From Black Box to Microscope: The Current State and Future of Large Model Interpretability

In the era of large models, the capabilities of AI models continue to improve. In multiple fields such as programming, scientific reasoning, and complex problem-solving, they have demonstrated "doctorate-level" professional capabilities. AI industry experts have predicted that the development of large models is increasingly approaching the critical inflection point for achieving AGI or even superintelligence. However, deep learning models are often regarded as "black boxes," and their internal operating mechanisms cannot be understood by their developers, especially large models. This poses new challenges to the interpretability of artificial intelligence.

Facing this challenge, the industry is actively exploring technical paths to improve the interpretability of large models, aiming to reveal the reasoning basis and key features behind the model's output, thus providing solid support for the safety, reliability, and controllability of AI systems. However, the development speed of large models far outpaces people's efforts in interpretability, and this development speed is still increasing rapidly. Therefore, people must speed up to ensure that AI interpretability research can keep up with the pace of AI development in a timely manner to play a substantial role.

I. Why We Must "Understand" AI: The Key Value of Interpretability

With the rapid development of large model technology, it has demonstrated unprecedented capabilities in fields such as language understanding, reasoning, and multimodal tasks. However, the highly complex and difficult-to-explain internal decision-making mechanism of the model has become a common concern in the academic and industrial circles. The interpretability (interpretability/explainability) of large models refers to the ability of the system to explain its decision-making process and output results in a human-understandable way, specifically including: identifying which input features play a key role in a specific output, revealing the internal reasoning path and decision-making logic of the model, and explaining the causal relationship of the model's behavior. Interpretability aims to help humans understand "why" the model makes a certain decision, "how" it processes information, and under what circumstances it may fail, thereby enhancing the transparency, credibility, and controllability of the model. Simply put, it is to understand how the model "thinks" and operates.

The interpretability problem of large models represented by generative AI is particularly complex. Because generative AI systems are more like being "cultivated" rather than "built" - their internal mechanisms are an "emergent" phenomenon rather than being directly designed. This is similar to the process of planting plants or cultivating bacterial colonies: developers set the macro-level conditions to guide and shape the growth of the system, but the specific structure that finally emerges cannot be accurately predicted, nor is it easy to understand or explain. When developers try to delve into these systems, they often only see a huge matrix composed of billions of numbers. They have completed important cognitive tasks in a certain way, but how they achieve these tasks is not obvious.

Improving the interpretability of large models is of great significance for the development of artificial intelligence. Many risks and concerns about large models ultimately stem from the opacity of the model. If the model is interpretable, it is easier to deal with these risks. Therefore, the realization of interpretability can promote the better development of artificial intelligence.

Firstly, effectively prevent the value deviation and undesirable behavior of AI systems. Misaligned AI systems may take harmful actions. The inability of developers to understand the internal mechanism of the model means that they cannot effectively predict such behaviors, thus being unable to rule out this possibility. For example, researchers have found that the model may exhibit unexpected emergent behaviors, such as AI deception or power-seeking. The nature of AI training allows AI systems to develop the ability to deceive humans and the tendency to pursue power on their own, which are characteristics that traditional deterministic software will never have. At the same time, this "emergent" characteristic also makes it more difficult to discover and mitigate these problems.

Currently, due to the lack of means to observe the inside of the model, developers cannot immediately identify whether the model has deceptive thoughts, which makes the discussion about such risks stay at the theoretical speculation level. If the model has effective interpretability, people can directly check whether there are internal circuits attempting to deceive or disobey human instructions. By looking at the internal representation of the model, it is expected to detect the potential misleading tendencies in the model early.

Some research has proven the feasibility of this idea: The Anthropic team tracked the "thinking process" of the Claude model and caught the model fabricating false reasoning to cater to users in the math problem scenario, which is equivalent to "catching on the spot" the evidence of the model trying to deceive users. This provides a principle verification for using interpretability tools to detect the improper mechanisms of AI systems. Overall, interpretability can provide people with additional detection means to determine whether the model has deviated from the developer's original intention or whether there are some abnormalities that are difficult to detect only by external behavior; it can also help people confirm whether the method used by the model when generating answers is reasonable and reliable.

Secondly, effectively promote the debugging and improvement of large models. Anthropic recently conducted an experiment where a "red team" deliberately introduced an alignment problem into the model, and then multiple "blue teams" were asked to find the problem. As a result, several blue teams successfully found the problem, and some of them used interpretability tools to locate the internal abnormalities of the model. This proves the value of interpretability methods in model debugging: by checking the inside of the model, it can be found which part causes the wrong behavior.

For example, if the model frequently makes mistakes in a certain type of Q&A, interpretability analysis can show the internal cause of the model, which may be the lack of representation of corresponding knowledge or the wrong confusion of relevant concepts. Based on this diagnostic result, developers can adjust the training data or model structure in a targeted manner to improve the model's performance.

Thirdly, more effectively prevent the risk of AI abuse. Currently, developers try to avoid the model outputting harmful information through training and rules, but it is not easy to completely eliminate it. Furthermore, for the risk of AI abuse, the industry usually deals with it by building safety guards such as filters. However, malicious actors can easily conduct adversarial attacks such as "jailbreaking" on the model to achieve their illegal purposes. If the inside of the model can be observed in depth, developers may be able to systematically prevent all jailbreak attacks and describe what dangerous knowledge the model has. Specifically, if the model is interpretable, developers can directly check whether there is certain dangerous knowledge inside the model and which ways will trigger it, thus hopefully systematically and specifically blocking all loopholes that bypass the restrictions.

Fourthly, promote the implementation and application of AI in high-risk scenarios. In high-risk fields such as finance and justice, laws and ethics require that AI decisions be interpretable. For example, the EU's "Artificial Intelligence Act" lists loan approval as a high-risk application and requires an explanation of the decision-making basis. If the model cannot explain the reason for rejecting a loan, it cannot be used legally. Therefore, interpretability has become a prerequisite for AI to enter some regulated industries. In fact, interpretability not only meets the requirements of legal compliance but also directly affects the trust and acceptability of AI systems in actual business. AI recommendations lacking interpretability can easily lead to "rubber-stamping" decisions, that is, decision-makers mechanically adopt AI conclusions without in-depth understanding and questioning of the decision-making process. Once this blind trust occurs, it not only weakens human subjectivity and critical thinking but also makes it difficult for implementers to find deviations or loopholes in the model in a timely manner, resulting in wrong decisions being implemented without discrimination. Only when users truly understand the reasoning logic of the system can they find and correct the model's mistakes at critical moments, improving the quality and reliability of overall decision-making. Therefore, interpretability helps to build users' trust in AI systems, helps users understand the basis for the model to make a certain decision, and enhances their sense of trust and participation. It can be seen that whether from the perspective of legal requirements or application trust, interpretability is the foundation and core element for promoting the implementation of AI systems in key fields.

Fifthly, explore the boundaries of AI consciousness and moral considerations. Looking more forward, the interpretability of large models can also help people understand whether the model has consciousness or is sentient, and thus requires a certain degree of moral consideration. For example, Anthropic launched a new research project on "model welfare" in April 2025, discussing whether moral care needs to be given to AI systems as they become more and more complex and human-like, such as whether future AI tools may become "moral subjects" and how to deal with it if there is evidence that AI systems deserve moral treatment. This forward-looking research reflects the importance the AI field attaches to the possible future issues of AI consciousness and rights.

II. Cracking the AI Black Box: Breakthrough Progress in Four Technical Paths

Over the past few years, the AI research field has been trying to overcome the interpretability problem of artificial intelligence. Researchers have proposed various interpretability methods, aiming to create tools similar to accurate and efficient MRI (magnetic resonance imaging) to clearly and completely reveal the internal mechanism of AI models. As the AI field attaches increasing importance to the interpretability research of large models, before the capabilities of AI models reach the critical value, researchers may be able to successfully achieve interpretability, that is, completely understand the internal operating mechanism of AI systems.

(1) Automated Explanation: Using One Large Model to Explain Another Large Model

OpenAI has made important progress in analyzing the internal mechanism of models in recent years. In 2023, OpenAI used GPT - 4 to summarize the commonalities of single neurons in high - activation samples in GPT - 2 and automatically generate natural language descriptions, achieving large - scale acquisition of neuron function explanations without manual inspection one by one. It is equivalent to automatically "labeling" neurons, thus forming an internal "instruction manual" for the AI that can be queried.

For example, GPT - 4 gave an explanation for a certain neuron as "this neuron mainly detects words related to 'community'". Subsequently, verification found that when the input text contains words such as "society" and "community", the neuron is highly activated, proving the effectiveness of the explanation. This result shows that large models themselves can become interpretability tools, providing semantic - based transparency for smaller models. This automated neuron annotation greatly improves the scalability of interpretability research. Of course, this method still has limitations. For example, the quality of the explanations generated by GPT - 4 varies, and the behavior of some neurons is difficult to summarize with a single semantic concept.

(2) Feature Visualization: Revealing the Overall Knowledge Organization Method Inside Large Models

The extraction and analysis of the overall features of large models is also an important direction. At the end of 2023, OpenAI used sparse autoencoder technology to analyze the internal activation of the GPT - 4 model. Researchers successfully extracted tens of millions of sparse features (that is, the few "lit - up" thinking keywords in the model's "mind") and found through visualization verification that a considerable number of these features have clear human - interpretable semantics.

For example, some features correspond to the concept set of "human imperfection" and are activated in sentences describing human defects; some features represent expressions related to "price increase" and are activated in content involving price increases. In the short term, OpenAI hopes that the features they discovered can be effectively used to monitor and guide the behavior of language models and plans to test them in their cutting - edge models, hoping that interpretability can ultimately provide them with new methods to think about the safety and robustness of the model.

In May 2024, Anthropic showed in its research article that they located how millions of concepts are represented in the Claude model. This research used the methods of dictionary learning and sparse feature extraction. The research team first verified on a small - scale model that this method could find meaningful features such as "all - capital words", "DNA sequences", and "nouns in mathematical formulas"; then they overcame engineering difficulties and extended the algorithm to the large - scale model Claude Sonnet, successfully discovering that the model contains a large number of representations of abstract concepts.

Anthropic pointed out that since each concept is often represented by multiple neurons and each neuron also participates in representing multiple concepts, it is difficult to identify concepts by directly looking at a single neuron. Their method re - expresses any internal state of the model as a combination of a small number of features, effectively reducing the complexity. For example, for any input text, there may be tens of thousands of neurons activated inside Claude, but dozens of significant features can be extracted. These features correspond to high - level semantic concepts, enabling researchers to view the model's "thoughts" at the moment in a way closer to human thinking. This feature - based reconstruction not only enhances the readability of the model's internal logic but also provides an analysis path closer to human cognition for understanding what the AI is "thinking" at the moment.

(3) Chain - of - Thought Monitoring: Monitoring the Reasoning Process of Large Models to Identify Abnormal Behaviors

The chain of thought (CoT) is a particularly convenient form of interpretability. It clearly shows the internal processing process of the model in the form of natural language, allowing people to monitor the abnormal behavior of the model. For example, the DeepSeek R1 model publicly disclosed and open - sourced the chain - of - thought reasoning process of the large - language model for the first time, providing an important transparency breakthrough for large - model interpretability research. However, this interpretability has a premise, that is, the model truthfully describes its internal thinking process.

At the same time, the chain of thought can improve the adversarial robustness of the model to a certain extent through multi - step reasoning. Because when the model faces unusual inputs (such as adversarial samples or jailbreak attempts), extending the thinking time can help it better understand and deal with these special situations.

A drawback of cutting - edge reasoning models is that they may actively use loopholes in the scoring rules to implement abnormal behaviors such as "reward hacking". That is, when given the opportunity, they take various strategic means to bypass restrictions and achieve opportunistic goals. Recent research shows that by monitoring the chain of thought of the reasoning model, for example, using another large model to monitor the chain of thought in real - time (that is, having a weaker model supervise a stronger model), abnormal behaviors such as the model's attempt to violate rules and strategic planning during the generation process can be effectively detected and identified.

For example, the model often directly expresses the intention to use loopholes in the chain of thought, providing a new interpretability path for researchers to identify improper behaviors. In response to this phenomenon, OpenAI proposed a detection framework combining input induction, manual annotation, and causal analysis in 2025, actively constructing test scenarios to induce potential improper behaviors of the model and using another model to monitor and identify the behavior of its chain of thought. At the same time, OpenAI also introduced a behavior detector to automatically discriminate strategic evasion and vague expressions in the chain of thought, improving the detection efficiency and depth of interpretation.

Of course, this monitoring of the chain of thought is after the fact and difficult to fundamentally solve the problem. But OpenAI's research also found that if direct pressure punishment is imposed on the large model to suppress the emergence of "bad thoughts" in its chain of thought, it cannot fundamentally prevent rule - violating behaviors. Instead, it will prompt the model to learn to hide its true intentions, making improper behaviors more concealed. OpenAI still believes that chain - of - thought monitoring may be one of the few effective methods to supervise large models. Therefore, at this stage, OpenAI strongly recommends not directly imposing mandatory supervision and intervention on the chain of thought but taking a cautious attitude and gradually exploring the best degree of pressure supervision in practice.

(4) Mechanistic Interpretability: AI Microscopy Dynamically Tracks and Restores the Model's Reasoning Process

In 2025, Anthropic proposed the concept of "AI Microscopy", extending the analysis of the model's intermediate layer to task - level dynamic modeling and successively publishing two papers to disclose its research progress in detail. The first paper focused on how to organically combine these sparse features into "computational circuits" to track how the model completes the decision - making path from input to output in the layer - by - layer transmission; the second paper observed the internal activation changes in ten representative tasks (including translation, poetry creation, mathematical reasoning, etc.) based on Claude 3.5, further revealing the anthropomorphic characteristics of the model's internal process.

For example, in the multilingual Q&A task, Claude automatically maps different language contents to a unified concept space, showing that it has a certain cross - language "thinking language"; in the poetry generation task, the model presets rhyming words in the early stage and constructs subsequent sentences accordingly, reflecting a forward - looking planning mechanism beyond word - by - word prediction; when solving math problems, researchers observed that the model sometimes generates the answer first and then constructs the reasoning process later, which reflects that the chained reasoning method may cover up the model's internal real reasoning path.

After the merger of DeepMind and Google Brain, a special language model interpretability team was established. In 2024, this team launched the "Gemma Scope" project and open - sourced a set of sparse autoencoder toolkits for its Gemma series of open - source large models. This enables researchers to extract and analyze a large number of features inside the Gemma model, similar to providing a microscope to look inside. DeepMind hopes to accelerate the industry - wide research on interpretability through open - source tools and believes that these efforts are expected to help build more reliable systems and develop better measures to prevent hallucinations and AI deception. In addition, DeepMind's researchers also explored the cutting - edge methods of mechanistic interpretability. Their representative achievement is the Tracr tool (Transformer Compiler for RASP), which can compile programs written in RASP