LLM Agents Already Know When to Call Tools -- Even Without Reasoning

• URL: http://arxiv.org/abs/2605.09252v1
• Date: Sun, 10 May 2026 01:37:40 GMT
• Title: LLM Agents Already Know When to Call Tools -- Even Without Reasoning
• Abstract summary: LLM agents tend to call tools indiscriminately, even when the model can answer directly.<n>No existing benchmark systematically studies when a tool call is actually needed.<n>We propose When2Tool, a benchmark of 18 environments spanning three categories of tool necessity.
• Score: 25.40369702634587
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latency, yet no existing benchmark systematically studies when a tool call is actually needed. We propose When2Tool, a benchmark of 18 environments (15 single-hop, 3 multi-hop) spanning three categories of tool necessity -- computational scale, knowledge boundaries, and execution reliability -- each with controlled difficulty levels that create a clear decision boundary between tool-necessary and tool-unnecessary tasks. We evaluate two families of training-free baselines: Prompt-only (varying the prompt to discourage unnecessary calls) and Reason-then-Act (requiring the model to reason about tool necessity before acting). Both provide limited control: Prompt-only suppresses necessary calls alongside unnecessary ones, and Reason-then-Act still incurs a disproportionate accuracy cost on hard tasks. To understand why these baselines fail, we probe the models' hidden states and find that tool necessity is linearly decodable from the pre-generation representation with AUROC 0.89--0.96 across six models, substantially exceeding the model's own verbalized reasoning. This reveals that models already know when tools are needed, but fail to act on this knowledge during generation. Building on this finding, we propose Probe&Prefill, which uses a lightweight linear probe to read the hidden-state signal and prefills the model's response with a steering sentence. Across all models tested, Probe&Prefill reduces tool calls by 48% with only 1.7% accuracy loss, while the best baseline at comparable accuracy only reduces 6% of tool calls, or achieves a similar tool call reduction but incurs a 5$\times$ higher accuracy loss. Our code is available at https://github.com/Trustworthy-ML-Lab/when2tool

Related papers

• Tunable Tool-Call Rates in LLM Agents via Representation Steering [45.332672398938136]
  We show that a tool can be controlled by a single linear direction in its residual stream, extracted without any training from the model's own tool-use preference signal.<n>We also show that the direction generalizes to unseen tools with strength comparable to each tool's own direction without favoring any specific tool choice.
  arXiv  Detail & Related papers  (2026-08-25T22:35:18Z)
• Joint Optimization of Tool Creation and Use for Large Language Model Agents [69.07066297088187]
  SMITH (-grounded Multi-task Iterative Tool Honing) trains tool creation and tool use inside a single policy.<n>A 4B Qwen3 trained with SMITH on 13 procedural reasoning tasks with exact verifiers reaches 79.8 macro-average accuracy on held-out tasks.<n>It also reaches 40.4 on TabMWP-Hard and 42.6 on out-of-domain GQA (+7.6 over the best same-backbone inference-time baseline)
  arXiv  Detail & Related papers  (2026-08-25T13:59:34Z)
• ToolFailBench: Diagnosing Tool-Use Failures in LLM Agents [0.0]
  ToolFailBench is a diagnostic benchmark for measuring tool-use failures.<n>We label each trace with Tool-Skip, Result-Ignore, Output-Fabrication, and Unnecessary-Tool-Use.
  arXiv  Detail & Related papers  (2026-07-06T05:25:21Z)
• ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents [9.793491545168575]
  ToolGate predicts execute/skip decisions from trajectory text and simple structural features.<n>Across two Qwen3-VL backbones, ToolGate reduces token cost to 64-69% of the unrestricted ReAct baseline.
  arXiv  Detail & Related papers  (2026-06-02T02:44:27Z)
• Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use [47.29360932085394]
  Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools.<n>We introduce a model-adaptive definition of tool-necessity, grounded in each model's empirical performance.<n>We find substantial mismatches of 26.5-54.0% and 30.8-41.8%, respectively.
  arXiv  Detail & Related papers  (2026-05-13T18:59:28Z)
• Teaching Thinking Models to Reason with Tools: A Full-Pipeline Recipe for Tool-Integrated Reasoning [59.74608632210439]
  We show how to inject natural tool-use behavior into a strong thinking model without sacrificing its no-tool reasoning ability.<n>Our recipe yields models that achieve state-of-the-art performance in a wide range of benchmarks among open-source models.
  arXiv  Detail & Related papers  (2026-05-07T14:23:21Z)
• To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling [13.42769424615184]
  We introduce a principled framework inspired by decision-making theory to evaluate web search tool-use decisions.<n>We find that models' perceived need and utility of tool calls are often misaligned with their true need and utility.<n>Our estimators enable simple controllers that can improve decision quality and lead to stronger task performance.
  arXiv  Detail & Related papers  (2026-05-01T15:38:13Z)
• When to Trust Tools? Adaptive Tool Trust Calibration For Tool-Integrated Math Reasoning [58.75883713573783]
  Tool-Integrated Reasoning has emerged as a promising paradigm that incorporates tool call and execution within the reasoning trajectory.<n>We find that when the reasoning of the model conflicts with the tool results, the model tends to believe in its own reasoning.<n>We introduce Adaptive Tool Trust (ATTC), a framework that guides the model to adaptively choose to trust or ignore the tool results.
  arXiv  Detail & Related papers  (2026-04-09T14:14:37Z)
• ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning [11.99927786717109]
  ToolMATH turns math problems into a controlled, correctness-checkable benchmark with tool sets.<n>ToolMATH provides actionable diagnostic evidence of failure modes in tool-augmented agents.
  arXiv  Detail & Related papers  (2026-02-24T09:23:12Z)
• One Model to Critique Them All: Rewarding Agentic Tool-Use via Efficient Reasoning [54.580646706013965]
  Reward models (RMs) play a critical role in aligning large language models with human preferences.<n>We introduce ToolRM, a family of lightweight generative RMs tailored for general tool-use scenarios.<n>To build these models, we propose a novel pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling.
  arXiv  Detail & Related papers  (2025-10-30T06:08:27Z)
• Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning [63.2198957755528]
  We propose Tool-MVR, a novel Tool-Augmented LLM that achieves comprehensive System 2 reasoning through two key innovations.<n>Specifically, we first introduce Multi-Agent Meta-Verification (MAMV), a systematic pipeline that rigorously validates APIs, queries, and reasoning trajectories.<n>Second, we propose Exploration-based Reflection Learning (EXPLORE), which enhances tool reflection capabilities by leveraging tool feedback.
  arXiv  Detail & Related papers  (2025-06-05T04:35:49Z)
• Acting Less is Reasoning More! Teaching Model to Act Efficiently [87.28134636548705]
  Tool-integrated reasoning augments large language models with the ability to invoke external tools to solve tasks.<n>Current approaches typically optimize only for final correctness without considering the efficiency or necessity of external tool use.<n>We propose a framework that encourages models to produce accurate answers with minimal tool calls.<n>Our approach reduces tool calls by up to 68.3% and improves tool productivity by up to 215.4%, while maintaining comparable answer accuracy.
  arXiv  Detail & Related papers  (2025-04-21T05:40:05Z)
• Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
  We propose MeCo, an adaptive decision-making strategy for external tool use.<n>MeCo quantifies metacognitive scores by capturing high-level cognitive signals in the representation space.<n>MeCo is fine-tuning-free and incurs minimal cost.
  arXiv  Detail & Related papers  (2025-02-18T15:45:01Z)
• ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models [43.895478182631116]
  Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. To address this challenge, we introduce a comprehensive diagnostic benchmark, ToolBH. For breadth, we consider three scenarios based on the characteristics of the toolset: missing necessary tools, potential tools, and limited functionality tools. The results show the significant challenges presented by the ToolBH benchmark.
  arXiv  Detail & Related papers  (2024-06-28T16:03:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.