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A controlled study across several versions of two major TFM families finds a consistent pattern: feature engineering gains are concentrated in earlier model generations and become negligible for the strongest models, suggesting that stronger TFMs depend less on explicitly engineered input representations.
This paper proposes HYSET, short for HYperedge-based SEt-level Tool retrieval, which forms tool retrieval as query-conditioned hyperedge prediction on a tool co-invocation hypergraph, under which the tool set itself becomes the unit of scoring and most existing retrieval paradigms reduce to restricted instances.
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