Public articles linked to the same research event.
arXiv The work proposes Conformal Interval-Driven Self-Evolution (CISE), which builds candidate-specific reward intervals via conditional conformal inference and iteration-wise online density-ratio estimation, uses conservative interval-based rewards for evolutionary feedback, and returns candidates only when all required property intervals lie entirely within their feasible regions; across three self-evolving search tasks in materials science, all candidates returned by CISE are true positives under high-fidelity evaluation, whereas baselines return more candidates but include false positives.
The work proposes Conformal Interval-Driven Self-Evolution (CISE), which builds candidate-specific reward intervals via conditional conformal inference and iteration-wise online density-ratio estimation, uses conservative interval-based rewards for evolutionary feedback, and returns candidates only when all required property intervals lie entirely within their feasible regions; across three self-evolving search tasks in materials science, all candidates returned by CISE are true positives under high-fidelity evaluation, whereas baselines return more candidates but include false positives.
The work proposes Conformal Interval-Driven Self-Evolution (CISE), which builds candidate-specific reward intervals via conditional conformal inference and iteration-wise online density-ratio estimation, uses conservative interval-based rewards for evolutionary feedback, and returns candidates only when all required property intervals lie entirely within their feasible regions; across three self-evolving search tasks in materials science, all candidates returned by CISE are true positives under high-fidelity evaluation, whereas baselines return more candidates but include false positives.
The work proposes Conformal Interval-Driven Self-Evolution (CISE), which builds candidate-specific reward intervals via conditional conformal inference and iteration-wise online density-ratio estimation, uses conservative interval-based rewards for evolutionary feedback, and returns candidates only when all required property intervals lie entirely within their feasible regions; across three self-evolving search tasks in materials science, all candidates returned by CISE are true positives under high-fidelity evaluation, whereas baselines return more candidates but include false positives.