Koo and colleagues demonstrate that Un1Cas12f1 recognizes an expanded PAMs, and enables multiplexed genome editing from a single CRISPR array. Delivered as an all-in-one AAV, this compact Un1Cas12f1 system excises Dmd exon 23 in vivo, restores the reading frame and dystrophin expression in a mouse model of Duchenne muscular dystrophy.

Opposing oncogenic and tumor-suppressive forces establish a progenitor-like state that builds a self-reinforcing niche to drive benign-to-malignant transition in pancreatic cancer models. Disruption of p53 activity or KRAS inhibits malignancy by collapsing the niche.

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10369-y

This work demonstrates industrial-scale roll-to-roll fabrication of high-efficiency visible metalenses using nanoimprinting and TiO2 coating, achieving low cost, high throughput and uniform performance, enabling commercialization of metasurfaces.

Synergizing with senescence-inducing therapies, uPAR CAR T cells eradicate primary and metastatic solid tumors with minimal myelodepletion by targeting both tumor cells and the stroma in preclinical models.

arXiv:2510.13829v2 Announce Type: replace-cross Abstract: As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is particularly essential for fostering a trustworthy AI ecosystem. A central challenge persists: balancing text quality against detection robustness. Recent studies have sought to navigate this trade-off by leveraging signals from model output distributions (e.g., token-level entropy); however, their reliance on these model-specific signals presents a significant barrier to public verification, as the detection process requires access to the logits of the underlying model. We introduce STELA, a novel framework that aligns watermark strength with the linguistic degrees of freedom inherent in language. STELA dynamically modulates the signal using part-of-speech (POS) n-gram-modeled linguistic indeterminacy, weakening it in grammatically constrained contexts to preserve quality and strengthen it in contexts with greater linguistic flexibility to enhance detectability. Our detector operates without access to any model logits, thus facilitating publicly verifiable detection. Through extensive experiments on typologically diverse languages-analytic English, isolating Chinese, and agglutinative Korean-we show that STELA surpasses prior methods in detection robustness. Our code is available at https://github.com/Shinwoo-Park/stela_watermark.

npj Digital Medicine, Published online: 06 April 2026; doi:10.1038/s41746-026-02600-x

Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation

arXiv:2507.07847v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, introducing ambiguity that disrupts in-context learning. In this study, we systematically investigate how entity coreference affects both document retrieval and generative performance in RAG-based systems, focusing on retrieval relevance, contextual understanding, and overall response quality. We demonstrate that coreference resolution enhances retrieval effectiveness and improves question-answering (QA) performance. Through comparative analysis of different pooling strategies in retrieval tasks, we find that mean pooling demonstrates superior context capturing ability after applying coreference resolution. In QA tasks, we discover that smaller models benefit more from the disambiguation process, likely due to their limited inherent capacity for handling referential ambiguity. With these findings, this study aims to provide a deeper understanding of the challenges posed by coreferential complexity in RAG, providing guidance for improving retrieval and generation in knowledge-intensive AI applications.

arXiv:2602.19065v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving into autonomous agents, yet current "frameless" development--relying on ambiguous natural language without engineering blueprints--leads to critical risks such as scope creep and open-loop failures. To ensure industrial-grade reliability, this study proposes Agentic Problem Frames (APF), a systematic engineering framework that shifts focus from internal model intelligence to the structured interaction between the agent and its environment. The APF establishes a dynamic specification paradigm where intent is concretized at runtime through domain knowledge injection. At its core, the Act-Verify-Refine (AVR) loop functions as a closed-loop control system that transforms execution results into verified knowledge assets, driving system behavior toward asymptotic convergence to mission requirements (R). To operationalize this, this study introduces the Agentic Job Description (AJD), a formal specification tool that defines jurisdictional boundaries, operational contexts, and epistemic evaluation criteria. The efficacy of this framework is validated through two contrasting case studies: a delegated proxy model for business travel and an autonomous supervisor model for industrial equipment management. By applying AJD-based specification and APF modeling to these scenarios, the analysis demonstrates how operational scenarios are systematically controlled within defined boundaries. These cases provide a conceptual proof that agent reliability stems not from a model's internal reasoning alone, but from the rigorous engineering structures that anchor stochastic AI within deterministic business processes, thereby enabling the development of verifiable and dependable domain agents.