Is the current AI simply reciting answers or actually reasoning them out?

My former physics teacher used to ask this question a lot, “Did you memorize it, or did you really understand it?”

If you answer “I understand” at this point, there’s a high probability that the teacher will test you with a few more questions. Therefore, to figure out whether AI has just memorized the answers or “truly understood” them, we also need to ask several “whys”.

The “reasoning” of AI

Some people liken AI’s response mechanism to a mechanized “knowledge repetition,” but OpenAI clearly disagrees with this view. Recently, they introduced the o1 model, which incorporates reinforcement learning and chain-of-thought reasoning technologies, claiming to achieve true deep reasoning capabilities. Notably, the model’s breakthrough performance in complex scenarios such as scientific computation, program development, and mathematical proofs does not rely solely on pattern recognition from pre-trained data. Instead, it dynamically constructs a “thought tree” for self-validation—analogous to the human tendency to pause and reflect on problem-solving steps—enabling it to autonomously correct errors and optimize solutions.

The core of the chain-of-thought mechanism lies in constructing a virtual thinking space: before generating a final answer, o1 creates numerous potential reasoning paths under the guidance of a reward-punishment model. Using a trial-and-error strategy similar to AlphaGo’s Monte Carlo Tree Search, the system systematically explores the feasibility of different problem-solving strategies. This multi-layered thinking architecture equips the model with the ability to deconstruct complex problems into actionable subtasks, significantly enhancing its effectiveness in handling intricate propositions.

However, the essential definition of “reasoning” remains controversial. OpenAI interprets it as an engineering concept that “improves result quality by extending computation time,” which differs significantly from the traditional understanding of reasoning as a process based on logical deduction. A recent paper published by Apple Research seemingly supports this skepticism, suggesting that the reasoning abilities of current large models are essentially advanced pattern-matching systems supported by massive parameters.

Despite the academic debate over whether AI truly possesses reasoning capabilities, models like GPT-4 and Claude have already sparked a wave of transformation in the commercial sector. From intelligent customer service to drug discovery, from code generation to financial modeling, the trend of AI technology reshaping industries is accelerating. This commercial revolution, driven by technological advancements, is becoming unstoppable.

The “memory” of AI

The memory mechanism of humans is naturally intertwined with cognitive architecture and emotional dimensions. Even in the process of rote memorization, the associative properties of neural networks and emotional hormones continue to play a role. We often reinforce memory retention through contextual associations, and events that trigger intense emotional fluctuations leave particularly profound cognitive imprints. In contrast, artificial intelligence systems are entirely devoid of emotional dimensions. Their memory capability is essentially a pattern recognition system based on mathematical modeling.

The construction of AI’s “memory” begins with the structured processing of massive heterogeneous data: the system first captures raw data from multimedia information streams such as text, images, and audio, followed by feature cleaning and dimensionality reduction. This preprocessing stage acts as a cognitive filter, transforming chaotic raw information into standardized inputs for algorithmic analysis through techniques such as noise reduction, data normalization, and complexity compression. Subsequently, the system enters the core phase of feature engineering—leveraging mathematical tools such as convolution kernels and attention mechanisms to extract key feature vectors with discriminatory power from the vast ocean of data. These quantified indicators form the cognitive codes that distinguish different patterns.

Once the feature map is completed, machine learning algorithms construct classification decision models: based on supervised learning frameworks or deep neural network architectures, the system continuously optimizes parameters to approach the human perception boundary of patterns. The ultimately formed cognitive engine functions as a precision sorter, capable of performing real-time pattern matching on input information streams. When new data features resonate strongly with the trained pattern matrix in high-dimensional space, the AI can accurately complete cognitive tasks such as image recognition and semantic parsing. This probability-based associative mapping mechanism is the technical essence of artificial intelligence’s ability to identify countless faces and respond to knowledge-based queries.

From this perspective, AI is indeed just “parroting”，This is clearly not the future.

In his keynote speech at the AAAI 2020 conference, Turing Award laureate Bengio made a forward-looking observation that future deep neural networks need to transcend the limitations of the current System 1 (intuitive response system) and evolve towards System 2 (logical analysis system) in human cognitive architecture. This involves constructing an intelligent system capable of explicit logical encoding, planned reasoning paths, and self-explanation. The core of this cognitive leap lies in transforming human knowledge systems into computable logical frameworks and achieving knowledge increment through deductive reasoning mechanisms. This aligns closely with my understanding of the true essence of reasoning.

From the perspective of cognitive science, reasoning can be decomposed into two levels: associative reasoning and causal reasoning. Current AI systems have demonstrated significant advantages in associative reasoning. Leveraging statistical learning from massive datasets, models can effectively capture high-dimensional statistical associations among variables. This capability has enabled practical breakthroughs in applications such as recommendation algorithms and semantic understanding. However, when it comes to causal reasoning, systems must go beyond superficial correlations and establish causal transmission mechanisms among variables through methods such as experimental control, confounder adjustment, and causal graph modeling. Tasks requiring counterfactual inference, such as evaluating medical treatment efficacy or simulating economic policies, hold irreplaceable value in decision-critical domains.

The current causal reasoning capabilities of AI still face fundamental challenges. A recent research review presented at ICLR 2024 highlights that the academic community is advancing exploration along paths such as causal discovery, counterfactual learning, and causal reinforcement learning. For instance, the study *”Robust agents learn causal world models”* included in the conference raises ongoing theoretical debates about whether intelligent agents must inherently build causal world models for generalization in new domains. These cutting-edge explorations corroborate Bengio’s assertion: while causal representation learning, intervention effect computation, and other techniques have made incremental progress, artificial intelligence remains in a critical phase of theoretical breakthroughs and technical validation in its pursuit of building truly interpretable causal reasoning systems.

Break the limitations

In the technological journey of advancing AI from statistical association to causal reasoning, achieving a dual breakthrough has become an essential path. The first is to resolve the dilemma of model interpretability. By extending the chain-of-thought technique into the causal dimension, we may open a transparent observation window within the “black box” system. The construction of such “causal observability,” though not yet fully realizing the ultimate goal of causal modeling, will provide a critical criterion for verifying whether AI systems truly possess reasoning capabilities. After all, only intelligent systems with traceable reasoning trajectories can pass the visualization validation of their decision-making processes and ultimately gain access to highly sensitive application domains such as medical diagnosis and judicial decision-making.

Secondly, it is imperative to overcome the universal challenge of capability generalization. The reasoning abilities that AI currently demonstrates in structured scenarios, such as mathematical logic, urgently need to be transformed into a general framework capable of addressing open-domain problems. A particularly enlightening example is the Core Plan Step Learning (CPL) technology introduced by Microsoft Research Asia. This framework constructs a hierarchical reinforcement learning architecture, attempting to abstract causal reasoning patterns from specific domains into transferable meta-strategies. This enables cross-domain generalization in complex real-world problems such as supply chain optimization and climate modeling. This paradigm shift—from “specialized breakthroughs” to “universal adaptation”—is reshaping our understanding of the cognitive boundaries of intelligent systems.

Notably, these two breakthrough directions exhibit a profound technical coupling. The visual analysis of causal graphs can effectively enhance the interpretability of model generalization paths, while the new patterns emerging during cross-domain generalization provide validation scenarios for causal modeling. This spiraling, mutually reinforcing mechanism may well be the key to breaking through the current cognitive ceiling of AI.

Conclusion

The memory architecture of artificial intelligence systems is essentially based on a knowledge reproduction mechanism rooted in statistical pattern recognition. Its response process primarily relies on probabilistic matching with training datasets—a mode of operation often likened to the mechanical functioning of a “knowledge retriever.” However, technological advancements are pushing the boundaries of cognition: taking OpenAI’s o1 model as a representative example, the integration of reinforcement learning algorithms with chain-of-thought architectures has enabled multi-step logical reasoning and self-verification of conclusions, demonstrating breakthrough progress in complex scenarios such as mathematical proofs.

It is worth emphasizing that current AI systems are still in the technical R&D phase for causal inference. The latest research from ICLR 2024 highlights that achieving true causal reasoning requires overcoming two major technical barriers: first, deciphering the black-box nature of neural networks and constructing a decision path visualization system that is traceable; second, transcending domain-specific limitations by establishing meta-reasoning transfer mechanisms, such as those explored in Microsoft’s CPL framework. These two breakthrough directions are not only critical to the construction of causal models but also determine whether intelligent systems can break free from the cognitive constraints of being “pattern matchers” and truly advance to a higher stage of reasoning that is interpretable and generalizable.