What is recorded here?
Solving a problem can produce a useful procedure or reveal a mistake worth avoiding. EvoLib stores these as modular skills and reflective insights in an evolving library. A new problem retrieves candidate abstractions, the model attempts a solution, and the system updates which abstractions are sampled. Its weighting mechanism considers both immediate usefulness and whether an item helped produce a useful successor. The researchers test this no-weight-update approach on mathematics, coding and interactive tasks. The contribution is the evolving-library algorithm, with its reported comparisons and ablations.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The library relies on self-evaluation, synthetic tests or majority judgments during learning; these can be wrong. Static experiments revisit a task pool, while continual experiments use a stream, so their results are not interchangeable. Some results tie a strong baseline. Weighted token counts are a specified cost proxy, not measured current spending. Generalization to open-ended scientific work remains unknown. The library’s internal credit heuristic is not a determination of scientific authorship or a proof that its abstractions are valid.
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People and systems
Weijia Xu; Alessandro Sordoni; Chandan Singh; Zelalem Gero; Michel Galley; Xingdi Yuan; Jianfeng Gao
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
Research locations and affiliations
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English catalogue narrative
EvoLib: retaining useful skills across test-time problems
Solving a problem can produce a useful procedure or reveal a mistake worth avoiding. EvoLib stores these as modular skills and reflective insights in an evolving library. A new problem retrieves candidate abstractions, the model attempts a solution, and the system updates which abstractions are sampled. Its weighting mechanism considers both immediate usefulness and whether an item helped produce a useful successor. The researchers test this no-weight-update approach on mathematics, coding and interactive tasks. The contribution is the evolving-library algorithm, with its reported comparisons and ablations.
Scientific scope
The library relies on self-evaluation, synthetic tests or majority judgments during learning; these can be wrong. Static experiments revisit a task pool, while continual experiments use a stream, so their results are not interchangeable. Some results tie a strong baseline. Weighted token counts are a specified cost proxy, not measured current spending. Generalization to open-ended scientific work remains unknown. The library’s internal credit heuristic is not a determination of scientific authorship or a proof that its abstractions are valid.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Weijia Xu; Alessandro Sordoni; Chandan Singh; Zelalem Gero; Michel Galley; Xingdi Yuan; Jianfeng Gao
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.