This paper explores the integration of predictive coding, a core mechanism in neuroscience, with symbolic computation to create a novel approach to artificial intelligence reasoning. The central argument is that by combining the pattern recognition capabilities of neural networks with the rule-based reasoning of symbolic systems, we can develop more robust and efficient AI systems. The proposed architecture utilizes neural networks to predict future inputs within a sequence, while symbolic systems generate rules to correct these predictions, establishing a continuous feedback loop. This approach offers a more grounded and explainable form of AI reasoning, addressing limitations of purely neural or purely symbolic systems. We detail the core components of this hybrid system, focusing on the dynamics of prediction and correction, and outline potential avenues for future research. The goal is to move beyond current AI paradigms by creating systems that learn and reason in a manner more aligned with how the human brain operates. The key contribution lies in the formalized process of integrating predictive signals with symbolic constraints, leading to a dynamic system capable of adapting to complex and noisy environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing deployment of deep learning models in critical applications necessitates a means to understand and trust their decisions. However, deep learning models are often 'black boxes,' offering limited insight into the reasoning behind their predictions. This paper proposes a novel approach to explain model decisions by leveraging graph-based causal inference. We represent the model's decision-making process as a graph, where nodes represent input features and the model's output, and edges represent causal relationships inferred from the model's behavior. By analyzing the structure of this graph, we can identify the key factors driving a specific prediction, providing a clear and interpretable explanation. The core of this approach is to move beyond simply identifying correlations between features and the output and instead to explicitly model the causal influences. This offers a more robust and reliable explanation than traditional methods. We demonstrate the effectiveness of this technique through a theoretical framework and discuss its potential applications in various domains. The resulting visual representation of the causal graph provides a significantly improved understanding of model decision-making compared to purely correlational explanations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to enhancing the interpretability and robustness of neural models by integrating symbolic reasoning. We propose a hybrid model architecture where a neural network's attention weights are constrained and guided by symbolic rules. Simultaneously, the neural network learns to refine these rules, creating a feedback loop that improves both interpretability and robustness. The core mechanism leverages the precision and explainability of symbolic systems to address the inherent opacity of deep neural networks. We demonstrate that this approach significantly improves the model's ability to justify its decisions, particularly in scenarios where logical constraints are relevant. The proposed method provides a framework for building more trustworthy and understandable AI systems. This work contributes to the growing field of neuro-symbolic AI and offers a practical solution for enhancing the explainability of complex neural models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to explainable artificial intelligence (XAI) by leveraging causal intervention methods. The core idea is to model systems using causal graphs and then employ techniques like do-calculus for precise intervention and control. Traditional intervention methods often lack interpretability, leading to uncertainties in control outcomes. This research addresses this limitation by integrating causality into XAI, aiming to enhance the accuracy and reliability of intervention strategies. The methodology focuses on understanding the 'what' and 'why' behind interventions through causal reasoning, offering a pathway to more trustworthy and explainable AI systems. The presented framework provides a robust mechanism for evaluating and refining intervention policies, ultimately contributing to a greater degree of transparency and control in AI-driven decision-making. This work emphasizes the importance of causal reasoning in building truly intelligent and accountable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Explainable AI (XAI) is a critical area of research aimed at increasing the transparency and interpretability of complex AI models. Traditional XAI techniques often focus on post-hoc explanations, which can be misleading or lack a deep understanding of the underlying causal relationships. This paper proposes a novel methodology leveraging probabilistic programming to achieve truly explainable AI by explicitly modeling and exploring causal chains. The core idea is to represent a system's causal graph using probabilistic programming, allowing for systematic tracing of the pathways leading to a specific prediction. We quantify the associated probabilities at each step within these causal chains, providing a rigorous and transparent explanation. This approach moves beyond simple feature importance analysis, offering a deeper understanding of how factors contribute to a model's decision-making process. The methodology offers a robust framework for building XAI systems, particularly in domains where causal understanding is paramount.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The increasing sophistication of cyber threats necessitates robust and reliable cybersecurity systems. However, many current AI-based threat detection systems operate as "black boxes," offering limited transparency and explainability. This paper proposes a novel approach to cybersecurity threat detection that integrates explainable artificial intelligence (XAI) with causal reasoning. The core idea is to move beyond simply identifying suspicious events and instead, to understand the *reasons* behind those detections by uncovering the causal relationships involved. This system will provide clear explanations for threat detection decisions, allowing security analysts to validate findings, improve response strategies, and ultimately enhance the overall effectiveness of cybersecurity defenses. We outline a framework for building such a system, focusing on the integration of anomaly detection techniques with causal inference methods. The proposed system addresses the critical need for trust and accountability in AI-driven cybersecurity, offering a pathway to more intelligent and understandable threat mitigation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the integration of causal inference with explainable artificial intelligence (XAI) to enhance decision support systems. Traditional XAI methods often struggle with providing true explanations for decisions, particularly in complex scenarios where correlation doesn't imply causation. This research proposes a novel framework leveraging causal inference techniques to build more robust and transparent decision support systems. We construct causal graphs representing the relationships between variables, apply causal inference algorithms to identify and quantify these relationships, and then translate the resulting insights into human-understandable explanations. The core aim is to move beyond simply identifying correlations to understanding the underlying causal mechanisms driving decisions. This approach addresses the critical need for transparency and trust in AI-driven decision-making, particularly in domains where accountability and understanding are paramount. The framework is demonstrated through a conceptual analysis and outlines potential implementation strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the integration of causal inference with explainable artificial intelligence (XAI) to enhance decision support systems. Traditional XAI methods often struggle with providing true explanations for decisions, particularly in complex scenarios where correlation doesn't imply causation. This research proposes a novel framework leveraging causal inference techniques to build more robust and transparent decision support systems. We construct causal graphs representing the relationships between variables, apply causal inference algorithms to identify and quantify these relationships, and then translate the resulting insights into human-understandable explanations. The core aim is to move beyond simply identifying correlations to understanding the underlying causal mechanisms driving decisions. This approach addresses the critical need for transparency and trust in AI-driven decision-making, particularly in domains where accountability and understanding are paramount. The framework is demonstrated through a conceptual analysis and outlines potential implementation strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent vulnerability of FL to malicious participants, known as Byzantine attacks, poses a significant threat to the integrity and convergence of the learning process. This paper proposes a novel framework for decentralized federated optimization with Byzantine fault tolerance leveraging blockchain technology. Our approach utilizes a blockchain network to establish a tamper-proof record of model updates, ensuring data integrity and enabling robust aggregation even in the presence of malicious actors. The core mechanism involves a distributed consensus algorithm on the blockchain to validate and aggregate model updates, guaranteeing Byzantine fault tolerance. We demonstrate the feasibility and effectiveness of this approach, providing a secure and reliable foundation for decentralized FL. The key contributions include a blockchain-based architecture for FL, a Byzantine fault tolerance mechanism, and a system for ensuring model integrity. The proposed system addresses the critical challenge of security in FL, offering a pathway toward trustworthy and resilient distributed learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized datasets while preserving data privacy. However, current FL security mechanisms often rely on centralized trust assumptions, posing significant vulnerabilities. This paper proposes a novel security framework leveraging Proof-Carrying Cryptography (PCC) within the FL architecture. The core idea is to enable each participant to generate and maintain cryptographic proofs attesting to the correctness of their local model updates. These proofs are then presented to the central aggregator, who can verify the updates without needing direct access to the underlying data. This approach establishes a robust security layer based on verifiable computation, effectively mitigating the risks posed by malicious or compromised participants. We outline the technical details of integrating PCC into the FL process, addressing key challenges related to proof generation, verification, and scalability. The proposed framework significantly enhances the security and trustworthiness of FL systems, paving the way for broader adoption in sensitive applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to enhancing data privacy in federated learning by integrating differential privacy with homomorphic encryption. Federated learning, a distributed machine learning paradigm, allows training models across multiple devices without directly sharing raw data. However, this approach still exposes participants to potential privacy risks. Our method leverages homomorphic encryption to enable computations on encrypted data, mitigating the risk of direct data exposure. Subsequently, we apply differential privacy techniques to the aggregated model updates, providing a rigorous guarantee against individual data disclosure. The core claim is that this combined strategy offers a robust solution for preserving data privacy in federated learning settings. We outline the key mechanisms involved, demonstrating how homomorphic encryption is used to perform computations on encrypted data and how differential privacy is applied to the resulting aggregated model updates. The proposed framework addresses the unique challenges posed by federated learning, offering a significant improvement in privacy protection.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to achieving robust differential privacy within federated learning systems, specifically designed to mitigate the risks posed by Byzantine attacks. Traditional federated learning methods frequently struggle to provide rigorous privacy guarantees when confronted with malicious participants who intentionally distort model updates. Our method integrates a Byzantine fault-tolerant consensus mechanism with adaptive noise injection, leveraging participant trustworthiness scores to dynamically adjust privacy protection. The key innovation lies in the proactive assessment of participant reliability and the subsequent tailoring of noise parameters, resulting in a significantly more resilient privacy framework. We demonstrate the effectiveness of this approach through a theoretical analysis and outline its potential for deployment in scenarios where data heterogeneity and adversarial behavior are significant concerns. The core claim is achieving robust differential privacy guarantees in federated learning scenarios, specifically addressing the vulnerability to Byzantine attacks where malicious participants attempt to compromise privacy. The core mechanism employs a Byzantine fault-tolerant consensus mechanism within the federated learning framework, combined with adaptive noise injection based on participant trustworthiness scores derived from local model variations.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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