ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
Researchers propose ToolSense, an open-source diagnostic framework to evaluate how well large language models truly understand tools. It generates three benchmarks to expose a knowledge-retrieval dissociation: models that excel on standard ToolBench benchmarks often collapse by 50-64 percentage points on more realistic queries, sometimes falling below embedding-based baselines.
[2606.12451] ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
[Submitted on 4 Jun 2026]
Title:ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
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Abstract:Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck. As embedding-based retrieval approaches rely on compact encoders that may under-capture specialized tool semantics, parametric tool retrieval addresses this by encoding each tool as a virtual token appended to the LLM vocabulary, fine-tuned in two stages (memorization then retrieval SFT) to use the LLM as a retriever, achieving strong performance on standard ToolBench retrieval benchmarks. Yet these benchmarks use verbose, fully-specified queries, and their evaluation applies constrained decoding that restricts outputs to valid token paths, neither reveals whether the model actually understands its tools. We introduce \textbf{ToolSense}, an open-source LLM-powered diagnostic framework that takes any tool catalog as input and automatically generates three benchmarks: a Realistic Retrieval Benchmark (RRB) with queries at three ambiguity tiers, an MCQ probing benchmark, and a QA probing benchmark. Applying ToolSense to ToolBench (~47k tools) and evaluating five parametric model training configurations reveals a knowledge-retrieval dissociation: on RRB queries, several configurations collapse by ~50-64 percentage points compared to fully-specified ToolBench benchmarks, falling below the embedding-model baseline. Additionally, despite strong retrieval performance, some models score near-random on factual probes, suggesting a knowledge-retrieval dissociation. We open-source the ToolSense framework and the ToolBench diagnostic benchmarks at this https URL.
Subjects:
Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2606.12451 [cs.AI]
(or arXiv:2606.12451v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12451
arXiv-issued DOI via DataCite
Submission history
From: Ashutosh Hathidara [view email] [v1] Thu, 4 Jun 2026 14:59:56 UTC (665 KB)
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