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Fuse raises $25M to disrupt aging loan origination systems used by US credit unions
The startup also announced a $5 million "rescue fund" to help credit unions ditch legacy software for its AI-native platform.
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Pulse desynchronization of neural populations by targeting the centroid of the limit cycle in phase space
arXiv:2603.12878v1 Announce Type: new
Abstract: The synchronized activity of neuronal populations can lead to pathological over-synchronization in conditions such as epilepsy and Parkinson disease. Such states can be desynchronized by brief electrical pulses. But when the underlying oscillating system is not known, as in most practical applications, to determine the specific times and intensities of pulses used for desynchronizaton is a difficult inverse problem. Here we propose a desynchronization scheme for neuronal models of bi-variate neural activity, with possible applications in the medical setting. Our main argument is the existence of a peculiar point in the phase space of the system, the centroid, that is both easy to calculate and robust under changes in the coupling constant. This important target point can be used in a control procedure because it lies in the region of minimal return times of the system.
AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Agents
arXiv:2603.12564v1 Announce Type: cross
Abstract: Tool-augmented LLM agents increasingly serve as multi-turn advisors in high-stakes domains, yet their evaluation relies on ranking-quality metrics that measure what is recommended but not whether it is safe for the user. We introduce a paired-trajectory protocol that replays real financial dialogues under clean and contaminated tool-output conditions across seven LLMs (7B to frontier) and decomposes divergence into information-channel and memory-channel mechanisms. Across the seven models tested, we consistently observe the evaluation-blindness pattern: recommendation quality is largely preserved under contamination (utility preservation ratio approximately 1.0) while risk-inappropriate products appear in 65-93% of turns, a systematic safety failure poorly reflected by standard NDCG. Safety violations are predominantly information-channel-driven, emerge at the first contaminated turn, and persist without self-correction over 23-step trajectories; no agent across 1,563 contaminated turns explicitly questions tool-data reliability. Even narrative-only corruption (biased headlines, no numerical manipulation) induces significant drift while completely evading consistency monitors. A safety-penalized NDCG variant (sNDCG) reduces preservation ratios to 0.51-0.74, indicating that much of the evaluation gap becomes visible once safety is explicitly measured. These results motivate considering trajectory-level safety monitoring, beyond single-turn quality, for deployed multi-turn agents in high-stakes settings.
Human-in-the-Loop LLM Grading for Handwritten Mathematics Assessments
arXiv:2603.13083v1 Announce Type: cross
Abstract: Providing timely and individualised feedback on handwritten student work is highly beneficial for learning but difficult to achieve at scale. This challenge has become more pressing as generative AI undermines the reliability of take-home assessments, shifting emphasis toward supervised, in-class evaluation. We present a scalable, end-to-end workflow for LLM-assisted grading of short, pen-and-paper assessments. The workflow spans (1) constructing solution keys, (2) developing detailed rubric-style grading keys used to guide the LLM, and (3) a grading procedure that combines automated scanning and anonymisation, multi-pass LLM scoring, automated consistency checks, and mandatory human verification. We deploy the system in two undergraduate mathematics courses using six low-stakes in-class tests. Empirically, LLM assistance reduces grading time by approximately 23% while achieving agreement comparable to, and in several cases tighter than, fully manual grading. Occasional model errors occur but are effectively contained by the hybrid design. Overall, our results show that carefully embedded human-in-the-loop LLM grading can substantially reduce workload while maintaining fairness and accuracy.
Clustering Astronomical Orbital Synthetic Data Using Advanced Feature Extraction and Dimensionality Reduction Techniques
arXiv:2603.13177v1 Announce Type: cross
Abstract: The dynamics of Saturn's satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for analysing such systems, including Fourier analysis and stability metrics, struggle with the scale and complexity of modern datasets. This study introduces a machine learning-based pipeline for clustering approximately 22,300 simulated satellite orbits, addressing these challenges with advanced feature extraction and dimensionality reduction techniques. The key to this approach is using MiniRocket, which efficiently transforms 400 timesteps into a 9,996-dimensional feature space, capturing intricate temporal patterns. Additional automated feature extraction and dimensionality reduction techniques refine the data, enabling robust clustering analysis. This pipeline reveals stability regions, resonance structures, and other key behaviours in Saturn's satellite system, providing new insights into their long-term dynamical evolution. By integrating computational tools with traditional celestial mechanics techniques, this study offers a scalable and interpretable methodology for analysing large-scale orbital datasets and advancing the exploration of planetary dynamics.
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
arXiv:2502.21123v5 Announce Type: replace-cross
Abstract: Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal methods into machine learning to navigate the trade-offs among key principles of trustworthy ML, including fairness, privacy, robustness, accuracy, and explainability. While these objectives should ideally be satisfied simultaneously, they are often addressed in isolation, leading to conflicts and suboptimal solutions. Drawing on existing applications of causality in ML that successfully align goals such as fairness and accuracy or privacy and robustness, this paper argues that a causal approach is essential for balancing multiple competing objectives in both trustworthy ML and foundation models. Beyond highlighting these trade-offs, we examine how causality can be practically integrated into ML and foundation models, offering solutions to enhance their reliability and interpretability. Finally, we discuss the challenges, limitations, and opportunities in adopting causal frameworks, paving the way for more accountable and ethically sound AI systems.
Development of Ontological Knowledge Bases by Leveraging Large Language Models
arXiv:2601.10436v2 Announce Type: replace-cross
Abstract: Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems. However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in Generative AI, particularly Large Language Models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimize knowledge acquisition, automate ontology artifact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.
First-line zolbetuximab plus mFOLFOX6 and nivolumab in unresectable CLDN18.2-positive gastric or gastroesophageal junction adenocarcinoma: a phase 2 trial
Nature Medicine, Published online: 16 March 2026; doi:10.1038/s41591-026-04306-9
In cohort 4 of the ILUSTRO trial, combination of anti-CLDN18.2 zolbetuximab plus mFOLFOX6 and nivolumab in patients with CLDN18.2-positive, HER2-negative metastatic gastric or gastroesophageal junction adenocarcinoma led to encouraging clinical efficacy, supporting the testing of this combination in a phase 3 trial.
Liquid Biopsy in Non-Metastatic Prostate Cancer: Clinical Evidence and Future Directions
Cancers (Basel). 2026 Feb 28;18(5):800. doi: 10.3390/cancers18050800.
ABSTRACT
BACKGROUND AND OBJECTIVE: Liquid biopsy has transformed the management of advanced prostate cancer, yet its clinical role in non-metastatic disease remains uncertain. Conventional biomarkers such as PSA, imaging, and pathology have limited ability to capture minimal residual disease and biological aggressiveness. The objective of this review was to critically evaluate the current evidence on circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in non-metastatic prostate cancer, focusing on feasibility, prognostic value, and potential clinical applications.
METHODS: A narrative review of PubMed-indexed original studies evaluating liquid biopsy in clinically localized or non-metastatic prostate cancer was performed. Eligible studies included patients treated with curative-intent local therapy or experiencing biochemical recurrence without radiologic metastases. Study designs were predominantly prospective or retrospective observational cohorts. Liquid biopsy analytes included CTCs and ctDNA assessed from peripheral blood plasma using EpCAM-based enrichment, targeted next-generation sequencing, whole-genome sequencing, or ultra-sensitive tumor-informed assays. Primary outcomes included detection rates, associations with clinicopathologic features, biochemical recurrence, metastasis-free survival, and overall survival. Key Findings and Limitations: Across 11 studies, CTC detection using EpCAM-based platforms was infrequent in localized disease and biochemical recurrence and showed limited prognostic value (10-11% in preoperative settings). In contrast, ctDNA was detectable in a minority of patients but consistently identified biologically aggressive disease and a higher risk of recurrence when present, particularly using tumor-informed ultra-sensitive assays. Limitations include low detection rates, heterogeneous methodologies, small sample sizes, and predominantly exploratory study designs.
CONCLUSIONS AND CLINICAL IMPLICATIONS: Currently, its most promising application is not broad screening, but as a selective, biology-driven tool for detecting minimal residual disease and refining risk assessment. CtDNA acts as a biological risk modifier, potentially guiding the escalation or de-escalation of adjuvant therapy. However, prospective biomarker-driven trials are required to validate these strategies before routine clinical implementation.
PMID:41827734 | PMC:PMC12984391 | DOI:10.3390/cancers18050800
Orally Administered Porcine Intestinal Lactobacilli Improve the Respiratory Innate Immune Response Against <em>Streptococcus pneumoniae</em>
Animals (Basel). 2026 Mar 6;16(5):825. doi: 10.3390/ani16050825.
ABSTRACT
BACKGROUND: Respiratory bacterial infections represent a major health challenge in swine production, highlighting the need for novel immunomodulatory strategies that enhance host resistance. In this study, we investigated whether porcine intestinal lactobacilli could modulate the gut-lung axis and improve respiratory innate immunity in a mouse model of Streptococcus pneumoniae infection, as a surrogate of Streptococcus suis pneumonia.
METHODS: Three strains of Ligilactobacillus salivarius (LAFF998, LAFF1071, and LAFF1095) were orally administered to Swiss mice prior to pneumococcal challenge. The resistance to the infection, the lung damage and the respiratory innate immune response were evaluated.
RESULTS: Only strain LAFF998 significantly reduced pulmonary bacterial loads, prevented bacteremia, and attenuated lung injury. This protective effect was associated with selective modulation of respiratory immunity, characterized by reduced neutrophilic inflammation, increased lymphocyte recruitment, and enhanced activation of alveolar macrophages expressing MHC-II. LAFF998 markedly increased the production of IFN-β, IFN-γ, IL-6, IL-10, and IL-27 in the respiratory tract, without inducing excessive inflammatory damage. Ex vivo and in vitro analyses confirmed that alveolar macrophages from LAFF998-treated mice exhibited a primed phenotype with heightened cytokine responses to pneumococcal stimulation. In contrast, strains LAFF1071 and LAFF1095 failed to confer protection or significantly modulate respiratory immune responses.
CONCLUSIONS: These findings demonstrate a strict strain-dependent effect among porcine L. salivarius isolates and identify LAFF998 as a potent immunobiotic capable of enhancing respiratory innate immunity through the gut-lung axis. This work supports further studies of LAFF998 as an immunobiotic strategy for the prevention of respiratory infections in pigs.
PMID:41829035 | PMC:PMC12985233 | DOI:10.3390/ani16050825
Nyne, founded by a father-son duo, gives AI agents the human context they’re missing
The data infrastructure startup raised $5.3 million in seed funding led by Wischoff Ventures and South Park Commons.
Sales automation startup Rox AI hits $1.2B valuation, sources say
Rox, founded in 2024 by the former chief growth officer of New Relic, offers an AI-native alternative to CRM tools.
A structure-based mRNA vaccine for Nipah virus in healthy adults: a phase 1 trial
Nature Medicine, Published online: 12 March 2026; doi:10.1038/s41591-026-04265-1
In this phase 1, open-label dose-escalation study in healthy adults found that the mRNA vaccine (mRNA-1215), encoding the Nipah virus Malaysian strain chimeric pre-fusion F protein linked to glycoprotein G, was safe and induced elevated immune responses at 1 year of follow-up, indicating that this is a promising vaccine candidate for further development.
A cognitive layer architecture to support large-language model performance in psychotherapy interactions
Nature Medicine, Published online: 12 March 2026; doi:10.1038/s41591-026-04278-w
A real-world study showed that introducing a cognitive layer architecture to support specialized psychotherapeutic reasoning capabilities in general-purpose chatbots improved depression and anxiety symptoms compared to chatbots or therapists alone.
Multidimensional profiling of heterogeneity in supratentorial ependymomas
Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10214-2
Individual supratentorial ependymoma tumour subgroups have two distinct progenitor-like cell states—neuroepithelial-like and embryonic-like—that are reminiscent of early human brain development and diverge in the extent of their neuronal or ependymal differentiation.
Intestinal interoceptive dysfunction drives age-associated cognitive decline
Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10191-6
Age-related microbiome changes increase medium-chain fatty acid-producing bacteria, driving GPR84-mediated myeloid inflammation, impaired vagal signalling and hippocampal dysfunction; targeting this gut–brain pathway restores memory in aged mice.
A sorghum pangenome reference improves global crop trait discovery
Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10229-9
A pangenome reference for the phenotypically diverse crop sorghum aims to help accelerate future efforts to breed crops that are better adapted to changing environments.
Blood phosphorylated tau elevation as a biomarker in immunoglobulin light chain and transthyretin amyloidosis
Nature Medicine, Published online: 11 March 2026; doi:10.1038/s41591-026-04272-2
Elevated serum levels of phosphorylated tau are not specific to Alzheimer’s disease and may also serve as a diagnostic tool for the most common types of systemic amyloidosis, with potential utility in distinguishing amyloidosis-related polyneuropathy from polyneuropathy of other etiologies.
Ageing promotes metastasis via activation of the integrated stress response
Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10216-0
Ageing reprograms the evolutionary trajectory of KRAS-driven lung adenocarcinoma, limiting primary tumour growth while promoting metastatic dissemination through epigenetic activation of the integrated stress response, and a therapeutic opportunity in older patients is revealed.