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Graph Engineering is introduced, an emerging paradigm for next-generation agent systems that provides a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution.
A Structure-Internalized Rule Language Model (SIRLM) is proposed, which centers on structural rule generation to couple the parametric learning of structural knowledge with the faithfulness evaluation of reasoning logic, enabling LLMs to anchor tightly to KG-grounded evidence.
TTS-Design is proposed, a test-time compute scaling framework that enhances protein sequence design without retraining models or relying on larger training datasets, and can consistently improve sequence recovery and structural reliability across different backbone models, without retraining or increasing model size.
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