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Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across inputs. We introduce RISR, a residual-informed method that uses these error patterns to guide formula discovery and learn which corrections are worth fitting. A residual...
Codebook Agent is the most accurate method on all six benchmarks the authors compare, and an MLP proxy that reads the flattened adjacency, regressed on measured utility and per-task normalized token cost, reranks the top decoded candidates in a single batched forward pass.
This work introduces RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem and shows that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators.
A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views, is proposed, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.
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