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Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper claims that the apparent failure of reasoning models on controlled puzzles disappears when both models get Python-interpreter and scratchpad tools, and that proper tool use makes reasoning models consistently outperform their…

desk verdict A small empirical study with a useful narrow finding on PoT-augmented reasoning models, but the abstract overclaims and the thinking-versus-code-skill confound is never addressed. read the letter →

arxiv 2507.17699 v1 pith:JU6VNNPP submitted 2025-07-23 cs.AI

Zhao Song , Song Yue , Jiahao Zhang This is my paper · ORCID
classification cs.AI
keywords largereasoningmodelstoolaugmentationProgram-of-Thoughtscratchpadthinking-illusionbenchmarkRiverCrossingpuzzleBlocksWorldchain-of-thought
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that the 'thinking is an illusion' result — reasoning models appearing no better than ordinary LLMs on controlled puzzles — reverses once both model types are given external tools. The authors evaluate DeepSeek-R1 and Qwen 3 Thinking against DeepSeek-V3 and Qwen 3 on the thinking-illusion benchmark's four puzzles, adding a Python interpreter via Program-of-Thought and a scratchpad. Under Program-of-Thought, the reasoning variants solve River Crossing and Blocks World at every tested problem size, while their non-reasoning counterparts mostly fail; on Tower of Hanoi, PoT makes all models perfect. The paper's central claim is that tool augmentation removes the output-length and bookkeeping limits that hid the reasoning models' advantage, making the earlier negative conclusion an artifact of the evaluation setup rather than a genuine absence of reasoning ability.

What carries the argument

The machinery is a tool-augmented evaluation protocol built on two tools. Program-of-Thought (PoT) prompts the model to write executable Python code that an external interpreter runs, replacing long serial move lists with a compact program. The scratchpad is an external memory interface: the model outputs a partial answer plus a JSON state each step, with early stopping, so long solutions can be spread over multiple rounds. These tools do the work of removing output-length limits and externalizing bookkeeping, which the paper argues is what was suppressing LRM performance.

What would settle it

Run the same PoT and scratchpad prompts on Qwen 3 with its thinking mode toggled on and off for River Crossing and Blocks World at $N=7,9,11,13$, holding the tool and prompt fixed; if the non-thinking mode matches the thinking mode's accuracy, the paper's paired-model comparison does not isolate the thinking process. The deciding observation is whether the success gap survives when the only thing that changes is the presence of explicit reasoning.

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Extended reading notes

Core claim

The paper claims that the earlier conclusion that explicit reasoning does not help — the 'thinking illusion' — was drawn under tool-free conditions that hard-limited reasoning models by capping their output length. When the same benchmark puzzles are given to both reasoning and non-reasoning models together with a Python interpreter or a scratchpad, reasoning models such as DeepSeek-R1 and Qwen 3 Thinking no longer look weak: with Program-of-Thought they solve River Crossing and Blocks World for every tested size $N=3,\dots,13$, while DeepSeek-V3 and Qwen 3 mostly fail on those tasks. On Tower of Hanoi, PoT gives all four models perfect accuracy at every tested $N$, and scratchpad use lifts Blocks World performance substantially. The paper concludes that proper tool use lets LRMs consistently outperform their non-reasoning counterparts, and that the limitation observed in earlier studies was an evaluation artifact rather than a missing reasoning ability.

Load-bearing premise

The paired-model design assumes that each reasoning model and its non-reasoning counterpart are otherwise equivalent, so any gap under tool use measures the value of thinking rather than differences in training, data, or code-generation skill between the two model releases.

Editorial extensions

If this is right

  • Under Program-of-Thought, DeepSeek-R1 and Qwen 3 Thinking solve River Crossing and Blocks World for every tested size from $N=3$ to $N=13$, while their base counterparts mostly fail or degrade sharply.
  • On Tower of Hanoi, PoT lifts all four models to perfect accuracy at every tested $N$, including sizes where direct prompting produced zero successes for the non-reasoning models.
  • Checker Jumping remains unsolved for $N \geq 3$ across all models and tool frameworks, so tool access helps only with tasks that admit an algorithmic expression.
  • Among the three tool frameworks, PoT gives the largest gains, followed by scratchpad, while Think-and-Execute gives the smallest, indicating that the tool's design matters as much as its presence.
  • Tool use does not necessarily increase token consumption: for Qwen 3 Thinking, multi-step tool frameworks reduced both thinking tokens and output tokens on some tasks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the paper leaves implicit is that the cleanest test of its central claim uses a single model family with thinking as a decoding toggle, so the reasoning process is the only variable; Qwen 3's setup already approximates this.
  • The results predict that reasoning models will keep failing on tasks like Checker Jumping where the hard part is search rather than bookkeeping, because no compact program removes the combinatorial difficulty.
  • A practical extension of the finding is that tool-augmented reasoning models can act as dependable small planners in constrained domains, with the interpreter handling state tracking the language model cannot sustain over long horizons.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper revisits Apple's 'thinking illusion' benchmark and asks whether Large Reasoning Models (LRMs) gain an advantage over non-reasoning LLMs when both are augmented with external tools. The authors evaluate DeepSeek-V3/R1 and Qwen 3/Qwen 3 Thinking on four puzzles (Hanoi Tower, Checker Jumping, River Crossing, Blocks World) under direct prompting, Program-of-Thought, Think-and-Execute, and a scratchpad-based multi-step protocol. They report that with proper tool use, LRMs consistently outperform their non-reasoning counterparts across all levels of task complexity, and use this to challenge the 'reasoning is an illusion' narrative. The paper also studies scratchpad usage and token consumption for one reasoning model.

Significance. The question addressed is timely and the study has some strengths: the evaluation reuses a controlled external benchmark, the code is publicly released, and the comparison across four tool-use regimes is systematic. If the main claim were supported, the result that Program-of-Thought unlocks LRM performance on River Crossing and Blocks World would be a useful empirical contribution. However, the paper's headline claim is considerably stronger than what the data show: the reported tables contain multiple conditions where LRMs do not outperform LLMs, and the paired-model comparisons do not isolate the contribution of 'thinking' from other model differences. The significance of the work is thus currently limited by overgeneralized conclusions and insufficient experimental controls.

major comments (4)
  1. [Abstract and Section 4.2, Tables 2 and 3] The abstract claims that 'with proper tool use, LRMs consistently outperform their non-reasoning counterparts across all levels of task complexity,' but this is contradicted by the paper's own results. In Table 2, under PoT all four models achieve 5/5 on Hanoi Tower for every N, so there is no LRM advantage on that task; in Checker Jumping all models score 0/5 for N >= 3 under every tool condition. In Table 3, the LRM advantage under PoT appears only on River Crossing and Blocks World. The claim of consistency across all levels and tasks is therefore not supported; the supported claim is narrower and should be restated accordingly.
  2. [Section 4.1, Tables 2 and 3] The paired-model design does not isolate 'thinking' from other model differences. DeepSeek-R1 and DeepSeek-V3 are separately trained model releases with different post-training, alignment, and possibly different base code-generation abilities, while Qwen 3 and Qwen 3 Thinking are the same base model under different decoding modes. Treating both pairs as controlled experiments on reasoning is therefore not valid. To support the claim that the thinking process causes the observed tool-use gains, the authors should provide a control that matches the models on code generation or ablates thinking within the same model.
  3. [Section 4.2, observation 1, and Table 3] The central PoT result is plausibly explained by code-generation skill rather than reasoning ability, because PoT requires the model to write an executable Python program. The paper does not report any control for programming competence, and the prompt for code generation is taken from a different source (CKK+24) than the original benchmark prompts. Without such a control, the River Crossing and Blocks World gaps under PoT may reflect differences in Python coding capability between the compared models rather than a 'thinking' advantage.
  4. [Section 4.1, 'Parameter Settings' and all result tables] All experiments use only five runs per condition, and the paper reports only success counts without confidence intervals or significance tests. Several observed gaps (e.g., 4/5 vs. 0/5 in River Crossing under PoT) may be meaningful, but the paper also relies on smaller differences and on equal scores to make broad claims about tool-use effectiveness and model comparisons. Reporting variance or a statistical comparison would be necessary to secure the headline claims.
minor comments (4)
  1. [Table 1] The 'Thinking Output Tokens' column lists Qwen 3 as 'Yes,' but the text describes Qwen 3 as a non-thinking LLM and Qwen 3 Thinking as its reasoning counterpart; the table entry appears inconsistent with the experiment setup.
  2. [Figure 2] The scratchpad panel contains a typo: 'Sctratchpad' should be 'Scratchpad'.
  3. [Section 4.2, observation 3] The statement that tool use helps only when the base model is strong is not quantified or supported by any metric of base-model strength; consider either removing or substantiating this claim.
  4. [Figures 5-8] The token-consumption figures show only Qwen 3 Thinking and not the other models, while the text discusses tool effects generally; the figures should be clearly labeled as a case study or additional comparisons should be provided.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper is an empirical comparison against Apple's external benchmark with no fitted parameters or self-citation chain.

full rationale

This paper contains no derivation chain, no fitted parameters, and no prediction that reduces to its inputs by construction. It evaluates four models on Apple's externally defined thinking-illusion benchmark under several tool-use protocols and reports accuracy counts, scratchpad usage, and token consumption. The tools and prompts are adopted from prior published work ([CMWC23], [CKK+24], [SMA+25]) rather than being defined in terms of the measured outcomes, so there is no self-definitional loop and no fitted-input-called-prediction pattern. The authors do not invoke a uniqueness theorem or an ansatz from their own prior work; indeed, the reference list contains no self-citations by the present authors. The central claim that LRMs 'consistently outperform' LLMs under tool use is overstated relative to the paper's own tables, since on Hanoi Tower all models reach 5/5 under PoT and on Checker Jumping all models score 0/5 for N>=3; however, that is a correctness and evidence-interpretation concern, not circularity. The paper is self-contained as an empirical study against an external benchmark, and any limitation lies in experimental control and statistical robustness, not in reasoning that presupposes its conclusion. Accordingly, the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central empirical comparison has no fitted parameters in the derivation sense. The only hand-set quantities are scratchpad hyperparameters T=5 and m=3, which influence results and are not swept except in the chain-length plot. The core assumptions are domain-level: the benchmark measures reasoning, the paired models are comparable, and five API runs are stable.

free parameters (2)
  • Scratchpad maximum steps T = 5
    Chosen by hand in Section 4.1. It caps how many partial outputs models may produce, so it directly bounds scratchpad-based performance and limits what the scratchpad method can achieve on long tasks.
  • Scratchpad in-context examples m = 3
    Chosen in Section 4.1. The number of human-annotated scratchpad examples changes how well models learn the interface and therefore changes measured accuracy.
assumptions (3)
  • domain assumption Apple's thinking-illusion puzzles are valid and representative tests of reasoning ability.
    Adopted as the evaluation framework in Section 3.1 without independent validation that puzzle performance reflects general reasoning rather than code-generation skill.
  • domain assumption The reasoning and non-reasoning models within each pair are comparable except for the reasoning mechanism.
    Section 4.1 pairs DeepSeek-V3 with DeepSeek-R1 and Qwen 3 with Qwen 3 Thinking and attributes observed gaps to thinking; training differences and code-generation ability are not controlled.
  • domain assumption Five API runs per condition are enough to support the reported rankings.
    Section 4.1 reports five repeats without temperature control, seeds, or statistical tests; the paper implicitly assumes the small samples are stable.

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Cite this review

Pith. "Pith review of Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations." pith.science (2026). https://pith.science/paper/JU6VNNPP

@misc{pith2026250717699,
  author       = {Pith},
  title        = {Pith review of: Thinking Isn't an Illusion: Overcoming the Limitations of Reasoning Models via Tool Augmentations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JU6VNNPP}},
  note         = {Machine review of arXiv:2507.17699}
}
read the original abstract

Large Reasoning Models (LRMs) have become a central focus in today's large language model (LLM) research, where models are designed to output a step-by-step thinking process before arriving at a final answer to handle complex reasoning tasks. Despite their promise, recent empirical studies (e.g., [Shojaee et al., 2025] from Apple) suggest that this thinking process may not actually enhance reasoning ability, where LLMs without explicit reasoning actually outperform LRMs on tasks with low or high complexity. In this work, we revisit these findings and investigate whether the limitations of LRMs persist when tool augmentations are introduced. We incorporate two types of tools, Python interpreters and scratchpads, and evaluate three representative LLMs and their LRM counterparts on Apple's benchmark reasoning puzzles. Our results show that, with proper tool use, LRMs consistently outperform their non-reasoning counterparts across all levels of task complexity. These findings challenge the recent narrative that reasoning is an illusion and highlight the potential of tool-augmented LRMs for solving complex problems.

Figures

Figures reproduced from arXiv: 2507.17699 by the authors.

Figure 1
Figure 1. Research Question. Previous empirical results, such as Apple’s Thinking-Illusion Benchmark [SMA+25], suggest that Large Reasoning Models (LRMs) do not show clear advan￾tages over standard LLMs when solving complex reasoning problems under controlled problem complexity. In this work, we introduce a new evaluation framework to revisit this conclusion, dif￾fering from Apple’s setting by allowing LRMs and LLMs to use ex… view at source ↗
Figure 2
Figure 2. Evaluation Setting of Tool Use. (a) Python Interpreter: To address the limited output length issue in LRM evaluation, we introduce two types of Python-based tool usage. The puzzle is first reformulated into Python code by the evaluated LLM, and then executed using either the Program-of-Thought (PoT) or Think-then-Execute framework. In PoT, the external Python interpreter directly executes the code to obtain the answ… view at source ↗
Figure 3
Figure 3. Number of Scratchpads Used on DeepSeek-V3 and DeepSeek-R1. 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 0 1 2 3 4 5 #Scratchpads 3 5 7 9 11 13 N 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy Qwen3 Tower of Hanoi 3 5 7 9 11 13 N 0.0 0.2 0.4 0.6 0.8 1.0 Accuracy Checker Jumping 3 5 7 9 11 13 N 0.0 0.2 0… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Number of Scratchpads Used on Qwen 3 and Qwen 3 Thinking. 3) The effectiveness of tool use depends on the base model’s strength. Not all models benefit equally from tool use. While DeepSeek-R1 shows strong improvements with PoT and 9 [PITH_FULL_IMAGE:figures/full_fig_…
Figure 5
Figure 5. Figure 5: Token Consumption of Qwen 3 Thinking on Tower of Hanoi. 3 5 7 9 11 13 N Direct Prompt 3 5 7 9 11 13 N Think & Execute 3 5 7 9 11 13 N PoT 3 5 7 9 11 13 N Scratchpad 0 20000 40000 60000 Tokens Output tokens Thinking tokens 0 20000 40000 60000 Tokens Output tokens Thinki…
Figure 6
Figure 6. Figure 6: Token Consumption of Qwen 3 Thinking on Checker Jumping. Token Consumption. In this study, we investigate whether the use of external tools increases the number of tokens consumed by reasoning models. Specifically, we evaluate Qwen 3 Thinking across all four tool-use b…
Figure 7
Figure 7. Figure 7: Token Consumption of Qwen 3 Thinking on River Crossing. 3 5 7 9 11 13 N Direct Prompt 3 5 7 9 11 13 N Think & Execute 3 5 7 9 11 13 N PoT 3 5 7 9 11 13 N Scratchpad 0 20000 40000 60000 Tokens Output tokens Thinking tokens 0 20000 40000 60000 Tokens Output tokens Thinki…
Figure 8
Figure 8. Figure 8: Token Consumption of Qwen 3 Thinking on Blocks World. as Scratchpad or Think-and-Execute, do not necessarily lead to higher token consumption. This advantage is especially evident on the Checker Jumping and Blocks World tasks. Second, when analyzing the composition of …

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Forward citations

Cited by 2 Pith papers

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  1. Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits

    stat.ML 2026-02 conditional novelty 6.0 of 10

    GCN-parameterized reasoning probes on looped ν-ary Boolean circuits achieve O(1/√N) transductive generalization error with high probability, independent of circuit size when the snowflake loss exponent α≍1/h.

  2. Entropy-Guided Loop: Achieving Reasoning through Uncertainty-Aware Generation

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A lightweight entropy-triggered refinement loop improves a small LLM's answer quality to roughly 95% of a reasoning model's, at about one-third the cost.

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