues have risen 108%, while average employee compensation has increased 60%, funded in part by those operational gains. Yet the picture is more complicated. Klarna still reported a 95 million dollar quarterly loss, and its CEO has warned that further staff reductions are likely. It shows that automation alone does not create stability. Without accountability and structure, the experience breaks down long before the AI does. As Jason Roos, CEO of CCaaS provider Cirrus, puts it, “Any transformation that unsettles confidence, inside or outside the business, carries a cost you cannot ignore. it can leave you worse off.”

We have already seen what happens when autonomy runs ahead of accountability. The UK’s Department for Work and Pensions used an algorithm that wrongly flagged around 200,000 housing-benefit claims as potentially fraudulent, even though the majority were legitimate. The problem wasn’t the technology. It was the absence of clear ownership over its decisions. When an automated system suspends the wrong account, rejects the wrong claim or creates unnecessary fear, the issue is never just “why did the model misfire?” It’s “who owns the outcome?” Without that answer, trust becomes fragile.

“The missing step is always readiness,” says Roos. “If the process, the data and the guardrails aren’t in place, autonomy doesn’t accelerate performance, it amplifies the weaknesses. Accountability has to come first. Start with the outcome, find where effort is being wasted, check your readiness and governance, and only then automate. Skip those steps and accountability disappears just as fast as the efficiency gains arrive.”

Part of the problem is an obsession with scale without the grounding that makes scale sustainable. Many organisations push toward autonomous agents that can act decisively, yet very few pause to consider what happens when those actions drift outside expected boundaries. The Edelman Trust Barometer [PDF] shows a steady decline in public trust in AI over the past five years, and a joint KPMG and University of Melbourne study found that workers prefer more human involvement in almost half the tasks examined. The findings reinforce a simple point. Trust rarely comes from pushing models harder. It comes from people taking the time to understand how decisions are made, and from governance that behaves less like a brake pedal and more like a steering wheel.

The same dynamics appear on the customer side. PwC’s trust research reveals a wide gulf between perception and reality. Most executives believe customers trust their organisation, while only a minority of customers agree. Other surveys show that transparency helps to close this gap, with large majorities of consumers wanting clear disclosure when AI is used in service experiences. Without that clarity, people do not feel reassured. They feel misled, and the relationship becomes strained. Companies that communicate openly about their AI use are not only protecting trust but also normalising the idea that technology and human support can co-exist.

Some of the confusion stems from the term “agentic AI” itself. Much of the market treats it as something unpredictable or self-directing, when in reality it is workflow automation with reasoning and recall. It is a structured way for systems to make modest decisions inside parameters designed by people. The deployments that scale safely all follow the same sequence. They start with the outcome they want to improve, then look at where unnecessary effort sits in the workflow, then assess whether their systems and teams are ready for autonomy, and only then choose the technology. Reversing that order does not speed anything up. It simply creates faster mistakes. As Roos says, AI should expand human judgement, not replace it.

All of this points toward a wider truth. Every wave of automation eventually becomes a social question rather than a purely technical one. Amazon built its dominance through operational consistency, but it also built a level of confidence that the parcel would arrive. When that confidence dips, customers move on. AI follows the same pattern. You can deploy sophisticated, self-correcting systems, but if the customer feels tricked or misled at any point, the trust breaks. Internally, the same pressures apply. The KPMG global study [PDF] highlights how quickly employees disengage when they do not understand how decisions are made or who is accountable for them. Without that clarity, adoption stalls.

As agentic systems take on more conversational roles, the emotional dimension becomes even more significant. Early reviews of autonomous chat interactions show that people now judge their experience not only by whether they were helped but also by whether the interaction felt attentive and respectful. A customer who feels dismissed rarely keeps the frustration to themselves. The emotional tone of AI is becoming a genuine operational factor, and systems that cannot meet that expectation risk becoming liabilities.

The difficult truth is that technology will continue to move faster than people’s instinctive comfort with it. Trust will always lag behind innovation. That is not an argument against progress. It is an argument for maturity. Every AI leader should be asking whether they would trust the system with their own data, whether they can explain its last decision in plain language, and who steps in when something goes wrong. If those answers are unclear, the organisation is not leading transformation. It is preparing an apology.

Roos puts it simply, “Agentic AI is not the concern. Unaccountable AI is.”

When trust goes, adoption goes, and the project that looked transformative becomes another entry in the 95% failure rate. Autonomy is not the enemy. Forgetting who is responsible is. The organisations that keep a human hand on the wheel will be the ones still in control when the self-driving hype eventually fades.

The post Autonomy without accountability: The real AI risk appeared first on AI News.

DNA doesn’t just sit still inside our cells — it folds, loops, and rearranges in ways that shape how genes behave. Researchers have now mapped this hidden architecture in unprecedented detail, showing how genome structure changes from cell to cell and over time. These insights reveal why many disease-linked mutations outside genes can still cause harm. The findings could speed up the discovery of genetic risks and inspire new ways to target diseases.

This Special Communication introduces a cross-disciplinary framework to integrate computer science principles with public health to address data inequities and ensure that digital and technological advances improve health outcomes for all.

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.

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.

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

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.

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.

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.

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.

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.

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.}

arXiv:2601.04218v1 Announce Type: cross Abstract: The artificial intelligence value chain is one of the main concepts underpinning the European legislation on the subject, especially the Artificial Intelligence Act. It is an economic concept that has become a legal one. i.e., a concept of legal governance, due to its continued use in policy documents and legal texts. This article (i) analyses its significance and function within the framework of the regulatory strategy established by recent EU programs (the Compass for Competitiveness, the Action Plan, Apply AI Strategy, and the Digital Omnibus on AI), (ii) identifies its limitations, and (iii) advances the theoretical construction of value chains that capture intangible dimensions that are not directly monetizable (such as language, culture, and, especially, ethical and legal values) but have a significant impact on the social environment. It also briefly compares three different legal frameworks for the regulation of AI (EU, Commonwealth and USA). It proposes at the end a specific framework for the analysis of the ethical and legal AI value chain to preserve democratic values and foster the digital implementation of the rule of law.

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.

arXiv:2601.04486v1 Announce Type: cross Abstract: Detection systems that utilize machine learning are progressively implemented at Security Operations Centers (SOCs) to help an analyst to filter through high volumes of security alerts. Practically, such systems tend to reveal probabilistic results or confidence scores which are ill-calibrated and hard to read when under pressure. Qualitative and survey based studies of SOC practice done before reveal that poor alert quality and alert overload greatly augment the burden on the analyst, especially when tool outputs are not coherent with decision requirements, or signal noise. One of the most significant limitations is that model confidence is usually shown without expressing that there are asymmetric costs in decision making where false alarms are much less harmful than missed attacks. The present paper presents a decision-sensitive trust signal correspondence scheme of SOC alert triage. The framework combines confidence that has been calibrated, lightweight uncertainty cues, and cost-sensitive decision thresholds into coherent decision-support layer, instead of making changes to detection models. To enhance probabilistic consistency, the calibration is done using the known post-hoc methods and the uncertainty cues give conservative protection in situations where model certainty is low. To measure the model-independent performance of the suggested model, we apply the Logistic Regression and the Random Forest classifiers to the UNSW-NB15 intrusion detection benchmark. According to simulation findings, false negatives are greatly amplified by the presence of misaligned displays of confidence, whereas cost weighted loss decreases by orders of magnitude between models with decision aligned trust signals. Lastly, we describe a human-in-the-loop study plan that would allow empirically assessing the decision-making of the analysts with aligned and misaligned trust interfaces.

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.

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.

arXiv:2601.04563v1 Announce Type: cross Abstract: Our experience of the world is multisensory, spanning a synthesis of language, sight, sound, touch, taste, and smell. Yet, artificial intelligence has primarily advanced in digital modalities like text, vision, and audio. This paper outlines a research vision for multisensory artificial intelligence over the next decade. This new set of technologies can change how humans and AI experience and interact with one another, by connecting AI to the human senses and a rich spectrum of signals from physiological and tactile cues on the body, to physical and social signals in homes, cities, and the environment. We outline how this field must advance through three interrelated themes of sensing, science, and synergy. Firstly, research in sensing should extend how AI captures the world in richer ways beyond the digital medium. Secondly, developing a principled science for quantifying multimodal heterogeneity and interactions, developing unified modeling architectures and representations, and understanding cross-modal transfer. Finally, we present new technical challenges to learn synergy between modalities and between humans and AI, covering multisensory integration, alignment, reasoning, generation, generalization, and experience. Accompanying this vision paper are a series of projects, resources, and demos of latest advances from the Multisensory Intelligence group at the MIT Media Lab, see https://mit-mi.github.io/.

arXiv:2601.04587v1 Announce Type: cross Abstract: This paper introduces FedKDX, a federated learning framework that addresses limitations in healthcare AI through Negative Knowledge Distillation (NKD). Unlike existing approaches that focus solely on positive knowledge transfer, FedKDX captures both target and non-target information to improve model generalization in healthcare applications. The framework integrates multiple knowledge transfer techniques--including traditional knowledge distillation, contrastive learning, and NKD--within a unified architecture that maintains privacy while reducing communication costs. Through experiments on healthcare datasets (SLEEP, UCI-HAR, and PAMAP2), FedKDX demonstrates improved accuracy (up to 2.53% over state-of-the-art methods), faster convergence, and better performance on non-IID data distributions. Theoretical analysis supports NKD's contribution to addressing statistical heterogeneity in distributed healthcare data. The approach shows promise for privacy-sensitive medical applications under regulatory frameworks like HIPAA and GDPR, offering a balanced solution between performance and practical implementation requirements in decentralized healthcare settings. The code and model are available at https://github.com/phamdinhdat-ai/Fed_2024.