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Knowledge, Rules and Their Embeddings: Two Paths towards Neuro-Symbolic JEPA

arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems offer rigorous, interpretable logic but suffer from discrete boundaries and NP-hard combinatorial explosion. To bridge this divide, we propose a bidirectional neuro-symbolic framework centered around Rule-informed Joint-Embedding Predictive Architectures (RiJEPA). In the first direction, we inject structured inductive biases into JEPA training via Energy-Based Constraints (EBC) and a multi-modal dual-encoder architecture. This fundamentally reshapes the representation manifold, replacing arbitrary statistical correlations with geometrically sound logical basins. In the second direction, we demonstrate that by relaxing rigid, discre

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Yongchao Huang, Hassan Raza · · 1 min read · 11 views
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arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems offer rigorous, interpretable logic but suffer from discrete boundaries and NP-hard combinatorial explosion. To bridge this divide, we propose a bidirectional neuro-symbolic framework centered around Rule-informed Joint-Embedding Predictive Architectures (RiJEPA). In the first direction, we inject structured inductive biases into JEPA training via Energy-Based Constraints (EBC) and a multi-modal dual-encoder architecture. This fundamentally reshapes the representation manifold, replacing arbitrary statistical correlations with geometrically sound logical basins. In the second direction, we demonstrate that by relaxing rigid, discrete symbolic rules into a continuous, differentiable logic, we can bypass traditional combinatorial search for new rule generation. By leveraging gradient-guided Langevin diffusion within the rule energy landscape, we introduce novel paradigms for continuous rule discovery, which enable unconditional joint generation, conditional forward and abductive inference, and marginal predictive translation. Empirical evaluations on both synthetic topological simulations and a high-stakes clinical use case confirm the efficacy of our approach. Ultimately, this framework establishes a powerful foundation for robust, generative, and interpretable neuro-symbolic representation learning.

Executive Summary

This article proposes the Rule-informed Joint-Embedding Predictive Architectures (RiJEPA) framework, a bidirectional neuro-symbolic approach that bridges the divide between self-supervised predictive architectures and traditional rule-based inference systems. By injecting structured inductive biases and relaxing rigid symbolic rules, RiJEPA enables the internalization of verifiable human logic and bypasses traditional combinatorial search for new rule generation. Empirical evaluations confirm its efficacy on both synthetic topological simulations and a high-stakes clinical use case. The framework establishes a powerful foundation for robust, generative, and interpretable neuro-symbolic representation learning. This has significant implications for applications in artificial intelligence, machine learning, and cognitive science.

Key Points

  • ▸ RiJEPA framework combines the strengths of self-supervised predictive architectures and traditional rule-based inference systems
  • ▸ Structured inductive biases are injected into JEPA training via Energy-Based Constraints (EBC) and a multi-modal dual-encoder architecture
  • ▸ Rigid symbolic rules are relaxed into a continuous, differentiable logic, enabling novel paradigms for continuous rule discovery

Merits

Strength

RiJEPA's ability to internalize verifiable human logic and bypass traditional combinatorial search for new rule generation

Interpretability

RiJEPA's framework enables robust, generative, and interpretable neuro-symbolic representation learning

Flexibility

RiJEPA's ability to handle both unconditional joint generation and conditional forward and abductive inference

Demerits

Limitation

The computational complexity of RiJEPA's framework, particularly when dealing with large-scale datasets

Scalability

The potential challenges in scaling RiJEPA to handle complex, high-dimensional data

Robustness

The need for further investigation into RiJEPA's robustness to noisy or adversarial data

Expert Commentary

The RiJEPA framework represents a significant advancement in the field of neuro-symbolic learning, offering a powerful approach to representation learning that combines the strengths of self-supervised predictive architectures and traditional rule-based inference systems. While the framework shows great promise, further research is needed to fully explore its potential and address its limitations. In particular, the need for more robust and scalable frameworks is critical for widespread adoption in high-stakes applications.

Recommendations

  • ✓ Further research is needed to develop more robust and scalable versions of RiJEPA
  • ✓ The development of more practical applications of RiJEPA in areas such as natural language processing and computer vision
  • ✓ The need for policymakers to engage with the research community to inform policy decisions related to the development and deployment of AI systems

Sources

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