The failure of AI models in EnigmaEval benchmark: Limitation of AI agents in automation | By The Digital Insider

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Large Language Models (LLMs) have demonstrated extraordinary performance in various benchmarks, ranging from complex mathematical problem-solving to nuanced language comprehension.

However, these same models fail almost completely on EnigmaEval—a test suite specifically designed to measure spatial reasoning and puzzle-solving skills. This glaring gap in AI competency not only highlights the current shortcomings of LLMs but also raises important questions about how to improve them, especially for practical applications in business, engineering, and robotics.

In this article, we will explore:

  1. LLM performance in math benchmarks vs. EnigmaEval
  2. Why LLMs Struggle with simple spatial reasoning
  3. The implications for AI-powered automation
  4. Potential solutions: Enhancing spatial intelligence through humans, reinforcement learning, and mixture-of-experts (MoE) models

1. LLM performance in math benchmarks vs. EnigmaEval

LLMs have proven their worth on a variety of math-focused benchmarks but falter on spatial puzzles:

Fig-1 : Excellent in Math, faltering in simple spatial puzzles
Fig-1 : Excellent in Math, faltering in simple spatial puzzles

While these models excel in complex abstract reasoning and numerical computations, their near-total failure in EnigmaEval exposes a significant deficit in spatial reasoning capabilities.

Fig-2 : Actual Score
Fig-2 : Actual Score
Fig-3 : Sample Questions : Link for the entire Q:
Fig-3 : Sample Questions : Link for the entire Q:

2. Why do LLMs struggle with simple spatial reasoning?

A. Text-based training bias

LLMs are predominantly trained on textual data and are optimized to find linguistic and statistical patterns.

Spatial reasoning, particularly when it involves 3D object manipulation or visual geometry, is not well-represented in text corpora. Consequently, these models lack the “visual scaffolding” that humans naturally acquire from interacting with the physical world.

B. Lack of embodied experience

Humans develop spatial intuition through embodied experiences—seeing objects, picking them up, navigating spaces, and manipulating items in real life. LLMs, in contrast, have no direct sensory inputs; they rely solely on textual descriptions, limiting their ability to form the mental models required for spatial or causal reasoning.

C. Absence of geometric and physical intuition

LLMs often fail to:

  • Grasp geometric relationships (angles, distances, rotations)
  • Understand physical laws (gravity, balance, collisions)
  • Simulate transformations in 3D space

Even if an LLM can parse a textual description of a puzzle, the lack of spatial or physical “muscle memory” leads to misguided outputs.

D. Limitations of current architectures

Models like Transformers are exceptionally good at sequence-to-sequence transformations (i.e., text in, text out) but are not natively designed for spatial manipulation.

While some architectures (e.g., Mixture-of-Experts, hierarchical or multimodal systems) have begun to incorporate specialized “expert” modules, mainstream LLMs often do not focus on dedicated spatial-reasoning subcomponents—yet.

3. What does this mean for businesses?

A. LLMs may struggle in key business automation areas

Business processes that implicitly involve spatial understanding can suffer if they rely solely on traditional LLM outputs. Examples include:

  • Debugging git issues – While text-based merges can be handled, any refactoring that requires visualizing complex dependencies or branching structures may lead to poor results.
  • Data visualization & analysis – LLMs often fail to interpret charts, graphs, and heatmaps effectively, limiting their utility in business intelligence.
  • Manufacturing & robotics – Spatially dependent tasks such as assembly line coordination or robotic manipulation demand spatial cognition that current LLMs lack.
  • Navigation & mapping – Autonomous vehicles and logistics optimizations require AI to handle maps, sensor data, and 3D structures—a challenge for text-anchored models.

B. Prevalence of spatial reasoning tasks

A surprising amount of business and engineering work involves spatial reasoning:

  • Most of engineering applications (CAD design, architecture)
  • Some of business analytics tasks (interpreting graphical trends, dashboards)
  • Some of coding tasks (complex code refactoring, dependency resolution)

Without improvements in spatial understanding, LLMs will remain limited in real-world automation and problem-solving.

4. Potential solutions: Enhancing spatial intelligence

A. Multimodal learning

One pathway to better spatial reasoning is to fuse text-based LLMs with vision and 3D simulation models. In a Mixture-of-Experts (MoE) architecture, different “experts” handle specific modalities—text, images, point clouds—while a high-level gating network decides which expert to consult. For instance, an “expert” in geometric transformations could help parse and manipulate visual puzzle data, supplementing the LLM’s linguistic strengths.

B. Reinforcement learning and simulation

Reinforcement learning (RL) provides an interactive framework for models to learn from trial and error. By placing AI agents in 3D simulated environments—think robotics simulators, game engines, or specialized puzzle platforms—they can develop an embodied sense of how objects move and interact.

  • Reward functions – Encouraging correct spatial manipulations or puzzle solutions
  • Curriculum learning – Gradually increasing puzzle complexity to build robust spatial intuitions

C. Human-in-the-loop approaches

Humans can act as on-demand “experts” to guide AI systems during training or real-time decision-making:

  • Active learning – Human annotators can correct or guide models on spatial tasks, refining their understanding.
  • Hybrid systems – Combining a human’s intuitive spatial reasoning with an LLM’s processing power can lead to better outcomes, especially in high-stakes scenarios like architecture or surgical robotics.

D. Neural-symbolic and knowledge-based methods

Some researchers advocate blending neural networks with symbolic reasoning engines that can encode geometric and physical laws. Symbolic modules could handle geometric constraints (e.g., angles, distances, volume) while the neural net handles pattern recognition. This hybrid approach aims to give AI a “grounded” understanding of space.

The dismal performance of LLMs on EnigmaEval is not an isolated data point; it underscores a core limitation in current AI models—namely, the lack of spatial reasoning. For businesses and developers relying on AI-driven automation, this shortfall can be a significant barrier. Yet, the path forward is promising:

  • Mixture-of-experts (MoE) architectures can incorporate specialized spatial or vision “experts.”
  • Reinforcement learning and simulated 3D environments can imbue AI with a more embodied sense of space.
  • Human collaboration ensures that AI remains grounded in real-world tasks that require physical intuition and problem-solving.

Ultimately, bridging the gap between text-based reasoning and spatial understanding will be essential for AI’s next leap forward.

Models that can genuinely perceive, manipulate, and reason about the physical world will transform a wide array of industries—from logistics and robotics to design and data analytics—ushering in an era of more versatile, reliable, and cognitively flexible AI systems.


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