Understanding Beyond Patterns: Transforming AI from Retrieval to Reasoning

The concept of “understanding” in AI has long been constrained by the technical focus of existing research frontiers, which prioritize advancements in neural networks (NNs), tokenization, and pattern recognition. While these innovations have enabled AI to excel in language processing and retrieval-augmented generation (RAG) systems, they fall short in achieving deeper comprehension — particularly in contexts where logic, adaptation, and strategic objectives intersect. Language, with its encoded relational complexity, and tokenized processing, the atomic units of NN operations, offer a foundation for AI’s mimicry of understanding but lack the dynamic reasoning humans employ to integrate knowledge meaningfully. To explore this gap, we propose testing AI’s comprehension against a strategic gaming scenario within Genshin Impact, where a new meta-character boasts extraordinary 3D motorbike mobility and 8-million-damage potential. This case mirrors the diverse goals of players: from narrative-driven objectives like completing Archon quests across Teyvat’s evolving regions (Mondstadt to Natlan and beyond) to achieving gameplay mastery within constraints like resource optimization. These strategic objectives highlight the limitations of current AI benchmarks, which fail to capture the layered complexities of such tasks. By deconstructing how advanced “understanding” can be modeled and tested, we aim to establish a new paradigm for AI systems that transcends retrieval and surface-level synthesis, paving the way for reasoning-based comprehension in practical applications.

Part 1: The Frustration Between RAG and Knowledge Understanding

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful tool in artificial intelligence, combining retrieval capabilities with generative language models to provide contextually enriched responses. However, their reliance on surface-level retrieval and pattern-based generation has become a source of growing frustration when these systems are tested against the deeper requirements of knowledge understanding. While RAG systems excel at retrieving and presenting information, they often fail to process, filter, or validate this information in a way that reflects true comprehension. This leads to outputs that may be fluent and seemingly accurate but are often irrelevant, logically inconsistent, or dependent on unverified claims.

The heart of the problem lies in the overemphasis on retrieval fidelity rather than reasoning. Current RAG systems rely heavily on retrieving fragments of data that match the input query but lack mechanisms to test or contest the validity of this data. For instance, a claim retrieved from a forum or user guide that a character in Genshin Impact deals “8 Million DMG” might be incorporated into the response without scrutiny, even though such an outcome is achievable only under highly specific conditions that are unrealistic in normal gameplay. This inability to filter or challenge retrieved information highlights a fundamental limitation: RAG systems do not inherently understand the knowledge they process — they merely reassemble patterns without logical evaluation.

Compounding the issue is the overreliance on benchmarks to evaluate RAG systems. Traditional metrics like precision, recall, or fluency fail to capture whether the system has synthesized meaningful and practical knowledge from its retrieved data. A higher retrieval accuracy score, for instance, does not necessarily indicate that the system has grasped the contextual relevance or applicability of the data. This creates a false sense of progress, where technical improvements in retrieval mechanisms are celebrated, even as fundamental flaws in comprehension persist.

A critical part of addressing this issue is clarifying the terminology. The distinction between “understanding” and “comprehension” is essential here. While often used interchangeably, understanding emphasizes the ability to integrate and apply knowledge dynamically, whereas comprehension suggests a more passive, albeit complete, grasp of information. In the case of RAG, the debate lies in whether such systems truly “comprehend” their retrieved data or merely simulate understanding by producing plausible responses. This distinction is vital, as it underscores the gap between the sophisticated mimicry of knowledge seen in current RAG systems and the genuine reasoning and adaptability that characterize true understanding.

Part 2: Human Comprehension vs. AI Comprehension

Human comprehension and AI comprehension share certain structural similarities but diverge fundamentally in their mechanisms and depth. At its core, human comprehension is an integrative, dynamic process rooted in experience, intuition, and abstract reasoning. Humans can connect disparate pieces of information, test their coherence against lived realities, and adapt their understanding based on changing contexts. By contrast, AI comprehension, as it currently exists, is a synthetic process based on pattern recognition, probabilistic associations, and pre-defined algorithms. While AI can simulate certain aspects of human reasoning, it lacks the experiential grounding and self-directed adaptability that are hallmarks of genuine comprehension.

For humans, comprehension begins with the assimilation of knowledge through sensory, emotional, and contextual experiences. These inputs are processed within a framework of prior understanding, allowing for connections that go beyond the explicit data. For instance, a human analyzing the utility of a character like Mavuika in Genshin Impactconsiders not only the numerical damage output but also contextual factors like team synergy, survivability, and real-world constraints on resources. This capacity for synthesis and abstraction is supported by intuition, a uniquely human faculty that bridges gaps in information and fosters creative solutions to complex problems.

AI comprehension, on the other hand, is grounded in the mechanics of neural networks and tokenized language processing. Neural networks operate on weighted connections learned from massive datasets, enabling them to identify patterns and predict relationships between data points. Tokens — the smallest units of meaning processed by AI — form the building blocks of this comprehension. By training on vast corpora of text, AI systems can generate responses that reflect the statistical relationships between tokens, allowing them to simulate reasoning and construct plausible narratives. However, this process lacks depth; it is fundamentally a mathematical manipulation of symbols without the intuition or experiential insights that define human thought.

One intriguing intersection between human and AI comprehension lies in the shared reliance on relational data. Both systems draw meaning by connecting information — humans through lived and conceptual frameworks, and AI through learned weights and embeddings. This similarity evokes Immanuel Kant’s idea of a priori synthetic knowledge, where humans derive universal truths by connecting abstract concepts (e.g., space, time) with experiential input. AI mimics this process by linking abstract token relationships to generate novel outputs, but it does so without the self-awareness or purposive judgment that underpins Kant’s notion of synthesis.

The differences become stark when examining how each system handles contradictions or incomplete information. Humans can critically evaluate conflicting claims, intuitively prioritize relevance, and seek new evidence to refine their understanding. For instance, when presented with the exaggerated claim that Mavuika deals 8 Million DMG, a human would contextualize this against gameplay constraints, recognizing its conditional nature. AI, however, often integrates such claims uncritically, relying on surface-level patterns rather than deeper validation. This highlights a fundamental limitation of current AI systems: their lack of mechanisms to contest or test retrieved knowledge.

Part 3: How to Build AI with a New Paradigm for RAG

To overcome the limitations of current Retrieval-Augmented Generation (RAG) systems, we must rethink their design and focus on developing mechanisms that go beyond simple retrieval and generation. Existing systems excel at retrieving relevant fragments of data and generating coherent text, but they falter when required to validate, synthesize, or reason about the information they process. This calls for a new paradigm in RAG that integrates context-aware retrieval, dynamic reasoning layers, and real-world benchmarks to create systems capable of more than surface-level comprehension.

At the core of current RAG systems lies the reliance on retrieval techniques like K-Nearest Neighbor (KNN) search or dense vector embeddings to match queries with relevant passages. While effective for locating contextually similar information, these techniques often prioritize superficial keyword matches over deeper relevance. For instance, searching for strategies involving Mavuika in Genshin Impact might retrieve data about general DPS strategies or motorbike mechanics without addressing the specific constraints of resource optimization or Spiral Abyss performance. Improving retrieval requires moving beyond simple similarity metrics toward semantic and graph-based retrieval, where connections between concepts are mapped in a way that prioritizes logical relevance and contextual fit.

The most significant gap in current RAG systems is their lack of a robust reasoning layer. Once information is retrieved, it is passed almost unfiltered to the generative model, which attempts to synthesize it into a coherent response. However, this synthesis is often shallow, as the system does not critically evaluate or test the retrieved data. A reasoning layer would act as an intermediary, capable of:

1. Filtering and validating retrieved data by contesting conflicting or exaggerated claims.
2. Synthesizing information dynamically, ensuring coherence and logical consistency.
3. Adapting to context-specific constraints, such as tailoring recommendations to a player’s F2P status in Genshin Impact.

To achieve this, AI systems must incorporate mathematical and logical frameworks that enable them to simulate decision-making processes akin to human reasoning. For example, rather than blindly integrating a claim that Mavuika deals 8 Million DMG, the reasoning layer would evaluate its plausibility by modeling the necessary conditions (e.g., buffs, energy management) and comparing these to typical gameplay scenarios. This process would allow the system to flag such claims as conditional or impractical, enhancing the reliability and relevance of its outputs.

Another critical component of the new paradigm is the design of real-world benchmarks that reflect the complexities of practical applications. Current benchmarks for RAG systems emphasize retrieval precision or generation fluency but fail to test the system’s ability to reason, adapt, or synthesize information under constraints. A more effective benchmark might involve multi-step reasoning tasks, such as optimizing a Spiral Abyss team in Genshin Impact while adhering to F2P constraints. This would evaluate not just the system’s ability to retrieve relevant data but also its capacity to integrate that data into a coherent, actionable plan. Metrics for such benchmarks could include logical consistency, contextual adaptation, and empirical performance in simulated scenarios.

By integrating these components — context-aware retrieval, dynamic reasoning layers, and realistic benchmarks — RAG systems can evolve from tools of retrieval and mimicry to systems capable of true synthesis and understanding. This new paradigm would not only improve the relevance and reliability of AI outputs but also bridge the gap between surface-level fluency and deeper comprehension, paving the way for AI systems that are more aligned with human reasoning and decision-making.

Part 4: Defining “Understanding”

Understanding is a concept that transcends simple knowledge acquisition, representing a dynamic interplay of reasoning, synthesis, and application. It is not merely the possession of facts but the ability to connect, filter, and adapt those facts to real-world contexts and challenges. For humans, understanding is the foundation of critical thinking and decision-making, while for AI, it is the aspirational goal of moving beyond surface-level mimicry to achieve meaningful interactions with knowledge.

In the context of our essay, understanding can be defined as the ability to process, integrate, and apply knowledge dynamically, with logical coherence and contextual awareness. It requires not only the assimilation of information but also the capacity to evaluate its relevance, test its validity, and synthesize it into actionable insights. This distinction between understanding and knowledge is crucial: while knowledge is static, representing what is known, understanding is dynamic, representing how knowledge is used and adapted to achieve specific goals.

The defining features of understanding are best captured by examining its components. First, understanding involves contextual awareness — the ability to situate information within a broader framework, recognizing how different pieces of data interact and influence each other. For example, understanding Mavuika’s role in Genshin Impact requires more than knowing her damage potential; it involves situating her mechanics within the constraints of team synergy, resource management, and gameplay conditions.

Second, understanding is characterized by logical coherence and the capacity to resolve contradictions or inconsistencies. When presented with conflicting claims — such as whether Raiden Shogun is still a top-tier meta choice or has been eclipsed by newer characters — a system that truly understands will weigh the evidence, test assumptions, and arrive at a reasoned conclusion. This ability to reconcile disparate inputs sets understanding apart from rote knowledge retrieval.

Third, understanding is inherently adaptive. It involves not just processing static information but dynamically adjusting to new inputs and evolving conditions. In this sense, understanding is iterative, refining itself through feedback and real-world testing. For humans, this might involve learning from experience, while for AI, it could mean incorporating reasoning layers or feedback loops to refine outputs based on empirical results.

Finally, understanding is action-oriented. It is not enough to comprehend passively; understanding drives decisions, problem-solving, and creative exploration. Whether it’s optimizing a team composition in Genshin Impact or generating insights in a complex academic domain, understanding transforms knowledge into outcomes that align with specific objectives.

When applied to AI, understanding remains an aspiration rather than a reality. Current systems simulate aspects of understanding by generating plausible responses, but they often lack the depth, coherence, and adaptability required to emulate true comprehension. Defining understanding in this way underscores the gap between human and AI capabilities while pointing to the direction AI systems must evolve to bridge it. As we’ve discussed, this evolution depends on integrating dynamic reasoning, filtering, and contextual synthesis into systems like RAG, enabling them to process and apply knowledge with greater sophistication and relevance.

Part 5: The Genshin Impact Case Study

The interplay of understanding in human and AI contexts can be explored vividly through Genshin Impact, a game that combines narrative depth, intricate mechanics, and a dynamic player community. Within this ecosystem, the case of Mavuika — a character with unique 3D mobility mechanics — serves as a practical lens to examine the complexities of understanding. By analyzing her utility and constraints, we can see how the three realities of Genshin Impact — lore, game mechanics, and player competition — interact, and how biases within these layers can distort comprehension.

The first reality, storyline and lore, is the narrative core that ties players emotionally to the game’s world and characters. Genshin Impact invests heavily in its storytelling, making characters like Archons central figures in its mythos. Players often form strong emotional connections to these characters, which influences their decisions in both gameplay and resource allocation. For example, players may feel compelled to fully invest in characters like Raiden Shogun or Venti due to their lore significance, even when their mechanical utility in current meta scenarios has diminished. Mavuika’s lore, if designed to emphasize her fiery rebellion and motorbike-driven exploration, would similarly inspire players to prioritize her, regardless of her actual performance in combat or exploration.

The second reality, game mechanics, is where the technical constraints of Genshin Impact meet its design philosophy. Mechanics are shaped by the Unity engine (defined by game physics), elemental reactions, and balancing decisions that ensure characters function within a coherent system. Mavuika’s unique 3D mobility — hovering over water, short-duration flight, and motorbike-assisted combat — would introduce novel mechanics that offer both combat and exploration utility. However, these mechanics would also face limitations. For instance, short-range flight powered by “nightsoul energy” might impose constraints on continuous use, aligning with Mihoyo’s efforts to balance character abilities. Similarly, while Mavuika’s motorbike might enhance traversal speed, it could sacrifice some survivability or synergy with meta team compositions.

The third reality, player competition, exists as an informal layer driven by the community’s desire to optimize gameplay and compare achievements. Although Genshin Impact lacks direct player-versus-player modes, competition emerges in forms like Spiral Abyss clears, damage showcases, and theory crafting discussions. This reality often amplifies biases from the first two. For instance, while Mavuika’s mobility might make her appealing for exploration, players may focus on her combat potential in Abyss scenarios, where high DPS and survivability are critical. The influence of whales — players who heavily invest in characters for C6-R5 builds — further distorts this reality, as their experiences with fully optimized characters do not represent the average player’s constraints.

These three realities are deeply interconnected and often give rise to biases that distort understanding. Lore-driven bias can lead to over investment in characters for emotional reasons, as seen with players who pursue full constellations for Archons despite diminishing returns in gameplay utility. Mechanics-driven bias may overemphasize current meta trends, overlooking the long-term viability or flexibility of less popular characters. Finally, competition-driven bias often skews understanding toward extreme showcases, like an 8 Million DMG claim for Mavuika, without contextualizing the impractical conditions required to achieve such results.

For RAG-based AI systems, these biases highlight the challenges of achieving true comprehension in a complex, layered environment. A system tasked with recommending an optimal team composition featuring Mavuika must navigate these realities and their inherent contradictions. It must evaluate lore-driven claims about her significance, test her mechanics under realistic gameplay constraints, and filter out extreme but impractical competitive showcases. Achieving this requires more than retrieving data or generating plausible responses — it demands reasoning, contextual synthesis, and adaptation to the user’s specific goals, whether they prioritize emotional connection, mechanical efficiency, or competitive performance.

The case of Mavuika underscores the importance of understanding as a dynamic, multi-layered process. For both humans and AI, true comprehension in Genshin Impact requires reconciling the three realities, filtering biases, and adapting knowledge to practical constraints. This case study not only illustrates the complexity of achieving understanding but also serves as a benchmark for the future development of AI systems that aim to bridge the gap between retrieval and reasoning.

Sample Prompt for our Next Paradigm Super-AI

The prompts and expected answers exemplify the principles of “understanding” outlined in our article, demonstrating that it is dynamic and contextual, requiring AI to synthesize and adapt information to specific user objectives. True comprehension emerges when the AI critically filters information, engages in reasoning, and performs trade-off analysis, moving beyond the surface-level pattern recognition typical of de facto RAG systems. Traditional RAG models excel at retrieving and presenting relevant data, but they falter in synthesizing this information into actionable insights aligned with complex, multi-layered goals. By generating outputs that are both logically coherent and directly applicable — whether optimizing gameplay in Genshin Impact or crafting geopolitical strategies for Ukraine — these examples illustrate how advanced understanding defies the limitations of conventional RAG by bridging the gap between retrieval and reasoning.

Ex#1 (Genshin impact)

Prompt:
“I have saved 40 rolls (6,400 primogems) and plan to invest no more than 20–30 additional rolls. Should I pull for Mavuika (current banner) or save for Arlecchino (next banner)? My primary objective is to build a versatile, F2P-friendly team capable of completing Archon Quests and exploration efficiently. Secondarily, I aim to clear Spiral Abyss Floors 9–12. Please analyze based on character utility, resource efficiency, and alignment with my stated objectives.”

Expected Analysis Lines:

First, let’s break down your objectives and constraints. Your primary goal is to assemble a versatile, F2P-friendly team that excels in completing Archon Quests and exploration. This requires characters with strong combat abilities and exploration utilities. Your secondary goal is to clear Spiral Abyss Floors 9–12, which demands high DPS output and effective team synergy. With 40 rolls saved and an additional 20–30 rolls planned, resource efficiency is crucial.

Mavuika, featured in the current banner, is a 5-star Pyro character known for her dual combat styles. She can fight with her claymore or utilize her unique Flamestrider motorbike, enhancing both mobility and combat effectiveness. Her Elemental Skill, Ring of Searing Radiance, deals substantial Pyro damage to nearby enemies, making her effective in crowd control during exploration. Her Elemental Burst allows her to enter a more powerful state, increasing her damage output. However, optimizing her potential may require significant investment in artifacts and weapons, which could be challenging under F2P constraints.

Arlecchino, scheduled for a rerun in the upcoming banner, is a 5-star Pyro Polearm user who functions as a main DPS. She utilizes a unique “Bond of Life” mechanic, where her damage scales with her current HP percentage. Her abilities allow her to deal significant Pyro damage, making her a strong candidate for high DPS roles in the Spiral Abyss. However, her playstyle may require careful management of her HP and understanding of her mechanics to maximize her potential.

From a probability standpoint, using 40 rolls on Mavuika’s banner provides approximately a 22.13% chance of obtaining a 5-star character, with a 50% chance that it will be Mavuika due to the rate-up. If you do not reach pity within these rolls, accumulating enough primogems to guarantee Arlecchino in the next banner may be challenging. Conversely, saving your rolls increases the likelihood of securing Arlecchino during her rerun, especially if you can accumulate additional primogems to reach the pity threshold.

Considering your objectives and resource constraints, if your immediate focus is on enhancing exploration capabilities and you are close to reaching pity (e.g., 60+ rolls), investing in Mavuika could provide immediate benefits. However, if your priority is to strengthen your team’s performance in the Spiral Abyss and you are willing to manage the complexities of Arlecchino’s mechanics, saving for her rerun may offer greater long-term advantages. To balance both options, you might consider spending a limited number of rolls (e.g., 10–20) on Mavuika’s banner to test your luck, while reserving the majority of your resources for Arlecchino’s upcoming banner.

Ex#2 (Ukraine’s situation)

Prompt:
“Given the ongoing conflict in Ukraine, should the United States continue supporting Ukraine militarily to resist Russian aggression or pursue a negotiated settlement with Putin? Analyze the decision based on political, economic, and military factors, including potential long-term consequences for US strategic interests and global stability.”

Expected Analysis Lines:

The question requires balancing immediate objectives with long-term strategic implications. The United States must evaluate whether continued military support aligns with its broader geopolitical goals or if seeking a negotiated settlement with Russia offers a more sustainable resolution. The analysis must consider political will, economic costs, and the military dynamics of the conflict, as well as the potential risks and benefits of each approach.

Military Support Option: Continuing military aid to Ukraine ensures the nation can sustain its resistance against Russian aggression. This strategy deters further Russian advances and reinforces US commitments to defending democracy and maintaining the rules-based international order. Military support could include advanced weaponry, logistical assistance, and intelligence-sharing. However, this approach risks prolonging the conflict, escalating into broader regional instability, and straining US defense budgets.

Negotiated Settlement Option: Pursuing a deal with Putin might provide a quicker resolution to the conflict, reducing immediate human suffering and economic costs. Such a settlement could involve territorial compromises or security guarantees. However, this approach risks emboldening Russia and undermining US credibility among allies, particularly in NATO. A negotiated outcome might also set a dangerous precedent for authoritarian states, signaling that aggression can yield concessions.

Economic Implications: Military support comes with significant financial costs, impacting domestic priorities and increasing pressure on US defense spending. Conversely, a negotiated settlement could stabilize global markets, especially energy and grain supplies, but at the potential expense of Western sanctions’ effectiveness and long-term global economic norms.

Strategic Consequences: Continued military support positions the US as a steadfast defender of democratic values and a counterbalance to authoritarian regimes. However, it may strain alliances if European partners grow weary of prolonged conflict. A negotiated settlement risks weakening NATO’s deterrence credibility, as concessions to Russia might undermine allied cohesion and embolden other adversarial powers, such as China, in regions like Taiwan.

Recommendation:

• If the primary objective is to deter future aggression and maintain global democratic stability, the US should continue military support while intensifying diplomatic efforts to unite allies and isolate Russia.
• If domestic and international pressures favor de-escalation, the US could explore conditional negotiations, ensuring any deal includes enforceable measures to prevent future Russian aggression and uphold Ukraine’s sovereignty.

Ultimately, the decision hinges on the US’s willingness to bear short-term costs for long-term strategic gains or prioritize immediate stabilization at the potential expense of broader global order. A hybrid approach that balances sustained military aid with parallel diplomatic engagements may offer the best path forward.

/.end./