Towards Cost-Effective ZK-Rollups: Modeling and Optimization of Proving Infrastructure
- URL: http://arxiv.org/abs/2509.16581v1
- Date: Sat, 20 Sep 2025 09:03:54 GMT
- Title: Towards Cost-Effective ZK-Rollups: Modeling and Optimization of Proving Infrastructure
- Authors: Mohsen Ahmadvand, Pedro Souto,
- Abstract summary: Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints.<n>As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs.<n>We propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Zero-knowledge rollups rely on provers to generate multi-step state transition proofs under strict finality and availability constraints. These steps require expensive hardware (e.g., GPUs), and finality is reached only once all stages complete and results are posted on-chain. As rollups scale, staying economically viable becomes increasingly difficult due to rising throughput, fast finality demands, volatile gas prices, and dynamic resource needs. We base our study on Halo2-based proving systems and identify transactions per second (TPS), average gas usage, and finality time as key cost drivers. To address this, we propose a parametric cost model that captures rollup-specific constraints and ensures provers can keep up with incoming transaction load. We formulate this model as a constraint system and solve it using the Z3 SMT solver to find cost-optimal configurations. To validate our approach, we implement a simulator that detects lag and estimates operational costs. Our method shows a potential cost reduction of up to 70\%.
Related papers
- EvoRoute: Experience-Driven Self-Routing LLM Agent Systems [100.64399490164959]
EvoRoute is a self-evolving model routing paradigm that transcends static, pre-defined model assignments.<n> Experiments on challenging agentic benchmarks demonstrate that EvoRoute, when integrated into off-the-shelf agentic systems, not only sustains or enhances system performance but also reduces execution cost by up to $80%$ and latency by over $70%$.
arXiv Detail & Related papers (2026-01-06T04:06:46Z) - Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization [0.0]
Microreactors are well-suited for access-challenged remote areas where costly fuels dominate.<n>They suffer from diseconomies of scale, and their financial viability remains unconvincing.<n>We present a novel unifying geometric design optimization approach that accounts for techno-economic considerations.
arXiv Detail & Related papers (2025-12-17T23:28:13Z) - e1: Learning Adaptive Control of Reasoning Effort [88.51897900019485]
Increasing the thinking budget of AI models can significantly improve accuracy, but not all questions warrant the same amount of reasoning.<n>Users may prefer to allocate different amounts of reasoning effort depending on how they value output quality versus latency and cost.<n>We propose Adaptive Effort Control, a self-adaptive reinforcement learning method that trains models to use a user-specified fraction of tokens.
arXiv Detail & Related papers (2025-10-30T23:12:21Z) - xRouter: Training Cost-Aware LLMs Orchestration System via Reinforcement Learning [104.63494870852894]
We present x, a tool-calling-based routing system in which a learned router can either answer directly or invoke one or more external models.<n>Our implementation encompasses the full reinforcement learning framework, including reward and cost accounting.<n>Across diverse benchmarks, x achieves strong cost-performance trade-offs.
arXiv Detail & Related papers (2025-10-09T16:52:01Z) - PT$^2$-LLM: Post-Training Ternarization for Large Language Models [52.4629647715623]
Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment.<n>We propose PT$2$-LLM, a post-training ternarization framework tailored for LLMs.<n>At its core is an Asymmetric Ternary Quantizer equipped with a two-stage refinement pipeline.
arXiv Detail & Related papers (2025-09-27T03:01:48Z) - Dynamic Speculative Agent Planning [57.630218933994534]
Large language-model-based agents face critical deployment challenges due to prohibitive latency and inference costs.<n>We introduce Dynamic Speculative Planning (DSP), an online reinforcement learning framework that provides lossless acceleration with substantially reduced costs.<n>Experiments on two standard agent benchmarks demonstrate that DSP achieves comparable efficiency to the fastest acceleration method while reducing total cost by 30% and unnecessary cost up to 60%.
arXiv Detail & Related papers (2025-09-02T03:34:36Z) - Scalable Chain of Thoughts via Elastic Reasoning [61.75753924952059]
Elastic Reasoning is a novel framework for scalable chain of thoughts.<n>It separates reasoning into two phases--thinking and solution--with independently allocated budgets.<n>Our approach produces more concise and efficient reasoning even in unconstrained settings.
arXiv Detail & Related papers (2025-05-08T15:01:06Z) - From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs [23.253571170594455]
Large Language Models (LLMs) have significantly advanced artificial intelligence.<n>This paper introduces a three-stage cost-efficient end-to-end LLM deployment pipeline.<n>It produces super-tiny online models with enhanced performance and reduced costs.
arXiv Detail & Related papers (2025-04-18T05:25:22Z) - Adaptive Orchestration for Large-Scale Inference on Heterogeneous Accelerator Systems Balancing Cost, Performance, and Resilience [0.46040036610482665]
This paper proposes a hardware-agnostic control loop that adaptively allocates requests across heterogeneous accelerators.<n>The framework consistently meets latency targets, automatically redirects traffic during capacity shortfalls, and capitalizes on lower-cost accelerators.
arXiv Detail & Related papers (2025-03-25T21:20:11Z) - Stochastic Bridges as Effective Regularizers for Parameter-Efficient
Tuning [98.27893964124829]
We propose regularizing PETs that use bridges as the regularizers (running costs) for the intermediate states.
In view of the great potential and capacity, we believe more sophisticated regularizers can be designed for PETs.
arXiv Detail & Related papers (2023-05-28T09:22:44Z) - T*$\varepsilon$ -- Bounded-Suboptimal Efficient Motion Planning for
Minimum-Time Planar Curvature-Constrained Systems [7.277760003553328]
We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles.
We show that by finding bounded-suboptimal solutions, one can dramatically reduce the number of time-optimal transitions used.
arXiv Detail & Related papers (2022-04-04T17:38:36Z) - Learning Stabilizing Controllers for Unstable Linear Quadratic
Regulators from a Single Trajectory [85.29718245299341]
We study linear controllers under quadratic costs model also known as linear quadratic regulators (LQR)
We present two different semi-definite programs (SDP) which results in a controller that stabilizes all systems within an ellipsoid uncertainty set.
We propose an efficient data dependent algorithm -- textsceXploration -- that with high probability quickly identifies a stabilizing controller.
arXiv Detail & Related papers (2020-06-19T08:58:57Z)
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.