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EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification

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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.

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Authors: Da Li, Xinxin Li, Xingyu Cui, Jin Xu, Juan Zhang, Junping Yin
Released: 2026-06-09 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: rect, correct, rate, chain, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.07915

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