編譯|李言
Nature, 18 September 2025, Volume 645 Issue 8081
《自然》2025年9月18日，第645卷，8081期
物理學Physics
Experimental demonstration of logical magic state distillation
邏輯魔態蒸餾的實驗演示
▲ 作者：Pedro Sales Rodriguez, John M. Robinson et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09367-3
▲摘要：
在此，我們在中性原子量子計算機上實現了邏輯量子比特的魔態蒸餾實驗。我們采用動態可重構架構對多個邏輯量子比特的并行編碼與量子操作。
通過基于d=3和d=5色碼的編碼方案，實驗觀察到輸出魔態的邏輯保真度較輸入邏輯魔態顯著提升。這些實驗證明了通用容錯量子計算的關鍵核心組件，標志著向大規模邏輯量子處理器邁出重要一步。
▲ Abstract：
Here we present the experimental realization of magic state distillation with logical qubits on a neutral-atom quantum computer. Our approach uses a dynamically reconfigurable architecture to encode and perform quantum operations on many logical qubits in parallel. We demonstrate the distillation of magic states encoded in d=3 and d=5 colour codes, observing improvements in the logical fidelity of the output magic states compared with the input logical magic states. These experiments demonstrate a key building block of universal fault-tolerant quantum computation and represent an important step towards large-scale logical quantum processors.
生物學Biology
Supervised learning in DNA neural networks
DNA神經網絡中的監督學習
▲ 作者：Kevin M. Cherry & Lulu Qian
▲鏈接：
https://www.nature.com/articles/s41586-025-09479-w
▲摘要：
在此，我們首次實現DNA分子在體外自主執行監督學習的功能，該系統能夠通過輸入分子與預期響應分子的示例完成模式分類學習。我們展示了一個經過訓練的DNA神經網絡，成功實現對三組不同100位模式的分類任務——該網絡將訓練數據直接整合為分子濃度記憶，并利用這些記憶處理后續測試數據。
我們的研究表明分子電路能夠學習比簡單自適應行為更復雜的任務，為在生物醫學和軟材料等眾多物理系統中開發具有嵌入式學習與決策能力的分子機器開辟了新途徑。
▲ Abstract：
Here we show that DNA molecules can be programmed to autonomously carry out supervised learning in vitro, with the system learning to perform pattern classification from molecular examples of inputs and desired responses. We demonstrate a DNA neural network trained to classify three different sets of 100-bit patterns, integrating training data directly into memories of molecular concentrations and using these memories to process subsequent test data. Our work suggests that molecular circuits can learn tasks more complex than simple adaptive behaviours. This opens the door to molecular machines capable of embedded learning and decision-making in a wide range of physical systems, from biomedicine to soft materials.
A gut sense for a microbial pattern regulates feeding
腸道對微生物模式的感知調控攝食行為
▲ 作者：Winston W. Liu, Naama Reicher et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09301-7
▲摘要：
在此，我們展示了在小鼠結腸中，微生物界普遍存在的鞭毛——這一跨菌門統一特征——能刺激PYY標記的結腸神經鞘細胞中的Toll樣受體5（TLR5）。這種刺激促使PYY釋放至表達NPY2R的迷走神經結狀神經元，從而調控攝食行為。
敲除該類細胞TLR5的小鼠相較于對照組攝食量增加，且體重增長更顯著。我們發現鞭毛蛋白并不直接作用于神經，而是通過刺激結腸腔內的神經鞘細胞，經由腸—腦感覺神經環路抑制進食。
此外，鞭毛蛋白的攝食調控作用獨立于免疫應答、代謝變化或腸道菌群存在。這種感知機制使宿主能根據常駐微生物的分子模式調整行為，我們將這種介于生物群與大腦之間的感知系統稱為神經生物感應。。
▲ Abstract：
Here we show that in the mouse colon, the ubiquitous microbial pattern flagellin—a unifying feature across phyla—stimulates Toll-like receptor 5 (TLR5) in peptide YY (PYY)-labelled colonic neuropod cells. This stimulation leads to PYY release onto NPY2R vagal nodose neurons to regulate feeding. Mice lacking TLR5 in these cells eat more and gain more weight than controls. We found that flagellin does not act on the nerve directly. Instead, flagellin stimulates neuropod cells from the colonic lumen to reduce feeding through a gut–brain sensory neural circuit. Moreover, flagellin reduces feeding independent of immune responses, metabolic changes or the presence of gut microbiota. This sense enables the host to adjust its behaviour in response to a molecular pattern from its resident microorganisms. We call this sense at the interface of the biota and the brain the neurobiotic sense.
動物學Zoology
Flourishing chemosynthetic life at the greatest depths of hadal trenches
在海溝最深處蓬勃生長的化能合成生命
▲ 作者：Xiaotong Peng, Mengran Du et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09317-z
▲摘要：
在此，我們報告了在“奮斗者”號載人深潛器對千島—堪察加海溝和阿留申海溝西部的科考中，發現的目前已知地球上分布最深、規模最大的化能合成生命群落。這些以管棲多毛類和雙殼類生物為主導的群落綿延2500公里，分布于5800米至9533米的深淵帶。
同位素分析表明，富含硫化氫和甲烷的流體沿海溝沉積層深處的斷層上涌，其中甲烷由沉積有機質經微生物作用產生，為這些群落提供了能量來源。鑒于其他超深淵海溝具有類似地質特征，此類化能合成群落的分布范圍可能遠超既往認知。這些發現對現有極端環境生命模型和深海碳循環理論提出了重要挑戰。
▲ Abstract：
Here we report the discovery of the deepest and the most extensive chemosynthesis-based communities known to exist on Earth during an expedition to the Kuril–Kamchatka Trench and the western Aleutian Trench using the manned submersible Fendouzhe. The communities dominated by siboglinid Polychaeta and Bivalvia span a distance of 2,500 km at depths from 5,800 m to 9,533 m. These communities are sustained by hydrogen sulfide-rich and methane-rich fluids that are transported along faults traversing deep sediment layers in trenches, where methane is produced microbially from deposited organic matter, as indicated by isotopic analysis. Given geological similarities with other hadal trenches, such chemosynthesis-based communities might be more widespread than previously anticipated. These findings challenge current models of life at extreme limits and carbon cycling in the deep ocean.
信息技術Information Technology
DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning
DeepSeek-R1通過強化學習激勵大語言模型推理能力提升
▲ 作者：Daya Guo, Dejian Yang et al.
▲鏈接：
https://www.nature.com/articles/s41586-025-09422-z
▲摘要：
在此，我們展示了通過純強化學習（RL）可有效激發大語言模型（LLMs）的推理能力，無需依賴人類標注的推理軌跡。所提出的強化學習框架促進了高級推理模式的自然涌現，包括自我反思、結果驗證與動態策略調整等能力。
經此訓練的模型在數學計算、編程競賽和STEM領域等可驗證任務中表現出卓越性能，顯著超越基于人類示范的傳統監督學習方法。更重要的是，這些大規模模型展現的涌現式推理模式，可系統性地用于指導并增強小型模型的推理能力。
▲ Abstract：
Here we show that the reasoning abilities of LLMs can be incentivized through pure reinforcement learning (RL), obviating the need for human-labelled reasoning trajectories. The proposed RL framework facilitates the emergent development of advanced reasoning patterns, such as self-reflection, verification and dynamic strategy adaptation. Consequently, the trained model achieves superior performance on verifiable tasks such as mathematics, coding competitions and STEM fields, surpassing its counterparts trained through conventional supervised learning on human demonstrations. Moreover, the emergent reasoning patterns exhibited by these large-scale models can be systematically used to guide and enhance the reasoning capabilities of smaller models.
A generic non-invasive neuromotor interface for human-computer interaction
通用型非侵入式神經運動人機交互接口
▲ 作者：Patrick Kaifosh, Thomas R. Reardon & CTRL-labs at Reality Labs
▲鏈接：
https://www.nature.com/articles/s41586-025-09255-w
▲摘要：
在此，我們開發了一種通用型非侵入式神經運動接口，可通過表面肌電圖（sEMG）解碼實現計算機輸入。我們研制了一種高靈敏度、易穿戴的sEMG腕帶設備，并建立了從數千名受試者收集訓練數據的可擴展基礎設施。這些數據支持我們開發出具有跨個體泛化能力的通用sEMG解碼模型。
測試用戶展示了在連續導航任務中達到每秒0.66次目標選擇的閉環手勢解碼中位性能，在離散手勢任務中實現每秒0.88次手勢識別，并以每分鐘20.9個單詞的速度完成手寫輸入。我們表明，通過個性化定制sEMG解碼模型，手寫識別性能可進一步提升16%。據我們所知，這是首款具備開箱即用跨個體泛化能力的高帶寬神經運動接口。
▲ Abstract：
Here, we describe the development of a generic non-invasive neuromotor interface that enables computer input decoded from surface electromyography (sEMG). We developed a highly sensitive, easily donned sEMG wristband and a scalable infrastructure for collecting training data from thousands of consenting participants. Together, these data enabled us to develop generic sEMG decoding models that generalize across people. Test users demonstrate a closed-loop median performance of gesture decoding of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task and handwriting at 20.9 words per minute. We demonstrate that the decoding performance of handwriting models can be further improved by 16% by personalizing sEMG decoding models. To our knowledge, this is the first high-bandwidth neuromotor interface with performant out-of-the-box generalization across people.
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