BlkMaC: A Blockchain-based Multi-Agents System with LLM Communication Framework
Bibliographic record
Abstract
Large Language Models (LLMs) face limitations like hallucinations, poor long-context processing, and insufficient analytical depth, while existing Multi-Agent Systems (MAS) have issues with external agent compatibility, structural vulnerability, and opaque decision-making. This study proposes BlkMaC, a Blockchain-based Multi-Agent System with an LLM communication framework, featuring a "Multi-Agent–Blockchain–LLM" architecture, an agent evaluation mechanism with an idleness parameter, encryption, and hybrid storage. Experiments on the MMLU dataset show BlkMaC improves GPT-5-mini’s accuracy by 5%, reaching levels comparable to larger models like Doubao-1.6, reduces blockchain gas fees by 2 USD per conversation round via hybrid storage, and confirms encryption/decryption latency is negligible, validating its secure, efficient, and cost-effective multi-agent collaboration capabilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".