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TNO

Academic institutioneurope · nl
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Research library51linked papers
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Selected work

Representative Papers

Evolving Executable Pipeline Programs for AutoML with Language Models

Aug 17, 2026

This study addresses the limitations of restricted search spaces and non-editable outputs in traditional AutoML by proposing LACE, a novel code-based tabular AutoML framework. Leveraging large language models as mutation operators within an evolutionary algorithm, LACE optimizes populations of scikit-learn-compatible pipelines at the code level. Unlike conventional approaches constrained by predefined structures, this paradigm generates transparent, editable code rather than opaque black-box models, thereby enhancing reusability and interpretability. Extensive evaluations across 68 OpenML tasks demonstrate that LACE significantly outperforms auto-sklearn and achieves performance comparable to AutoGluon. By unifying comprehensive search coverage with high transparency, LACE effectively overcomes the rigidity of existing AutoML systems while delivering human-readable solutions that facilitate downstream refinement and domain-specific adaptation.

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Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration

Jun 17, 2026

This study addresses the challenge of low initial coordination efficiency in urban search and rescue scenarios, where robots struggle to rapidly adapt to dynamic human-robot collaborative environments. To overcome this limitation, the work introduces a reusable episodic memory mechanism that encodes historical collaboration patterns into a knowledge graph. By integrating graph representation learning, the system automatically retrieves and initializes optimal behavioral policies prior to new tasks, enabling effective cross-task knowledge transfer. Experimental evaluations on the MATRX simulation platform demonstrate significant improvements in collaborative performance: rescue success rates increase from 25.7% to 41.3%, and average task completion time is reduced by 283 seconds, with particularly pronounced gains during the early phases of missions.

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Recent publications

Latest Papers

Evolving Executable Pipeline Programs for AutoML with Language Models

Aug 17, 2026

This study addresses the limitations of restricted search spaces and non-editable outputs in traditional AutoML by proposing LACE, a novel code-based tabular AutoML framework. Leveraging large language models as mutation operators within an evolutionary algorithm, LACE optimizes populations of scikit-learn-compatible pipelines at the code level. Unlike conventional approaches constrained by predefined structures, this paradigm generates transparent, editable code rather than opaque black-box models, thereby enhancing reusability and interpretability. Extensive evaluations across 68 OpenML tasks demonstrate that LACE significantly outperforms auto-sklearn and achieves performance comparable to AutoGluon. By unifying comprehensive search coverage with high transparency, LACE effectively overcomes the rigidity of existing AutoML systems while delivering human-readable solutions that facilitate downstream refinement and domain-specific adaptation.

0 citationsRead paper

Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration

Jun 17, 2026

This study addresses the challenge of low initial coordination efficiency in urban search and rescue scenarios, where robots struggle to rapidly adapt to dynamic human-robot collaborative environments. To overcome this limitation, the work introduces a reusable episodic memory mechanism that encodes historical collaboration patterns into a knowledge graph. By integrating graph representation learning, the system automatically retrieves and initializes optimal behavioral policies prior to new tasks, enabling effective cross-task knowledge transfer. Experimental evaluations on the MATRX simulation platform demonstrate significant improvements in collaborative performance: rescue success rates increase from 25.7% to 41.3%, and average task completion time is reduced by 283 seconds, with particularly pronounced gains during the early phases of missions.

0 citationsRead paper