arXiv:2512.11509v1 Announce Type: cross
Abstract: Large Language Models (LLMs) exhibit remarkable capabilities in natural language understanding and reasoning, but suffer from hallucination: the generation of factually incorrect content. While numerous methods have been developed to reduce hallucinations, their impact on creative generations remains unexplored. This gap is particularly critical for AI-assisted scientific discovery, which requires both factual accuracy and creative hypothesis generation. We investigate how three hallucination-reduction techniques: Chain of Verification (CoVe), Decoding by Contrasting Layers (DoLa), and Retrieval-Augmented Generation (RAG), affect creativity in LLMs. Evaluating multiple model families (LLaMA, Qwen, Mistral) at varying scales (1B - 70B parameters) on two creativity benchmarks (NeoCoder and CS4), we find that these methods have opposing effects on divergent creativity. CoVe enhances divergent thinking, DoLa suppresses it, and RAG shows minimal impact. Our findings provide guidance for selecting appropriate hallucination-reduction methods in scientific applications, where the balance between factual accuracy and creative exploration is crucial.
arXiv:2512.11661v1 Announce Type: cross
Abstract: Large Language Models (LLMs) are increasingly embedded in academic writing practices. Although numerous studies have explored how researchers employ these tools for scientific writing, their concrete implementation, limitations, and design challenges within the literature review process remain underexplored. In this paper, we report a user study with researchers across multiple disciplines to characterize current practices, benefits, and \textit{pain points} in using LLMs to investigate related work. We identified three recurring gaps: (i) lack of trust in outputs, (ii) persistent verification burden, and (iii) requiring multiple tools. This motivates our proposal of six design goals and a high-level framework that operationalizes them through improved related papers visualization, verification at every step, and human-feedback alignment with generation-guided explanations. Overall, by grounding our work in the practical, day-to-day needs of researchers, we designed a framework that addresses these limitations and models real-world LLM-assisted writing, advancing trust through verifiable actions and fostering practical collaboration between researchers and AI systems.
arXiv:2409.13115v2 Announce Type: replace-cross
Abstract: Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively leveraging multimodal data, particularly digital pathology whole slide images (WSIs) and genomic sequencing, remains a significant challenge due to the intrinsic heterogeneity of these modalities and the need for scalable and interpretable frameworks. Existing diagnostic models typically operate on unimodal data, overlooking critical cross-modal interactions that can yield richer clinical insights. We introduce MarbliX (Multimodal Association and Retrieval with Binary Latent Indexed matriX), a self-supervised framework that learns to embed WSIs and immunogenomic profiles into compact, scalable binary codes, termed ``monogram.'' By optimizing a triplet contrastive objective across modalities, MarbliX captures high-resolution patient similarity in a unified latent space, enabling efficient retrieval of clinically relevant cases and facilitating case-based reasoning. \textcolor{black}{In lung cancer, MarbliX achieves 85-89\% across all evaluation metrics, outperforming histopathology (69-71\%) and immunogenomics (73-76\%). In kidney cancer, real-valued monograms yield the strongest performance (F1: 80-83\%, Accuracy: 87-90\%), with binary monograms slightly lower (F1: 78-82\%).
arXiv:2503.05860v3 Announce Type: replace-cross
Abstract: Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development.
We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5.
To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.
arXiv:2509.12421v2 Announce Type: replace-cross
Abstract: The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem.
arXiv:2512.10041v2 Announce Type: replace-cross
Abstract: Modern deep learning methods have achieved impressive results across tasks from disease classification, estimating continuous biomarkers, to generating realistic medical images. Most of these approaches are trained to model conditional distributions defined by a specific predictive direction with a specific set of input variables. We introduce MetaVoxel, a generative joint diffusion modeling framework that models the joint distribution over imaging data and clinical metadata by learning a single diffusion process spanning all variables. By capturing the joint distribution, MetaVoxel unifies tasks that traditionally require separate conditional models and supports flexible zero-shot inference using arbitrary subsets of inputs without task-specific retraining. Using more than 10,000 T1-weighted MRI scans paired with clinical metadata from nine datasets, we show that a single MetaVoxel model can perform image generation, age estimation, and sex prediction, achieving performance comparable to established task-specific baselines. Additional experiments highlight its capabilities for flexible inference. Together, these findings demonstrate that joint multimodal diffusion offers a promising direction for unifying medical AI models and enabling broader clinical applicability.
SLAS Technol. 2025 Dec 11:100376. doi: 10.1016/j.slast.2025.100376. Online ahead of print.
ABSTRACT
The existing multi-omic analyses are frequently confined to individual tissues, and the regulatory picture of the systemic regulator of complex physiology and disease is hidden. To fill this gap, we have created a unified systems biology model of the high-throughput dissection of inter-organ genetic networks. Our model incorporates transcriptomic, epigenomic and proteomic analysis of five major organs (liver, kidney, heart, lung, brain) using the Multi-Omics Factor Analysis (MOFA+) tool, specifically, cross-tissue coordination. We characterized 27 evidence-heavy cross-tissue modules (FDR < 0.05) that are major hubs such as *HNF4Aenda NRF2cheng8loadmasterregulatingconstitutionembryonicstemcellularinfoncogenes recognize them. One notable observation was liver-kidney metabolic axis, significant cross-talks in hepatocyte organoids are confirmed with CRISPR knockdown, which suppresses the expression of transporters expressed by the kidney. Our work offers a scalable validated framework that goes beyond organ-centric perspectives, which can be used as a potent tool of systemic disease modelling and precision medicine.
Background: Computer perception (CP) technologies hold significant promise for advancing precision mental health care systems, given their ability to leverage algorithmic analysis of continuous, passive sensing data from wearables and smartphones (eg, behavioral activity, geolocation, vocal features, and ambient environmental data) to infer clinically meaningful behavioral and physiological states. However, successful implementation critically depends on cultivating well-founded stakeholder trust. Objective: This study aims to investigate, across adolescents, caregivers, clinicians, and developers, the contingencies under which CP technologies are deemed trustworthy in health care. Methods: We conducted 80 semistructured interviews with a purposive sample of adolescents (n=20) diagnosed with autism, Tourette syndrome, anxiety, obsessive-compulsive disorder, or attention-deficit/hyperactivity disorder and their caregivers (n=20); practicing clinicians across psychiatry, psychology, and pediatrics (n=20); and CP system developers (n=20). Interview transcripts were coded by 2 independent coders and analyzed using multistage, inductive thematic content analysis to identify prominent themes. Results: Across stakeholder groups, 5 core criteria emerged as prerequisites for trust in CP outputs: (1) epistemic alignment—consistency between system outputs, personal experience, and existing diagnostic frameworks; (2) demonstrable rigor—training on representative data and validation in real-world contexts; (3) explainability—transparent communication of input variables, thresholds, and decision logic; (4) sensitivity to complexity—the capacity to accommodate heterogeneity and comorbidity in symptom expression; and (5) a nonsubstitutive role—technologies must augment, rather than supplant, clinical judgment. A novel and cautionary finding was that epistemic alignment—whether outputs affirmed participants’ preexisting beliefs, diagnostic expectations, or internal states—was a dominant factor in determining whether the tool was perceived as trustworthy. Participants also expressed relational trust, placing confidence in CP systems based on endorsements from respected peers, academic institutions, or regulatory agencies. However, both trust strategies raise significant concerns: confirmation bias may lead users to overvalue outputs that align with their assumptions, while surrogate trust may be misapplied in the absence of robust performance validation. Conclusions: This study advances empirical understanding of how trust is formed and calibrated around artificial intelligence–based CP technologies. While trust is commonly framed as a function of technical performance, our findings show that it is deeply shaped by cognitive heuristics, social relationships, and alignment with entrenched epistemologies. These dynamics can facilitate intuitive verification but may also constrain the transformative potential of CP systems by reinforcing existing beliefs. To address this, we recommend a dual strategy: (1) embedding CP tools within institutional frameworks that uphold rigorous validation, ethical oversight, and transparent design; and (2) providing clinicians with training and interface designs that support critical appraisal and minimize susceptibility to cognitive bias. Recalibrating trust to reflect actual system capacities—rather than familiarity or endorsement—is essential for ethically sound and clinically meaningful integration of CP technologies.
Generative AI’s experimental phase is concluding, making way for truly autonomous systems in 2026 that act rather than merely summarise.
2026 will lose the focus on model parameters and be about agency, energy efficiency, and the ability to navigate complex industrial environments. The next twelve months represent a departure from chatbots toward autonomous systems executing workflows with minimal oversight; forcing organisations to rethink infrastructure, governance, and talent management.
Autonomous AI systems take the wheel
Hanen Garcia, Chief Architect for Telecommunications at Red Hat, argues that while 2025 was defined by experimentation, the coming year marks a “decisive pivot towards agentic AI, autonomous software entities capable of reasoning, planning, and executing complex workflows without constant human intervention.”
Telecoms and heavy industry are the proving grounds. Garcia points to a trajectory toward autonomous network operations (ANO), moving beyond simple automation to self-configuring and self-healing systems. The business goal is to reverse commoditisation by “prioritising intelligence over pure infrastructure” and reduce operating expenditures.
Technologically, service providers are deploying multiagent systems (MAS). Rather than relying on a single model, these allow distinct agents to collaborate on multi-step tasks, handling complex interactions autonomously. However, increased autonomy introduces new threats.
Emmet King, Founding Partner of J12 Ventures, warns that “as AI agents gain the ability to autonomously execute tasks, hidden instructions embedded in images and workflows become potential attack vectors.” Security priorities must therefore shift from endpoint protection to “governing and auditing autonomous AI actions.”
As organisations scale these autonomous AI workloads, they hit a physical wall: power.
King argues energy availability, rather than model access, will determine which startups scale. “Compute scarcity is now a function of grid capacity,” King states, suggesting energy policy will become the de facto AI policy in Europe.
KPIs must adapt. Sergio Gago, CTO at Cloudera, predicts enterprises will prioritise energy efficiency as a primary metric. “The new competitive edge won’t come from the largest models, but from the most intelligent, efficient use of resources.”
Horizontal copilots lacking domain expertise or proprietary data will fail ROI tests as buyers measure real productivity. The “clearest enterprise ROI” will emerge from manufacturing, logistics, and advanced engineering—sectors where AI integrates into high-value workflows rather than consumer-facing interfaces.
AI ends the static app in 2026
Software consumption is changing too. Chris Royles, Field CTO for EMEA at Cloudera, suggests the traditional concept of an “app” is becoming fluid. “In 2026, AI will start to radically change the way we think about apps, how they function and how they’re built.”
Users will soon request temporary modules generated by code and a prompt, effectively replacing dedicated applications. “Once that function has served its purpose, it closes,” Royles explains, noting these “disposable” apps can be built and rebuilt in seconds.
Rigorous governance is required here; organisations need visibility into the reasoning processes used to create these modules to ensure errors are corrected safely.
Data storage faces a similar reckoning, especially as AI becomes more autonomous. Wim Stoop, Director of Product Marketing at Cloudera, believes the era of “digital hoarding” is ending as storage capacity hits its limit.
“AI-generated data will become disposable, created and refreshed on demand rather than stored indefinitely,” Stoop predicts. Verified, human-generated data will rise in value while synthetic content is discarded.
Specialist AI governance agents will pick up the slack. These “digital colleagues” will continuously monitor and secure data, allowing humans to “govern the governance” rather than enforcing individual rules. For example, a security agent could automatically adjust access permissions as new data enters the environment without human intervention.
Sovereignty and the human element
Sovereignty remains a pressing concern for European IT. Red Hat’s survey data indicates 92 percent of IT and AI leaders in EMEA view enterprise open-source software as vital for achieving sovereignty. Providers will leverage existing data centre footprints to offer sovereign AI solutions, ensuring data remains within specific jurisdictions to meet compliance demands.
Emmet King, Founding Partner of J12 Ventures, adds that competitive advantage is moving from owning models to “controlling training pipelines and energy supply,” with open-source advancements allowing more actors to run frontier-scale workloads.
Workforce integration is becoming personal. Nick Blasi, Co-Founder of Personos, argues tools ignoring human nuance – tone, temperament, and personality – will soon feel obsolete. By 2026, Blasi predicts “half of workplace conflict will be flagged by AI before managers know it exists.”
These systems will focus on “communication, influence, trust, motivation, and conflict resolution,” Blasi suggests, adding that personality science will become the “operating system” for the next generation of autonomous AI, offering grounded understanding of human individuality rather than generic recommendations.
The era of the “thin wrapper” is over. Buyers are now measuring real productivity, exposing tools built on hype rather than proprietary data. For the enterprise, competitive advantage will no longer come from renting access to a model, but from controlling the training pipelines and energy supply that power it.
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Cancer Cell. 2025 Dec 11:S1535-6108(25)00498-2. doi: 10.1016/j.ccell.2025.11.005. Online ahead of print.
ABSTRACT
How early precursor cells and their surrounding microenvironment cooperate to drive oncogenic progression in lung adenocarcinoma (LUAD) remains elusive. In this issue of Cancer Cell, Peng et al. conducted multimodal spatial-omics to comprehensively profile precancerous lung and LUAD tissues, uncovering alveolar progenitors and proinflammatory niches that co-evolve during cancer progression.
J Int Med Res. 2025 Dec;53(12):3000605251403252. doi: 10.1177/03000605251403252. Epub 2025 Dec 11.
ABSTRACT
BackgroundGastric cancer remains a major global health challenge due to its high mortality rate and complex pathophysiological mechanisms. Emerging evidence highlights that dysregulated lipid metabolism contributes to gastric cancer progression and prognosis, but the associations between lipid metabolism-associated genes, gastric cancer patient survival, and tumor immune microenvironment remodeling are not fully elucidated.MethodsWe analyzed publicly available omics and clinical data, including RNA sequencing data from 371 gastric cancer samples in The Cancer Genome Atlas database and 433 gastric cancer samples in the Gene Expression Omnibus database. We first curated the top 100 lipid metabolism-associated genes based on relevance scores. Then, univariate Cox regression was used to identify genes significantly associated with overall survival. Consensus clustering was applied to these survival-related genes to define gastric cancer molecular subtypes. Copy number variation analysis was performed to assess genomic alterations of these genes in tumor samples. A prognostic risk model was constructed using least absolute shrinkage and selection operator regression and validated via multivariate Cox regression. Immune infiltration analysis using CIBERSORT and ESTIMATE algorithms was conducted to explore associations between lipid metabolism-associated genes and tumor immune microenvironment characteristics.ResultsA total of 3911 differentially expressed genes were identified between gastric cancer and adjacent normal tissues. Among the top 100 lipid metabolism-associated genes, 43 were significantly linked to patient survival, most of which were considered as poor prognostic factors. Copy number variation analysis revealed frequent copy number gains of these genes in tumor samples. Consensus clustering stratified patients into two molecular subtypes (LMAGcluster A and LMAGcluster B), with LMAGcluster A showing significantly worse survival outcomes (median survival: 2.6 years vs. 8.3 years in LMAGcluster B, p < 0.001). LMAGcluster A was also characterized by elevated infiltration of pro-tumor immune cells, such as regulatory T cells and follicular helper T cells. The prognostic model based on 14 key lipid metabolism-associated genes exhibited robust predictive performance, with area under the receiver operating characteristic curve values of 0.702-0.761 in The Cancer Genome Atlas cohort and 0.621-0.638 in the Gene Expression Omnibus cohort for 1-, 3-, and 5-year survival.ConclusionLipid metabolism-associated genes are closely associated with gastric cancer prognosis and tumor immune microenvironment remodeling. The identified gene-based molecular subtypes and prognostic model provide novel insights into gastric cancer progression, and the 14 key genes may serve as potential biomarkers and therapeutic targets.
Trump signed an AI executive order targeting state laws and promising one national rulebook. Critics warn it could trigger court battles and prolong uncertainty for startups while Congress debates federal rules.
Int J Mol Sci. 2025 Dec 3;26(23):11708. doi: 10.3390/ijms262311708.
ABSTRACT
Gynecologic cancers remain a major global health burden, particularly in low- and middle-income countries, with high incidence and mortality rates around 45-50%. The detection of minimal residual disease (MRD) is transforming the management of recurrence risk in gynecologic cancers through highly sensitive molecular technologies. MRD encompasses small populations of residual cancer cells or post-treatment molecular traces but remain undetectable by conventional methods. Its detection relies on circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and advanced next-generation sequencing (NGS), with ctDNA-based MRD assays having sensitivity levels between 85% and over 99%. Other technologies, such as liquid biopsies and digital PCR, are also in development. MRD status has demonstrated high predictors of recurrence and survival with positive MRD strongly associated with poor outcomes and negative MRD indicates sustained remission. However, MRD detection faces significant limitations, such as tumor heterogeneity, inconstant ctDNA levels, technical issues of false-negative results, and limited clinical accessibility. Therefore, this review presents current evidence regarding the molecular detection of MRD in gynecologic malignancies and assesses its prognostic and predictive relevance. Ultimately, MRD continuous integration into clinical practice offers a promising modality to enable early relapse detection, more precise therapeutic decision-making, and the improvement of personalized medicine access to gynecologic cancers worldwide.
arXiv:2512.09935v1 Announce Type: new
Abstract: Fact-checking health-related claims has become increasingly critical as misinformation proliferates online. Effective verification requires both the retrieval of high-quality evidence and rigorous reasoning processes. In this paper, we propose a two-stage framework for health misinformation detection: Agreement Score Prediction followed by Multi-Agent Debate. In the first stage, we employ large language models (LLMs) to independently evaluate retrieved articles and compute an aggregated agreement score that reflects the overall evidence stance. When this score indicates insufficient consensus-falling below a predefined threshold-the system proceeds to a second stage. Multiple agents engage in structured debate to synthesize conflicting evidence and generate well-reasoned verdicts with explicit justifications. Experimental results demonstrate that our two-stage approach achieves superior performance compared to baseline methods, highlighting the value of combining automated scoring with collaborative reasoning for complex verification tasks.
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.
arXiv:2512.10100v1 Announce Type: new
Abstract: This manuscript establishes information-theoretic limitations for robustness of AI security and alignment by extending G\"odel's incompleteness theorem to AI. Knowing these limitations and preparing for the challenges they bring is critically important for the responsible adoption of the AI technology. Practical approaches to dealing with these challenges are provided as well. Broader implications for cognitive reasoning limitations of AI systems are also proven.
arXiv:2512.10611v1 Announce Type: new
Abstract: Data center (DC) infrastructure serves as the backbone to support the escalating demand for computing capacity. Traditional design methodologies that blend human expertise with specialized simulation tools scale poorly with the increasing system complexity. Recent studies adopt generative artificial intelligence to design plausible human-centric indoor layouts. However, they do not consider the underlying physics, making them unsuitable for the DC design that sets quantifiable operational objectives and strict physical constraints. To bridge the gap, we propose Phythesis, a novel framework that synergizes large language models (LLMs) and physics-guided evolutionary optimization to automate simulation-ready (SimReady) scene synthesis for energy-efficient DC design. Phythesis employs an iterative bi-level optimization architecture, where (i) the LLM-driven optimization level generates physically plausible three-dimensional layouts and self-criticizes them to refine the scene topology, and (ii) the physics-informed optimization level identifies the optimal asset parameters and selects the best asset combination. Experiments on three generation scales show that Phythesis achieves 57.3% generation success rate increase and 11.5% power usage effectiveness (PUE) improvement, compared with the vanilla LLM-based solution.
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