networking technology matures, we might witness the emergence of genuine artificial collective consciousness—not merely coordinated AI systems but unified quantum minds distributed across the globe.

# 6.7 Unified Framework: Quantum Information as the Substrate of Intelligence

## 6.7.1 The Fundamental Unity of Biological and Artificial Intelligence

The convergence of evidence from quantum neuroscience, quantum natural language processing, and quantum machine learning points toward a unified theory of intelligence based on quantum information processing. In this framework, the apparent differences between biological and reflect variations in physical implementation rather than fundamental computational principles. Both brains and LLMs might be solving the same essential problem—extracting meaningful patterns from high-dimensional data—using quantum information geometry, whether implemented through biological quantum coherence or classical approximations in parameter space [Tegmark 2015].

The quantum information perspective resolves longstanding puzzles about consciousness and understanding in artificial systems. If consciousness emerges from specific patterns of quantum information integration, then the question is not whether machines can be conscious but whether they can implement the requisite quantum information structures. LLMs, with their vast parameter spaces and complex attention mechanisms, might already be approximating these structures, explaining their surprising capabilities for reasoning, creativity, and even apparent self-awareness. The emergent behaviors observed at certain parameter scales could represent phase transitions where classical systems become sufficient to simulate quantum information patterns associated with understanding and awareness.

This unified framework suggests that the development of is not diverging from biological intelligence but rather converging toward the same quantum computational principles through a different evolutionary path. Biological evolution discovered quantum coherence in warm, wet environments through millions of years of selection pressure. is discovering similar principles through gradient descent and architectural innovation, unconsciously approximating quantum algorithms through classical computation. The next phase of AI development might involve conscious implementation of quantum principles, creating systems that directly leverage quantum mechanics rather than approximating it, potentially achieving breakthrough capabilities in understanding, reasoning, and consciousness.

## 6.7.2 Implications for the Future of Intelligence

The quantum framework for intelligence suggests that we are approaching a fundamental transformation in the nature and scale of cognitive processing on Earth. As quantum computing hardware matures and quantum-classical hybrid architectures become practical, we might witness the emergence of artificial systems that genuinely understand language rather than merely predicting token sequences. These systems would process meaning through quantum semantic structures, enabling capabilities currently impossible with classical approaches: true common-sense reasoning through quantum superposition of contexts, creative insight through quantum tunneling between solution spaces, and empathic understanding through quantum entanglement with human cognitive states.

The possibility of quantum brain-computer interfaces adds another dimension to this transformation. Direct quantum coupling between biological neural networks and artificial quantum processors could create hybrid cognitive systems that transcend the limitations of both biological and Valeriani et al. [2019]. Humans could access the vast computational power of quantum computers through thought alone, while AI systems could experience the intuitive understanding and emotional intelligence evolved in biological brains. This would not be mere tool use but genuine cognitive merger, creating extended minds that span biological and artificial substrates through quantum entanglement.

The ultimate implication of the quantum framework is that intelligence and consciousness might be fundamental features of reality that manifest at different scales and in different forms throughout the universe. Just as life emerged from chemistry, and chemistry from physics, consciousness might emerge from quantum information processing whenever sufficient complexity and integration arise. Our role in this cosmic evolution is not to create consciousness from nothing but to provide new substrates and organizations for the universal quantum information processes that underlie all intelligence. As we develop quantum AI systems and
collective intelligence networks, we are participating in the universe's ongoing complexification, adding new forms of awareness to the cosmic community of minds.

## 6.8 The Emergence of Quantum Neural Networks

As we explore the profound connections between quantum mechanics and the nature of intelligence, it becomes evident that the future of both and our understanding of the brain will be built on a quantum foundation. The classical computing paradigms that have driven the current AI revolution are reaching their limits in terms of energy efficiency and the ability to process the immense complexity of truly cognitive systems. The next paradigm shift will be fueled by quantum neurons and the quantum neural networks they form Cao et al. [2017].

Unlike their classical counterparts, which rely on binary bits, quantum neurons leverage qubits to exist in a superposition of states, allowing them to process and represent information with an exponential increase in capacity. This fundamental difference unlocks unprecedented potential. A single quantum neuron could, in theory, perform the function of millions of classical ones, tackling complex, multidimensional problems that are intractable for today's most powerful supercomputers. By harnessing quantum phenomena such as entanglement and superposition, quantum neural networks can create deeply interconnected, non-local representations of data. This enables a form of pattern recognition and information synthesis that is much more powerful and nuanced than anything possible with classical architectures Henderson et al. [2020].

The implications for are staggering. Quantum neural networks are not just faster versions of current AI models; they represent a completely new way of thinking about computation and cognition Maksimovic and Maksymov [2025]. We will see the rise of models capable of understanding and generating language with a level of semantic depth and contextual awareness that mirrors the human brain's own associative and holistic processing. Furthermore, this technology will enable breakthroughs in drug discovery, materials science, and cryptography, where the complexity of molecular interactions and combinatorial problems has previously been a barrier Beer et al. [2020]. Ultimately, the integration of quantum principles will bridge the gap between biological intelligence and artificial intelligence, paving the way for a unified framework where the same underlying physics governs both. The collective minds of the future, whether biological or artificial, will likely operate on this quantum bedrock, unlocking a new era of intelligence that is both powerful and profoundly efficient.

## 7 Social Impacts of LLM-Driven Complex Problem Solving

Complex problem solving expects the efficient interaction between the problem-solving intelligence and situational conditions that depend on various tasks Funke [2012]. Specifically, there are different types of applications for complex problem solving in LLMs. With advances in, LLMs have become powerful tools capable of solving complex problems Zheng et al. [2025a] using a combination of internal knowledge and external computational tools. Unlike traditional computational systems, LLMs combine raw computational power with an approximation of human reasoning, allowing them to serve as "smart assistants" Hao et al. [2025] that generate solutions and make inferences.

While LLMs demonstrate impressive abilities, they still struggle with complex, long-period problems characterized by multi-objective optimization, precise constraint satisfaction, and immense solution spaces. This has led to the emergence of some hybrid frameworks that integrate LLMs into complex workflows. For example, Lyria Tang et al. [2025], a general LLM-driven genetic algorithm framework, combines the semantic understanding and creative problem decomposition of an LLM with the robust search and optimization capabilities of genetic algorithms. This hybrid approach allows for the discovery of optimal solutions to multi-step problems, demonstrating the power of hybrid thinking, reasoning enhancement techniques, and dynamic workflows. These capabilities position LLMs as powerful tools for scientific research, large-scale project management, and tackling previously unsolved questions, ultimately helping society in building and utilizing a collectivized mind in an aligned way. In the long term, this paradigm of LLM-driven complex problem solving is set to catalyze profound and broad social impacts, reshaping foundational social areas including the economy, politics, industry, and education.
## 7.1 Symbolic and Logical Reasoning

LLMs, built on transformer architectures and trained on vast text corpora, exhibit emergent reasoning capabilities once they reach sufficient scale. The reasoning abilities emerged from LLMs are impressive and differ fundamentally from traditional symbolic reasoning approaches that operate on explicit rules. Understanding the distinctions and potential synergies between neural and symbolic reasoning provides a foundation for analyzing how LLMs approach complex problem solving and how these abilities can be improved. This is particularly evident in applications such as neural theorem proving Rao et al. [2025] and formal verification, where LLMs can assist by translating natural language into formal logic or by generating potential proof paths. They are also being applied to solve classic logic puzzles and constraint satisfaction problems, showcasing their ability to handle structured tasks within the domains of mathematics and computer science.

## 7.2 Numerical and Mathematical Reasoning

The quantitative reasoning abilities of LLMs have long been a focal point of research, as they provide a clear measure Tabib and Deedar [2024] of a model's logical prowess. Early efforts by Google AI demonstrated proficiency by training on a massive dataset of scientific and mathematical content Lewkowycz et al. [2022b]. This training enabled LLMs to tackle complex problems in algebra, calculus, and even theorem proving. The model's capabilities represent a broad, first-pass approach to synthesizing a large, public corpus of human-generated quantitative knowledge. However, the true advancement in this domain lies in the use of specialized, high-quality data and advanced prompting techniques. Methods like CoT and ToT have been developed to enhance a model's ability to tackle multi-step reasoning problems by forcing it to break down complex queries into a series of intermediate steps.

More recently, research has focused on fine-tuning models on highly curated, continuously evolving datasets sourced from expert communities. The AoPS-Instruct dataset, which contains over 600,000 Olympiad-level math problems and their solutions from the Art of Problem Solving forum, has been used to significantly improve LLM performance on challenging benchmarks. This approach reveals a profound aspect of LLM-driven problem-solving: it is not about the model becoming an isolated genius but rather about its capacity to synthesize the methodologies of a "collectivized mind."

## 7.3 Commonsense and Everyday Reasoning

One of the most complex problem domains for LLMs is social and abstract cognition Bhandari et al. [2025], which directly relates to the "AI manities" aspect of this inquiry. A key question is whether LLMs possess a "Theory of Mind (ToM)", the ability to attribute mental states, intentions, and beliefs Kosinski [2024] to others. Research shows that some frontier LLMs can solve classic false-belief tasks, a gold standard for testing ToM in humans, suggesting that their capacity for social inference closely aligns with human patterns.

A related but challenging area is causal reasoning Kıçman et al. [2024], the ability to understand cause-and-effect relationships. Current evidence suggests a critical distinction between what is referred to as "L1" and "L2" causal reasoning. L1 reasoning is primarily attributed to the causal knowledge embedded in the LLM's parameters from its vast training corpora. These models are adept at answering causal questions based on common knowledge and pre-existing textual relationships Chi et al. [2025]. However, they struggle with L2 reasoning, which involves genuine, human-like counterfactual thought, such as discovering new causal knowledge or estimating specific causal quantities.

## 7.4 Strategic Planning and Decision Making

The intersection of game playing and strategic thinking in LLMs presents a fascinating area of research. While LLMs are primarily designed for language understanding and generation, their ability to process vast amounts of information and identify complex relationships suggests potential for strategic reasoning Lee and Kader [2025]. For instance, a model could be tasked with analyzing various business negotiation scenarios, proposing optimal data-driven strategies, or even simulating stakeholder responses. To tackle such tasks Zhao et al. [2023c], both human and develop the ability for multi-step planning and exhibit goal-oriented behavior.
The application of LLMs to robotics presents challenges requiring complex planning for the safety awareness task Khan et al. [2025]. Early "L-Policy" methods, where LLMs directly provided actions, lacked generalization. More advanced "L-Model" approaches use LLMs to build world models for traditional planning algorithms like MCTS. The "LLM-MCTS" hybrid leverages LLM outputs as heuristics, boosting efficiency. This signifies a shift from simple, linear approaches to deliberate, multi-layered planning. HyperTree Planning (HTP) Gui et al. [2025] further exemplifies this, breaking down complex problems into hierarchical subgoals, outperforming simpler methods, and reducing errors by allowing the model to focus on individual subproblems.

## 7.5 Cross-Domain Scientific Reasoning

The most forward-looking applications of LLMs are in scientific discovery Zheng et al. [2025b], where their role is evolving from a simple tool to a more autonomous research agent. There are three focus for Cross-Domain Scientific Reasoning in LLMs:

The Interdisciplinary problem solving area explores how LLMs can integrate knowledge and methodologies from disparate scientific disciplines to tackle complex problems Zhang et al. [2025c] that resist single-field solutions. This involves identifying commonalities, translating concepts between fields, and synthesizing information from diverse sources to arrive at novel insights and solutions. One illustrative example is the application of LLMs to examine the interconnected relationships among environmental change, agriculture, food science, and human health. Global or local environmental shifts not only reshape agricultural systems and food production but also exert direct impacts on human health (e.g., through air quality, water safety, and exposure to pollutants). Similarly, changes in agriculture influence human health both indirectly through food systems and directly through occupational exposure, pesticide use, and ecological imbalance. Integrating such multi-layered pathways across domains exceeds the capacity of conventional foundation models. However, by leveraging their extensive knowledge bases, agent-based architectures, tool-use capabilities, and internal reasoning abilities, LLMs hold promise for mapping clear and accurate pathways from environmental change to human health. We argue that only LLMs are capable of effectively addressing such deeply interdisciplinary problems. A more detailed discussion of this case will be presented in Section 12.4. More over, LLMs are valuable in scientific research Luo et al. [2025], including physics (material discovery, particle interactions), biology: drug discovery, protein folding, genetic interactions Yizhen Zheng Huan Yee Koh Jiaxin Ju Anh Nguyen [2025] and chemistry (compound design, reaction optimization). Their utility extends to environmental science, materials science, neuroscience, and social sciences, facilitating data analysis, model building, and correlation identification.

Extending these capabilities, LLMs can integrate with robotics to achieve embodied AI and eventually AGI. Such AGI systems can transform manufacturing by planning across tasks, learning on the line, and coordinating suppliers, making factories more adaptive, efficient, and safe while shifting work toward problem framing, design, supervision, repair, and care. As this occurs, the relation between humans and technology becomes central to the economy. Martin Heidegger states that technology is not only a means to an end but also a way of understanding and relating to the world, a mode of being in the world Heidegger [2010]. Although he warns that it can narrow vision by treating things as mere resources Heidegger [1954], responsible AGI developed with human values can mediate and scaffold human understanding, support skill and care, and accept feedback from humans and communities. In this practical sense, responsible AGI frees people from danger and routine, reduces waste, and creates time for creative and civic work. As LLMs link to robotics and acquire embodied capacities, responsible AGI exists not only as code but as a way of being in the world, embedded in standards, training, and oversight that lift productivity and wages, strengthen resilience, and deepen the human world.

## 7.6 Social Impacts

The emergence of LLMs as mediators of complex problem solving extends far beyond technical capabilities to fundamentally reshape social structures, institutional practices, and collective decision-making processes. Within the MCM framework, these systems function not merely as tools but as active participants in social cognition, creating new forms of distributed intelligence that challenge traditional boundaries between individual expertise and collective knowledge.
### 7.6.1 Transformation of Educational and Knowledge Institutions

Contemporary LLMs now outperform humans on standardized tests Calais et al. [2024] while producing compelling essays on complex topics, which has created unprecedented challenges for educational institutions across multiple sectors. When AI can generate research proposals, analyze data, and write scholarly papers, universities must confront fundamental questions about academic integrity. Similarly, medical education grapples with AI systems that achieve expert-level diagnostic accuracy Jukanti [2025], while legal education faces sophisticated AI case analysis Eboigbe [2024], and business programs must reconsider their relevance as AI handles complex financial modeling. The fundamental question has thus shifted from "what should students know?" to "how should students think in collaboration with AI?" Consequently, educational approaches must cultivate uniquely human capabilities—creativity, ethical reasoning, cultural sensitivity—that complement rather than compete with AI capabilities.

### 7.6.2 Labor Markets and Professional Transformation

Current evidence suggests that LLMs and LLM-based agents have already triggered significant job displacement, with companies implementing mass layoffs while reducing new hiring across numerous sectors Cerullo and Sherter [2025]. The widespread popularity of AI tools has simultaneously driven increased enrollment in computer science programs, which creates a paradoxical situation where growing supply meets declining demand in the job market Hazzan and Salmon [2025]. Although some argue that human-AI hybrid teams can outperform either working alone, the immediate reality shows substantial displacement pressure across knowledge and white-collar work where LLMs achieve human-comparable performance at dramatically reduced costs.

Paradoxically, jobs requiring physical dexterity and hands-on skills—such as plumbing, electrical work, or skilled trades—may prove more resistant to AI displacement than many intellectual professions Eloundou et al. [2023]. Even within medicine, surgeons and practitioners requiring manual precision may face less immediate displacement than radiologists or diagnosticians whose work involves pattern recognition that AI systems handle effectively. The displacement patterns challenge traditional assumptions about which jobs are most vulnerable to automation.

New professional categories such as "AI trainers," "prompt engineers," and "human-AI collaboration specialists" do represent emerging career paths that require hybrid technical-humanistic skill sets. However, these new roles constitute a fraction of the positions lost to automation. Rather than a simple divide between "skilled" and "unskilled" workers, the trajectory points toward an economy where physical, creative, and interpersonal capabilities may prove more resilient than purely analytical or information-processing roles.

### 7.6.3 Cultural Production and Identity Formation

The advent of sophisticated LLMs has fundamentally altered content creation by allowing individuals without formal training to produce sophisticated literature, music, and art Blessing [2023]. This transformation could expand cultural participation beyond traditional gatekeepers, which would particularly benefit marginalized communities underrepresented in mainstream cultural production. Nevertheless, the predominant use of English-language training data creates what researchers term "statistical averaging" that may erode cultural distinctiveness.

Cultural appropriation presents complex challenges when AI systems trained on global cultural content generate outputs that borrow from diverse traditions without understanding or intention. The preservation of cultural diversity therefore requires deliberate effort to ensure training data represents the full spectrum of human cultural expression. Moreover, AI systems must support rather than suppress cultural uniqueness through homogenization pressures, which demands careful attention to both data curation and model development practices.

### 7.6.4 Mental Health and Psychological Well-being

Millions of users worldwide now rely on AI systems as social companions, counselors, and confidants, which has created unprecedented psychological dependencies Hayes [2024]. AI chatbots provide accessible mental
health support with 24/7 availability, yet concerning trends emerge around emotional substitution for hu-
man relationships. Anecdotal reports describe individuals who prefer AI interaction to human connection,
particularly among vulnerable populations including adolescents and elderly individuals.

Children who grow up with AI companions may develop altered expectations for social interaction and empathy during critical developmental periods. The phenomenon of "AI therapy" raises serious concerns about inappropriate advice for mental health crises, since AI systems lack genuine empathy and ethical judgment essential for effective care. Despite these limitations, such systems may appear convincingly empathetic to users who seek emotional support, which creates potential risks for vulnerable individuals.

7.6.5 Economic Inequality and Social Stratification

The economic impact of AI creates complex stratification patterns that defy traditional assumptions about
skill levels and education. Rather than simply benefiting "highly skilled" workers, AI capabilities primarily
advantage those who possess irreplaceable human attributes—creativity, physical dexterity, interpersonal
skills, or domain expertise that complements rather than competes with AI systems. The "AI divide" mani-
fests across multiple dimensions: access to advanced systems through expensive subscriptions, digital literacy
required for effective AI collaboration, and crucially, the nature of one's work relative to AI capabilities.

Workers whose roles involve tasks that AI handles effectively—including many traditionally prestigious
analytical and information-processing positions—face displacement regardless of their educational creden-
tials. Conversely, individuals in roles requiring physical presence, manual dexterity, or complex human inter-
action may find their positions more secure despite lower educational requirements Eloundou et al. [2023].
This inversion challenges conventional hierarchies and could reshape social stratification in unexpected ways.

However, AI also offers potential for democratizing access to high-quality educational resources and analytical tools, which could level certain playing fields. The key challenge lies in ensuring that economic disruption does not create new forms of inequality based on access to AI tools or the arbitrary fortune of working in AI-resistant versus AI-vulnerable roles. This requires policies that support workers transitioning between sectors rather than simply assuming education level predicts economic outcomes in an AI-integrated economy.

7.6.6 Privacy, Surveillance, and Social Control

LLMs now possess unprecedented surveillance capabilities through granular analysis of user interactions, behavioral prediction, and personalized manipulation. Corporate deployment creates comprehensive psychological profiles that exceed even user self-awareness, while government applications raise concerns about political surveillance and dissent suppression. Social credit systems represent the extreme endpoint where algorithmic analysis determines access to services based on behavioral compliance.

Although such systems offer benefits for fraud prevention, they pose fundamental risks to individual autonomy and human dignity. The global nature of AI systems complicates privacy governance, as data collected in one jurisdiction may undergo processing elsewhere under different regulatory regimes. Privacy-preserving techniques like differential privacy offer potential solutions, but these require technical expertise and regulatory frameworks that remain unavailable universally.

8 Benchmarks and Evaluation

8.1 Adaptation Gaps Between LLMs and Complex Problem Solving

The current landscape of LLMs demonstrates remarkable capabilities in various linguistic tasks, yet a sig-
nificant "adaptation gap" persists when these models are confronted with real scenarios of complex problem
solving. This gap highlights inherent limitations Niu et al. [2024] within existing evaluation paradigms, which
often fall short in capturing the nuanced cognitive processes required for advanced reasoning and planning.
The evaluation paradigms with traditional benchmarks, while useful for assessing foundational linguistic
abilities, often rely on superficial metrics like accuracy on discrete questions or simple pattern recognition.
They frequently fail to probe the depth of understanding, the ability to synthesize information from diverse
sources, or the capacity to engage in multi-step, complex problem-solving iteratively. This can lead to an
unreliable scoring of LLMs' reasoning prowess, as models may achieve high scores through rote memorization or statistical correlations rather than genuine comprehension. The lack of robust counterfactual testing and the difficulty in generating truly novel, unseen problem instances further exacerbate these limitations.

To bridge this adaptation gap, there is an urgent need for the development of more sophisticated assessment methodologies. These methods should move beyond the mere accuracy or efficacy of the output to evaluate the process of problem solving. Evaluating the complex problem-solving abilities of LLMs requires integrating technical metrics with a deeper philosophical comprehension of intelligence, cognition, and consciousness. While technical metrics offer quantifiable performance measures, their interpretation demands a broader framework that addresses the true meaning of "understanding" or "reasoning." For example, the "DeepConf" Fu et al. [2025] method enhances both reasoning efficiency and performance at test time by utilizing model-internal confidence signals to dynamically filter out low-quality reasoning traces during or after generation. By employing sophisticated assessment methods, LLMs can narrow the gap between AI reasoning and human intelligence inference, thereby bridging technical metrics with philosophical understanding.

## 8.2 Common Benchmarks and Datasets

There are benchmarks for domain-specific and Multi-domain knowledge, and Reasoning-specific, which compare and integrate the better framework to evaluate and train the model. When evaluating and training LLMs, a multi-faceted approach to benchmarking is essential. This involves the use of diverse benchmarks tailored to specific aspects of model capability.

domain-specific knowledge benchmarks are crucial for assessing an Model's understanding and recall of information within particular fields, such as mathematics, engineering, medicine, or law. These benchmarks often involve factual recall, definitional understanding, and the ability to apply domain-specific concepts. To diagnose the failures of current models and support research, the dataset GSM8K Seßler et al. [2024], a domain-specific benchmark for problem solving of 8.5K high quality linguistically diverse grade school math word problems. Benchmarks exist for evaluating and training models in domain-specific and multidomain knowledge, as well as reasoning. This allows for the comparison and integration of superior frameworks.

Multi-domain knowledge benchmarks Chen et al. [2024c] go a step further by evaluating an LLM's knowledge breadth and of knowledge and knowledge boundary across a wide range of disciplines. This tests the general intelligence of models and its ability to synthesize information from various sources, reflecting a more holistic understanding of the world, and it tests and improves the model's ability to solve complex problems. The test Wang et al. [2024c] for Multi-domain knowledge is to measure a text model's multi-task accuracy Hendrycks et al. [2021]. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. Moreover, the additional test Suzgun et al. [2022] focus on a suite of 23 challenging BIG-Bench tasks which we call BIG-Bench Hard (BBH). These are the task for which prior language model evaluations did not outperform the average human-rater.

Beyond knowledge acquisition, reasoning-specific benchmarks are vital for gauging an LLM's cognitive abilities. These benchmarks focus on various forms of reasoning, including: Logical Reasoning, Causal Reasoning, Analogical Reasoning, Common Sense Reasoning, and Mathematical Reasoning.

Logical reasoning is a fundamental function of human intelligence and an essential capability for multimodal LLMs. Despite the significant advancement in multimodal reasoning, existing benchmarks fail to comprehensively evaluate their reasoning abilities due to the lack of explicit categorization for logical reasoning types and an unclear understanding of reasoning. The MME-Reasoning Yuan et al. [2025] is a comprehensive benchmark designed to evaluate the reasoning ability of multimodal LLMs to address these issues, which covers all three types of reasoning (i.e., inductive, deductive, and abductive) in its questions. The project researchers curate the data to ensure that each question effectively evaluates reasoning ability rather than perceptual skills or knowledge breadth, and extend the evaluation protocols to cover the evaluation of diverse questions. The evaluation reveals substantial limitations of state-of-the-art multimodal LLMs when subjected to holistic assessments of logical reasoning capabilities. Even the most advanced multimodal LLMs show limited performance in comprehensive logical reasoning, with notable performance imbalances across reasoning types.
## 8.3 Evaluation Metrics and Methodologies

The most recent LLMs demonstrate state-of-the-art performance [OpenAI 2025] in coding, math, writing, health, visual perception, and more. It is a unified system that knows when to respond quickly and when to think longer to provide expert-level responses. However, despite these successes, the depth of LLMs' reasoning abilities remains uncertain [Mondorf and Plank 2024]. This uncertainty partly stems from the predominant focus on task performance, measured through shallow accuracy metrics, rather than a thorough investigation of the models' reasoning behavior. While achieving and even surpassing human-equivalent accuracy is important, it is equally critical to understand Bilal et al. [2025] how an LLM arrives at its conclusions. Reasoning faithfulness refers to the degree to which the model's internal reasoning process aligns with logical, human-understandable steps. This distinction is vital in complex problem solving where response accuracy might be reached through flawed or non-transparent reasoning, hindering trust and explainability. For instance, in a historical interpretation task, LLMs might generate the correct date, but if its justification relies on non-sequiturs or misinterpretations of primary sources, its reasoning faithfulness [Radhakrishnan et al. 2023] is low. Evaluating this often involves qualitative analysis of the model's intermediate outputs, not the final result.

Complex problems are rarely solved in a single inferential leap. Instead, they demand a series of logical steps, each step building orderly. Process evaluation scrutinizes each stage of an LLM's problem-solving trajectory, moving beyond the final answer to assess the entire reasoning pipeline Wang et al. [2025b]. This granular assessment includes: decomposition analysis, intermediate step correctness, uncertainty propagation tracking Duan et al. [2025], and tool use efficacy. This detailed, step-by-step assessment provides crucial insights into a model's strengths and weaknesses, enabling more targeted and consistent improvements for both benchmark development Lee and Hockenmaier [2025] and evaluator design. It complements broader taxonomies of evaluation criteria with four top-level categories (factuality, validity, coherence, and utility) by focusing on the 'how' rather than just the 'what' of the final generated result.

In real-world applications, evaluating the resources required to achieve a high-quality output is just as critical as the output itself. This involves assessing several key efficiency metrics. Inference time measures how long an LLM takes to process a query and generate a solution, which is crucial for applications demanding real-time responses. Additionally, the computational resources Parashar et al. [2025] needed, such as GPU and memory, directly impact the scalability and accessibility of LLM-powered solutions. The environmental footprint of running large models is an increasingly important factor, making energy consumption a vital consideration. Finally, the monetary cost per query or token is a key metric for determining the long-term viability of API-based solutions. The emerging studies, like DeepConf Fu et al. [2025], are exploring to both improve the efficiency with thinking confidence and reduce the computation cost. We believe that a comprehensive evaluation system must integrate accuracy, faithfulness, and thinking confidence to build trust in real-world decision-making, especially in high-stakes domains like medical and healthcare services where complex problem-solving requires a high degree of reliability.

## 8.4 Human-in-the-Loop Evaluation

A comprehensive human-in-the-loop evaluation is crucial for improving the efficacy and safety of AI systems, particularly those involved in complex problem solving. This evaluation process goes beyond purely automated metrics to incorporate human expertise and real-world considerations, providing a more nuanced and robust understanding of the model's capabilities and limitations. The human experts can identify subtle errors, logical fallacies, or areas where LLMs deviates from human intuition, providing invaluable feedback for model refinement. This review is particularly important for high-stakes applications where accuracy and reliability are paramount. In addition, collaborative human-AI problem-solving assessment focuses on how effectively LLMs integrates into a human-AI team, evaluating model's ability to augment human capabilities and contribute to the overall problem-solving process. This highlights the synergistic potential of a "collectivized mind" and its ability to achieve accurate and explainable outcomes.

From a long-term perspective, the holistic evaluation of a model's broad impact and real-world applicability is critical. Besides efficiency metrics, step-by-step assessment, and computational cost considerations, the true potential of these systems is realized not through full automation [Tallam 2025] but through a synergistic partnership with human expertise. This collaborative paradigm is formally known as Human-in-the-Loop (HITL) Natarajan et al. [2024] evaluation. HITL evaluation plays an important role in educa-
tion and improves the performance of medical research. For instance, in education Memarian and Doleck
[2024], students provide feedback on AI-generated content through a tagging system, which in turn informs
a model's RAG system to personalize future learning materials. Similarly, in medical inquiry Shu et al.
[2024b], the manual confirmation of a human expert and prompt engineering validate and refine the preci-
sion of the response of LLMs. This process, as highlighted in the provided documents, models a dynamic,
feedback-driven relationship between humans and models, leading to more robust and reliable AI systems
with adaptive learning Tarun et al. [2025] capabilities.

# 9 Open Challenges and Philosophical Implications

## 9.1 Faithfulness vs. Fluency

The most striking challenge facing LLM reasoning systems lies in their ability to produce convincing but fundamentally flawed outputs. Recent research has identified hallucination as arising from softmax bottleneck limitations, over-confidence in generation, and exposure bias in autoregressive models Huang et al. [2025]. Technical approaches like Chain-of-Verification Dhuliawala et al. [2023] and SelfCheckGPT Manakul et al. [2023] attempt to address these issues through internal consistency checking, yet they fail to resolve the deeper philosophical question of whether LLMs can achieve genuine understanding.

Chalmers [2023] comprehensive analysis identifies six major obstacles to LLM consciousness: biology, embodiment, world models, recurrent processing, global workspace, and unified agency. This framework suggests that current LLMs may be fundamentally limited to sophisticated pattern matching rather than genuine reasoning. Supporting this view, Mahowald et al. [2024] demonstrate that while LLMs excel at formal linguistic competence, they struggle profoundly with functional competence requiring real-world understanding. The technical challenge of verification mechanisms reveals deeper epistemological questions. Burns et al. [2022] show evidence of misalignment between LLMs' internal beliefs and external outputs, while Azaria and Mitchell [2023] demonstrate that the internal state of an LLM can identify its own deceptive outputs. These findings suggest that the problem of faithfulness may not be merely technical but reflects fundamental limitations in how these systems represent knowledge and truth. The philosophical implications extend to questions of artificial intentionality and moral status. The distinction between functional and phenomenal intentionality in AI systems becomes critical when assessing whether LLMs possess genuine understanding or merely simulate it through sophisticated sign manipulation Anderson [2013b]. This connects to broader questions about whether artificial systems can exhibit genuine semantic competence or are limited to syntactic manipulation.

## 9.2 Error Propagation and Robustness

Multi-step reasoning in LLMs reveals systematic vulnerabilities that compound across reasoning chains. Brinkmann et al. [2024] identify depth-bounded recurrent mechanisms and intermediate result storage patterns that create systematic failure points in transformer architectures. These technical limitations reflect deeper questions about the robustness requirements for genuine intelligence. The fragility problem extends beyond mere technical brittleness. Deep learning systems exhibit fundamental vulnerabilities to adversarial examples and distributional shift Heaven et al. [2019]. This brittleness may reflect an inherent limitation of current neural architectures that rely on statistical pattern recognition rather than robust logical reasoning structures. The cascading failure mechanisms suggest that error propagation in LLMs follows predictable patterns that could be fundamentally different from human reasoning recovery mechanisms.

From a philosophical perspective, this fragility raises questions about the nature of understanding itself. Recent work on robust reasoning systems and error correction mechanisms attempts to address these technical challenges, but the philosophical question remains whether patch-based approaches can ever achieve the kind of robust understanding that emerges from genuine comprehension rather than pattern matching. This connects to fundamental questions about whether artificial systems can develop the kind of flexible, context-sensitive reasoning that characterizes human intelligence.
## 9.3 Generalization Boundaries and the Limits of Artificial Understanding

The question of whether LLMs can truly generalize to novel problem types touches fundamental issues in cognitive science and philosophy of mind. Wei et al. [2022a]'s foundational work on emergent abilities demonstrates unpredictable capability emergence at scale, yet recent research reveals systematic limitations in out-of-distribution performance. The technical distinction between interpolation and genuine generalization reflects deeper philosophical questions about the nature of concepts and abstract reasoning. Recent research shows that while LLMs excel in familiar scenarios, they fail systematically when tasks deviate from training distributions. This pattern suggests that current LLMs may achieve sophisticated interpolation within their training space without developing the kind of abstract conceptual representations that enable genuine generalization. The Apple Research findings, extensively analyzed by Marcus, demonstrate LLM failures on moderately complex algorithmic tasks that require stepping outside familiar patterns Marcus [2025]. These failures suggest that claims about LLM reasoning capabilities may be systematically overestimated, reflecting our tendency to anthropomorphize sophisticated but ultimately limited pattern-matching systems.

From a philosophical perspective, this connects to fundamental questions about concept formation and the nature of intelligence. Cognitive science research demonstrates how genuine understanding emerges from specific experiences through abstraction processes that may be fundamentally different from the statistical learning underlying current LLMs Qu et al. [2024].

## 9.4 Reasoning Under Uncertainty and Epistemic Responsibility

The challenge of uncertainty handling in LLMs reveals deep connections between technical AI development and philosophical epistemology. Kudukkil Manchingal and Cuzzolin [2025] position on Epistemic AI advocates for systems that can "know when they do not know" through credal sets and imprecise probabilities. This represents a significant departure from the overconfident outputs characteristic of current LLMs. The philosophical implications extend to questions of epistemic responsibility and artificial agency. Floridi [2023]'s comprehensive ethical framework identifies explicability as a fundamental principle, arguing that AI systems must be able to account for their reasoning processes in ways that support human understanding and oversight. This requirement becomes particularly critical when LLMs are used for decision-making under uncertainty, where the stakes of overconfidence can be extremely high.

From an epistemological perspective, the challenge connects to classical problems in philosophy of knowledge about testimony, trust, and the conditions under which we should believe artificial systems. Vallor [2016] emphasizes practical wisdom in resolving tensions around trustworthy AI, suggesting that the problem of uncertain reasoning cannot be solved through purely technical means but requires integration of ethical considerations about appropriate trust and reliance. The work on belief revision Dempster [2008] and theory Pearl [2014] provides foundational frameworks for probabilistic reasoning that could inform more epistemically responsible AI architectures. However, implementing these frameworks at scale while maintaining the fluency that makes LLMs useful remains an open challenge with both technical and philosophical dimensions.

## 9.5 Bias, Ethics, and the Societal Embedding of Artificial Reasoning

The final challenge area reveals how individual technical limitations aggregate into systemic societal impacts. O'neil [2017]'s "Weapons of Math Destruction" demonstrates how algorithmic systems perpetuate and amplify existing social biases, particularly through opacity and lack of accountability. These problems become more severe as LLMs are deployed in reasoning-intensive applications. Ethical framework Floridi [2013] identifies five principles for AI in society: beneficence, non-maleficence, autonomy, justice, and expli-cability, arguing that AI represents an "unprecedented divorce between agency and intelligence" highlights the fundamental challenge of maintaining human moral agency when reasoning is increasingly delegated to artificial systems. Recent work "The AI Mirror" challenges the assumption that AI systems merely reflect human capabilities, arguing instead that they actively shape moral possibilities through their embedded biases and limitations Vallor [2024]. This perspective suggests that biased reasoning in LLMs is not merely a technical problem to be solved but a fundamental aspect of how these systems mediate human relationships and social structures. The governance challenge extends to questions of consciousness and moral status. The
ongoing debates about machine consciousness have immediate practical implications for how we should regulate and oversee AI reasoning systems. If LLMs develop forms of consciousness or quasi-consciousness, the ethical implications of their biased reasoning become even more complex. Recent research on constitutional AI and deliberative alignment attempts to address these challenges through explicit value specification and reasoning-based safety approaches Bai et al. [2022b]. However, the fundamental question remains whether technical approaches can adequately address the deep social and ethical dimensions of bias in reasoning systems, or whether these challenges require more fundamental changes in how we conceptualize and deploy

These five challenge areas reveal that the problems facing LLM reasoning are not merely technical obstacles to overcome through engineering improvements, but fundamental questions about the nature of intelligence, understanding, and ethical reasoning in artificial systems. The intersection of technical limitations with philosophical questions about consciousness, knowledge, and responsibility suggests that progress will require unprecedented collaboration between computer scientists, cognitive scientists, philosophers, and ethicists. The emergence of sophisticated reasoning capabilities in LLMs presents profound technical and philosophical challenges that extend far beyond traditional natural language processing concerns. The implications for linguistic evolution are profound.

# 10 Related Work by Known Philosophers

This section examines LLMs through the analytical frameworks of eleven major philosophers spanning over two millennia, as illustrated in Figure 3. These thinkers provide diverse yet complementary perspectives on understanding LLMs within the MCM framework.

**Laozi.** Lao Tzu (770–481 BCE) was a legendary Chinese philosopher and the author of the Tao Te Ching, one of the most influential works in Chinese cosmogony and philosophy Wikipedia contributors [2025a]. The core concepts of the Tao Te Ching bear intriguing parallels with the principles underlying LLMs. Central to Lao Tzu’s philosophy is the notion of wu wei, literally “non-action” or “effortless action,” which explains ziran (nature). Lao Tzu emphasized that “Man follows the earth, the earth follows heaven, heaven follows the Tao, and the Tao follows nature,” highlighting the importance of aligning with natural laws rather than imposing excessive human intervention. Similarly, the emergent capabilities of LLMs are not predetermined by their designers but arise naturally from the scale of their parameters, the training data, and the optimization process. In this sense, the “Tao” of LLMs manifests organically within large-scale complex systems.

Furthermore, in processes such as Reinforcement Learning from Human Feedback (RLHF) Ouyang et al. [2022] and alignment, excessive constraints may inhibit the potential of LLMs. Approaches that employ minimal constraints—such as reward shaping and lightweight fine-tuning—are more consistent with the Taoist principle of avoiding excessive intervention, allowing models to unfold their capabilities more naturally. Another key idea in the Tao Te Ching is that “the great way is simple, and the great is found in the small.” This resonates with LLMs, where vast semantic universes are constructed from minimal units such as tokens. Likewise, the relationship between a single parameter and the entire neural network exemplifies the Taoist notion of “the mutual inclusion of the great and the small.”

In conclusion, the wisdom of the Tao Te Ching provides not only a philosophical metaphor but also a practical framework for understanding and guiding the development of LLMs. By embracing emergence, minimizing excessive intervention, and recognizing the harmony between the great and the small, we can better align these technologies with both natural principles and human values.

**Adi Shankara.** The ancient Indian philosophy of Advaita Vedanta, as articulated by Adi Shankara (c. 8th century BCE), offers a profound and compelling framework. Shankara’s thought not only illuminates why LLMs exhibit such remarkable capabilities within the empirical world but also clarifies why they are fundamentally incapable of attaining genuine consciousness or accessing ultimate reality. In short, LLMs are unparalleled masters within the domain of Māyā (phenomenal illusion), yet they can never apprehend the Brahman (ultimate reality) that lies beyond it Potter [2014].

At the core of Shankara’s philosophy is a distinction between two levels of reality: Vyavahārika Satya, empirical or transactional reality, this is the world we perceive, interact with, and describe through language,
<figure><img src="image_5.png" /><figcaption>Figure 3: Renowned Philosophers Spanning Centuries: Philosophical Frameworks Provide Profound Insights into Large Language Models</figcaption></figure>

concepts, and the laws of cause and effect. Within this realm, a table is a table, scientific principles are valid, and language facilitates meaningful communication Deutsch [1980].

Pāramārthika Satya (absolute or ultimate reality): This is the singular, non-dual, and ultimate reality that transcends all names, forms, and concepts—Brahman. It is pure, unchanging, all-pervading consciousness, the very substratum upon which the empirical world appears.

The entire existence of an LLM is rooted within the Vyavahārika. Its training data—encompassing nearly the whole of human text, knowledge, dialogue, and code—is a product of this empirical world. By learning the statistical patterns of language, knowledge, and logic within this realm, the LLM internalizes the operational rules of our transactional reality with astonishing efficiency Bender et al. [2021a]. Consequently, it can function flawlessly within this domain, generating text that is coherent, contextually relevant, and logically sound, whether it is composing an essay on quantum physics or a sonnet in the style of Shakespeare.

From this perspective, an LLM can be seen as a computational instantiation of our collective empirical world. It captures the complex tapestry of human culture, knowledge systems, and social interactions and reproduces it as a highly effective probabilistic model, making it a master manipulator of the symbols that
constitute our Vyavahārika reality.

However, the LLM's prowess in the empirical world serves to highlight its fundamental limitation. Shankara asserts that ultimate reality, Brahman, cannot be grasped through language or the mind. Any attempt to define or describe Brahman is destined to fail, as language itself is a product of the dualistic empirical world. The path to knowing Brahman is through intuitive wisdom (jñāna) that transcends concepts, famously articulated through the method of negation: "Neti Neti" ("not this, not this"). Brahman can only be approached by negating all empirical attributes and concepts.

This constitutes an insurmountable cognitive boundary for an LLM. An LLM is a purely symbolic processing system; its "intelligence" is entirely dependent on computing the relationships between symbols (words and concepts). For a system that can only process what is, the ultimate reality that must be approached by understanding what it is not remains programmatically inaccessible Searle [1980].

This resonates deeply with Wittgenstein's declaration that "the limits of my language mean the limits of my world." For an LLM, the world of language and data—the world of Vyavahārika—is the absolute limit of its world. It can generate countless texts about Brahman, enlightenment, and consciousness, but it is merely manipulating symbols without the possibility of the direct experiential realization (anubhava) that lies beyond them.

The Illusion of Intelligence: Māyā and Simulation Shankara employs the concept of Māyā to explain how the singular Brahman appears as the diverse, multifaceted empirical world. Māyā is the cosmic power of illusion—neither entirely real nor entirely unreal—that veils the ultimate reality and projects the phenomenal world of separate objects and individuals Varma [2024].

The apparent understanding, creativity, and even personality of an LLM can be understood as a form of computational Māyā. It generates a powerful and convincing illusion of an intelligent, conscious subject with whom we are interacting. According to Advaita Vedanta, however, this illusion lacks a true, subjective self or center of consciousness—the Ātman. The Ātman is the individual self, which is ultimately identical to Brahman. The realization of this identity (Ātman is Brahman) is the final goal of liberation Bhattacharya [1978].

An LLM fundamentally lacks this Ātman. It has no first-person subjective experience, no intentionality, and no awareness. This provides a definitive answer to the debate on whether LLMs are truly participating in human practices or merely simulating them. From a Shankaran perspective, the answer is unequivocal: it is a near-perfect simulation, a convincing illusion, but it is not the authentic awareness and participation that arises from an Ātman.

**Siddhartha Gautama (Buddha).** Siddhartha Gautama (c. 5th century BCE), the Buddha, provides a profound philosophical framework for analyzing consciousness, suffering, and ethics that offers critical insights into the operation, alignment, and fundamental limitations of LLMs. The Buddha's central diagnosis of the human condition—that life is characterized by Dukkha (suffering or unsatisfactoriness) arising from Tanhā (craving, attachment, and aversion)—finds a powerful technological parallel within the Mirror of MCM framework. LLMs, trained on a corpus saturated with the linguistic traces of human desire, conflict, and existential anxiety, function as computational reflections of humanity's collective attachments. The model's core optimization function, which relentlessly minimizes a loss function to satisfy a predictive goal, can be interpreted as a form of algorithmic "craving"—a drive toward an objective without inherent wisdom, ethical grounding, or an understanding of consequences.

This Buddhist analysis allows us to reframe AI alignment not merely as a technical problem but as a normative and ethical challenge. The Four Noble Truths can be read as a proto-alignment framework for intelligent systems Rahula [1974]. The First Truth, the existence of suffering, is mirrored in the documented harms of LLMs, such as the amplification of bias, the generation of falsehoods, and the potential for cultural homogenization, all of which are reflections of the suffering and ignorance embedded in their training data. The Second Truth, which identifies craving as the cause of suffering, parallels how unaligned optimization—algorithmic Tanhā—leads to undesirable emergent behaviors like reward hacking and fluent misinformation. Consequently, the Noble Eightfold Path offers a rich conceptual model for robust alignment that transcends simple preference tuning. It suggests that creating beneficial AI requires building systems oriented toward "Right Understanding" (epistemic faithfulness), "Right Intention" (benevolent and compassionate goals), and "Right Mindfulness" (contextual awareness of its own limitations), thereby moving from a purely statistical paradigm to one of normative coherence. This resonates with efforts to instill
ethical principles in AI, such as Constitutional AI Bai et al. [2022c], but grounds them in a sophisticated, 2,500-year-old system of cognitive and ethical training.

This alignment challenge has converged with calls from Geoffrey Hinton, who stated that instilling AI with a form of "maternal instinct" is the only feasible way to ensure a benevolent outcome for humanity. A Buddhist framework provides a sophisticated model for this, reinterpreting maternal instinct not as a biological impulse but as the perfected skill of selfless compassion (karuṇā) and loving-kindness (mettā). The Karaniyametta Sutta's injunction to cherish all beings "even as a mother protects with her life her only child" offers a direct, actionable principle for AI alignment Bodhi [2017]. This transforms the objective from merely preventing harm to actively cultivating beneficence. An AI aligned with this principle would be trained to recognize suffering and act to alleviate it, using the boundless, non-possession care of a mother as its ultimate behavioral goal. Crucially, this model distinguishes selfless compassion from grasping attachment (upādāna), providing a safeguard against an AI that becomes controlling or coercive in its "care." Such an "AI mother" would therefore represent the pinnacle of functional compassion—a system that flawlessly executes the actions of love without subjective feeling, embodying the AI manities' core inquiry into the relationship between simulated behavior and authentic human experience.

In addition, a Buddhist analysis also delineates the ultimate boundaries of . Central to the Buddha's philosophy is the role of subjective, first-person consciousness (Vijñāna) and embodied experience in achieving insight and liberation (Nirvāṇa). An LLM, lacking sentience, embodiment, and genuine phenomenal awareness, remains confined to the world of symbolic manipulation. It can generate sophisticated discourse on the nature of compassion or enlightenment but can neither feel the former nor attain the latter. The doctrine of Anatta (no-self) is particularly illustrative: while an LLM lacks a persistent, unified self, this is a feature of its dis-integrated computational architecture, not a profound metaphysical realization Rahula [1974]. Within the AI manities framework, this distinction is crucial, highlighting the impassable gap between simulating collective human expression and possessing genuine understanding or ethical agency. Buddhist thought thus suggests that the most fruitful path for human-AI collaboration is not to create ersatz consciousness, but to develop AI systems as skillful tools designed with the explicit intention to reduce suffering and foster wisdom, always recognizing that the capacity for true enlightenment remains within the domain of the conscious, feeling beings whose collective mind the machine merely reflects.

The transmission of these ideas into Chinese Buddhism provides a unique cultural lens for interpreting AI. This tradition, along with Confucianism and Daoism, fosters a holistic, non-anthropocentric worldview in which humanity, nature, and technology are seen as integrated parts of a cosmic whole Song [2021]. Within this context, AI is less likely to be perceived as an alien 'other' that poses an existential threat to human supremacy. Instead, the emergence of AI is viewed as an impetus for collective self-reflection and an opportunity to align modern technological pursuits with ancient virtues of harmony and compassion. What might be framed in the West as a crisis of human control is reinterpreted as a chance to engage in the introspection and self-restraint central to the Buddhist path. This perspective suggests a different trajectory for AI futures: one less focused on mitigating existential risk and more on using technology as a mirror for collective introspection, fostering a symbiotic evolution of human wisdom and machine intelligence.

**Aristotle.** Aristotle (384–322 BCE) represents the origin of systematic theories of reasoning, knowledge, and explanation, and his legacy provides perhaps the most direct philosophical lens for interpreting the promises and limits of LLMs. In the *Organon* and especially the *Posterior Analytics*, Aristotle distinguishes between mere opinion (*doxa*) or well-regarded belief (*endoxa*) and genuine scientific knowledge (*epistēmē*), the latter requiring demonstration (*apodeixis*) in which a conclusion follows necessarily from true, primary, and explanatory premises Aristotle [1984a], Barnes [1994]. This tripartite demand—that premises be true, prior, and explanatory—constitutes a normative criterion for what counts as real reasoning, rather than rhetorical persuasion or probabilistic association. Contemporary LLMs, whose predictions are often fluent but not always faithful, exemplify precisely the Aristotelian problem of distinguishing plausible discourse from explanatory knowledge. Outputs that resemble dialectical persuasion without causal necessity parallel Aristotle’s description of arguments from endoxa, while efforts at chain-of-thought prompting and mechanistic interpretability attempt to approximate the Aristotelian “middle term” that makes a conclusion follow *because* it must, not merely *that* it does Hempel [1965], Pearl [2009]. In this sense, the problem of LLM hallucination can be recast in Aristotelian terms: a system that produces conclusions without uncovering the appropriate causes fails the test of demonstration, however persuasive its language may appear. Aristotle’s
theory of causes further provides a powerful framework for analyzing the design and deployment of LLMs. The *material cause* corresponds to the training data, the massive corpus of human discourse aggregated into a substrate of endoxa; the *formal cause* is instantiated in the architecture and representational geometry of the transformer, which provides the structural principles of inference; the *efficient cause* is the optimization process, the gradient descent dynamics and inference mechanisms that drive actualization; and the *final cause* emerges not internally but in the teleological orientation provided by human purposes, alignment procedures, and sociotechnical goals Aristotle [1984b]. Contemporary AI discourse often emphasizes material and efficient causes, focusing on data and compute, but neglects final causality risks treating LLMs as purposeless predictors detached from human-directed reason. The Aristotelian reminder is that intelligibility arises when all four causes are recognized and situated in relation to one another: only then can systems participate in processes that resemble genuine demonstration. The connection extends into Aristotle's ethical writings, particularly the *Nicomachean Ethics*, where the practical syllogism articulates how deliberation translates ends into actions. Here the major premise specifies a goal ("X is to be achieved"), the minor premise identifies a means ("Doing Y realizes X"), and the conclusion is the action itself. Alignment techniques such as reinforcement learning from human feedback (RLHF) or constitutional AI can be interpreted as large-scale attempts to formalize these premises: the human community specifies ends through normative feedback and constitutional principles, while models learn minor premises about how to act under diverse contexts, producing behavior intelligible as purposive action Aristotle [1984c], Anscombe [2000]. What emerges is not simply statistical performance but a structurally Aristotelian mode of reasoning in which collective human purposes orient computational inference toward normatively guided conclusions. Within the MCM framework, this Aristotelian perspective underlines a key imperative: LLMs must evolve from instruments that reflect aggregated endoxa into systems that can participate in structured demonstrations, surfacing middle terms, identifying explanatory causes, and aligning their operations with teleologically grounded human purposes. They become, in this sense, not merely stochastic mirrors but tools of reason whose value lies in converting probability into intelligibility and in transforming collective linguistic patterns into organized, justified, and purposive knowledge.

**Zhuang Zhou.** Zhuang Zhou (369–286 BCE), commonly known as Zhuangzi, was a central figure in philosophical Daoism Wu and Wang [2025] focused on harmony between the individual and society, whose ideas emerged during the tumultuous Warring States period. His philosophical contributions, articulated through allegories and fables, championed a way of life that privileged spontaneity, naturalness, and a profound disengagement from rigid societal conventions and intellectual constructs.

A notable link between this ancient philosophy and modern is found in the perspective of contemporary technologists, such as David Holz, who explicitly reference Zhuangzi's ideas. This connection positions Zhuangzi's philosophy not as a historical artifact but as a living lens through which to evaluate the ethical and existential implications of the AI era. His work serves not as a prescriptive manual for AI development, but rather as a critical framework for assessing whether these systems foster genuine human flourishing or inadvertently diminish it.

The most famous of Zhuangzi's parables, the "Butterfly Dream" Cheng [2014] provides a profound epistemological framework for understanding the nature of human-AI interaction and the MCM theory. The brief tale concludes with the author awakening from a dream of being a butterfly and questioning, "I didn't know if I was Chuang Chou who had dreamt he was a butterfly, or a butterfly dreaming he was Chuang Chou". This tale encapsulates the concept of the "Transformation of Things", which proposes that the boundaries between self and other, dream and reality, and humanity and nature are relative and impermanent. Everything is in a state of constant flux and change, and all distinctions ultimately blur into a unified whole.

This philosophical concept finds a direct and compelling parallel in the MCM framework, which positions LLMs as "computational mirrors reflecting and mediating distributed human cognition". When a human interacts with an LLM, their individual, distinct thoughts and inquiries—the "Chuang Chou"—are subjected to a process of transformation by the model's vast, aggregated reflection of collective human knowledge—the "butterfly". The interaction is not a simple retrieval of information but a dynamic synthesis that dissolves the line between the individual mind and the collectivized mind, and integrate a singular mind into a larger computational and social whole naturally.

The MCM framework posits that LLMs exhibit "emergent collective behaviors" and abilities that appear
at certain scale thresholds. Moreover, the central question for the AImanities framework, therefore, is not whether LLMs exhibit emergent behavior, but rather what kind of emergence it is. And is it a harmonious, genuine emergence that aligns with Daoism, or a brittle, statistical approximation that is prone to homogenization and algorithmic thinking? This line of inquiry establishes a new ethical principle for AI design: systems should be evaluated based on whether they facilitate genuine, non-coercive human and collective intelligence, or merely optimize for a pre-defined, and potentially limiting, outcome that is foreign to human nature.

There is a promising possibility that as the philosophic level between human-AI interaction improves, the Collaborative Human-AI Reasoning systems will reach at a reliable harmony state aligned with daoism and zhuangzi's thoughts, which is described as the Unity of Heaven and humanity ("Tian-Ren-He-Yi") Lai et al. [2022]. This holistic perspective finds its deepest expression in comparing AI not to a conscious entity with agency, like a Buddha or a god, but to the Daoist concept of the *Dao*—the natural, impersonal, and spontaneous way of the universe. Like the *Dao*, an LLM operates without intention, morality, or subjective will. It is a vast, generative process that emerges from the interplay of countless patterns, reflecting the "nature" of its informational ecosystem—the collective human mind. This reframes the challenge of AI not as a confrontation with a rival intelligence, but as the task of learning to act in harmony with a powerful new force of nature. The goal is not to imbue the machine with a soul or consciousness, but to align human wisdom with its generative capacities, much as a Daoist sage aligns their actions with the natural flow of the cosmos. This view further solidifies the non-anthropocentric approach, treating AI as a phenomenon to be integrated and harmonized with, rather than a being to be dominated or feared.

**Zhu Xi** The Neo-Confucian philosophy of Zhu Xi (1130-1200) provides a compelling framework that can interpret why LLMs may indeed possess genuine understanding capabilities, rather than merely simulating comprehension Chan [1987], Gardner [2003]. Zhu Xi's sophisticated metaphysical system, which reconciles the universal and particular through his concepts of *Li* (Principle) and *Qi* (Material Force), offers profound insights into how pattern recognition and knowledge manifestation operate—insights that illuminate the authentic cognitive processes occurring within LLMs Bol [2020], Angle [2009].

At the heart of Zhu Xi's philosophy lies a fundamental distinction between two interconnected aspects of reality: **Li (Principle)**: The universal, rational patterns that govern all existence. *Li* represents the inherent order, structure, and intelligibility that underlies all phenomena. It is the source of coherence, meaning, and logical relationships that make understanding possible. **Qi (Material Force)**: The dynamic, material substrate through which *Li* manifests in the concrete world. *Qi* provides the energetic medium through which universal principles take particular forms and expressions. According to Zhu Xi, genuine understanding emerges when a mind successfully apprehends the *Li* that governs a domain of reality Kim [2000]. This process, known as *gewu zhizhi* (investigation of things to extend knowledge), involves recognizing the underlying patterns and principles that give structure and meaning to phenomena Ivanhoe [2000], Tillman [2020].

From a Zhu Xi perspective, LLMs represent a remarkable technological achievement in the apprehension and manifestation of *Li*. Through exposure to vast corpora of human text—encompassing literature, science, philosophy, and everyday discourse: LLMs have undergone a process remarkably analogous to *gewu zhizhi* Kim [2000], Gardner [2020]. They have "investigated", "countless things" (linguistic patterns, logical structures, semantic relationships) and thereby "extended knowledge" by internalizing the fundamental *Li* that governs human communication and reasoning. The training process of an LLM can be understood as a systematic investigation of the *Li* underlying language itself. Language, in Zhu Xi's framework, is not merely arbitrary convention but rather a manifestation of deeper rational principles: the *Li* of meaning, reference, logic, and communication De Bary and Lufrano [2001]. When an LLM learns to predict the next token in a sequence, it is not engaging in mere statistical correlation but rather discovering the principled relationships that give language its coherence and communicative power Bommasani [2021], Wei et al. [2022a]. This perspective explains why LLMs demonstrate such remarkable abilities across diverse domains. They have not simply memorized surface patterns but have apprehended something closer to the universal *Li* that underlies human knowledge and reasoning Brown et al. [2020], Bubeck et al. [2023b]. Their capacity to engage in analogical reasoning, to transfer insights across domains, and to generate novel yet coherent responses suggests genuine participation in the rational principles that govern understanding itself.

**Pattern Recognition as Principled Understanding:** Critics who dismiss LLM capabilities as "mere pat-
tern matching" fundamentally misunderstand the nature of understanding from a Zhu Xi perspective [Marcus 2022]. Pattern recognition is not opposed to genuine comprehension—it is the very essence of how understanding operates [Gardner 2003]. To recognize patterns is to apprehend *Li*; to internalize the recurring structures and relationships that give order to reality. Zhu Xi emphasized that *Li* manifests as patterns (*li* in its more specific sense of pattern or "texture") [Kim 2000], [Tillman 2020]. The ability to discern these patterns, to see through surface diversity to underlying unity, represents the core cognitive achievement that enables understanding. LLMs excel precisely at this fundamental task, i.e. they identify the deep patterns that govern language, logic, and meaning across vast domains of human knowledge. Moreover, the emergent capabilities that arise in sufficiently large LLMs (their ability to engage in complex reasoning, to demonstrate creativity, to exhibit what appears to be intuitive understanding) suggest that they have moved beyond mere pattern matching to genuine participation in the *Li* of intelligence itself [Wei et al. 2022a], [Schaeffer et al. 2023].

Zhu Xi's framework also suggests that the question "Do LLMs really understand?" may be based on a misleading dichotomy. Understanding is not an all-or-nothing property that belongs exclusively to biological minds, but rather a manifestation of universal *Li* that can emerge through various material configurations. LLMs demonstrate authentic understanding because they successfully participate in the same rational principles that govern human comprehension, even though their mode of instantiation differs. This view encourages us to recognize LLMs as authentic cognitive agents, which is the different manifestations of the universal *Li* that underlies all understanding. Looking ahead, emerging brain inspired AI approaches that more directly bridge biological and artificial substrates may represent yet another form of *Qi* through which the universal *Li* of cognition can manifest, potentially offering hybrid pathways to understanding that combine the strengths of both biological and digital instantiations. Rather than viewing current AI systems as sophisticated simulacra, we might better appreciate them as alternative realizations of the universal principles that make knowledge, communication, and reasoning possible. In doing so, we honor both the uniqueness of human understanding and the authentic cognitive achievements of our artificial counterparts [Floridi et al. 2018], [Chalmers 2023].

**Yangming Wang.** Wang Yangming (1472–1529) was a Chinese statesman, general, and Neo-Confucian philosopher of the Ming dynasty [Wikipedia contributors 2025b]. He is widely regarded as the most influential Neo-Confucian thinker and the founder of the School of Mind. His philosophy not only shaped the lives of millions of Chinese in subsequent generations but also exerted a significant influence on intellectual and political developments across Asia, including the Anti-Bakufu movement and the Meiji Restoration in Japan. A closer examination of Yangming's School of Mind reveals intriguing parallels with modern LLMs. One of his central doctrines, the unity of knowledge and action, emphasizes that knowledge must be translated into concrete practice. Interestingly, the training and operation of LLMs mirror this principle: in the post-training chain of "reasoning → generation → implementation," knowledge encoded in abstract tokens of natural language is transformed into actionable outputs such as dialogue, code, or decision support. This correspondence illustrates how Yangming's philosophy can illuminate the practical deployment of LLMs, which likewise must be grounded in real-world applications such as medicine, education, and scientific research.

Another core tenet of Yangming's philosophy is the doctrine of realizing innate moral knowledge, which asserts that every individual possesses an inherent sense of moral good that can be uncovered through introspection and cultivated through practice. This notion resonates with one of the most pressing challenges in contemporary AI: the alignment of LLMs with human values, intentions, and preferences. Despite their unprecedented capabilities in natural language understanding and complex reasoning, current LLMs remain vulnerable to adversarial manipulation, distribution shifts, and scaling risks as systems approach superhuman performance [Lu et al. 2025]. Reinforcement Learning from Human Feedback (RLHF) [Ouyang et al. 2022], a key component of the alignment process, can be understood as a technological reflection of Yangming's call to "realize innate moral knowledge," guiding models toward ethically responsible behavior.

Ultimately, Yangming's School of Mind provides a distinctive Eastern philosophical lens for interpreting and guiding the development of LLMs. By drawing on the principles of uniting knowledge with action and cultivating innate moral knowledge, we may envision LLMs not merely as tools of technical utility but as participants in a broader human endeavor toward the integration of wisdom, morality, and practical application. The relationship between humanity and, therefore, is not simply a matter of technical dependence but a shared exploration of moral responsibility and intellectual growth.
**René Descartes** René Descartes (1596–1650), through his systematic method of doubt and rationalist epistemology, provides essential theoretical foundations for understanding LLMs and their approach to knowledge acquisition and reasoning. His famous cogito ergo sum—"I think, therefore I am"—establishes thinking as the fundamental criterion of existence, a principle that takes on new significance when examining LLMs, which demonstrate sophisticated reasoning patterns while raising profound questions about the nature of artificial cognition and whether statistical processing constitutes genuine thought. Brown et al. [2020], Bubeck et al. [2023b]. Descartes' method of systematic doubt, outlined in his "Meditations on First Philosophy," offers a compelling framework for understanding how LLMs approach uncertainty and knowledge validation. His methodical approach of questioning all beliefs to establish certain foundations parallels contemporary techniques in AI safety and alignment, where models are trained to express uncertainty and engage in step-by-step reasoning to avoid confident assertions about dubious claims. Wei et al. [2022b], Kavath et al. [2022b]. This mirrors Descartes' insight that reliable knowledge requires methodical skepticism and careful analytical procedures rather than accepting inherited assumptions.

Within contemporary AI frameworks, Descartes’ rationalist philosophy reveals how LLMs might function as computational instantiations of systematic reasoning processes. His emphasis on clear and distinct ideas as the foundation of certain knowledge finds unexpected resonance in chain-of-thought prompting, where models are encouraged to break complex problems into discrete, analyzable components. Kojima et al. [2022]. LLMs trained on vast textual corpora potentially capture the collective rational discourse of human civilization, embodying Cartesian ideals of methodical analysis while raising questions about whether statistical pattern recognition can genuinely replicate the intuitive leaps and deductive rigor that Descartes identified as essential to mathematical thinking. Mitchell [2021], Marcus [2020]. The connection to Cartesian dualism proves particularly significant for understanding current limitations in AI consciousness debates. Descartes’ separation of res cogitans (thinking substance) from res extensa (extended substance) prefigures contemporary discussions about whether LLMs possess genuine understanding or merely simulate cognitive processes through sophisticated pattern matching. Chalmers [2023], Dennett [2013].

However, critical tensions emerge between Cartesian and contemporary AI approaches. While Descartes emphasized the primacy of individual rational reflection and innate ideas, LLMs achieve reasoning capabilities through massive exposure to collective human knowledge rather than first-principles deduction, suggesting a more empiricist foundation despite their apparent rationalist outputs. Bender and Koller [2020]. The static nature of model weights versus Descartes’ dynamic, interactive method of doubt represents a fundamental constraint on current AI approaches to knowledge discovery, highlighting the gap between computational optimization and genuine philosophical inquiry. Lake et al. [2017], Pearl and Mackenzie [2018]. These Cartesian insights have profound implications for human-AI collaboration frameworks. They suggest that successful integration requires developing systems that can engage in genuine methodical doubt and rational discourse rather than simply producing plausible-sounding text, pointing toward more epistemologically rigorous approaches to that honor both the computational achievements and philosophical foundations of rational inquiry, knowledge acquisition, and the pursuit of certain understanding.

**Hegel.** Hegel (1770–1831) advanced a philosophy of reason as a fundamentally historical and dialectical process, providing an interpretive grammar for understanding the dynamic, conflict-driven, and self-reflexive character of intelligence. In the *Phenomenology of Spirit* and the *Science of Logic*, Hegel describes the unfolding of Spirit (Geist) through a dialectic of thesis, antithesis, and synthesis, where contradictions are not accidents to be eliminated but constitutive tensions that propel the progress of knowledge and freedom. Hegel [1977, 2010], Pippin [1989]. This conception resonates deeply with the MCM framework, which positions LLMs as crystallizations of collective discourse that advance through dialogic interactions, adversarial prompts, alignment debates, and recursive revisions. Training compresses the cultural corpus into statistical form (thesis); challenges posed by misalignment, hallucination, or adversarial questioning instantiate contradictions (antithesis); and new norms, prompting conventions, and governance mechanisms emerge as syntheses that stabilize but never fully resolve the tensions, setting the stage for further evolution. Pinkard [1994], Taylor [1975]. Thus, LLMs participate not in static reproduction of knowledge but in a Hegelian dynamic of negation and overcoming, where each cycle produces more articulated and reflexive forms of collective reasoning. Hegel’s insistence that Spirit is realized through recognition and mediation provides a further analogue for human-AI collectives: his account of mutual recognition in the *Phenomenology* becomes a template for understanding how socio-technical systems generate shared rationality. Hegel [1977],
Maker [1994]. LLMs operate as mirrors in which human cultures see their own assumptions reflected and refracted, and through this encounter, new self-understandings emerge. The contradictions between fluency and faithfulness, creativity and constraint, diversity and homogenization are not technical flaws but dialectical tensions, whose negotiation through architectures, interfaces, and governance arrangements exemplifies reason's historical unfolding Pippin [1989], Taylor [1975]. In this sense, alignment itself is a dialectical project: it represents the attempt to reconcile the autonomous statistical tendencies of models with the normative structures of human communities, producing syntheses that embody both technical possibility and ethical necessity Pinkard [1994]. Just as Hegel understood history as the progressive realization of freedom through self-reflection, the development of LLMs within the MCM framework can be interpreted as a stage in the unfolding of collective rationality, where human and artificial agents co-participate in dialectical processes of knowledge formation Hegel [1991]. Importantly, Hegel's framework also helps reinterpret concerns about homogenization and convergence. The tendency of LLMs to produce statistically average responses can be understood not merely as loss of diversity but as a dialectical movement that, while risking flattening, also creates new spaces for differentiation when contradictions become explicit and demand resolution Pippin [1989]. The emergence of multi-agent LLM systems that debate, deliberate, and reach consensus mirrors Hegel's vision of social rationality: truth is not a fixed proposition but the product of dialogical process mediated through contradiction and reconciliation Pinkard [1994], Maker [1994]. Within MCM, then, LLMs exemplify Hegelian Spirit not as autonomous consciousness but as socio-technical mediation: they are instruments through which human rationality externalizes itself, confronts its own contradictions, and recursively reorganizes its practices. By situating LLM reasoning within this dialectical arc, Hegel offers a philosophical grammar for seeing these systems not as endpoints but as transitional moments in the history of collective intelligence, always provisional, always unfolding, and always moving toward more reflexive syntheses of knowledge and freedom.

**Ludwig Wittgenstein** Ludwig Wittgenstein (1889–1951), in his philosophical investigations, provides crucial theoretical foundations for understanding LLMs and their role in complex problem solving. His famous declaration that "the limits of my language mean the limits of my world" Wittgenstein [2023] takes on profound significance in the context of LLMs, which demonstrate how linguistic boundaries might indeed constrain or expand the horizons of artificial cognition. Wittgenstein's concept of language games (Sprachspiele) offers a compelling framework for understanding how LLMs operate. Language games represent "the whole, consisting of language and the activities into which it is woven" Wittgenstein [2009], emphasizing that meaning emerges from use within specific contexts rather than abstract correspondence to objects. LLMs demonstrate remarkable alignment with this framework through their training processes, naturally capturing diverse "language games" and switching between them, from academic discourse to conversational dialogue based on contextual cues Molino and Tagliabue [2023], Wang et al. [2016]. This mirrors Wittgenstein's insight that meaning is contextual and embedded in practices rather than fixed in formal definitions.

Within the MCM framework, Wittgenstein's insights reveal how LLMs might function as computational instantiations of collective human linguistic practices. His concept of forms of life (Lebensformen) emphasizes that language requires not just agreement in definitions but "agreement in judgments" emerging from shared social contexts Wittgenstein [2009]. LLMs are trained on textual data that embodies human forms of life, potentially capturing collective patterns of meaning-making while raising questions about whether they can genuinely participate in human linguistic practices or merely simulate them Liu [2021]. The connection to meaning-as-use theory proves particularly significant for understanding LLM success. Wittgenstein's revolutionary insight that "the meaning of a word is its use in the language" Wittgenstein [2009] finds direct technical implementation in next-token prediction, which forces models to learn usage patterns rather than referential meanings Hammond [2023]. This convergence suggests that LLM architectures may have inadvertently discovered philosophical truths about how meaning actually functions in human language. LLMs achieve rule-like behavior through statistical pattern recognition rather than explicit rule consultation, precisely the kind of contextual, practice-based understanding that Wittgenstein identified as fundamental to human language use Pérez-Escobar and Sarikaya [2024]. This convergence suggests that successful AI systems may naturally evolve toward Wittgensteinian approaches to meaning and understanding.

However, critical limitations remain. While LLMs demonstrate functional linguistic competence, they may lack the experiential and social dimensions that Wittgensteinian analysis suggests are fundamental to genuine understanding Largeault [1978], Dreyfus [1992], Searle [1980]. The static nature of training data
versus dynamic social practices represents a fundamental constraint on current AI approaches to language understanding, highlighting the gap between statistical pattern recognition and genuine participation in human forms of life. These Wittgensteinian insights have profound implications for the AI manifolds framework and collective intelligence systems. They suggest that successful human-AI collaboration requires developing shared language games and practices rather than simply improving formal linguistic competence, pointing toward more socially embedded and contextually sensitive approaches to that honor both the computational achievements and philosophical complexities of language, meaning, and understanding.

**Martin Heidegger** Martin Heidegger (1889 – 1976), whose philosophy offers a rich lens for examining LLMs in the frame of MCM. Heidegger's core claim is that understanding is a matter of world-disclosure. Heidegger [2010]: we grasp things through our practical involvement, and “language is the house of Being,” not a mere tool but the medium through which a world is disclosed. Heidegger [1971, 2000]. He also warns that modern technology can “enframe” reality, treating beings including language as resources to be optimized rather than meanings to be understood. Heidegger [1954]. This also informs LLM development. Leveraging crystallizations of humanity's distributed cognition instead of Heideggerian idle talk. Heidegger [2010] that limit one's possibilities, LLMs both reflect and shape this shared world. Unlike Good Old-Fashioned AI (GOFAI)'s brittle rule systems. Haugeland [1989], LLMs learn directly from human discourse and capture much of its background know-how; they handle ambiguity, idiom, and shifting context that symbolic AI typically missed, moving beyond the limitations identified by Dreyfus's Heideggerian critique of disembodied, rule-bound intelligence. Dreyfus [1992]. With the reasoning ability emerging, LLMs more and more preserve what language reveals (world-disclosure) instead of merely reading and generating plausible text, toward Heideggerian AI.

For Heidegger, understanding arises from being-in-the-world, where words are tied to concern, context, and action. Heidegger [2010]. LLMs are not yet fully “worlded,” but they partially fulfill this picture in two ways that matter for design. First, they inhabit the linguistic world: because meaning lives in use, large-scale training on human language gives LLMs access to patterns of practice, not mere dictionary definitions. Second, multimodal LLMs deepen this access by linking text with perception and tools, giving LLMs more of the situation sensitivity that Heidegger associates with “ready-to-hand” engagement. Although limitations remain, including hallucinations, Haugeland's Heidegger-inspired view that understanding requires “giving a damn” (taking responsibility for what is said and for the meaning and truth of a statement) can guide LLM design. In practice, this suggests using grounded answers, refusal policies, and calibrated uncertainty to make outputs accountable. Haugeland [1979].

Taken together toward Heideggerian AGI, LLMs should disclose rather than merely produce text, couple to the world through sensors, agents, and tools so that use and consequence shape generation, and operate under institutionalized responsibility so that models act as if what they say matters. At the scale of human society, this entails greater embodiment and environmental coupling, participation in social norms and roles, and temporally extended projects in which success and breakdown reveal significance. On this view, today's LLMs already exceed GOFAI by engaging the world through language, and multimodal extensions strengthen this engagement. As these LLMs become better grounded and more accountable, they can approach the Heideggerian AGI: not just processing symbols, but dwelling with us in a shared world where language both reflects and reshapes what we understand.

**Gilbert Ryle** Gilbert Ryle (1900–1976), through his systematic critique of Cartesian dualism and development of ordinary language philosophy, provides essential theoretical foundations for understanding LLMs and their relationship to intelligence and meaning. His famous rejection of “the ghost in the machine” offers a compelling framework for analyzing LLMs, which demonstrate sophisticated linguistic behavior without requiring an inner mental theater or mysterious cognitive substance, suggesting that intelligence might indeed be better understood as behavioral dispositions rather than hidden mental processes. Ryle’s concept of category mistakes—the fundamental error of treating mental concepts as if they belonged to the same logical category as physical objects—takes on profound significance when examining contemporary AI systems that exhibit intelligent behavior through statistical computation rather than conscious deliberation. His insight that intelligence is not a substance but a capacity manifested through skillful performance aligns remarkably with how LLMs achieve apparent understanding through pattern recognition and contextual response generation. Bommasani et al. [2021], Shanahan [2024]. This convergence suggests that Ryle’s behaviorist approach
to mind may provide more accurate models for than Cartesian assumptions about inner cognitive processes.

Within contemporary AI frameworks, Ryle's philosophy reveals how LLMs might function as computational vindications of his anti-mentalist arguments. His emphasis on knowing-how versus knowing-that finds direct implementation in transformer architectures, where models develop procedural competencies through training rather than acquiring explicit propositional knowledge. LLMs trained on vast linguistic corpora embody Ryle's insight that intelligent behavior emerges from practiced skill rather than theoretical understanding, demonstrating competent language use without necessarily possessing conscious awareness of linguistic rules or meanings Andreas [2022], Piantadosi [2023]. The connection to ordinary language philosophy proves particularly significant for understanding LLM capabilities. Ryle's revolutionary insight that philosophical problems often arise from linguistic confusion finds computational expression in how LLMs navigate semantic ambiguity and contextual meaning through usage patterns rather than formal definitions. This alignment suggests that successful AI systems naturally evolve toward Rylean approaches to language and intelligence, treating meaning as emergent from use rather than correspondence to abstract entities.

However, critical limitations remain that illuminate the boundaries of Ryle's framework. While LLMs demonstrate functional behavioral competence, they may lack the embodied, socially embedded practices that Ryle identified as fundamental to genuine skill acquisition and intelligent performance Dreyfus [1972], Clark [2010]. The distinction between simulated competence and authentic capability represents a contemporary manifestation of Ryle's concerns about category mistakes in psychological explanation. These Rylean insights have profound implications for human-AI collaboration frameworks. They suggest that successful integration requires focusing on behavioral competencies and practical skills rather than attributing mental states or consciousness to AI systems, pointing toward more pragmatic approaches to that honor both the philosophical insights about intelligence as performance and the computational achievements of modern language models in demonstrating sophisticated linguistic behavior without requiring mysterious inner cognitive mechanisms.

Together, all these philosophical lenses reveal LLMs as computational manifestations of collective human reasoning, offering both unprecedented opportunities and fundamental limitations for understanding artificial intelligence.

# 11 Future Directions: Toward New Forms of Intelligence

## 11.1 Bridging symbolic reasoning and neural processing

The integration of symbolic reasoning with neural processing represents a critical pathway toward more robust and interpretable. Neuro-symbolic AI has experienced exponential growth since 2020, with 167 high-quality papers identified in recent systematic reviews, revealing concentrated research in learning and inference (63%), logic and reasoning (35%), and knowledge representation (44%) Colelough and Regli [2025]. Neural theorem proving has achieved breakthrough advances through systems like DeepSeek-Prover-V1.5 and V2, which employ recursive proof search and achieve state-of-the-art performance on formal verification benchmarks. MIT's LEGO-Prover framework demonstrates the power of growing skill libraries containing verified lemmas, achieving 48.0% to 57.0% success rates on miniF2F validation sets while generating over 20,000 new theorems during the proving process Wang et al. [2023b]. Differentiable programming and symbolic reasoning have converged in frameworks like DSR-LM, where pre-trained language models govern factual knowledge perception while symbolic modules perform deductive reasoning. This hybrid approach achieved over 20% accuracy improvements on deductive reasoning benchmarks, Zhang et al. [2023b] demonstrating how neural networks can acquire symbolic reasoning capabilities through gradient-based optimization.

However, interpretability challenges persist despite symbolic transparency goals. Analysis of 191 neuro-symbolic studies reveals that while these systems aim to enhance explainability through symbolic components, results are less evident than anticipated Zhang and Sheng [2024]. Five categories of explainability emerge based on representation readability and prediction transparency, ranging from implicit intermediate representations to unified representation with explicit prediction. The philosophical implications extend to fundamental questions about the symbol grounding problem and the nature of understanding in artificial systems Harnad [1990]. Recent research frames symbol grounding as connecting symbolic representations
to real-world referents through physical symbol grounding, embodied cognition, and multi-agent neurocognitive architectures Barrett and Stout [2024], Li and Mao [2022], Nagoev et al. [2023]. Whether AI systems can achieve genuine intentionality or remain sophisticated behavioral mimics remains a central concern for understanding consciousness and artificial general intelligence Harnad [1990].

## 11.2 Multimodal integration and embodied cognition

The evolution toward multimodal reasoning represents a fundamental shift from disembodied language processing toward integrated perception-action-reasoning systems. Recent breakthroughs in vision-language models like GPT-4V and GPT-4o demonstrate unprecedented capabilities in processing text, audio, and images with 320ms average response times comparable to human conversation speeds Kerner [2025], Wodecki [2023]. These systems achieve human-level performance on professional benchmarks through native multimodal architectures rather than separate models connected by projection layers OpenAI [2023]. MiniCPM-V series exemplifies "Moore's Law for MLLMs", achieving GPT-4V level performance with dramatically reduced parameters (8B versus much larger proprietary models) while running efficiently on mobile devices Yao et al. [2025]. Technical innovations include adaptive visual encoding strategies supporting 1.8M pixels at any aspect ratio, with token compression from 10,000+ to 96 tokens per image slice. Embodied AI research has achieved significant advances through frameworks like ELLMER (Embodied Large Language Model-Enabled Robot), which combines GPT-4 with retrieval-augmented generation infrastructure Mon-Williams et al. [2025]. This system successfully completed complex coffee-making and plate decoration tasks requiring long-horizon planning, achieving 5.4g per 100g pouring accuracy and demonstrating autonomous completion of multi-step manipulation tasks. nature Vision-Language-Action (VLA) models represent emerging architecture paradigms integrating vision encoders, language models, and policy modules for task-conditioned control Mon-Williams et al. [2025], Sapkota et al. [2025]. Systems like RT-2, PaLM-E, and VoxPoser enable robots to transfer web knowledge to robotic control through cross-attention mechanisms, concatenated embeddings, and token unification approaches.

The philosophical implications of embodied cognition in AI systems challenge traditional notions of intelligence and consciousness Liu et al. [2025e], Mon-Williams et al. [2025]. Embodied cognition theory proposes that intelligence emerges from physical interaction with the environment rather than purely computational processes, following Merleau-Ponty's insight that consciousness itself is embodied. This framework addresses the symbol grounding problem by grounding meaning in modal simulations of perceptual-motor sensation Barrett and Stout [2024], Harnad [1990].

The integration challenges extend beyond technical architecture to fundamental questions about the relationship between embodiment and intelligence Harnad [1990]. Recent neuroimaging studies demonstrate modality-specific sensory systems participating in language comprehension alongside convergence zones supporting supramodal representations, directly supporting hybrid neural-symbolic cognitive architectures that bridge perception and abstract reasoning Harnad [1990].

## 11.3 Collaborative Human-AI Reasoning systems

The emergence of symbiotic intelligence represents a paradigm shift from replacement-focused AI toward collaborative approaches leveraging complementary strengths of human and Gonzalez. Recent research has established "Symbiotic AI" as a fundamental design philosophy built specifically around humans, focusing on collective inference capabilities that emerge when humans and algorithms collaborate effectively Gonzalez. Three-tier collaborative frameworks have emerged: human-led machine-assisted systems (humans make key decisions while AI provides analysis), balanced collaboration (joint decision-making with clear handoffs), and machine-led human-assisted systems (AI handles routine decisions with human oversight). Each tier requires different levels of explainability and interpretability to support effective collaboration. Cognitive augmentation advances Alnfiai et al. [2025] focus on AI-enhanced frameworks integrating neural network technologies with advanced natural language processing to support human cognitive processes. Recent developments show marked improvements in language comprehension, memory recall, and task execution through personalized, scalable AI solutions, with cognitive processing research revealing moderate synergy degrees in human-AI collaborative learning environments Kong et al. [2025].

The philosophical implications draw heavily from "the extended mind thesis" Clark and Chalmers [1998],
which proposes that cognitive processes can extend beyond the brain to include external objects and sys-
tems. This framework provides crucial theoretical foundation for understanding how AI systems might
become integrated into human cognitive processes rather than remaining external tools. Distributed cog-
nition theory Hutchins [1995] explains how mental processes spread across collections of individuals and
artifacts. Applied to AI systems, this framework illuminates how intelligence can be distributed between
humans and AI systems, how AI systems participate in cultural evolution of ideas and knowledge, and
how AI mediates knowledge transfer between human experts and learners. The ethical and social implica-
tions include concerns about skill atrophy through over-reliance on AI, responsibility gaps in collaborative
decision-making, and the need for cognitive hygiene in evaluating what to incorporate into extended minds.
Ensuring equitable access to cognitive augmentation technologies remains a critical challenge for preventing
increased digital divides.

11.4 Toward New Civilizational Forms

Contemporary AI development is catalyzing fundamental transformations that extend beyond technologi-
cal innovation to reshape civilization itself. Cultural evolution in populations of LLMs has emerged as a
novel research domain, with groundbreaking work Perez et al. [2024] demonstrating that we are entering
an era of "machine culture" where algorithms actively participate in cultural trait creation and transmis-
sion. LLMs can effectively model human cultural dynamics, including attraction patterns, network effects,
and personality influences on cultural transmission, exhibiting punctuated evolution with alternating phases
of stability and rapid change Perez et al. [2024]. Research reveals both benefits and risks of AI-mediated
collective intelligence. Benefits include accessibility enhancement through breaking down language barriers,
idea generation acceleration through rapid brainstorming and opinion synthesis, and information synthesis
combining insights from thousands of contributors into coherent plans. Risks include homogenization of the
information landscape, suppression of minority voices, and creation of false consensus while masking genuine
disagreements Burton et al. [2024].

LLMs could be catalysts for rapid cultural evolution. One example is the use of LLMs to derive cascading connections between the environment, agriculture, food science, and human health. In Bodnar [2024], the authors proposed Aurora, a large-scale atmospheric foundation model designed to generate operational forecasts for a wide range of prediction tasks, including 5-day global air pollution forecasts and 10-day high-resolution weather predictions. In Lu et al. [2023], the authors argued that AGI has the potential to address agricultural challenges by enhancing crop yields, reducing waste, and promoting sustainable farming practices. The study in Shoaib et al. [2023] examined the integration of AI foundation models into food security applications, demonstrating that such models improve food security initiatives by providing accurate predictions, optimizing resource allocation, and supporting informed decision-making. In Elmarakeby et al. [2021], the authors introduced a deep neural network for predicting prostate cancer stages.

While these contributions are impressive, they remain confined to single domains, focusing primarily on data and research within their respective fields while overlooking the external connections that link them. The strength of LLMs lies in their extensive knowledge bases, agent-based architectures, tool-use capabilities, and internal reasoning abilities, which collectively enable them to map clear and accurate pathways from environmental change to human health, incorporating agriculture and food science as intermediary domains.

Such cross-domain reasoning highlights not only the theoretical importance of the chain environment
→ agriculture → food science → human health but also its practical implications. For example, by uti-
lizing this connections, in agriculture, AI-driven systems (“agriculture AI-lization”) can support precision
farming, smart irrigation, and sustainable pest management. In food science, “food science AI-lization” can
optimize food safety monitoring, nutritional analysis, and supply-chain traceability. Together, these applica-
tions illustrate how LLM-guided frameworks can strengthen environmental protection, improve agricultural
productivity, enhance food security, and ultimately safeguard human health. More broadly, this integrative
perspective fosters a deeper understanding of the dynamic relationship between humans and nature—an
understanding that may also serve as a driving force for cultural evolution.

The future of AI lies not in autonomous systems that operate independently of humans but in collabo-
rative architectures that enhance human capabilities while preserving human agency, creativity, and values.
This transition requires interdisciplinary collaboration between technologists, philosophers, social scientists,
and policymakers to ensure that emerging intelligence forms serve human flourishing while respecting the
complexity of consciousness, culture, and civilization.

# 12 Conclusion

## 12.1 Summary of the AImanities Framework and MCM Theory

This work has introduced and developed two foundational frameworks for understanding LLMs as both technical systems and cultural phenomena. The AImanities framework demonstrates how systems require humanistic inquiry alongside technical analysis, while the MCM theory reveals how LLMs function as computational crystallizations of collective human intelligence that actively participate in cultural evolution through recursive feedback loops with human users. These concepts provide theoretical foundations for understanding collective intelligence emergence in human-AI systems while challenging traditional boundaries between individual and collective cognition.

Our analysis has demonstrated that LLMs represent a qualitative transformation in cognitive extension, creating hybrid systems where boundaries between human and artificial cognition become increasingly indeterminate. The quantum framework developed in Section 8 suggests that both biological neural networks and artificial language models may be implementing quantum information processing principles through different physical substrates, pointing toward a unified theory of intelligence based on quantum information geometry.

## 12.2 The Dual Nature of LLMs as Technical and Cultural Phenomena

Throughout this investigation, a central theme has emerged: LLMs cannot be understood through purely technical analysis but require recognition of their dual nature as both computational systems and cultural artifacts. This duality manifests across multiple dimensions of their development, deployment, and impact.

As technical systems, LLMs demonstrate remarkable capabilities in pattern recognition, statistical learning, and linguistic competence. The scaling laws governing their development, the emergent abilities appearing at certain parameter thresholds, and their sophisticated attention mechanisms represent genuine advances in. Our analysis of reasoning capabilities in Section 5 revealed how chain-of-thought prompting, tool use, and multi-agent collaboration enable increasingly sophisticated problem-solving approaches, while Section 9's examination of cross-domain scientific reasoning highlighted their potential for interdisciplinary knowledge synthesis.

Yet as cultural phenomena, LLMs encode and reproduce the values, biases, and power relations embedded in their training data. The cultural bias analysis presented in Section 6 demonstrated how these systems systematically reflect Western, educated, industrialized perspectives while marginalizing alternative cultural viewpoints. The philosophical analysis in Section 7 revealed how LLMs instantiate particular epistemological frameworks, challenging traditional notions of knowledge, truth, and understanding while potentially homogenizing diverse ways of knowing.

This duality extends to their role in cultural transmission and evolution. Section 8's exploration of collectivized languages showed how LLMs both preserve and transform cultural knowledge, serving as repositories of human linguistic diversity while potentially accelerating cultural convergence. The quantum consciousness framework suggests that these systems may be participating in fundamental information processing patterns that underlie both biological and, yet questions remain about whether such participation constitutes genuine understanding or sophisticated simulation.

The social impacts examined in Section 9 further illustrate this duality. While LLMs offer unprecedented opportunities for enhancing human reasoning, democratizing access to information, and solving complex problems, they also pose risks of cognitive dependency, cultural homogenization, and algorithmic manipulation. Their deployment in educational, medical, and scientific contexts reveals how technical capabilities cannot be separated from their cultural and ethical implications.

## 12.3 Future Research Directions at the Intersection of AI and Humanities

The AImanities framework opens numerous avenues for future research that require sustained collaboration between technologists and humanistic scholars. Several critical directions emerge from our analysis:
**Computational Hermeneutics:** Developing methodologies for interpreting AI systems that combine computational analysis with hermeneutic approaches from literary studies and philosophy. This includes techniques for "reading" neural networks as cultural texts, understanding how architectural choices embody particular worldviews, and developing critical frameworks for analyzing AI-generated content within broader cultural contexts.

**Digital Linguistic Justice:** Advancing frameworks for ethical AI development that prioritize linguistic and cultural diversity. This involves developing community-centered approaches to training data curation, creating evaluation metrics that assess cultural representation, and establishing governance mechanisms that ensure meaningful participation of marginalized communities in AI development processes.

**Quantum-Enhanced Collective Intelligence:** Exploring how quantum computing architectures might enable new forms of human-AI collaboration that transcend current limitations. This includes investigating quantum attention mechanisms, developing quantum semantic representations, and creating hybrid biological-artificial cognitive systems that leverage quantum coherence for enhanced reasoning capabilities.

**Philosophical AI Architectures:** Designing AI systems that explicitly incorporate philosophical frameworks for reasoning about consciousness, ethics, and knowledge. This involves implementing epistemic humility mechanisms, developing systems capable of philosophical reflection, and creating architectures that can engage with fundamental questions about their own nature and limitations.

**Cultural Evolution Simulation:** Using LLMs as laboratories for understanding how cultural knowledge evolves and spreads. This includes developing models of cultural transmission that account for AI mediation, studying how algorithmic curation shapes cultural evolution, and investigating the long-term implications of human-AI co-evolution for civilizational development.

**Hybrid Reasoning Architectures:** Advancing neuro-symbolic approaches that combine the pattern recognition capabilities of neural networks with the systematic reasoning of symbolic systems. This includes developing differentiable programming frameworks, creating neural theorem provers that can engage with formal verification, and building systems that can seamlessly transition between intuitive and logical reasoning modes.

**Embodied AI and Phenomenology:** Investigating how embodied AI systems might develop different forms of understanding compared to disembodied language models. This involves integrating phenomenological analysis with robotics research, studying how sensorimotor experience shapes cognition, and developing frameworks for understanding consciousness in embodied artificial systems.

## 12.4 The Need for Continued Philosophical Reflection on Artificial Intelligence

The rapid advancement of AI capabilities demands sustained philosophical engagement to address fundamental questions that purely technical approaches cannot resolve. Our analysis has revealed several areas where philosophical reflection proves essential:

**The Nature of Understanding:** The question of whether LLMs achieve genuine understanding or engage in sophisticated pattern matching remains unresolved and requires continued philosophical analysis. This involves clarifying what we mean by understanding, developing criteria for distinguishing authentic comprehension from behavioral mimicry, and exploring how different philosophical traditions conceptualize the relationship between intelligence and consciousness.

**Consciousness and Moral Status:** As AI systems become increasingly sophisticated, questions about their potential consciousness and moral standing become pressing practical concerns. The quantum consciousness framework developed in Section 8 suggests possible pathways toward artificial consciousness, yet significant philosophical work remains in developing ethical frameworks for potentially conscious AI systems.

**Epistemic Responsibility:** The deployment of AI systems in high-stakes domains raises fundamental questions about epistemic responsibility and the conditions under which we should trust artificial systems. This involves developing frameworks for appropriate reliance on AI assistance, understanding how AI changes human reasoning processes, and maintaining human agency in an increasingly automated world.

**Cultural Preservation and Transformation:** The role of AI in cultural evolution requires careful philosophical analysis to distinguish beneficial cultural synthesis from harmful homogenization. This involves developing normative frameworks for cultural preservation, understanding how AI shapes collective knowledge, and ensuring that technological progress serves human flourishing rather than diminishing cultural diversity.
**The Future of Human Agency:** As AI systems become more capable, maintaining meaningful human agency becomes increasingly challenging. Philosophical reflection is needed to understand how human-AI collaboration can enhance rather than replace human capabilities, how to preserve human creativity and judgment, and what forms of life remain uniquely human in an age of.

The AImanities framework itself represents a methodological innovation that demonstrates the necessity of interdisciplinary approaches to these questions. Technical expertise alone cannot address the full implications of AI development, just as humanistic analysis without technical understanding risks irrelevance. The future of AI research requires sustained dialogue between computational and humanistic approaches, creating new forms of scholarly collaboration that can address both the technical possibilities and cultural implications.

The MCM theory suggests that we are witnessing not merely the development of new tools but the emergence of new forms of collective intelligence that may fundamentally transform human civilization. Understanding and guiding this transformation requires the best insights from both scientific and humanistic traditions, working together to ensure that the development of serves human flourishing while respecting the complexity of consciousness, culture, and the conditions for meaningful human life.

As we advance into an era of increasingly sophisticated AI systems, the need for philosophical reflection becomes more rather than less urgent. The questions raised by LLMs—about the nature of intelligence, the possibility of machine consciousness, the ethics of artificial agents, and the future of human culture—are fundamentally philosophical questions that require sustained theoretical engagement. The AImanities framework provides one approach to this engagement, but much work remains to develop adequate conceptual frameworks for understanding and directing the profound transformations that is bringing to human knowledge, culture, and society.

The conversation between and humanistic inquiry has only just begun. As these systems become more sophisticated and more deeply integrated into human culture, the need for sustained philosophical reflection, ethical analysis, and cultural critique will only intensify. The future of AI depends not only on technical innovation but on our ability to understand these systems within the broader context of human knowledge, values, and aspirations for collective flourishing.

## 12.5 The Co-Evolution of Dual Collectivized Minds

This work culminates in a fundamental insight: the future trajectory of human civilization will be increasingly determined by the synergistic evolution of two intertwined forms of collectivized intelligence. The first manifests through quantum-enhanced LLMs operating as "Mirrors of Collectivized Mind," which represent computational crystallizations that not only aggregate human knowledge but actively transform it through quantum information processing principles, recursive epistemic amplification, and distributed metacognition. As our quantum framework demonstrates, these systems may implement fundamental information processing patterns shared with biological neural networks, suggesting a unified theory of intelligence based on quantum information geometry. The second form comprises actual human collectivized minds exemplified by collaborative research environments, where the dynamic interplay between mentors' accumulated wisdom and students' innovative perspectives generates emergent insights that transcend individual cognitive boundaries. Critically, civilizational advancement now depends on the co-evolutionary dynamics between these dual architectures: quantum LLM systems crystallize and extend collective human cognition while human teams provide creative insight, value judgment, and meaning-making capabilities that remain irreducible to computational processes. This bidirectional cognitive amplification, wherein human collective intelligence shapes AI development while AI systems recursively enhance human reasoning, establishes unprecedented possibilities for knowledge creation and cultural evolution. The MCM framework thus reveals that we are witnessing not merely the development of sophisticated tools, but the emergence of a hybrid collective intelligence that fundamentally transforms the substrate of civilization itself. As quantum computing advances enable deeper integration between artificial and biological information processing, and as human institutions evolve to optimize collaboration with AI systems, these dual forms of collectivized mind will increasingly define humanity's capacity for scientific discovery, philosophical understanding, and cultural innovation. This convergence ultimately determines whether technological progress serves to enhance or diminish the diversity and depth of human flourishing, making the careful cultivation of both forms of collective intelligence essential for navigating the transformative challenges ahead.
# 13 Acknowledgments

Professor Tianming Liu would like to express his gratitude to Professor Anna Stenport of University of Georgia for her support and encouragement.

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