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Lookahead-R 将工具检索重构为受资源约束的序贯决策问题，用轻量级执行感知代理世界模型驱动成本敏感的 MCTS，在 ToolBench 的 I3 分割上取得 91.40% 的 NDCG@5，超过 ToolGen (90.16%)。
Cartograph is a federated MCP proxy that enhances AI agent tool discovery by using operator-attested capability cards and a three-layer analysis to reduce token usage and improve retrieval accuracy.
This paper introduces the tool menu as an execution prior and proposes the State-Path Tool Menu framework to improve online agent success rates by learning execution routes for tool retrieval in multi-step tasks.
TRACE introduces a two-stage curriculum to preserve parametric tool knowledge in enterprise LLMs while enabling fast single-beam greedy decoding, achieving improved accuracy and recall over baselines.
ToolSense is an open-source diagnostic framework that generates three benchmarks (realistic retrieval, MCQ probing, QA probing) to audit LLMs' parametric tool knowledge, revealing a knowledge-retrieval dissociation where strong retrieval performance can coexist with poor factual understanding.
BioManus is an MCP-native biomedical agent system that uses graph-scaffolded planning over structured biological capabilities instead of flat prompt-based tool retrieval, achieving better context efficiency and execution accuracy on biomedical benchmarks. The system introduces a BioinfoMCP Compiler to standardize heterogeneous bioinformatics tools and organizes them as a typed heterogeneous MCP graph for scalable reasoning.
CoHyDE introduces an iterative co-training procedure for an LLM rewriter and a dense encoder to improve tool retrieval from large API catalogs. It outperforms single-component baselines, especially on vague queries, by training both components together using InfoNCE and DPO.