n-VM: A Multi-VM Layer-1 Architecture with Shared Identity and Token State

• URL: http://arxiv.org/abs/2603.23670v1
• Date: Tue, 24 Mar 2026 19:19:25 GMT
• Title: n-VM: A Multi-VM Layer-1 Architecture with Shared Identity and Token State
• Abstract summary: We present n-VM, a Layer-1 architecture that hosts n heterogeneous virtual machines as co-equal execution environments over shared consensus and shared state.<n>The design combines three components: a dispatcher that routes transactions by opcode prefix, a unified identity layer in which one 32-byte commitment anchors VM-specifific addresses, and a unified token ledger that exposes VM-native interfaces such as ERC-20 and SPL over a common balance store.
• Score: 1.7429038786735553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-chain ecosystems suffer from fragmented identity, siloed liquidity, and bridge-dependent token transfers. We present n-VM, a Layer-1 architecture that hosts n heterogeneous virtual machines as co-equal execution environments over shared consensus and shared state. The design combines three components: a dispatcher that routes transactions by opcode prefix, a unified identity layer in which one 32-byte commitment anchors VM-specifific addresses, and a unified token ledger that exposes VM-native interfaces such as ERC-20 and SPL over a common balance store. We formalize routing, identity derivation, and token transfer semantics, and prove cross-VM transfer atomicity and identity isolation under standard cryptographic assumptions. We describe a concrete instantiation with five VMs: a native runtime, EVM, SVM, Bitcoin Script, and TVM. We also present context-based sharding and a write-set scheduler for parallel execution. Under an analytical throughput model, the architecture admits a projected range of about 16,000 to 66,000 transactions per second on commodity hardware.

Related papers

• From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity [94.51565447227847]
  6G networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity.<n>Large models (LMs) with strong multimodal understanding and generation capabilities have accelerated this shift.<n>Current LM-driven semantic communication (SemCom) remains fragmented.<n>We argue that tokens provide a natural candidate for this missing abstraction.
  arXiv  Detail & Related papers  (2026-09-09T18:11:38Z)
• Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling [61.28599393707943]
  Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generation timesteps.<n>We propose a task-shared core scorer and progress-conditioned generation expansions, optimized through generation decomposition and cross-task core alignment.<n> Experiments on two representative UMM architectures demonstrate consistent quality--efficiency improvements across both tasks, retaining 98.03% of dense understanding performance with a 1.93times$ end-to-end inference speedup.
  arXiv  Detail & Related papers  (2026-08-29T14:27:55Z)
• Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties [2.617654879276715]
  Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality.<n>We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer.<n>Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code.
  arXiv  Detail & Related papers  (2026-06-09T09:15:15Z)
• Intent2Tx: Benchmarking LLMs for Translating Natural Language Intents into Ethereum Transactions [6.606052122056915]
  textscIntent2Tx grounds natural language intents in real-world protocol interactions across 11 categories.<n>textscIntent2Tx serves as a critical foundation for developing autonomous, reliable agents in intent-centric Web3 ecosystems.
  arXiv  Detail & Related papers  (2026-04-30T11:52:50Z)
• Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution [3.724127405825709]
  Zero-knowledge Virtual Machines (zkEVMs) must reconcile a fundamental contradiction: the Virtual Machine was designed for transparent sequential execution, while zero-knowledge proofs require circuit representations.<n>This survey provides the first systematic analysis of how existing major production zkEVM implementations resolve this tension through distinct constraint engineering strategies.
  arXiv  Detail & Related papers  (2025-10-06T21:10:11Z)
• Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture [3.746889836344766]
  This work elaborates on a High performance computing architecture based on Simple Linux Utility for Resource Management (SLURM)<n> Dynamic resource scheduling and seamless integration of containerized have been leveraged to manage CPU, GPU, and memory efficiently in multi-node clusters.<n>The obtained results pave ways for significantly more efficient, responsive, and fault-tolerant LLM inference on large-scale HPC infrastructures.
  arXiv  Detail & Related papers  (2025-08-25T09:11:27Z)
• TrapSIMD: SIMD-Aware Compiler Optimization for 2D Trapped-Ion Quantum Machines [14.239863509836864]
  We present FluxTrap, a SIMD-aware compiler framework that establishes a hardware-software co-design interface for TI systems.<n>F FluxTrap reduces execution time by up to $3.82 times$ and improves fidelity by several orders of magnitude.
  arXiv  Detail & Related papers  (2025-04-24T18:49:51Z)
• AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation [48.82264764771652]
  We introduce AsCAN -- a hybrid architecture, combining both convolutional and transformer blocks. AsCAN supports a variety of tasks: recognition, segmentation, class-conditional image generation. We then scale the same architecture to solve a large-scale text-to-image task and show state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-07T18:43:17Z)
• Designing and Implementing a Generator Framework for a SIMD Abstraction Library [53.84310825081338]
  We present TSLGen, a novel end-to-end framework for generating an SIMD abstraction library. We show that our framework is comparable to existing libraries, and we achieve the same performance results.
  arXiv  Detail & Related papers  (2024-07-26T13:25:38Z)
• FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels [57.05834683261658]
  We present FSDv2, an evolution that aims to simplify the previous FSDv1 while eliminating the inductive bias introduced by its handcrafted instance-level representation. We develop a suite of components to complement the virtual voxel concept, including a virtual voxel encoder, a virtual voxel mixer, and a virtual voxel assignment strategy.
  arXiv  Detail & Related papers  (2023-08-07T17:59:48Z)
• Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification [0.0]
  We introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities.
  arXiv  Detail & Related papers  (2023-06-02T13:24:37Z)
• Scalable Video Object Segmentation with Identification Mechanism [125.4229430216776]
  This paper explores the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object (VOS) We present two innovative approaches, Associating Objects with Transformers (AOT) and Associating Objects with Scalable Transformers (AOST) Our approaches surpass the state-of-the-art competitors and display exceptional efficiency and scalability consistently across all six benchmarks.
  arXiv  Detail & Related papers  (2022-03-22T03:33:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.