Quantum computers require both high-fidelity operations and large qubit numbers to surpass classical capabilities. Trapped-ion platforms have demonstrated the highest gate fidelities of any modality but scaling to larger...Quantum computers require both high-fidelity operations and large qubit numbers to surpass classical capabilities. Trapped-ion platforms have demonstrated the highest gate fidelities of any modality but scaling to larger qubit numbers while preserving performance has remained a central challenge. We report on Quantinuum Helios, a 98-qubit trapped-ion quantum processor based on the quantum charge-coupled device (QCCD) architecture. Helios features Ba hyperfine qubits, all-to-all connectivity enabled by a rotatable ion storage ring connecting two quantum operation regions by a junction, speed improvements from parallelized operations and a new software stack with real-time compilation of dynamic programs. Averaged over all operational zones in the system, we achieve average infidelities of 2.5(1) × 10 for single-qubit (1Q) gates, 7.9(2) × 10 for two-qubit (2Q) gates and 3.3(5) × 10 for state preparation and measurement (SPAM), none of which are fundamentally limited and probably able to be improved. These component infidelities are predictive of system-level performance in both random Clifford circuits and random circuit sampling (RCS), the latter demonstrating that Helios operates well beyond the reach of classical simulation and establishes a new frontier of fidelity and complexity for quantum computers.
Koch C, Khalid S, Khan MZ
… +75 more, Bandyadka S, Doyon B, Denning DP, Jahanzaib M, Mian MR, Gul W, Liaqat MB, Bano A, Dahar M, Saqib N, Kamani L, Butt N, Jalal A, Sultana R, Abbas S, Siddiqeh M, Haroon M, Khan A, Babar KP, Rasheed A, Iqbal J, Aslam F, Usman U, Bajwa MA, Hyder A, Memon MS, Hashmani N, Haroon MI, Muddassir A, Zaidi SAR, Akram M, Hussain M, Mohsin SN, Bugti S, Mehmood T, Rodeni AL, Mukhtar S, Rasool T, Mahmood A, Wazir MN, Khan SJ, Khan MA, Ghaffar R, Jan S, Ul Hadi N, Anjum R, Abdullah R, Musharraf MU, Bashir MT, Ali M, Majeed I, Bilal MH, Khan SA, Hata C, Kou I, Asaumi M, Morii W, Smith KR, Kundu K, Lythgow K, MacArthur S, Wasilewski S, Petrovski S, Regeneron Genetics Center, Rodriguez-Flores JL, Riaz M, Kapoor M, Backman JD, Shuldiner AR, Bradner JE, Splawski I, Rasheed A, Dominy JE, Gurtan AM, Saleheen D
Naturally occurring loss-of-function variants in human genes enable drug target discovery because they mimic pharmacological inhibition of proteins. However, the study of these genetic variants is constrained by their ra...Naturally occurring loss-of-function variants in human genes enable drug target discovery because they mimic pharmacological inhibition of proteins. However, the study of these genetic variants is constrained by their rarity. Sequencing of diverse populations, particularly those enriched in familial relatedness, has been postulated to promote discovery of rare genetic variants. Here we present the Pakistan Genome Resource, a South Asian biobank with high familial relatedness comprising 173,303 participants, who collectively carry naturally occurring homozygous loss-of-function variants in 6,476 genes. We describe the genetic architecture of this population, associations between genes and biomarkers, the distribution of loss-of-function variants across molecular pathways, and recall-by-genotype studies of therapeutically relevant genes. The Pakistan Genome Resource expands the catalogue of human genetic variants, provides a comprehensive genetic reference resource for the Pakistani population, and demonstrates the value of studying diverse cohorts to advance human health.
Liévin V, Palepu A, Weng WH
… +17 more, Saab K, Stutz D, Cheng Y, Kulkarni K, Mahdavi SS, Barral J, Webster DR, Chou K, Hassidim A, Matias Y, Manyika J, Tanno R, Natarajan V, Rodman A, Tu T, Karthikesalingam A, Schaekermann M
While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning-including disease progression, therapeutic response, and safe medication prescription-re...While large language models (LLMs) have shown promise in diagnostic dialogue, their capabilities for effective management reasoning-including disease progression, therapeutic response, and safe medication prescription-remain under-explored. We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimized for multi-visit clinical management and dialogue. To ground its reasoning in authoritative clinical knowledge, AMIE leverages Gemini's long-context capabilities, combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multi-visit case scenarios designed to reflect UK NICE Guidance and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning as assessed by specialists and scored better in both preciseness of treatments and investigations, and in its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark derived from two national drug formularies (US, UK) and validated by board-certified pharmacists. Though AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher difficulty questions. While further research would be needed before real-world translation, AMIE's strong performance across evaluations marks a significant step towards conversational AI as a tool in disease management.
Global rice production faces mounting challenges from abnormal temperature fluctuations and nitrogen-fertilizer-driven environmental pollution. Developing varieties that balance chilling resilience and nitrogen-use effic...Global rice production faces mounting challenges from abnormal temperature fluctuations and nitrogen-fertilizer-driven environmental pollution. Developing varieties that balance chilling resilience and nitrogen-use efficiency (NUE) offers a promising solution, but the molecular networks coordinating these traits remain poorly understood. Here we identify CHILLING PHOENIX (CHPO), a major gene underlying the quantitative trait locus shared by both chilling tolerance and resilience. It encodes a MYB transcription factor that acts as a key regulator coordinating post-chilling recovery with nitrogen use in rice. Natural variation in a GCG-repeat-encoded polyalanine tract alters CHPO DNA-binding preference and redirects regulatory outputs between the japonica-type (CHPO) and indica-type (CHPO), causing opposing effects on chilling tolerance and resilience. This allelic variation is shaped by domestication selection, with the CHPO allele probably derived from Chinese wild rice. CHPO directly targets OsTCP19 and OsNRT2.4 to fine-tune NUE, thereby enhancing chilling tolerance and resilience. These findings provide a mechanistic framework for a chilling-induced high-nitrogen-utilization module that alleviates the damage caused by chilling stress, and a potential molecular design strategy for breeding rice varieties with both chilling resilience and high NUE at the recovery stage.
Yue L, Jiang W, Li S
… +37 more, Luo M, Fan N, Zhan X, Sun R, Cheng H, Xue Z, Liu T, Zhou Q, Chen K, Lu T, Guo F, Li D, Ge W, Nie Z, Lyu M, A J, Wang Y, Chen Y, Fu Z, Xiang N, Li L, Yu F, Teo GC, Nesvizhskii AI, Wang M, Snyder MP, Collins BC, Xiao Q, Aebersold R, Xu F, Yang H, Zhang S, Han Y, Zhu Y, Ji Y, Li Y, Guo T
A detailed, spatially resolved quantitative map of the human proteome is essential for a deeper understanding of human biology and disease. Here we present a comprehensive human proteomic landscape, generated by profilin...A detailed, spatially resolved quantitative map of the human proteome is essential for a deeper understanding of human biology and disease. Here we present a comprehensive human proteomic landscape, generated by profiling more than 13,000 proteins across 2,856 samples using data-independent acquisition mass spectrometry. The dataset spans 58 major tissue types, 251 specific tissue subtypes and 25 distinct carcinomas. This resource enables the depiction of spatially resolved proteome trajectories across tissue types and physiological states, including fetal, tumour, adjacent non-tumour and healthy adult tissue, thereby providing insight into both developmental processes and oncogenic progression. Furthermore, quantitative proteomics comparisons across diverse tissue types and states facilitate the indication of organ-specific toxicity, the identification of repurposable anticancer drug candidates and the prioritization of therapeutic targets for cancers. This study establishes a quantitative resource for navigating the proteome in the human body and in common cancers.
When cells enter S phase, bidirectional DNA replication is initiated through the kinase-regulated recruitment of three activators (Cdc45, GINS and Pol ε) to a duplex-DNA-loaded double hexamer of minichromosome maintenanc...When cells enter S phase, bidirectional DNA replication is initiated through the kinase-regulated recruitment of three activators (Cdc45, GINS and Pol ε) to a duplex-DNA-loaded double hexamer of minichromosome maintenance (MCM) ATPases. Together, these proteins form two CMGE helicases that establish divergent replication forks as they become separated. Here, to gain an understanding of CMGE biogenesis, we reconstituted the pre-initiation complex with purified yeast proteins. The cryo-electron-microscopy structure shows a set of firing factors caught in the act of assembling two symmetrical CMGEs. We show how stepwise complex formation reshapes MCM in preparation for DNA opening, and we explain how ATP promotes firing-factor ejection and CMGE maturation. We find that although Sld2 facilitates the recruitment of GINS to MCM, as expected, it also aids the efficient separation of the CMGE dimer, and is essential for the ejection of the lagging strand from MCM. These findings have direct implications for our understanding of the metazoan Sld2 orthologue, RECQL4, and point to a replication-fork establishment mechanism that is conserved across eukaryotes.
Climate-induced permafrost thaw unlocks large stores of organic carbon that are mineralized and emitted as carbon dioxide (CO) from rivers to the atmosphere. Concurrently, warming and permafrost thaw can increase mineral...Climate-induced permafrost thaw unlocks large stores of organic carbon that are mineralized and emitted as carbon dioxide (CO) from rivers to the atmosphere. Concurrently, warming and permafrost thaw can increase mineral weathering rates, thus affecting the release and sequestration of inorganic carbon. Yet how these biological and geological carbon cycles interact and jointly affect CO dynamics (emission compared with drawdown) in permafrost rivers remains unknown. Here we combine CO emissions, organic and inorganic solute concentrations, dual carbon isotopes (δC-ΔC) and geochemical modelling to infer how permafrost thaw may affect river biogeochemistry over decades to centuries across the Qinghai-Tibet Plateau. Leveraging a gradient of thermal permafrost degradation, we find that river CO emissions decline, whereas solute fluxes from rock weathering increase with decreasing permafrost cover. Across this region, net CO drawdown fluxes from rock weathering are about 35% of river CO emissions, varying from around 15% in catchments with continuous permafrost to more than 100% in catchments with discontinuous or isolated permafrost. Thus, carbon fluxes from chemical weathering may become increasingly important with ongoing permafrost thaw, potentially even outpacing river CO emissions. Our findings disentangle the interplay between biological and geological carbon fluxes that are important for the cryosphere and the global carbon cycle.
Large-scale artificial intelligence (AI) models achieve notable performance in computer vision but require substantial computational resources, limiting their deployment on edge devices. Optical neural networks (ONNs) pr...Large-scale artificial intelligence (AI) models achieve notable performance in computer vision but require substantial computational resources, limiting their deployment on edge devices. Optical neural networks (ONNs) promise reduced latency and energy consumption by making use of the inherent parallelism of light. However, present ONNs struggle to scale and are confined to simple tasks, owing to the challenges of replicating exact algebraic operations of digital models using physical (analogue) systems. This work introduces a new paradigm that directly embeds core computer vision principles, including similarity-based recognition, attention-guided perception and detail-context fusion, into a large-scale optical metasurface. By unifying optical physics with these computer vision fundamentals, we develop a photonic-electronic engine that overcomes scalability and generality barriers, enabling high-accuracy, general-purpose computer vision at the edge. The resulting system combines a 41-million-parameter optical metasurface front end with a co-designed, ultraefficient 87,000-parameter digital back end, outperforming many digital models with tens of millions of parameters across object detection, segmentation, 3D reconstruction and video understanding. We build a deployable prototype and demonstrate real-time edge visual processing in natural scenes. This work represents a path towards practical optical computing for general vision tasks in complex natural environments, enabling a new paradigm for low-energy, low-latency, real-time on-device vision intelligence.
Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows. However, building phys...Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows. However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints. Yet it remains unproven whether such a system can manage patient cases with physician-level performance. Here we show that MIRA (Medical Intelligence for Reasoning and Action), an autonomous artificial intelligence agent operating in a sandboxed EHR environment, can navigate a large clinical action space to obtain patient histories; order and interpret laboratory, imaging and microbiology tests; generate differential diagnoses; and formulate treatment plans such as prescribing medications, scheduling surgical procedures and planning admissions. In simulations on real patient cases spanning multiple diagnoses, MIRA outperformed physicians in diagnostic accuracy and made guideline-concordant, medication-safe and appropriate admission decisions. Compared with previous LLM applications that addressed isolated subtasks or provided free-text advice, these results suggest that an EHR-integrated artificial intelligence agent can turn clinical intent into structured, actionable EHR operations, possibly making it a more effective decision-support partner for physicians. Further work is needed to establish generalization, safety and governance through prospective, real-world studies.
Fan M, Li J, Gao G
… +17 more, Jiang B, Fan L, Yuan Q, Zhang Y, Zheng H, Li S, Lin L, Chen Z, Ren Y, Liu Y, He W, Chen G, Sa B, Wang L, Lin J, Peng DL, Xie Q
Formation in lithium-ion battery manufacturing typically involves low-rate charge-discharge cycles to establish stable electrode-electrolyte interfaces-a time-consuming process. Here, our findings on lithium-rich layered...Formation in lithium-ion battery manufacturing typically involves low-rate charge-discharge cycles to establish stable electrode-electrolyte interfaces-a time-consuming process. Here, our findings on lithium-rich layered oxide cathodes challenge the necessity of conventional formation, which can even shorten battery lifespan. Fast formation, on the other hand, reduces production cost and enhances capacity and stability. Multiscale synchrotron-based techniques show that residual lithium ions after the initial charge are critical for subsequent structural evolution and cycling performance. Deep lithium de-intercalation causes severe structural degradation and capacity loss due to the inherently fragile lithium-deficient matrix. By contrast, the residual lithium ions from fast formation enhance reversibility through a self-pinning effect, preventing pernicious lattice deformation and reinforcing the ion-storage framework. Adjusting the initial charge current density from 0.2 C to 2 C improves reversible capacity by 20% and extends cycle life by more than 36%. This approach can also be extended to other electrode systems, providing insights for more-efficient battery production.
Charge transfer at solid-liquid interfaces plays a critical role in various energy-storage systems, particularly under dynamically varying reactant concentrations. Deciphering these intricate reaction pathways remains a...Charge transfer at solid-liquid interfaces plays a critical role in various energy-storage systems, particularly under dynamically varying reactant concentrations. Deciphering these intricate reaction pathways remains a substantial challenge, notably in lithium-sulfur (Li-S) batteries, in which achieving high energy density requires efficient conversion of highly concentrated lithium polysulfides (LiPSs). However, the mechanisms governing lithium sulfide (LiS) deposition and dissolution under lean electrolyte conditions remain poorly understood. Here, using in situ liquid-cell electron microscopy, we directly visualize concentration-driven phase segregation at the electrode-electrolyte interface. Within these high-concentration interfacial layers (HCILs), competitive surface and solution dictate the charge-transfer dynamics and ultimately govern LiS deposition at different phase boundaries. Density functional theory (DFT) calculations reveal that the aggregation of LiPSs alters molecular geometry, electronic properties and orbital hybridization, collectively facilitating charge transfer through highly concentrated LiPSs clusters. Guided by these insights, we design optimized electrodes that balance interfacial reaction pathways, enabling fast charging (4 C, 26.8 mA cm) and achieving high energy densities exceeding 400 Wh kg. These findings provide mechanistic understanding of interfacial reactions under practical working conditions and offer a design strategy to advance Li-S batteries.
Despite the functional diversity of over 100 causal genes, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogeni...Despite the functional diversity of over 100 causal genes, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.
Humans can convey new and highly diverse information through language. This ability to form and combine words into elaborate phrases and sentences enables us to express inexhaustible meanings and is fundamental to human...Humans can convey new and highly diverse information through language. This ability to form and combine words into elaborate phrases and sentences enables us to express inexhaustible meanings and is fundamental to human cognition. However, understanding the microscopic cellular building blocks and cortical landscape that precisely underlie human language has remained a challenge. Here we used wide-scale single-neuronal recordings combined with natural language processing models to identify fine-grained linguistic representations across the human frontotemporal cortex during language production. We find that, whereas certain neurons represented the detailed grammatical relationships between words or their parts of speech, others tracked the sentences' higher-order syntactic structure, their phrase transitions and sequence. Collectively, these neurons reliably captured the words' syntactic and semantic properties but also dynamically incorporated their specific sentence contexts, therefore enabling them to encode information combinatorially and at highly granular levels of detail. We show how these cell populations were locally organized and how their microscale representations differed from that of their wider field potential patterns. We also show how these neurons were distributed broadly across the frontotemporal cortex, but how their ability to encode linguistic information was left-lateralized and varied between cortical regions. Together, these findings identify some of the most basic cellular building blocks by which linguistic information is encoded in humans and begin to define the cortical landscape of language at a combined micro (cellular), meso (local population) and macro (regional) scale.
Zhang Z, Canela A, Kurisu J
… +13 more, Zou P, Kawaue T, Nakazawa N, Takeda N, Saeki M, Utsunomiya M, Bilgic M, Ishidate F, Grenci G, Furuta T, Kishi Y, Sasanuma H, Kengaku M
Migratory cells tend to have soft nuclei that deform and penetrate narrow spaces. Extensive nuclear deformation during migration can cause nuclear-envelope rupture and DNA damage in cancer cells, which may contribute to...Migratory cells tend to have soft nuclei that deform and penetrate narrow spaces. Extensive nuclear deformation during migration can cause nuclear-envelope rupture and DNA damage in cancer cells, which may contribute to malignant transformation during tumour progression. However, the importance of DNA damage in physiological migration is less well understood. Here we demonstrate that the migration of neurons in developing cerebral and cerebellar cortices is accompanied by massive DNA double-stranded breaks (DSBs) due to mechanostress during passage through narrow interstitial spaces. In contrast to many other migratory cells, these DSBs occur without detectable nuclear envelope rupture. Confined migration increases topoisomerase-IIβ covalently bound DSBs, and these lesions are repaired through non-homologous end-joining during brain development without causing cell death. Genome sequencing revealed that DSBs tend to occur at transcriptionally inactive regions. The deletion of ligase IV at the onset of neuronal migration leads to persistent DSB accumulation in cerebellar neurons with moderate transcriptional changes in genes related to synaptic function, neuronal development and stress and immune responses. The mutant mouse develops mild motor deficits in later life, suggesting that the DNA damage generated during normal brain development poses a potential disease risk if left unrepaired.
Ge Z, Smith C, He Z
… +17 more, Yang Y, Li Q, Kim HL, Xiang Z, Xiao J, Zhou W, Kahn S, Hu A, Erdi M, Banerjee R, Taniguchi T, Watanabe K, Tongay SA, Morales MA, Zhang S, Wang F, Crommie MF
Electron Wigner solids (WSs) provide an ideal system for understanding the competing effects of electron-electron and electron-disorder interactions, a central unsolved problem in condensed matter physics. Progress in th...Electron Wigner solids (WSs) provide an ideal system for understanding the competing effects of electron-electron and electron-disorder interactions, a central unsolved problem in condensed matter physics. Progress in this topic has been limited by a lack of single-defect-resolved experimental measurements as well as accurate theoretical tools to enable realistic experiment/theory comparison. Here we overcome these limitations by combining atomically resolved scanning tunnelling microscopy (STM) with neural-quantum-state quantum Monte Carlo (NQS-QMC) simulation of disordered 2D electron WSs to discover new disorder-induced physical regimes of correlated electron behaviour. STM was used to image the electron density (n)-dependent evolution of electron WSs in gate-tunable bilayer MoSe (BL-MoSe) devices with varying long-range (n) and short-range (n) disorder densities. These images were compared with NQS-QMC simulations using realistic disorder maps extracted from experiment, thus allowing the roles of different disorder types to be disentangled. We identify two distinct physical regimes for disordered electron WSs that depend on n. For n ≲ n, the WS behaviour is dominated by long-range disorder and features extensive mixed solid-liquid phases, a new type of local re-entrant melting/crystallization and prominent Friedel oscillations. By contrast, when n ≫ n, these features are suppressed and a more robust amorphous WS phase emerges that persists to higher n, highlighting the importance of short-range disorder in this regime. Our work establishes a powerful framework for studying disordered quantum solids through a combined experimental-theoretical approach.
Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale, characterization in human...Understanding how the body is represented in the motor cortex is key to understanding how the brain controls movement. Although the motor cortex has been mapped in animal models at a fine scale, characterization in humans remains primarily limited to low-resolution recording and stimulation techniques. Here we created a comprehensive map of the human motor cortex at single-neuron resolution, spanning microelectrode array recordings from 20 arrays across 8 individuals with paralysis from spinal cord injury, amyotrophic lateral sclerosis or brainstem stroke, all enrolled in brain-computer interface clinical trials. These arrays broadly sample the crown of the precentral gyrus (PCG; thought to be composed largely of the premotor cortex (Brodmann area 6)). We found that body parts were highly intermixed, such that the entire body was represented in all sampled locations of the PCG, although the relative strength of body parts was roughly consistent with the motor homunculus. We also found two speech-preferential areas with a broadly tuned, orofacial-dominant area in between them. Throughout the PCG, movement representations of the four limbs were interlinked, with homologous movements of different limbs (for example, toe curl and hand close) having correlated representations. These data provide evidence consistent with an intermixed, interrelated and behaviour-centred organization of the motor cortex. The resulting map also provides important targeting information for brain-computer interfaces that seek to restore motor function.
Pan D, Liang K, Xin C
… +17 more, Zhong L, Jiang S, Zhang C, Yang L, Wang Z, Lao Z, Ni J, Wang C, Li J, Zhen S, Yu B, Huang Z, Sun FW, Chu J, Hu Y, Zhang L, Wu D
Optical tweezers offer precise, non-contact control, but operate in a limited force regime and impose strict requirements on the characteristics of the targets as well as the environmental conditions. Millimetre-scale me...Optical tweezers offer precise, non-contact control, but operate in a limited force regime and impose strict requirements on the characteristics of the targets as well as the environmental conditions. Millimetre-scale mechanical tweezers can offer higher gripping force but are not suitable for precise manipulations. Integrating microgrippers directly at the optical fibres provides a new approach for precise micromanipulation. However, existing fibre-integrated tweezers still face challenges in achieving high-performance manipulation of micro-objects (for example, single cells) within narrow spaces, mainly due to simplified architectures, constrained designs and millimetre-scale footprints. Here we report a three-dimensional (3D) optical fibre gripper (OFG), which is fabricated by two-step, two-photon polymerization. The OFG consists of rigid photoresist microclaws and soft thermoresponsive hydrogel muscle doped with silver nanoparticles, and its size is only 38 × 38 × 61 μm. The OFG exhibits a force-to-mass ratio of about 340 μN mg, outperforming previously reported fibre-integrated tweezers by one to two orders of magnitude. The OFG can manipulate opaque particles, irregular micromechanical components and diverse single-cell types. We further demonstrated its potential in 3D microassembly of complex microdevices (bearings, shafts and gearboxes) and biomimetic sampling in the narrow environment (<300 μm). These results position the OFG as a compact fibre-tip manipulator for 3D micromanipulation, offering reversible and tunable gripping in an intermediate force regime between optical field trapping and millimetre-scale mechanical tweezers.