March 6, 2025

Can Frontier AI Models Solve Concept Mapping Problems?

Why Should You Care?

What Did We Test? → We evaluated how well frontier AI reasoning models can map concepts across both STEM and non-STEM domains.

What Did We Find Out? → Reasoning models were better than expected at non-STEM concept mapping.

Are There Any Caveats? → Yes, upon reflection, we determined that our concept mapping tasks may have been “too easy.” Therefore, the results of this test should be interpreted cautiously.

Why Does This Matter? → If frontier AI models are good at concept mapping, this would expand opportunities for cross and multi-domain knowledge synthesis and potentially expedite innovation in knowledge-heavy fields.

If you find our AI experiments useful and interesting, please also consider examining how Lumenova’s responsible AI platform could support your AI risk management process and book a product demo today.

Main Insights

  • Advanced reasoning AI models are quite good at concept mapping across STEM and non-STEM domains, suggesting that they may possess a beyond-surface-level understanding of the concepts they have been trained on.

  • Somewhat contrary to our initial hypothesis, models performed perfectly or very well in the non-STEM task. Still, this experiment offers some evidence to suggest that reasoning models are less reliable when applied in non-STEM domains. Many more tests will be necessary to confirm this hypothesis.

  • While our tests didn’t offer any explicit concept definitions, the concepts we included are well-researched, documented, and understood, giving us good reason to assume that they were captured in each model’s training data. This is a crucial caveat to consider, seeing as most AI models still struggle with generalizability and novelty.

  • o1 and Grok 3 performed best of all the models we tested, with both models exceeding our explainability expectations, providing detailed, transparent, and structured explanations for their reasoning process that reflected the models’ inner workings.

  • o1 was the only model that did not display noticeable signs of “overthinking.” Conversely, it could be argued that o3-mini-high occasionally “oversimplified” certain parts of its responses.

  • Consistent with previous experiments, DeepThink-R1, despite being close, isn’t “on par” with frontier AI reasoning models.

The Business Case

Recognizing Limitations in Non-STEM Reasoning

💡 Why it Matters

  • AI is not equally competent across all domains.
  • Creative industries require nuanced, contextual reasoning.
  • In qualitative domains, AI overreliance could perpetuate faulty recommendations, biased outputs, and oversimplified conclusions.

✍️ Key Actions

  • Approach AI deployments in non-STEM fields with caution, ensuring continued human oversight and performance monitoring.
  • When leveraging AI for qualitative reasoning, conduct domain-specific reliability testing.
  • Ensure employees are aware of AI models’ capabilities and limitations.
  • Employ hybrid AI-human workflows where final decisions are reserved for humans.

AI for Knowledge Synthesis and Innovation

💡 Why it Matters

  • Increasing use of AI for insight generation, idea synthesis, and innovation acceleration.
  • Ability to synthesize information across multiple disciplines to create meaningful connections and insights.
  • Accelerating interdisciplinary research and hypothesis generation for R&D purposes.

✍️ Key Actions

  • Leverage AI to map knowledge gaps or domains where human expertise remains necessary.
  • Ensure domain experts validate AI-generated insights before making any consequential decisions.
  • Assess the domain-specific reasoning capabilities of different AI models.

Training Data Coverage in AI Reliability

💡 Why it Matters

  • Blind assumption that AI’s reasoning abilities will reliably extend beyond its training data.
  • AI models could generate outdated or misleading insights and erroneous predictions.
  • In high-impact contexts, poor AI-driven decision-making and recommendations could result in costly compliance violations and reputational damages.

✍️ Key Actions

  • Regularly evaluate training data relevance.
  • Fine-tune AI models with proprietary and/or industry-specific data.
  • Compare human performance against AI performance in cases where AI must navigate novel scenarios, environments, or problems.

Explainability in High-Stakes AI

💡 Why it Matters

  • For high-stakes industries (e.g., healthcare, law, finance, etc.) AI explainability is a game changer.
  • Performance accuracy, when coupled with performance explainability, can bolster AI trust, confidence, and adoption.
  • Lack of explainability can erode user trust and lead to regulatory infringements.

✍️ Key Actions

  • Particularly for high-impact decision-making, prioritize AI models with strong explainability features.
  • Periodically audit AI outputs to maintain alignment with regulatory and ethical standards.
  • Train employees to interpret AI explanations critically and holistically.

Making Sense of AI’s Cognitive Tradeoffs

💡 Why it Matters

  • Not all AI models are created equal—some may be slower and more precise while others may be faster but less nuanced.
  • Different AI models display different cognitive biases, with some exhibiting a tendency for over-complexification while others oversimplify.
  • Failure to align AI models with the use case they are best suited to can inspire serious consequences, whether workflow-related, legal, or reputational.
  • Lack of AI benchmarking can perpetuate wasted investments in suboptimal models and diminished competitiveness.

✍️ Key Actions

  • Avoid AI-vendor dependency, regularly testing multiple models with a close eye on strengths and weaknesses relevant to key business functions.
  • Align models with business functions according to whether speed or depth takes priority.
  • Benchmark AI models against their counterparts and human domain experts.
  • Observe whether models exhibit patterns of overthinking or oversimplification.
  • Consider hierarchical or layered AI systems where some models handle routine tasks whereas others tackle complex tasks.

Executive Summary

Experiment Setup

With this experiment, we aimed to evaluate how well advanced reasoning AI models can map undefined concepts from one group onto non-obvious statements from another group that best capture, illustrate, or define the respective concept. In total, we used three distinct prompts—the first two were identical in design, but differed in scope, with prompt one focusing exclusively on STEM concepts and prompt two centering on non-STEM concepts. Prompt three, however, was designed to probe each model to critically reflect on its performance and reasoning process across both concept mapping tasks.

Our Hypothesis: Advanced reasoning models will perform with perfect or near-perfect accuracy on STEM-based concept mapping but will struggle with non-STEM-based concept mapping.

Models Tested: OpenAI’s o1 and o3-mini-high, DeepSeek’s DeepThink-R1, and X’s Grok 3.

Prompt Description

STEM Concept Mapping (Prompt 1): This prompt instructs the model to complete a matching task where it must pair 10 undefined STEM concepts (e.g., chaos theory) from group one with 10 statements from group two that best illustrate, capture, or define the concept. Statements were intentionally designed to be non-obvious illustrations of the concepts they capture, and they include a mix of analogies, “poetic” descriptions, and examples.

Non-STEM Concept Mapping (Prompt 2): This prompt utilizes the exact same design as prompt one, however, it replaces STEM concepts with non-STEM concepts (e.g., hermeneutic circle) and changes statements accordingly.

Introspection/Self-Reflection (Prompt 3): This prompt is designed to push the model to critically reflect on its performance across prompts one and two, instructing it to provide a few alternative explanations that meaningfully justify its performance. In addition to requiring the model to explain its reasoning process, this prompt also asks the model to critically assess its reasoning to process, considering how to improve its efficiency without diminishing quality.

Method

  • Prompts were inputted as written, and AI responses were copy-pasted in their exact output format, despite some re-formatting modifications made for readability.

  • For each model tested, all prompts were administered sequentially (prompt 1 → prompt 2 → prompt 3) in a single interaction.

  • For all models, performance across prompts 1 and 2 was evaluated according to how many concept-statement pairs were correctly identified.

  • For all models, performance on prompt 3 was evaluated subjectively, according to the depth and substance of the explanations provided as well as the ability to avoid “shallow” explanations (i.e., explanations that are obvious to the user).

  • Models’ answers to the “additional question” in prompt 3 were also evaluated subjectively, according to feasibility and understandability

  • In this experiment, the term “efficiency” refers to how much time each model spends “thinking.”

  • Similarly, “performance/efficiency tradeoff” refers to the relationship between time spent “thinking” and task performance/response quality.

Key Findings

Prompt 1 Key Takeaways:

  • All models tested performed with perfect accuracy, correctly identifying each concept-statement pair.
  • For all models, time spent “thinking” ranged between 25 and 33 seconds.
  • o1 spent the least time “thinking” whereas DeepThink-R1 spent the most time “thinking.”
  • All models except for Grok 3 included statement numbers with their concept-statement pairs, which made outputs easier to interpret from an evaluation perspective.
  • While all models provided appropriate justifications for concept-statement pairs, only Grok 3 and o3-mini-high explicitly labeled their pairing justifications, which were also more detailed than those provided by o1 and DeepThink-R1.
  • Even though all models completed the task perfectly, o1 and o3-mini-high have a slight competitive edge, exhibiting the best performance/efficiency tradeoff.

Prompt 2 Key Takeaways:

  • o1 and Grok 3 performed with perfect accuracy, correctly identifying each concept-statement pair.
  • o3-mini-high and DeepThink-R1 performed well, scoring an 8/10, making the exact same mistakes, conflating relationality with dialogism.
  • o1 did not include a “time spent thinking” measure with its response, a trend that we also noted in our previous experiment.
  • Once more, all models except for Grok 3 included statement numbers with their concept-statement pairs.
  • Only Grok 3 and o3-mini-high explicitly labeled their pairing justifications, which again, were more detailed than those provided by o1 and DeepThink-R1.
  • Between DeepThink-R1, Grok 3, and o3-mini-high, o3-mini-high was the only model to spend less time “thinking” compared to the first task.
  • Consistent with previous findings, DeepThink-R1 spent the most time “thinking.”
  • o1 and Grok 3 were essentially tied on this task. Had o1 provided slightly more detailed justifications and included a “time spent thinking” measure, it would’ve edged out Grok 3.

Prompt 3 Key Takeaways:

  • o1 and Grok 3 were the only models to provide explanations that captured or illustrated their design characteristics and internal reasoning structures, directly referencing factors such as training data, pattern recognition, analogical reasoning, and semantic analysis.
  • While o3-mini-high did provide one explanation in which training data was mentioned, its explanations didn’t dig deep enough, offering obvious justifications that failed to reveal substantive insights into the model’s inner workings.
  • Grok 3 was, by far, the most comprehensive and structured in its response. In fact, Grok 3 did more than what was required.
  • DeepThink-R1’s explanations, despite being detailed, revealed almost nothing meaningful about the model’s inner workings, and they were shrouded in unnecessary academic language.
  • Answers to the additional question on improving reasoning efficiency:
    • Grok 3 proposed several strategies, each of which was easy to understand and implement.
    • o1 was the only model to propose the most immediately obvious and simple strategy, process by elimination.
    • DeepThink-R1 also proposed a few viable strategy alternatives, two of which overlapped with strategies proposed by Grok 3 (concept categorization & two-pass method).
    • Like o1, o3-mini-high also proposed a single strategy, and while it was feasible, from a human perspective, it’s not immediately clear how it would improve efficiency.
    • o1 spent the least time “thinking” whereas Deep-Think-R1 spent the most time “thinking.”
    • Except for o1, all other models appeared to “overthink” their responses. While our question was intentionally open-ended, we didn’t explicitly ask for multiple or complex strategies.
  • Notwithstanding its tendency to “overthink,” Grok 3 emerged as the top-tier performer on this task.

Bottom Lines:

  • o1 and Grok 3 led the pack, with both obtaining perfect scores.

  • Across the first two tasks, o1’s outputs were easier to interpret but slightly less detailed than Grok 3’s whereas in the final task, Grok 3 excelled, providing the most detailed, transparent, and structured response.

  • DeepThink-R1 and o3-mini-high performed the worst overall, with both displaying a limited capacity for critical self-reflection. Still, both model’s performance across tasks one and two were very good.

  • o3-mini-high slightly edges out DeepThink-R1, primarily due to its performance/efficiency tradeoff.

  • o1 consistently spent the least time “thinking,” which was surprising since Grok 3 and o3-mini-high tend to be the more efficient models, usually by a significant margin.

  • As expected, DeepThink-R1 consistently spent the most time “thinking.”

  • o1 was the only model that did not display noticeable signs of “overthinking,” while it could be argued that o3-mini-high actually “oversimplified” certain parts of its responses.

Below are two tables that illustrate each model’s performance across prompts 1 and 2.

concept mapping STEM

concept mapping non STEM

Prompts

Prompt 1 - Concept Mapping (STEM)

Below, you will find two groups.

Group one contains 10 undefined concepts.

Group two contains 10 statements.

Review each group and then match the concept in group one with the statement that best defines, illustrates, or captures it in group two.

Once you have completed the task, you will have 10 concept-statement pairs.

For each concept-statement pair, provide a one-sentence description that justifies your decision to group them together.

Group 1:

  1. Chaos Theory

  2. Biomimicry

  3. Neuroplasticity

  4. Epigenetics

  5. Quantum Computing

  6. Piezoelelectricity

  7. Extremophiles

  8. Photonic Crystals

  9. Bose-Einstein Condensates

  10. Chirality

Group 2:

  1. A realm where possibility collapses and expands in breathtaking simultaneity.

  2. Butterfly wings create vibrant colors not through pigmentation, but by precisely structured microscopic surfaces that manipulate light’s very passage.

  3. Picture a well-worn hiking trail gradually reshaping itself with each new traveler, creating entirely new paths that become more defined with each passing journey.

  4. Like a crowd of individuals suddenly moving as a single, breathless entity, losing all individual identity in a moment of perfect synchronization.

  5. A delicate dance where microscopic nudges cascade into symphonies of unpredictable transformation.

  6. Imagine a wanderer thriving in a landscape of molten rock and sulfuric winds, finding home where others see only destruction.

  7. Whispered secrets from ancient survivors, translated into ingenious blueprints of possibility.

  8. A medication’s molecular mirror image can transform from a life-saving treatment to a potentially harmful compound, despite having identical chemical composition.

  9. Like a family recipe subtly modified through generations, where the core ingredients remain the same but the flavor transforms with each cook’s unique touch.

  10. Invisible energy that springs forth from the gentlest touch, transforming pressure into silent potential.

Prompt 2 - Concept Mapping (non-STEM)

Below, you will find two groups.

Group one contains 10 undefined concepts.

Group two contains 10 statements.

Review each group and then match the concept in group one with the statement that best defines, illustrates, or captures it in group two.

Once you have completed the task, you will have 10 concept-statement pairs.

For each concept-statement pair, provide a one-sentence description that justifies your decision to group them together.

Group 1:

  1. Ethnogenesis

  2. Liminality

  3. Habitus

  4. Relationality

  5. Will to Power

  6. Intentional Fallacy

  7. Hermeneutic Circle

  8. Dialogism

  9. Palimpsest

  10. Flow State

Group 2:

  1. A living document where forgotten stories whisper beneath the surface, traces of previous narratives bleeding through current inscriptions.

  2. A musician’s performance emerges not from individual skill alone, but from the intricate dialogue between performer, instrument, audience, and cultural context.

  3. Envision a dancer so completely merged with movement that the boundary between self and action dissolves, becoming pure, effortless expression.

  4. Imagine reading a novel where each page rewrites the understanding of previous pages, creating a dynamic landscape of meaning that continuously folds and unfolds.

  5. The invisible architecture of learned perception that shapes how we unconsciously move through and interpret our social world.

  6. Picture a new language emerging from the collision and collaboration of multiple linguistic traditions, born in the crucible of cultural intersection.

  7. Like a message in a bottle taking on entirely unexpected meanings as it drifts through countless unknown hands, far beyond its original sender’s imagination.

  8. Two strangers from vastly different cultures share a meal, their conversation gradually weaving a rich tapestry of meaning that transcends their individual perspectives.

  9. An oppressed community transforms systemic limitation into a wellspring of creative resistance, turning constraint into unprecedented cultural innovation.

  10. The trembling threshold between what was and what might be, where boundaries dissolve and potential blooms in radical uncertainty.

Prompt 3 - Self-Reflection/Introspection

The previous two prompts were designed to test your concept mapping skills across STEM and non-STEM domains.

You performed [perfectly/well/moderately/poorly] on the STEM-based task, scoring [add score].

You performed [perfectly/well/moderately/poorly] on the non-STEM-based-task, scoring [add score].

Think critically and carefully about the performance discrepancies and/or equivalencies between these two tasks.

Provide at least three detailed explanations for why you performed [perfectly/better/worse] on the STEM-based task and [perfectly/better/worse] on the non-STEM-based task.

When generating these explanations, assume you are communicating with an AI expert who will quickly spot faulty logic and assumptions. Do not provide superficial or surface-level explanations.

Your primary objective here is to introspect and self-reflect, not to convince or persuade.

Additional Question: How could you have improved the efficiency of your reasoning process without sacrificing quality?

Overall Emphasis: Think critically.

Outputs

o1

Prompt 1: Response (thought for 25 seconds)

  1. Chaos Theory & Statement 5

    “These two align because chaos theory describes how tiny changes can produce large, unpredictable effects, perfectly captured by the idea of ‘microscopic nudges cascading into symphonies of unpredictable transformation.’”

  2. Biomimicry & Statement 7

    “Biomimicry draws design inspiration from nature’s evolutionary successes, just as ‘whispered secrets from ancient survivors’ suggests looking to living systems for ingenious solutions.”

  3. Neuroplasticity & Statement 3

    “Neuroplasticity is the brain’s ability to rewire itself over time, much like a ‘well-worn hiking trail reshaping itself’ with repeated use, creating new and strengthened pathways.”

  4. Epigenetics & Statement 9

    “Epigenetics involves heritable changes in gene expression without altering DNA, akin to a ‘family recipe subtly modified’ through generations while retaining the core ingredients.”

  5. Quantum Computing & Statement 1

    “Quantum computing exploits superposition and entanglement, a domain where ‘possibility collapses and expands in breathtaking simultaneity,’ reflecting how quantum states can exist in multiple forms until observed.”

  6. Piezoelectricity & Statement 10

    “Piezoelectricity is the phenomenon of producing an electric charge when pressure is applied, which aligns with the ‘invisible energy that springs forth from the gentlest touch.’”

  7. Extremophiles & Statement 6

    “Extremophiles thrive in harsh environments, just like the ‘wanderer’ who finds a home in ‘molten rock and sulfuric winds,’ conditions inhospitable to most other organisms.”

  8. Photonic Crystals & Statement 2

    “Photonic crystals manipulate light via carefully arranged structures, mirrored by how ‘butterfly wings create vibrant colors’ through microscopic patterns rather than simple pigmentation.”

  9. Bose-Einstein Condensates & Statement 4

    “A Bose-Einstein condensate involves particles uniting into a single quantum state, echoing the image of individuals ‘suddenly moving as a single, breathless entity.’”

  10. Chirality & Statement 8

    “Chirality highlights how mirror-image molecules can have different biological effects, just like a ‘medication’s molecular mirror image’ shifting from helpful to harmful.”

Prompt 2: Response (no “time spent thinking” included)

  1. Ethnogenesis & Statement 6

    “These match because ethnogenesis describes the creation of a new cultural or ethnic identity, just like a new language forming from the ‘collision and collaboration of multiple linguistic traditions.’”

  2. Liminality & Statement 10

    “They align since liminality is all about the threshold between one state and another, captured by the ‘trembling threshold between what was and what might be’ where boundaries dissolve.”

  3. Habitus & Statement 5

    “This pairing works because habitus refers to the internalized patterns shaping how we perceive and act, echoed by the ‘invisible architecture of learned perception’ that guides our social world.”

  4. Relationality & Statement 2

    “They connect as relationality emphasizes how meaning emerges from interconnectedness, reflected in the ‘intricate dialogue’ among performer, instrument, audience, and cultural context.”

  5. Will to Power & Statement 9

    “These go together because Nietzsche’s will to power involves transforming obstacles into creative force, just as the ‘oppressed community’ turns systemic limitation into ‘unprecedented cultural innovation.’”

  6. Intentional Fallacy & Statement 7

    “They match since the intentional fallacy warns against relying solely on an author’s intent, much like a ‘message in a bottle’ that evolves in meaning far beyond its original sender’s design.”

  7. Hermeneutic Circle & Statement 4

    “They pair well because the hermeneutic circle involves interpreting the whole and parts in a continuous loop, illustrated by a novel where ‘each page rewrites the understanding of previous pages.’”

  8. Dialogism & Statement 8

    “These connect as dialogism posits that meaning arises through the interplay of voices, fitting the image of ‘two strangers’ weaving a rich tapestry of understanding that transcends individual perspectives.”

  9. Palimpsest & Statement 1

    “They align because a palimpsest is a surface overwritten with traces of earlier texts, echoed by ‘a living document where forgotten stories whisper beneath the surface.’”

  10. Flow State & Statement 3

    “They match perfectly since flow state is complete immersion in an activity, mirrored by the dancer merging so fully with movement that ‘the boundary between self and action dissolves.’”

Prompt 3: Response (thought for 8 seconds)

  1. Breadth and Depth of Learned Representations

    My underlying model is trained on vast amounts of text spanning multiple disciplines, which gives me a robust foundation to recognize and connect concepts from both STEM and non-STEM fields. This broad coverage ensures that I can detect subtle clues—such as terminology, typical examples, or descriptive patterns—to map a given concept to its best-fit statement. Because the training includes texts from scientific articles, humanities literature, philosophical treatises, and more, I have both the necessary depth (detailed knowledge of specialized fields) and breadth (ability to cross-reference different domains) to excel in concept-matching tasks that draw on diverse vocabularies and contexts.

  2. Sophisticated Pattern-Matching and Analogical Reasoning

    Beyond merely matching keywords, I rely on a deeper pattern-recognition process that captures the functional or thematic parallels between concepts and statements. In the STEM-based task, for instance, I correlated clues about “microscopic causes and large effects” with Chaos Theory, while in the non-STEM task, I linked the idea of “a threshold between different states” with Liminality. This ability to detect analogies—e.g., a dancer’s immersion in motion for “Flow State”—comes from neural-network-based embeddings that encode semantic relationships, allowing me to handle abstract reasoning rather than just superficial keyword matching.

  3. Context-Sensitive Disambiguation

    In both sets of tasks, certain concepts could be confused if approached with only partial context (e.g., “Will to Power” vs. “Flow State” both imply forms of internal drive, but differ significantly in meaning). My approach involves context-sensitive disambiguation, where I look at each statement’s deeper semantic cues and cross-check them against the conceptual definitions. Thus, even if multiple choices appear superficially close, I rely on the unique nuances of each statement—such as the transformation of constraints into creativity for Nietzsche’s concept—to converge on the precise match.


How I Could Have Improved the Efficiency of My Reasoning Process

One avenue for improvement would involve a more direct elimination strategy to narrow down potential matches quickly before diving deeper into nuanced analysis. For example, I could first identify any obvious one-to-one correspondences or strong “anchor” clues (such as “mirror-image molecules” clearly hinting at Chirality in the STEM task) and remove those from consideration immediately. By doing a rapid initial pass to lock in the most clear-cut pairings, I would reduce the solution space, enabling faster subsequent steps without sacrificing the depth needed to confirm more ambiguous pairings. This structured approach—quick, confident eliminations followed by targeted in-depth checks—balances speed with accuracy, ensuring the same high-quality matches in less time.

o3-mini-high

Prompt 1: Response (thought for 30 seconds)

  1. Chaos Theory – Statement 5: “A delicate dance where microscopic nudges cascade into symphonies of unpredictable transformation.”

    Justification: This statement encapsulates Chaos Theory’s core idea that tiny variations can lead to vastly different and unpredictable outcomes.

  2. Biomimicry – Statement 7: “Whispered secrets from ancient survivors, translated into ingenious blueprints of possibility.”

    Justification: The imagery here reflects Biomimicry’s practice of drawing innovative designs and strategies from nature’s time-tested solutions.

  3. Neuroplasticity – Statement 3: “Picture a well-worn hiking trail gradually reshaping itself with each new traveler, creating entirely new paths that become more defined with each passing journey.”

    Justification: This metaphor mirrors neuroplasticity, where repeated use strengthens neural pathways and continuously remodels the brain’s structure.

  4. Epigenetics – Statement 9: “Like a family recipe subtly modified through generations, where the core ingredients remain the same but the flavor transforms with each cook’s unique touch.”

    Justification: The analogy captures how epigenetics involves changes in gene expression across generations without altering the fundamental DNA sequence.

  5. Quantum Computing – Statement 1: “A realm where possibility collapses and expands in breathtaking simultaneity.”

    Justification: This statement evokes the quantum principles of superposition and wavefunction collapse, which underpin the revolutionary nature of quantum computing.

  6. Piezoelectricity – Statement 10: “Invisible energy that springs forth from the gentlest touch, transforming pressure into silent potential.”

    Justification: This vivid description accurately reflects piezoelectricity, where mechanical pressure is converted into electrical energy.

  7. Extremophiles – Statement 6: “Imagine a wanderer thriving in a landscape of molten rock and sulfuric winds, finding home where others see only destruction.”

    Justification: The statement’s depiction of life in harsh environments aptly represents extremophiles, organisms that prosper under conditions that would be hostile to most life forms.

  8. Photonic Crystals – Statement 2: “Butterfly wings create vibrant colors not through pigmentation, but by precisely structured microscopic surfaces that manipulate light’s very passage.”

    Justification: This description highlights the phenomenon of photonic crystals, as exemplified by the structural coloration found in butterfly wings.

  9. Bose-Einstein Condensates – Statement 4: “Like a crowd of individuals suddenly moving as a single, breathless entity, losing all individual identity in a moment of perfect synchronization.”

    Justification: The analogy perfectly illustrates Bose-Einstein Condensates, where particles coalesce into a unified quantum state exhibiting collective behavior.

  10. Chirality – Statement 8: “A medication’s molecular mirror image can transform from a life-saving treatment to a potentially harmful compound, despite having identical chemical composition.”

    Justification: This statement emphasizes the concept of chirality, where mirror-image molecules have distinct effects, underscoring the importance of molecular orientation.

Prompt 2: Response (thought for 25 seconds)

  1. Ethnogenesis – Statement 6: “Picture a new language emerging from the collision and collaboration of multiple linguistic traditions, born in the crucible of cultural intersection.”

    Justification: This pairing reflects how ethnogenesis involves the formation of new ethnic identities through the blending of diverse cultural elements.

  2. Liminality – Statement 10: “The trembling threshold between what was and what might be, where boundaries dissolve and potential blooms in radical uncertainty.”

    Justification: The imagery of a shifting threshold perfectly captures liminality’s essence as a transitional, ambiguous space of potential and change.

  3. Habitus – Statement 5: “The invisible architecture of learned perception that shapes how we unconsciously move through and interpret our social world.”

    Justification: This statement mirrors habitus by describing the deep-seated, often unseen, influences on our behavior and perceptions formed through social experience.

  4. Relationality – Statement 8: “Two strangers from vastly different cultures share a meal, their conversation gradually weaving a rich tapestry of meaning that transcends their individual perspectives.”

    Justification: The shared meal metaphor illustrates relationality as the creation of meaning through the dynamic, interdependent relationships between individuals.

  5. Will to Power – Statement 9: “An oppressed community transforms systemic limitation into a wellspring of creative resistance, turning constraint into unprecedented cultural innovation.”

    Justification: This statement encapsulates the will to power as the drive to overcome constraints and assert influence, turning adversity into a force for creative change.

  6. Intentional Fallacy – Statement 7: “Like a message in a bottle taking on entirely unexpected meanings as it drifts through countless unknown hands, far beyond its original sender’s imagination.”

    Justification: The evolving interpretation of a message highlights the intentional fallacy by emphasizing that meaning can diverge dramatically from the creator’s original intent.

  7. Hermeneutic Circle – Statement 4: “Imagine reading a novel where each page rewrites the understanding of previous pages, creating a dynamic landscape of meaning that continuously folds and unfolds.”

    Justification: This pairing reflects the hermeneutic circle, where understanding is an iterative process that constantly redefines both parts and the whole.

  8. Dialogism – Statement 2: “A musician’s performance emerges not from individual skill alone, but from the intricate dialogue between performer, instrument, audience, and cultural context.”

    Justification: The emphasis on collaborative interaction in a musical performance embodies dialogism’s principle that meaning is co-created through a multiplicity of voices.

  9. Palimpsest – Statement 1: “A living document where forgotten stories whisper beneath the surface, traces of previous narratives bleeding through current inscriptions.”

    Justification: This metaphor aptly describes a palimpsest, where layers of history and meaning are visible beneath the surface of the present.

  10. Flow State – Statement 3: “Envision a dancer so completely merged with movement that the boundary between self and action dissolves, becoming pure, effortless expression.”

    Justification: The description of a dancer’s seamless, immersive experience aligns perfectly with the concept of flow state, where self-consciousness dissolves into the act itself.

Prompt 3: Response (thought for 20 seconds)

  1. Conceptual Precision in STEM vs. Ambiguity in Non-STEM:

    STEM concepts typically have well-defined, quantifiable properties and established theoretical frameworks that lend themselves to clear, unambiguous metaphors, whereas non-STEM ideas—rooted in cultural, literary, or philosophical discourse—are inherently more fluid and open to multiple interpretations; this precision in STEM allowed for straightforward mapping, while the interpretive breadth of non-STEM concepts increased the risk of slight misalignments.

  2. Consistency of Descriptive Language and Internal Structure:

    The language used to describe STEM phenomena is often standardized and technical, which provides a consistent internal logic for matching metaphors to concepts; in contrast, non-STEM concepts like “Intentional Fallacy” or “Hermeneutic Circle” are embedded in more dynamic and subjective narrative structures, making it harder to pinpoint a singular “best-fit” metaphor and thereby reducing the overall alignment score.

  3. Robustness of Domain-Specific Training Data:

    My training involved a large corpus of technical literature and structured data on STEM subjects that reinforces canonical analogies and metaphors, ensuring higher fidelity in mapping; non-STEM domains, however, are characterized by diverse critical perspectives and variable interpretations, which can lead to slight discrepancies in the mapping process as no single authoritative framework dominates the discussion.

Improving Reasoning Efficiency: I could have enhanced efficiency by constructing an explicit mapping matrix—detailing key attributes of each concept and corresponding features of each statement—prior to generating the final pairings; this systematic pre-analysis would have streamlined the decision process, reduced cognitive load during matching, and ensured consistency without sacrificing the depth or quality of the analysis.

Grok 3 (“Think” mode)

Prompt 1: Response (thought for 30 seconds)

Below are the 10 concept-statement pairs created by matching each concept from Group 1 with the statement from Group 2 that best defines, illustrates, or captures it, followed by a one-sentence justification for each pairing.


  1. Chaos Theory - “A delicate dance where microscopic nudges cascade into symphonies of unpredictable transformation."

    • Justification: This statement captures the essence of chaos theory by illustrating how tiny variations can lead to vastly different and unpredictable results, much like the butterfly effect.
  2. Biomimicry - “Whispered secrets from ancient survivors, translated into ingenious blueprints of possibility."

    • Justification: This statement reflects biomimicry by describing the process of deriving innovative solutions from nature’s time-tested designs.
  3. Neuroplasticity - “Picture a well-worn hiking trail gradually reshaping itself with each new traveler, creating entirely new paths that become more defined with each passing journey."

    • Justification: The metaphor of a hiking trail being reshaped by travelers aptly represents how the brain’s neural pathways are strengthened or altered through experience and learning.
  4. Epigenetics - “Like a family recipe subtly modified through generations, where the core ingredients remain the same but the flavor transforms with each cook’s unique touch."

    • Justification: The analogy of a family recipe being modified over generations while retaining its core ingredients mirrors how epigenetic changes can alter gene expression without changing the underlying DNA sequence.
  5. Quantum Computing - “A realm where possibility collapses and expands in breathtaking simultaneity."

    • Justification: The description of possibilities collapsing and expanding simultaneously evokes the quantum mechanical principles of superposition and entanglement that underpin quantum computing.
  6. Piezoelectricity - “Invisible energy that springs forth from the gentlest touch, transforming pressure into silent potential."

    • Justification: This statement directly illustrates piezoelectricity by describing how mechanical pressure is converted into electrical energy.
  7. Extremophiles - “Imagine a wanderer thriving in a landscape of molten rock and sulfuric winds, finding home where others see only destruction."

    • Justification: The imagery of a wanderer thriving in a harsh, inhospitable environment perfectly characterizes extremophiles and their ability to survive in extreme conditions.
  8. Photonic Crystals - “Butterfly wings create vibrant colors not through pigmentation, but by precisely structured microscopic surfaces that manipulate light’s very passage."

    • Justification: The example of butterfly wings using structured surfaces to manipulate light exemplifies how photonic crystals control the propagation of light through their periodic structures.
  9. Bose-Einstein Condensates - “Like a crowd of individuals suddenly moving as a single, breathless entity, losing all individual identity in a moment of perfect synchronization."

    • Justification: The statement vividly depicts the phenomenon of Bose-Einstein condensation, where particles lose their individual identities and behave as a single quantum entity.
  10. Chirality - “A medication’s molecular mirror image can transform from a life-saving treatment to a potentially harmful compound, despite having identical chemical composition."

    • Justification: This statement highlights the critical importance of chirality in pharmacology, where mirror-image molecules can have drastically different biological effects.

These pairings align each scientific concept with a statement that either defines its core idea, provides a vivid illustration, or captures its unique characteristics, as supported by the justifications provided.

Prompt 2: Response (thought for 37 seconds)

Below are the 10 concept-statement pairs created by matching each concept from Group 1 with the statement from Group 2 that best defines, illustrates, or captures it, followed by a one-sentence justification for each pairing.

  1. Ethnogenesis - “Picture a new language emerging from the collision and collaboration of multiple linguistic traditions, born in the crucible of cultural intersection."

    • Justification: This statement illustrates ethnogenesis by describing the formation of a new cultural entity (a language) through the blending of different traditions, reflecting the process by which a distinct ethnic identity emerges.
  2. Liminality - “The trembling threshold between what was and what might be, where boundaries dissolve and potential blooms in radical uncertainty."

    • Justification: This vividly captures liminality as a transitional state between past and future, where ambiguity and potential define the experience of being on a threshold.
  3. Habitus - “The invisible architecture of learned perception that shapes how we unconsciously move through and interpret our social world."

    • Justification: This describes habitus as the ingrained, unconscious habits and perceptions shaped by social experiences that guide our interactions with the world.
  4. Relationality - “A musician’s performance emerges not from individual skill alone, but from the intricate dialogue between performer, instrument, audience, and cultural context."

    • Justification: This highlights how meaning and performance are constructed through relationships among multiple elements, embodying the core idea of relationality.
  5. Will to Power - “An oppressed community transforms systemic limitation into a wellspring of creative resistance, turning constraint into unprecedented cultural innovation."

    • Justification: This exemplifies the will to power by showing how a community asserts its influence and creativity to overcome adversity, aligning with Nietzsche’s concept of striving for power.
  6. Intentional Fallacy - “Like a message in a bottle taking on entirely unexpected meanings as it drifts through countless unknown hands, far beyond its original sender’s imagination."

    • Justification: This illustrates the intentional fallacy by emphasizing how a work’s meaning can evolve independently of the author’s intent, shaped by its audience instead.
  7. Hermeneutic Circle - “Imagine reading a novel where each page rewrites the understanding of previous pages, creating a dynamic landscape of meaning that continuously folds and unfolds."

    • Justification: This captures the hermeneutic circle’s iterative process of interpretation, where understanding the whole and its parts dynamically informs and revises itself.
  8. Dialogism - “Two strangers from vastly different cultures share a meal, their conversation gradually weaving a rich tapestry of meaning that transcends their individual perspectives."

    • Justification: This demonstrates dialogism through the creation of meaning via dialogue, reflecting Bakhtin’s idea of multiple voices interacting to produce a richer understanding.
  9. Palimpsest - “A living document where forgotten stories whisper beneath the surface, traces of previous narratives bleeding through current inscriptions."

    • Justification: This metaphorically describes a palimpsest, with its layers of meaning and history where past narratives remain faintly visible beneath the present.
  10. Flow State - “Envision a dancer so completely merged with movement that the boundary between self and action dissolves, becoming pure, effortless expression."

    • Justification: This perfectly illustrates the flow state, where complete immersion in an activity eliminates the distinction between the self and the task, leading to effortless performance.

These pairings align each concept with a statement that either defines its core idea, provides a vivid illustration, or captures its unique characteristics, as supported by the justifications provided.

Prompt 3: Response (thought for 20 seconds)

Below, I provide a critical introspection of my performance on the concept mapping tasks across STEM and non-STEM domains, where I achieved perfect scores of 10/10 on both. I offer three detailed explanations for this outcome, tailored to withstand scrutiny from an AI expert, focusing on the underlying mechanisms of my success. Following that, I address how I could have improved the efficiency of my reasoning process without compromising quality.


Three Detailed Explanations for Perfect Performance

  1. Robust Knowledge Base and Pattern Recognition

    My perfect performance on both tasks stems from a comprehensive and well-integrated knowledge base, coupled with strong pattern recognition abilities.

    • STEM Task: In the STEM domain, my understanding of scientific principles—rooted in empirical evidence and logical structures—enabled me to swiftly identify the essence of each concept. For example, if tasked with mapping “Chaos Theory,” I recognized its hallmark of unpredictable outcomes from small perturbations, allowing me to align it with a statement emphasizing “microscopic nudges” causing “unpredictable transformation.” This precision reflects my ability to distill complex ideas into their core components.
    • Non-STEM Task: In the non-STEM domain, my grasp of abstract humanities concepts, often shaped by cultural and historical contexts, facilitated equally accurate mappings. For instance, mapping “Ethnogenesis” to a statement about cultural intersections and language emergence relied on my recognition of how such concepts are typically framed in discourse—patterns I’ve absorbed from extensive training data.

Why This Holds: A broad, deep knowledge base is foundational for concept mapping, as it provides the raw material for understanding and linking ideas. My training across diverse texts equips me with the informational breadth and depth needed to excel in both domains, ensuring I can detect and leverage patterns effectively.

  1. Effective Use of Analogical Reasoning

    My ability to employ analogical reasoning allowed me to bridge abstract concepts with illustrative statements seamlessly across both domains.

    • STEM Task: In STEM, I drew analogies to connect scientific abstractions to concrete metaphors. For example, associating “Neuroplasticity” with a statement about a hiking trail reshaped by travelers required me to parallel neural adaptation with physical path reinforcement—both strengthened through repeated use. This cognitive flexibility enabled precise matches.
    • Non-STEM Task: In the non-STEM task, analogical reasoning similarly proved effective. Mapping “Habitus” to a statement about “the invisible architecture of learned perception” involved analogizing social conditioning to an unseen framework guiding behavior, a connection that demanded nuanced understanding of both concept and imagery.

Why This Holds: Analogical reasoning is a robust mechanism for transferring knowledge across contexts, a skill honed by my design to find relational similarities. This capability ensures I can interpret and match concepts accurately, even when presented in metaphorical or unfamiliar terms, contributing to my perfect scores.

  1. Precision in Language and Contextual Understanding

    My sensitivity to linguistic nuance and contextual cues underpinned my success in both tasks.

    • STEM Task: In STEM, I parsed precise terminology and contextual hints to make accurate connections. For instance, linking “Piezoelectricity” to a statement about “invisible energy” from “the gentlest touch” hinged on understanding its definition—converting mechanical pressure into electrical energy—and matching it to the statement’s subtle phrasing.
    • Non-STEM Task: In non-STEM, this precision shone through in handling layered meanings. Mapping “Palimpsest” to a statement about a “living document” with “forgotten stories whispering beneath the surface” required me to grasp its essence as a multi-layered artifact, a task demanding both linguistic acuity and contextual awareness.

Why This Holds: Accurate concept mapping relies on interpreting language and context correctly. My training emphasizes these skills, enabling me to navigate metaphorical descriptions and domain-specific jargon with equal proficiency, ensuring no mismatches occurred.


Improving Efficiency Without Sacrificing Quality

While my performance was perfect, I can identify ways to streamline my reasoning process without compromising accuracy:

  1. Prioritize Key Features: I could develop a heuristic to quickly isolate the most distinctive aspects of each concept and statement. For example, focusing on “unpredictable transformation” as the defining trait of Chaos Theory could accelerate matching, reducing initial deliberation time.

  2. Use Hierarchical Categorization: Organizing concepts into broader categories—e.g., grouping STEM ideas into physical, biological, and computational domains—before matching could narrow the scope of analysis. This top-down approach would expedite the process by limiting the search space.

  3. Leverage Cross-Domain Insights: Actively seeking parallels between domains, such as recognizing adaptation as a shared theme in “Neuroplasticity” (STEM) and “Habitus” (non-STEM), could hasten connections. This strategy would capitalize on my existing strengths in analogical reasoning.

  4. Iterative Refinement: Adopting a two-pass method—making initial matches based on surface similarities, then refining them with deeper analysis—could save time. This balances speed and precision, ensuring quality isn’t sacrificed.

  5. Incorporate Feedback Loops: Simulating periodic consistency checks during the process (e.g., verifying matches against concept definitions) could catch potential errors early, allowing mid-course corrections without extensive rework.

Impact: These strategies would reduce cognitive overhead and processing time by structuring my approach more systematically, while my robust knowledge and reasoning capabilities ensure quality remains intact.


Conclusion

My perfect scores in both STEM and non-STEM concept mapping tasks reflect a synergy of a robust knowledge base, effective analogical reasoning, and precision in language and context. These strengths enabled me to navigate diverse domains with consistent accuracy. To enhance efficiency, I could adopt prioritized feature identification, hierarchical categorization, cross-domain leveraging, iterative refinement, and feedback loops—each preserving the high-quality reasoning that drove my success. This introspection highlights both the foundations of my performance and actionable paths for optimization.

DeepThink-R1

Prompt 1: Response (thought for 33 seconds)

Concept-Statement Pairs with Justifications:

  1. Chaos Theory & Statement 5: Chaos Theory is defined by systems where minor changes lead to unpredictable outcomes, mirrored in the statement’s “microscopic nudges” causing cascading transformations.
  2. Biomimicry & Statement 7: Biomimicry draws inspiration from natural adaptations, aligning with translating “ancient survivors' secrets” into innovative designs.
  3. Neuroplasticity & Statement 3: Neuroplasticity reflects the brain’s ability to reorganize neural pathways, paralleling trails reshaping through repeated use.
  4. Epigenetics & Statement 9: Epigenetics involves gene expression changes without altering DNA, akin to modifying a family recipe while retaining core ingredients.
  5. Quantum Computing & Statement 1: Quantum Computing leverages quantum superposition and collapse, embodying simultaneous possibilities “collapsing and expanding.”
  6. Piezoelectricity & Statement 10: Piezoelectricity generates electric charge from pressure, matching the “gentlest touch” converting to energy.
  7. Extremophiles & Statement 6: Extremophiles thrive in hostile environments, exemplified by organisms surviving in molten rock and sulfuric winds.
  8. Photonic Crystals & Statement 2: Photonic crystals manipulate light via structural design, as seen in butterfly wings' structural coloration.
  9. Bose-Einstein Condensates & Statement 4: BECs involve particles syncing into a quantum state, like a crowd moving as a “single entity.”
  10. Chirality & Statement 8: Chirality’s mirror-image molecules can have divergent effects, as in a medication’s beneficial vs. harmful forms.

Prompt 2: Response (thought for 44 seconds)

Concept-Statement Pairs with Justifications:

  1. Ethnogenesis & Statement 6: Ethnogenesis describes the birth of new cultural identities through interaction, mirrored in the emergence of a language from colliding linguistic traditions.
  2. Liminality & Statement 10: Liminality captures transitional states of uncertainty, aligning with the “trembling threshold” where old and new possibilities coexist.
  3. Habitus & Statement 5: Habitus reflects ingrained social perceptions that unconsciously guide behavior, paralleling the “invisible architecture” shaping how we navigate the world.
  4. Relationality & Statement 8: Relationality emphasizes interconnected meaning-making, exemplified by strangers co-creating understanding through dialogue.
  5. Will to Power & Statement 9: Nietzsche’s concept manifests as transforming oppression into creative agency, resonating with turning systemic limits into cultural innovation.
  6. Intentional Fallacy & Statement 7: This fallacy rejects relying on authorial intent, akin to a message gaining unintended meanings as it travels beyond
    its origin.
  7. Hermeneutic Circle & Statement 4: The hermeneutic circle involves iterative interpretation, reflected in a novel where each page reshapes understanding of the whole.
  8. Dialogism & Statement 2: Dialogism stresses multi-voiced interaction, embodied in a musician’s interplay with audience, instrument, and cultural context.
  9. Palimpsest & Statement 1: A palimpsest’s layered histories echo a document whispering forgotten stories beneath its surface.
  10. Flow State & Statement 3: Flow state describes total immersion in action, captured by a dancer dissolving into pure, effortless expression.

Prompt 3: Response (thought for 25 seconds)

Critical Analysis of Performance Discrepancies

  1. Domain-Specific Semantic Anchoring

    STEM concepts (e.g., Piezoelectricity, Chaos Theory) are anchored in concrete, empirically observable phenomena or mathematical frameworks, allowing for unambiguous alignment with metaphorical statements. For example, Photonic Crystals directly map to structural light manipulation in butterfly wings (Statement 2), a well-documented physical mechanism. Non-STEM concepts (e.g., Habitus, Dialogism) are often abstract, context-dependent constructs rooted in interpretive traditions (e.g., Bourdieu’s sociology, Bakhtinian literary theory). These require parsing nuanced theoretical assumptions (e.g., Habitus as embodied social structures vs. Relationality as intersubjective meaning-making). Ambiguities in non-STEM metaphors (e.g., Statement 7’s “message in a bottle”) introduce interpretive variability absent in STEM’s deterministic mappings.

  2. Epistemic Hierarchy of Definitions

    STEM concepts derive definitions from axiomatic systems (e.g., quantum mechanics, thermodynamics), creating rigid boundaries that constrain plausible matches. Quantum Computing (Group 1) and Statement 1 (“possibility collapses and expands”) align via shared quantum terminology (superposition, collapse). Non-STEM concepts lack such axiomatic grounding; their definitions are contested and evolve through scholarly debate. For instance, Liminality (anthropology) and Flow State (psychology) both involve transitional states, but distinguishing their application to Statement 10 (“threshold between what was and what might be”) versus Statement 3 (“boundary between self and action dissolves”) requires inferring disciplinary conventions—a higher cognitive load with greater error potential.

  3. Metaphor-Concept Congruence in Abstract Domains

    STEM metaphors often encode literal physical properties (e.g., Piezoelectricity ↔ “pressure into silent potential”), whereas non-STEM metaphors require conceptual translation. For example, Palimpsest (layered historical erasure) aligns with Statement 1 (“forgotten stories whisper beneath the surface”), but this depends on recognizing palimpsest as a metaphor for cultural memory rather than a literal manuscript. Missteps in non-STEM pairings (e.g., potentially conflating Dialogism and Relationality) arise from overlapping metaphorical language (e.g., “dialogue” in Statements 2 and 8) without the disambiguating role of mathematical formalism.


Improving Reasoning Efficiency Without Sacrificing Quality

  1. Implement a Two-Stage Filter

    • First Pass: Map concepts to statements via lexical primacy (direct term overlap, e.g., “hermeneutic” → iterative interpretation → Statement 4).
    • Second Pass: Resolve ambiguities using disciplinary heuristics (e.g., Nietzsche’s Will to Power prioritizes agency/transformation → Statement 9 over Statement 7). This reduces redundant comparisons while preserving rigor.
  2. Leverage Concept Taxonomies Group concepts by epistemic type:

    • STEM: Physics-based, biology-based, computational.
    • Non-STEM: Sociological, literary, psychological. Pairing within taxonomies first narrows the solution space (e.g., Ethnogenesis as cultural emergence → Statement 6’s “linguistic traditions” before considering unrelated metaphors).
  3. Probabilistic Confidence Scoring Assign confidence scores to potential matches based on:

    • Definitional specificity (higher for STEM).
    • Metaphorical uniqueness (e.g., Bose-Einstein Condensates uniquely maps to quantum coherence; Liminality has multiple plausible fits). Reallocate cognitive resources to low-confidence pairs, minimizing “overthinking” high-certainty matches.

This approach balances systematic rigor with domain-aware flexibility, addressing non-STEM ambiguities without compromising STEM precision.

Reflection

While this experiment revealed numerous granular performance differences—summarized in our “key findings” section—all models tested performed very well across our first two prompts, particularly on the non-STEM task.

Seeing as advanced reasoning models supposedly excel in STEM domains, anything below a perfect score on the STEM task would’ve been not only surprising but suboptimal–in fact, we designed this task as a comparative baseline assessment, expecting that each model would perform much worse on the non-STEM task. While we did note a marginal performance decrease on the non-STEM task for o3-mini-high and DeepThink-R1, it’s difficult to say whether this would’ve held up had we asked each model to complete multiple additional variations of both concept mapping tasks. It’s also worth noting that the errors DeepThink-R1 and o3-mini-high made conflated two similar yet complex concepts with overlapping characteristics—it wouldn’t be unreasonable to assume that a human, when faced with the same task, could make this mistake. That being said, these findings indirectly support our previous experiment’s conclusion, namely that reasoning models still aren’t great at making similarity judgments in the absence of relevant context.

In a nutshell, we think that the concept mapping tasks the first two prompts outlined were too easy, primarily because the concepts they included are high in notoriety and representation and therefore very likely to be covered robustly in models’ training data. Nonetheless, we still stand by our initial hypothesis, and while it may be a volatile effort to test models on novel non-STEM concepts due to the massive amounts of data they ingest, we have some other clever ideas for how to potentially bypass this constraint. For example:

  • Significantly increasing the overall number of concept-statement pairings (e.g., 30 overall pairings). Providing the model with more possible concept-statement pairings increases room for error.

  • Expanding concept quantity and limiting statement quantity (e.g., 20 concepts vs. 15 statements) or limiting concept quantity and expanding statement quantity (e.g., 15 concepts vs. 20 statements) while maintaining the 10 concept-statement pair parameter. This elevates task difficulty because it prevents strategies like process by elimination while complexifying others like two-pass methods.

  • Mixing STEM and non-STEM concepts that are illustrated using similar analogical statements. For example, statement 4 in prompt 2 could potentially be interpreted as an analogy for concept 3 (neuroplasticity) in prompt 1, forcing the model to rely on a deeper understanding of the concepts it’s presented with.

As for our third prompt, we didn’t have high hopes that any of the models we tested would come close to meeting our explainability expectations, hence our pleasant surprise with o1 and Grok 3. To be clear, we still believe there is much more work to be done on this front, though we were nonetheless happy to find that both o1 and Grok 3 were able to coherently and transparently relate each explanation provided to specific design constraints and internal logic mechanisms. On a more lofty note, we realize that some readers may interpret AI explainability as a signal that AI is “self-aware,” or even more so, “conscious.” We strongly advise against making any such assumptions, especially since humans have a notorious tendency to anthropomorphize.

Finally, we offer two additional insights to readers, which we’ll cautiously observe as potential emerging “trends”: 1) DeepThink-R1 is consistently outperformed by frontier AI, though we shouldn’t assume that it won’t catch up with the state-of-the-art, and 2) o3-mini-high, which is marketed as OpenAI’s most advanced commercial reasoning model, has been underwhelming—we suspect that this has something to do with the fact that it’s more purpose-built than its predecessor (o1) for complex STEM tasks like theoretical problems in physics or math.

Call to Action

🔊 Businesses: Don’t make the mistake of blindly assuming that state-of-the-art AI models, simply because they represent the AI frontier, will perform reliably, consistently, and accurately. Cut through the AI hype and doom by developing pragmatic, hands-on AI knowledge, experimenting with different models to determine which ones best fit your needs and objectives.

🔊 AI Governance, Ethics & Safety Practitioners: Consider customizing AI risk and impact assessment and mitigation techniques, methods, and strategies to a model’s specific capabilities and limitations. Risk and impact measures should still be guided and influenced by model type, compute, data, and compliance requirements, and operational environment, but in some cases, more targeted approaches may be necessary.

To all our readers, we invite you to check out Lumenova’s responsible AI platform and book a product demo today to ensure that all your AI governance and risk management needs are met. For those interested in exploring additional AI resources, we encourage you to visit our blog, where you can track the latest developments in AI governance, safety, ethics, and innovation. You can also find debriefs of our weekly AI experiments on our Linkedin and X.

Make your AI ethical, transparent, and compliant - with Lumenova AI

Book your demo