Nature Medicine, Published online: 13 September 2026; doi:10.1038/s41591-026-04488-2

In a large international real-world study of non-small cell lung cancer, a multimodal explainable AI model outperformed established biomarkers for immunotherapy outcome prediction and improved physician decision-making.

Nature Medicine, Published online: 11 September 2026; doi:10.1038/s41591-026-04642-w

In the randomized phase 2 ImmunoPRISM trial, patients with high-risk smoldering multiple myeloma (MM) showed higher rates of complete clinical responses in response to treatment with teclistamab compared with lenalidomide–dexamethasone, although longer follow-up is required to determine durable prevention of progression to MM.

Kis and colleagues report an integrated sequential-batch IVT–oligo-dT process that links RNA synthesis and affinity capture through a shared buffer, enabling direct crude-IVT loading and flowthrough recycling. The workflow improves cap-analog utilization and raw-material efficiency while preserving functional mRNA expression across five cycles.

Blancafort and colleagues describe a non-viral polymeric system for the delivery of dCas9-KRAB silencers as ribonucleoprotein (RNP) payloads for EWSR1-FLI1 repression. They demonstrate highly efficient RNP delivery and robust silencing of EWSR1-FLI1 in both cell line and patient-derived xenografts of Ewing sarcoma, accompanied by potent anti-tumor effects.

The efficacy of vaccines remains suboptimal in many settings, underscoring the need for new strategies. Baydemir and colleagues show that modulation of acetyl-CoA metabolism reshapes metabolic and epigenetic programs underlying Bacille Calmette-Guérin-induced trained immunity, enhancing cellular innate immune responses and identifying immunometabolic targeting as a promising approach to improve vaccine efficacy.

Nature Nanotechnology, Published online: 02 September 2026; doi:10.1038/s41565-026-02268-0

Interband-engineered quantum wells integrated with a resonant metasurface yield giant near-infrared-to-visible optical nonlinearities.

arXiv:2609.09240v1 Announce Type: cross Abstract: Ultra-low-bit language models promise reductions in storage and memory traffic, but a nominal "1.58-bit" label does not specify the deployed representation or its execution cost. We study a scale-up of an aggressive post-training conversion pipeline from Qwen3-4B to Qwen3-8B. The conversion uses KOTMS rotation, E2M-ATQ adaptive ternarisation, and GPTQ-style error compensation in a weight-only A16 configuration. We do not claim these algorithms as new. Our contribution is the end-to-end scale-up characterisation: an external reproduction gate, matched 4B/8B capability analysis, cross-corpus perplexity, effective-bit accounting, lossless lattice-aware packing, and direct packed execution. The 8B model reaches a three-corpus perplexity ratio of 1.361x, with WikiText-2, C4, and PTB ratios of 1.318x, 1.393x, and 1.371x. On eight zero-shot tasks at n = 500, mean accuracy is 64.6% versus 72.4% for FP16, corresponding to 78.5% chance-corrected retention and a 7.8-point absolute cost. The matched 4B run retains 69.6%, yielding an 8.9-point 8B advantage. The packed checkpoint is 8.24 GiB and preserves the recorded perplexity to measurement precision. Direct packed execution reaches 15.52 tokens/s in 7.35 GiB, while a preliminary packed GEMV remains slower than FP16 cuBLAS. The result is a validated scale-up baseline: model size improves robustness to aggressive post-training discretisation, actual serialisation is solved for the measured artefact, and direct execution is feasible, while broader seeds, calibration distributions, and kernel optimisation remain open.

arXiv:2502.14043v3 Announce Type: replace-cross Abstract: Most learning algorithms with formal regret guarantees essentially rely on trying all possible behaviors, which is problematic when some errors cannot be recovered from. Instead, we allow the learning agent to ask for help from a mentor and to transfer knowledge between similar states. We show that this combination enables the agent to learn both safely and effectively. Under standard online learning assumptions, we provide an algorithm whose regret and number of mentor queries are both sublinear in the time horizon for Markov decision processes with irreversible dynamics and infinite state spaces. Our proof involves a sequence of three reductions, making our result more general than a single algorithm. Conceptually, our result may be the first formal proof that it is possible for an agent to obtain high reward while becoming self-sufficient in an unknown, unbounded, and high-stakes environment without resets.

arXiv:2609.08977v2 Announce Type: replace-cross Abstract: In this work, we present Gander, an end-to-end model that unifies omni perception, realtime interaction, and agentic capabilities within a single framework. In contrast to turn-based conventional paradigms, Gander continuously receives streaming inputs across multiple modalities, including video, speech, and text, enabling natural full-duplex interaction in both everyday conversations and complex workflow-oriented agent scenarios. Users can interrupt the model at any time, while the model can also proactively provide intermediate feedback or ask follow up questions. To natively support these capabilities, Gander adopts two key architectural designs: 1) It employs a Cerebellum-Brain collaborative framework, in which the Cerebellum is responsible for realtime interaction and omni conversational capabilities, while the Brain handles complex reasoning and higher-level agentic tasks. The two components interact continuously through tool calling and the agent orchestration runtime. 2) The Cerebellum is built upon a streaming Thinker-Talker architecture, user inputs and model outputs are further flattened into an ordered token stream at the chunk level, providing a unified representation for low latency, continuous interaction. We conduct comprehensive evaluations of Gander across four dimensions: conversational ability, omni understanding, interactive capability, and agentic intelligence. Internal human evaluations demonstrate that Gander maintains the natural and expressive spoken dialogue capabilities of SOTA open source models while achieving competitive performance in omni interaction. Gander also demonstrates robustness in challenging real-world scenarios, including background noise interference, multi-party interactions, and backchannel communication. We release Gander together with its models, code, and data to facilitate further research and development in the community.

Nature, Published online: 08 September 2026; doi:10.1038/s41586-026-11081-7

Author Correction: Contrasting roles of histone 3 lysine 27 demethylases in acute lymphoblastic leukaemia

arXiv:2605.25338v1 Announce Type: cross Abstract: Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and environment interaction. While such failures are typically logged or retried heuristically, they contain structured signals about where execution broke down. We introduce CausalFlow, an interventional framework that converts failed agent traces into minimal counterfactual repairs and reusable supervision. CausalFlow models execution traces as sequential chains of dependent steps and computes Causal Responsibility Scores(CRS) via step-level counterfactual intervention to identify failure-inducing steps. For these steps, we generate minimally edited repairs that flip the final outcome to success, producing validated contrastive pairs of the form (wrong step, corrected step). CausalFlow supports two complementary uses: targeted test-time repair that recovers from failures with minimal behavioral drift, and training-time supervision suitable for offline preference optimization or reward modeling. Across four benchmarks spanning mathematical reasoning, code generation, question answering, and medical browsing, CausalFlow converts failed executions into validated minimal repairs with high minimality and causal-consensus scores, and demonstrates that causal attribution is necessary for reliable improvement across diverse agent tasks, outperforming heuristic refinement in complex retrieval settings while producing more localized repairs throughout. These results demonstrate that interventional analysis over structured execution traces provides a principled and scalable mechanism for transforming agent failures into reliability gains and learning-ready supervision.

Nature Cancer, Published online: 25 May 2026; doi:10.1038/s43018-026-01165-8

In this phase 2 trial, Perez et al. reported the safety of the vitamin D receptor agonist paricalcitol with gemcitabine and nab-paclitaxel in patients with metastatic pancreatic ductal adenocarcinoma and the pharmacodynamic effects of paricalcitol in fibroblasts and immune cells.

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10464-0

Researchers designed two-component proteins forming quasisymmetric cages via geometric frustration, enabling tunable virus-like assemblies for cargo delivery, cellular uptake and studying intracellular diffusion and protein localization.

Oncogene, Published online: 11 May 2026; doi:10.1038/s41388-026-03821-w

Editorial Expression of Concern: Disruption of transforming growth factor-β signaling through β-spectrin ELF leads to hepatocellular cancer through cyclin D1 activation

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10319-8

During model distillation, large language models can subtly transmit traits unrelated to the training data.

Oncogene, Published online: 15 April 2026; doi:10.1038/s41388-026-03789-7

Correction to: Targeting Lyn regulates Snail family shuttling and inhibits metastasis

arXiv:2604.01594v1 Announce Type: new Abstract: How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study human teaching strategies. On each trial, a teacher LLM sees a hypothetical learner's trajectory through a reward-annotated directed graph and must reveal a single edge so the learner would choose a better path if they replanned. We run a range of LLMs as simulated teachers and fit their trial-by-trial choices with the same cognitive models used for humans: a Bayes-Optimal teacher that infers which transitions the learner is missing (inverse planning), weaker Bayesian variants, heuristic baselines (e.g., reward based), and non-mentalizing utility models. In a baseline experiment matched to the stimuli presented to human subjects, most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans. Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices. When given a scaffolding intervention, models follow auxiliary inference- or reward-focused prompts, but these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs and can sometimes reduce performance. Overall, cognitive model fits provide insight into LLM tutoring policies and show that prompt compliance does not guarantee better teaching decisions.

arXiv:2604.01262v1 Announce Type: cross Abstract: Traditional Online Public Access Catalogues (OPACs) are becoming less effective due to the rapid growth of scholarly literature. Conventional search methods, such as keyword indexing and Boolean queries, often fail to support efficient knowledge discovery. This paper proposes a Smart OPAC framework that transforms traditional OPACs into intelligent discovery systems using artificial intelligence and knowledge graph techniques. The framework enables semantic search, thematic filtering, and knowledge graph-based visualization to enhance user interaction and exploration. It integrates multiple open scholarly data sources and applies semantic embeddings to improve relevance and contextual understanding. The system supports exploratory search, semantic navigation, and refined result filtering based on user-defined themes. Quantitative evaluation demonstrates improvements in retrieval efficiency, relevance, and reduction of information overload. The proposed approach offers practical implications for modernizing digital library services and supports next-generation research workflows. Future work includes user-centric evaluation, personalization, and dynamic knowledge graph updates.

Oncogenesis, Published online: 01 April 2026; doi:10.1038/s41389-026-00611-7

Alternatively activated macrophages cause DNA damage through an arginase 1-mediated bystander effect and promote epithelial cell transformation

arXiv:2603.28925v1 Announce Type: cross Abstract: Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to experience emotions. We investigate whether suppressing mind-attribution tendencies degrades intimately related socio-cognitive abilities such as Theory of Mind (ToM). Through safety ablation and mechanistic analyses of representational similarity, we demonstrate that LLM attributions of mind to themselves and to technological artefacts are behaviorally and mechanistically dissociable from ToM capabilities. Nevertheless, safety fine-tuned models under-attribute mind to non-human animals relative to human baselines and are less likely to exhibit spiritual belief, suppressing widely shared perspectives regarding the distribution and nature of non-human minds.