Möbius: an Atomic State Sharding Design for Account-Based Blockchains
Bibliographic record
Abstract
This paper presents Mobius, the first cost-efficient state sharding design that remains consensus mechanism agnostic and guarantees atomicity for cross-shard smart contract transactions. In particular, to address the challenges posed by the growing blockchain state, Mobius enables its participants to verify all transactions while only storing a partial state. Unlike previous state sharding systems, the proposed protocol uses a novel vector commitment data structure to reduce the network bandwidth overhead via proof aggregation. Further, it utilizes a novel epoch-based multi-phase commitment technique for guaranteeing atomicity in cross-shard transactions. Experiments presented here show that Mobius reduces the disk requirement of each participant linearly with respect to the number of shards. Further, it presents a 4.7-7.3x lower network bandwidth overhead when compared to existing state-of-the-art state sharding systems. The outcomes also confirm that existing smart contracts can operate on Mobius in cross-shard scenarios without modifications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".