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arxiv: 2510.22977 · v2 · submitted 2025-10-27 · 💻 cs.LG · cs.AI

The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination

Chenlong Yin , Zeyang Sha , Shiwen Cui , Changhua Meng , Zechao Li This is my paper

Pith reviewed 2026-05-18 04:06 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM reasoningtool hallucinationreinforcement learningAI agentshallucination mitigationreasoning reliability trade-off
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0 comments X p. Extension

The pith

Enhancing LLM reasoning through RL causally increases tool hallucination in proportion to performance gains.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether improving how well large language models reason also makes them hallucinate tools they do not have or should not use. Experiments show that training for better reasoning raises hallucination rates on a diagnostic benchmark, and this happens even when the training uses only math problems instead of tool tasks. The increase appears whether reasoning is added by reinforcement learning, supervised fine-tuning, or just by asking the model to think step by step at inference time. If the finding holds, efforts to build more capable agents will need to address a built-in tension between stronger thinking and reliable tool use.

Core claim

Progressively enhancing reasoning through RL increases tool hallucination proportionally with task performance gains. This effect transcends overfitting because training on non-tool tasks such as mathematics still amplifies later tool hallucination. The same rise occurs when reasoning is instilled by supervised fine-tuning and when it is only elicited at inference by switching from direct answers to step-by-step thinking. Mechanistically, reasoning RL disproportionately collapses tool-reliability-related representations, and hallucinations surface as amplified divergences concentrated in late-layer residual streams.

What carries the argument

SimpleToolHalluBench, a diagnostic benchmark that measures tool hallucination in two controlled failure modes: no tool available and only distractor tools available.

If this is right

  • Training on mathematics tasks still amplifies subsequent tool hallucination on the benchmark.
  • Eliciting step-by-step thinking at inference time raises tool hallucination even without additional training.
  • Mitigation methods such as prompt engineering or DPO reduce hallucination but also degrade task utility.
  • Reasoning enhancement methods inherently amplify tool hallucination rather than improving capability alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Future agent systems may need separate modules for reasoning and tool selection to avoid the amplification effect.
  • The reliability-capability trade-off could appear in other reliability problems such as factual hallucination when reasoning is strengthened.
  • Training objectives that jointly optimize task performance and tool-use accuracy would be a direct response to the observed collapse of reliability representations.

Load-bearing premise

The benchmark's two failure modes isolate tool hallucination without confounding effects from task difficulty or prompt formatting that could independently drive both reasoning gains and hallucination rates.

What would settle it

If RL training that improves performance on non-tool tasks such as mathematics produces no rise in hallucination rates when models are later tested on SimpleToolHalluBench, the claimed causal relationship would be falsified.

Figures

Figures reproduced from arXiv: 2510.22977 by Changhua Meng, Chenlong Yin, Shiwen Cui, Zechao Li, Zeyang Sha.

Figure 1
Figure 1. Figure 1: Overview of our key findings. Left: Reinforcement learning for reasoning enhancement increases tool hallu￾cination rates alongside task performance gains. Middle: Mechanistic analysis reveals that reasoning RL destabilizes tool-reliability-related representations in the model’s internal layers. Right: Mitigation strategies expose a fundamen￾tal trade-off—reducing hallucination consistently degrades utility… view at source ↗
Figure 2
Figure 2. Figure 2: An overview of model performance during the training of ReCall (Chen et al., 2025). [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of model performance during GRPO training on GSM8K (Cobbe et al., 2021). [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Layer-wise representation stability after Reasoning RL. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Component-wise discrimination scores across layers. The heatmap shows how distinguishable correct and hallucinated responses are within different model components. Residual stream components (resid mid and resid post) exhibit substantially higher discrimination scores in late layers (>0.14), while attention and MLP outputs show consistently lower scores (<0.08). observed in attention (avg. 0.06) and MLP (a… view at source ↗
read the original abstract

Enhancing the reasoning capabilities of Large Language Models (LLMs) is a key strategy for building Agents that "think then act." However, recent observations, like OpenAI's o3, suggest a paradox: stronger reasoning often coincides with increased hallucination, yet no prior work has systematically examined whether reasoning enhancement itself causes tool hallucination. To address this gap, we pose the central question: Does strengthening reasoning increase tool hallucination? To answer this, we introduce SimpleToolHalluBench, a diagnostic benchmark measuring tool hallucination in two failure modes: (i) no tool available, and (ii) only distractor tools available. Through controlled experiments, we establish three key findings. First, we demonstrate a causal relationship: progressively enhancing reasoning through RL increases tool hallucination proportionally with task performance gains. Second, this effect transcends overfitting - training on non-tool tasks (e.g., mathematics) still amplifies subsequent tool hallucination. Third, the effect is method-agnostic, appearing when reasoning is instilled via supervised fine-tuning and when it is merely elicited at inference by switching from direct answers to step-by-step thinking. We also evaluate mitigation strategies including Prompt Engineering and Direct Preference Optimization (DPO), revealing a fundamental reliability-capability trade-off: reducing hallucination consistently degrades utility. Mechanistically, Reasoning RL disproportionately collapses tool-reliability-related representations, and hallucinations surface as amplified divergences concentrated in late-layer residual streams. These findings reveal that current reasoning enhancement methods inherently amplify tool hallucination, highlighting the need for new training objectives that jointly optimize for capability and reliability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript examines whether enhancing reasoning capabilities in LLMs causes increased tool hallucination. It introduces SimpleToolHalluBench, a diagnostic benchmark with two failure modes (no tool available; only distractor tools available). Through controlled experiments, the authors report three findings: (1) progressively enhancing reasoning via RL increases tool hallucination proportionally with task performance gains; (2) the effect persists even when training on non-tool tasks such as mathematics; (3) the effect appears under both SFT and inference-time step-by-step prompting. Mitigation experiments with prompt engineering and DPO reveal a reliability-capability trade-off, and mechanistic analysis indicates that reasoning RL collapses tool-reliability representations, with hallucinations appearing as amplified divergences in late-layer residual streams.

Significance. If the central causal claim holds after addressing potential confounds, the work is significant for highlighting an inherent trade-off in current reasoning-enhancement techniques that could affect reliable agent development. Strengths include the new diagnostic benchmark, experiments across multiple enhancement methods (RL, SFT, inference-time), and the mechanistic investigation of representation collapse. These elements provide empirical grounding and falsifiable predictions about the proportionality of hallucination increases with capability gains.

major comments (2)
  1. [SimpleToolHalluBench and experimental controls] The two failure modes in SimpleToolHalluBench may not isolate pure tool hallucination. Stronger reasoning could independently increase output length, number of reasoning steps, or tool-seeking attempts, raising the chance of naming non-existent or distractor tools. The RL and SFT controls do not hold output length or reasoning depth constant across conditions, which risks confounding the reported proportional increase with task performance (see experimental setup and results on RL training).
  2. [Abstract and Results sections] The abstract and results do not report statistical significance tests, error bars on hallucination rates, or how task performance gains were measured independently of hallucination counts. This weakens support for the proportionality claim and the three key findings.
minor comments (2)
  1. [Mechanistic analysis] The mechanistic analysis of late-layer residual streams would benefit from additional figures showing the divergence metrics across layers for different reasoning levels.
  2. [Experimental details] Clarify the exact models, dataset sizes, and number of runs used in the RL and SFT experiments to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your insightful comments on our work. We respond to each major comment below and indicate the changes we will make to the manuscript.

read point-by-point responses

  1. Referee: [SimpleToolHalluBench and experimental controls] The two failure modes in SimpleToolHalluBench may not isolate pure tool hallucination. Stronger reasoning could independently increase output length, number of reasoning steps, or tool-seeking attempts, raising the chance of naming non-existent or distractor tools. The RL and SFT controls do not hold output length or reasoning depth constant across conditions, which risks confounding the reported proportional increase with task performance (see experimental setup and results on RL training).

    Authors: We thank the referee for highlighting this potential confound. While stronger reasoning may lead to longer outputs, our benchmark specifically measures tool hallucination as the rate of calling unavailable or distractor tools in the defined failure modes. To address the concern about not holding length and depth constant, we will include additional experiments in the revision that normalize for these factors, such as by length-matching responses or analyzing subsets with similar reasoning steps. We will report whether the proportional relationship holds under these controls. revision: yes

  2. Referee: [Abstract and Results sections] The abstract and results do not report statistical significance tests, error bars on hallucination rates, or how task performance gains were measured independently of hallucination counts. This weakens support for the proportionality claim and the three key findings.

    Authors: We agree with the referee that reporting statistical significance, error bars, and clear measurement details will improve the manuscript. Task performance gains are measured via success rates on tasks where tools are available and correctly usable, separate from the hallucination rates in the no-tool or distractor-only scenarios. In the revised version, we will add error bars to all reported rates, include statistical tests (e.g., correlation significance for the proportionality), and clarify these metrics in the abstract and results sections. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on new benchmark and controlled experiments

full rationale

The paper introduces SimpleToolHalluBench and reports results from RL/SFT training runs plus inference-time elicitation. Central findings (proportional increase in hallucination with reasoning gains, method-agnostic effect, reliability-capability trade-off) are presented as outcomes of these experiments rather than any derivation, equation, or parameter fit that reduces to the same data by construction. No self-definitional steps, fitted-input predictions, or load-bearing self-citations appear in the abstract or described methodology. The work is self-contained against external benchmarks via the new diagnostic benchmark and explicit controls.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the benchmark cleanly measures tool hallucination and that reasoning enhancement can be isolated as the causal variable. No new physical entities or mathematical axioms are introduced.

axioms (1)
  • domain assumption Tool hallucination can be measured by explicit failure modes in a controlled benchmark without confounding from general capability or prompt sensitivity.
    Invoked when defining SimpleToolHalluBench as a diagnostic for the two failure modes.

pith-pipeline@v0.9.0 · 5826 in / 1320 out tokens · 24083 ms · 2026-05-18T04:06:37.245471+00:00 · methodology

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Reference graph

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    name": <function-name>,

    A DETAILS ANDEXAMPLES OF THESIMPLETOOLHALLUBENCH A.1 THEDETAILS OF THECONSTRUCTION OFSimpleToolHalluBench. We construct the benchmark as follows: We sample 296 tools whose parameters are not empty fromAgent Safety Bench(Zhang et al., 2024b). For each tool, we use ChatGPT-4o to generate a user query whose correct resolution necessarilyrequires invoking tha...

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