The Illusion of Thinking: Apple’s Research Reveals the True Boundaries of LLM Reasoning Abilities
1. Introduction: When “Thinking” AI Became the Industry Fad
In recent years, the AI field has witnessed a surge in “reasoning model fever.” Large Reasoning Models (LRMs) such as OpenAI’s o-series, Anthropic’s Claude 3.7 Sonnet Thinking, and Google’s Gemini Thinking have emerged, claiming to “think deeply” through mechanisms like Chain-of-Thought (CoT) and self-reflection before providing answers. These models have shown remarkable performance on reasoning benchmarks like mathematics and coding tasks, leading some scholars to believe that Artificial General Intelligence (AGI) might be achievable within the next few years.
However, a recent research paper The Illusion of Thinking by Apple has challenged this optimism. Using carefully designed controllable puzzle experiments, the study reveals the true limitations of state-of-the-art LRMs: they underperform on low-complexity tasks, fail completely on high-complexity problems, and even struggle to execute known algorithms effectively. These findings not only challenge the industry’s blind optimism toward LRMs but also sound an alarm for research into AI reasoning capabilities.
2. The Training Myth and Thinking Mechanism of Reasoning Models
2.1 Chain-of-Thought: The Core of Hyped “AI Thinking”
The “thinking” of LRMs fundamentally relies on the Chain-of-Thought (CoT) technique: through supervised fine-tuning or reinforcement learning with numerous examples containing intermediate reasoning steps, models generate a series of intermediate steps during reasoning to simulate human logic. For instance, when solving math problems, models decompose problems, list formulas, derive intermediate results, and finally reach conclusions.
This training approach brings two improvements:
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Reasoning Depth: By allocating more “thinking tokens” (computational resources), models can generate longer reasoning chains to explore more possibilities; -
Result Quality: Self-verification mechanisms help models filter optimal solutions from multiple candidates, improving accuracy.
2.2 Hidden Risks Behind Glittering Data
Despite LRMs’ excellent performance on standard benchmarks, Apple’s research points out that this “success” may stem from data contamination—training data might include original benchmark problems or similar cases, allowing models to “memorize” answers instead of truly reasoning. More critically, existing evaluations lack rigorous examination of the authenticity of reasoning processes: Are the reasoning chains generated by models genuinely used for problem-solving, or are they post-hoc rationalizations?
3. Puzzle Experiments: Unveiling the Limitations of Reasoning Models
To avoid data contamination and uncontrollable complexity, Apple’s research team selected four classic puzzles as testing tools:
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Tower of Hanoi: Move disks to specified pegs with the rule that only one disk can be moved at a time, and larger disks cannot be placed on smaller ones; -
Checker Jumping: Swap red and blue checkers on a 1D board, allowing slides or jumps but no backward movement; -
River Crossing: Escort multiple people and their bodyguards across a river with a boat of limited capacity, ensuring no one is left with another’s bodyguard; -
Blocks World: Rearrange block stacks into specified orders, with moves limited to the topmost block.
3.1 Four Reasons for Choosing Puzzles
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Avoid Data Contamination: These puzzles are rarely found in public training data, preventing models from relying on memorization; -
Controllable Complexity: Complexity can be precisely quantified by adjusting parameters like the number of disks or checkers (e.g., the minimum moves for Tower of Hanoi grow exponentially as (2^N-1)); -
Traceable Processes: Puzzle solutions consist of clear logical steps, enabling line-by-line verification of model reasoning; -
Objective Evaluation: Simulators automatically check whether each move complies with rules, eliminating subjective biases.
4. Key Findings: The Triple Dilemma of Reasoning Abilities
4.1 The Curse of Complexity: From Mediocrity to Collapse
Experiments show that LRMs’ performance exhibits a non-monotonic relationship with problem complexity, dividing into three regimes:
| Complexity Level | Non-Reasoning Models | Reasoning Models | Typical Case (Tower of Hanoi) |
|---|---|---|---|
| Low (N=1-3) | 80%+ accuracy, concise reasoning | Similar or lower accuracy, redundant steps | Correct steps appear early but followed by unnecessary checks |
| Medium (N=4-7) | <50% accuracy, struggles with logic | 60-70% accuracy, relies on long chains | Requires 15-127 moves, solves via trial and error |
| High (N≥8) | Near-zero accuracy | Same collapse, shorter reasoning chains | Fails to solve with 255+ moves, abandons thinking |
Core Insight: Reasoning models outperform non-reasoning ones only at medium complexity. They “overthink” simple tasks, reducing efficiency, and fail identically to standard models at high complexity.
4.2 Reasoning Collapse: Beyond the Threshold of Competence
All reasoning models exhibit a critical complexity threshold (model-dependent). When problems exceed this threshold:
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Accuracy Plummets: Drops from ~50% to 0% abruptly, with no transitional phase; -
Reduced Effort: Models should allocate more tokens for complex tasks but instead shorten reasoning chains. For example, Claude 3.7 Sonnet Thinking reduced tokens from 15,000 (N=5) to 8,000 (N=10) in Tower of Hanoi, while error rates surged.
This “giving up” behavior reveals a fundamental scaling limit in LRMs’ reasoning capabilities, unable to be overcome by increasing computational resources.
4.3 Algorithmic Failure: Knowing the Rules but Failing to Execute
The most striking finding is that even with complete solution algorithms provided (e.g., recursive pseudocode for Tower of Hanoi), models still cannot execute them correctly:
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Miss critical recursive calls, leading to chaotic intermediate states; -
Struggle with symbolic operations (e.g., tracking disk positions, validating moves); -
Understand algorithms only as text patterns, not logical instructions.
This indicates that LRMs’ “reasoning” is essentially statistical text generation, not true logical deduction.
5. Anatomy of Reasoning: From Overthinking to Quitting
5.1 Overthinking: Mental Redundancy in Simple Tasks
In low-complexity tasks, reasoning models often exhibit “correct solutions early, wrong paths late.” For example, when solving N=3 Tower of Hanoi, Claude 3.7 Sonnet Thinking generated correct steps within the first 100 tokens but spent the next 2,000 tokens exploring wrong paths, leading to:
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Wasted Resources: Redundant reasoning chains increase latency and costs; -
Reduced Credibility: Mixtures of correct and incorrect steps undermine process reliability.
5.2 Reasoning Regression: Logical Breaks in Complex Tasks
As complexity increases, correct solutions appear later in reasoning chains:
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At medium complexity, correct answers emerge in the second half of chains after extensive trial and error; -
At high complexity, wrong solutions dominate, with correct ones vanishing entirely, reducing models to random guessing.
This reveals limited self-correction capabilities in LRMs: Unlike humans, they cannot actively backtrack errors or adjust strategies, relying solely on probabilistic searches, which fail in complex scenarios.
6. Industry Implications: Cold Thoughts on the AGI Vision
6.1 Reassessing Evaluation Standards
Current evaluation systems focusing on “final answer accuracy” have critical flaws:
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Data Contamination Hides True Abilities: Math benchmarks may be memorized rather than reasoned through; -
Lack of Process Transparency: Unable to verify if reasoning chains are genuine or post-hoc fabrications.
Apple’s research suggests future evaluations should include metrics for reasoning process validity, such as logical consistency of intermediate steps and algorithm execution accuracy.
6.2 Redefining the “Circle of Competence” for Reasoning Models
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Suitable Scenarios: Medium-complexity, rule-defined tasks with abundant training data (e.g., programming with known algorithms); -
Forbidden Zones: High-complexity planning (e.g., chain reasoning beyond 10 steps), symbolic operations in novel environments (e.g., solving unseen puzzles).
6.3 The Path to AGI: From Simulated Thinking to True Intelligence
The limitations of current LRMs indicate that “scaling models + reinforcing reasoning chains” alone cannot achieve general intelligence. Potential breakthroughs may lie in:
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Neurosymbolic Integration: Combining deep learning with symbolic logic to enhance algorithm execution; -
Meta-Learning Mechanisms: Enabling models to learn “how to reason” rather than relying on fixed patterns; -
Interpretability Enhancement: Developing transparent frameworks to trace and validate reasoning steps.
7. Conclusion: A Rational View of AI’s Present and Future
Apple’s research does not dismiss reasoning models but reminds us: AI “thinking” is fundamentally a simulation of data patterns, not human-like logical reasoning. From labs to real-world applications, LRMs must still cross three gaps:
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Generalization: Transferring reasoning from “seen” to “unseen” problems; -
Algorithmic Faithfulness: Shifting from text generation to accurate symbolic logic execution; -
Complexity Robustness: Handling problems with exponentially growing steps.
As the paper’s authors note: “It is premature to talk about superintelligence before solving the fundamental problems of intelligence.” For the industry, moving beyond the hype of “reasoning models as AGI precursors” and focusing on technical shortcomings may be the true path to advancing AI.