arXiv:2606.07915v1 Announce Type: new Abstract: Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding is prone to error accumulation, which may lead to generating structurally incorrect expressions, especially in complex expression generation scenarios. Existing rectification strategies can alleviate this issue, but they often depend on restarting global search, thereby weakening the efficiency advantage of neural models, and remain susceptible to error accumulation. In this paper, we propose Edit
The continuous drive for more efficient and accurate AI models, especially in complex symbolic regression tasks, necessitates novel approaches to address error accumulation in autoregressive decoding.
Improving the efficiency and accuracy of neural symbolic regression can accelerate scientific discovery and enhance agentic AI systems by providing more robust methods for deriving mathematical expressions and causal relationships.
This paper proposes a method to rectify errors in neural symbolic regression without fully restarting global search, potentially making these models more practically usable and efficient for complex problem-solving.
- · AI researchers and developers
- · Scientific research (physics, chemistry, engineering)
- · AI agent developers
- · Industries that rely on complex modeling
- · Traditional symbolic regression methods (if EditSR proves more efficient)
More accurate and efficient neural symbolic regression models become available for research and applications.
Accelerated discovery of mathematical formulas and underlying data relationships across various scientific and engineering fields.
Enhanced capabilities for AI agents to autonomously formulate hypotheses and models, leading to more sophisticated problem-solving and decision-making.
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Read at arXiv cs.AI