A Hashgraph-Inspired Consensus Mechanism for Reliable Multi-Model Reasoning

• URL: http://arxiv.org/abs/2505.03553v1
• Date: Tue, 06 May 2025 14:05:12 GMT
• Title: A Hashgraph-Inspired Consensus Mechanism for Reliable Multi-Model Reasoning
• Authors: Kolawole E. Ogunsina, Morayo A. Ogunsina,
• Abstract summary: Inconsistent outputs and hallucinations from large language models (LLMs) are major obstacles to reliable AI systems.<n>This paper proposes a novel consensus mechanism, inspired by distributed ledger technology, to validate and converge these outputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Inconsistent outputs and hallucinations from large language models (LLMs) are major obstacles to reliable AI systems. When different proprietary reasoning models (RMs), such as those by OpenAI, Google, Anthropic, DeepSeek, and xAI, are given the same complex request, they often produce divergent results due to variations in training and inference. This paper proposes a novel consensus mechanism, inspired by distributed ledger technology, to validate and converge these outputs, treating each RM as a black-box peer. Building on the Hashgraph consensus algorithm, our approach employs gossip-about-gossip communication and virtual voting to achieve agreement among an ensemble of RMs. We present an architectural design for a prototype system in which RMs iteratively exchange and update their answers, using information from each round to improve accuracy and confidence in subsequent rounds. This approach goes beyond simple majority voting by incorporating the knowledge and cross-verification content of every model. We justify the feasibility of this Hashgraph-inspired consensus for AI ensembles and outline its advantages over traditional ensembling techniques in reducing nonfactual outputs. Preliminary considerations for implementation, evaluation criteria for convergence and accuracy, and potential challenges are discussed. The proposed mechanism demonstrates a promising direction for multi-agent AI systems to self-validate and deliver high-fidelity responses in complex tasks.

Related papers

• AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models [78.08374249341514]
  The rapid development of AI-generated content (AIGC) has led to the misuse of AI-generated images (AIGI) in spreading misinformation.<n>We introduce a large-scale and comprehensive dataset, Holmes-Set, which includes an instruction-tuning dataset with explanations on whether images are AI-generated.<n>Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control.<n>In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization
  arXiv  Detail & Related papers  (2025-07-03T14:26:31Z)
• An Efficient and Mixed Heterogeneous Model for Image Restoration [71.85124734060665]
  Current mainstream approaches are based on three architectural paradigms: CNNs, Transformers, and Mambas.<n>We propose RestorMixer, an efficient and general-purpose IR model based on mixed-architecture fusion.
  arXiv  Detail & Related papers  (2025-04-15T08:19:12Z)
• Mechanistic understanding and validation of large AI models with SemanticLens [13.712668314238082]
  Unlike human-engineered systems such as aeroplanes, the inner workings of AI models remain largely opaque.<n>This paper introduces SemanticLens, a universal explanation method for neural networks that maps hidden knowledge encoded by components.
  arXiv  Detail & Related papers  (2025-01-09T17:47:34Z)
• A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends [67.43992456058541]
  Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their effectiveness in real-world scenarios with complex distortions. The all-in-one image restoration (AiOIR) paradigm has emerged, offering a unified framework that adeptly addresses multiple degradation types.
  arXiv  Detail & Related papers  (2024-10-19T11:11:09Z)
• Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design [63.24275274981911]
  Compound AI Systems consisting of many language model inference calls are increasingly employed. In this work, we construct systems, which we call Networks of Networks (NoNs) organized around the distinction between generating a proposed answer and verifying its correctness. We introduce a verifier-based judge NoN with K generators, an instantiation of "best-of-K" or "judge-based" compound AI systems.
  arXiv  Detail & Related papers  (2024-07-23T20:40:37Z)
• LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks [52.46420522934253]
  We introduce LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks.<n>By employing a single pre-trained self-attention network with weights shared across all members, we train member-specific low-rank matrices for the attention projections.<n>Our method exhibits superior calibration compared to explicit ensembles and achieves similar or better accuracy across various prediction tasks and datasets.
  arXiv  Detail & Related papers  (2024-05-23T11:10:32Z)
• Probabilistic ML Verification via Weighted Model Integration [11.812078181471634]
  Probability formal verification (PFV) of machine learning models is in its infancy. We propose a unifying framework for the PFV of ML systems based on Weighted Model Integration (WMI) We show how successful scaling techniques in the ML verification literature can be generalized beyond their original scope.
  arXiv  Detail & Related papers  (2024-02-07T14:24:04Z)
• Cloud-based XAI Services for Assessing Open Repository Models Under Adversarial Attacks [7.500941533148728]
  We propose a cloud-based service framework that encapsulates computing components and assessment tasks into pipelines. We demonstrate the application of XAI services for assessing five quality attributes of AI models.
  arXiv  Detail & Related papers  (2024-01-22T00:37:01Z)
• Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability [28.67753149592534]
  This study bridges the accountability gap by introducing our effort towards a comprehensive metrics catalogue. Our catalogue delineates process metrics that underpin procedural integrity, resource metrics that provide necessary tools and frameworks, and product metrics that reflect the outputs of AI systems.
  arXiv  Detail & Related papers  (2023-11-22T04:43:16Z)
• Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence [0.0]
  This paper contributes to the discourse on XAI by presenting an empirical evaluation based on a novel framework. It is a hybrid architecture where VAE combined with ensemble stacking and SHapley Additive exPlanations are used for imbalanced classification. The finding reveals that combining ensemble stacking, VAE, and SHAP can. not only lead to better model performance but also provide an easily explainable framework.
  arXiv  Detail & Related papers  (2023-09-25T02:46:19Z)
• LAMBO: Large AI Model Empowered Edge Intelligence [71.56135386994119]
  Next-generation edge intelligence is anticipated to benefit various applications via offloading techniques. Traditional offloading architectures face several issues, including heterogeneous constraints, partial perception, uncertain generalization, and lack of tractability. We propose a Large AI Model-Based Offloading (LAMBO) framework with over one billion parameters for solving these problems.
  arXiv  Detail & Related papers  (2023-08-29T07:25:42Z)
• Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning [22.443211209959497]
  A zero-shot QA system transforms a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings. We propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation.
  arXiv  Detail & Related papers  (2022-06-08T07:36:31Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• KAT: A Knowledge Augmented Transformer for Vision-and-Language [56.716531169609915]
  We propose a novel model - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an end to end encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. An additional benefit of explicit knowledge integration is seen in improved interpretability of model predictions in our analysis.
  arXiv  Detail & Related papers  (2021-12-16T04:37:10Z)

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.