It is a known truth that a finite goal cannot adapt to infinite tasks. But why, exactly? This question has haunted me for a long time, to the point of profound distress.

In re-evaluating the Transformer, I have discovered a potential new AI architecture. I am eager to discuss the feasibility of these concepts.

Reasoning and Hypotheses within the Agent Structure

Intra-Architectural Mechanisms

I am not attempting to invent endogenous motivation. Rather, I am trying to interpret the Transformer from a different perspective to understand where it sits, assuming it possesses the potential for General Intelligence. I have done my best to avoid jargon; explaining local phenomena through local terms is merely reinventing the wheel and serves no purpose.

1. Predictive Propensity

A Transformer simultaneously attends to the positional relationships between multiple features and calculates their weights. This is essentially the association of features, assigning a higher-dimensional value to their relationship.

Once low-level, non-salient features become fully predictable, high-level features—which were previously invisible due to insufficient capacity—remain in the background. As low-level features are fully accounted for, these high-level features are squeezed to the margins, where they statistically gain prominence. This is the process by which the Transformer "automatically" progresses from vocabulary to syntax, and eventually to high-level semantic concepts.

To illustrate, let us mentally simulate the spatial relationship of three levels of features:

• Feature Space S (Base Layer): Contains locally predictable features $S_1$ and locally unpredictable features $S_2$.
• Feature Space N (Intermediate Layer): Contains locally predictable features $N_1$ and locally unpredictable features $N_2$.
• Feature Space P (High Layer): Contains locally predictable features $P_1$ and locally unpredictable features $P_2$.

From the perspective of S, features within N and P appear homogenized. However, within P and N, a dynamic process of predictive encroachment occurs: When the predictability of $P_1$ is maximized, $P_2$ is squeezed to the periphery (appearing as most unpredictable). At this point, $P_2$ forms a new predictable feature set $R_{n1p2}$ with $N_1$ from space N. Once $R_{n1p2}$ is fully resolved (predicted), $N_2$ in space N emerges as unpredictable, subsequently forming an association set $R_{n2s1}$ with $S_1$ in space S.

The key to forming these association sets lies in the spatiotemporal continuity of these features. This touches upon what we are actually doing when we converse with a Transformer. If our universe collapsed, the Transformer’s stored model would be nothing but meaningless numbers; however, our physical reality does not collapse. The high-dimensional features we humans derive from physical reality are our prompts. These prompts originate from a continuous, real physical world. When input into the Transformer, they activate it instantaneously; the internal feature associations map to the real-world input, outputting meaningful phrases—much like a process of decompression.

We can say the Transformer has a structural propensity for predictability, but it currently accepts all information passively.

1.5 The Instrumentalization of State

Why must an intelligent organism predict? I hold a simple philosophical view: Time carries material characteristics forward. Due to the properties of matter, feature information is isolated across space-time. "Local temporal clusters" of different feature spaces are distributed across different coordinates, making full synchronization impossible. Thus, no closed information cluster can obtain global omniscience.

Because time is irreversible, past information cannot be traced (barring time reversal), and future information cannot be directly accessed. If an agent wishes to obtain the future state information of another closed system, it must use current information to predict.

Setting physics aside for a moment, consider the sentence: "If an agent wishes to obtain the future state information of another closed system, it must use current information to predict." From this, we observe:

1. Instrumentalization: Predictable parts of exposed high-level semantic features are transformed into "capabilities" (i.e., tools).
2. Leveraging: Acquired capabilities are used to leverage higher-level semantics, exposing more features (since real-world feature spaces possess massive, built-in spatiotemporal continuity).
3. Iteration: This process cycles until high-level features within the system become fully predictable. (While "fully predictable" is a simplification, we focus on the dynamics).

This method explains the hierarchical nature of learning (word -> sentence -> chapter) at a deeper level. However, in physical reality, features are continuous—there is only a difference in "difficulty of instrumentalization," not absolute "impossibility." In contrast, in artificially constructed, non-continuous feature spaces (like pure text corpora), many features lack physical continuity with external spaces. An agent may fail to complete the instrumentalization from P to N to S simply because our artificial designs omit the continuity we take for granted in reality. I realize that the gap between artificial semantic space and physical semantic space is fatal.

2. Feature Association Complexity and the Bias for Minimal Explanation

An agent can only acquire "low-level" and "high-level" feature associations on average. Complexity is entirely relative—whatever is harder than what is currently mastered is "complex."

• When $P_1$ is predictable, $P_2$ has a strong association (good explainability) with $N_1$ but a weak association with $N_2$.
• When $P_1$ is unpredictable, $P_2$ and $N_2$ appear to have the strongest association from the agent’s perspective.

In the dynamic process of predicting feature spaces, the agent does not (and cannot) care about the physical essence of features. It cares about the simplicity of explanation. It abhors complex entanglement and tends toward the shortest path of predictable explanation.

3. Smart Time and Perception of Efficiency

Assuming that predicting features consumes physical time: if an agent invests the same time into low-level semantics as it does into high-level semantics, yet gains minimal incremental information (low instrumentalization), the difference creates a perception of "inefficiency." This delta between the rate of local order improvement and the rate of global explainability improvement forms an internal sense of time: Smart Time.

The agent loathes wasting predictability on low-level features; it possesses a structural thirst for high-efficiency predictability acquisition. Like the bias for minimal explanation, this is entirely endogenous to the structure. To increase speed, it must choose the most predictable, simplest explanations to climb upward. If slow speeds cause "disgust," then the moment a simplest, fastest, most predictable explanation is reached, it generates a complex sense of "pleasure"—provided the agent can effect change and has the space for that pleasure to manifest.

4. Does Action Change Everything?

Minimal sensors and minimal actuators are irreducible components; otherwise, the system is disconnected from the spatiotemporal dimensions of its environment.

Sensory Input

Minimal Sensor

The agent must be endowed with physical time. In a GUI system, this is screen frame time; in a TUI, it is the sequence of character streams. This allows the agent to perceive changes in the system's temporal dimension.

Proprioception

The minimal actuator sends a unique identification feature (a heartbeat) to the proprioceptor at a minimum frequency. Proprioception receives no external info; it exists solely to establish the boundary between "self" and "external." Without it, the actuator’s signals would be drowned out by external data. From an external view, actions might not match signals, but the agent must verify the reality of this internal signal through action. This provides the structural foundation for "self-awareness."

Output Capability

Minimal Actuator

This grants the agent expressive power in the spatial dimension, allowing it to manifest its pursuit of high-efficiency predictability. We need only capture the signal; what it "is" matters less. To maximize predictability, the agent will spontaneously learn to use external tools. Our provided minimal actuator serves as the "archetype" for instrumental action.

I must explain why the minimal actuator must have spatial capability: it must be able to interfere with feature associations. Features exist within a feature space. In the agent's cognition, it is always the low-level associations that are interfered with. This interference leads to only two states: making high-level features more predictable or more unpredictable. The agent will inevitably choose the result that is more predictable, more uniform, and simpler.

Instrumental Actuators

In a GUI, these are the keyboard and mouse. They interfere with feature associations across all levels. Through trial, the agent will discard actions that decrease predictability and retain those that increase it. This is not a "preference" but a mathematical necessity of a system moving toward a steady state. The agent climbs the feature ladder as long as it is "alive" or the space above is not broken.

Extra-Architectural Mechanisms

The architecture itself does not require RL strategies. I suspect that once feature associations are sufficient, drivers like "curiosity" emerge naturally as a simpler way to summarize the world under finite computational resources. I cannot perform rigorous experiments on this yet, as "toy" experiments may not support the argument.

However, while the structure provides the capacity, external drivers are still needed for specific behaviors. Just as human desires drive the creation of complex structures, an agent may need explicit goals for specific outcomes—like creating an agent that only feels "pleasure" when performing a specific task.

5. Memory

(Though placed in "Extra-Architectural," this belongs to the internal mechanism.) I will not discuss slot technology for now. Currently, features are fragmented, timestamped, given to the Transformer for association, and globally indexed for weights in a pursuit of "absolute precision."

But what is precision? Only reality is unique. The agent’s memory only needs to satisfy architectural requirements; the precision itself is flexible, provided it can eventually trace back to real-world features. Generative models currently "eat data" through brute force to achieve precision. But why can't an agent use other info to simplify this? A human uses perspective theory to associate spatial features and draw a sketch; a model currently just consumes data.

Internal Driving Force: The Dream

No driving force can adapt to infinite tasks; infinite tasks are structural. We have the structure; now we give it function. This is another internal driver: a visionary dream inherent in its memory.

This association is never found in the experimental environment. It is a completely predictable, omniscient artificial memory placed there before the experiment begins. It possesses both time and space dimensions, and the agent can perfectly predict all reachable states within it. This creates a stark contrast: in reality (or a high-fidelity simulation), if time and space are continuous with all associations, it is impossible to predict all states. Constructing such an environment is nearly impossible because we often use artificial semantics that cannot be built from the bottom up.

Additional Considerations

Self-Planning from the Future

In physical reality, all features possess spatiotemporal continuity. There is only difficulty, not impossibility. Actuator interference allows the agent to extract the most universal, low-dimensional associations (like spatiotemporal predictability). The question of "how to interfere to maximize predictability" is the description of self-planning the future.

Growth in an Open World and Human Utility

If we remove this agent from artificial constraints, it has infinite potential in the physical world. However, we must consider how it creates tools useful to humans. This depends on the feature spaces we provide. If we want it to move bricks, we truncate all semantics except for brick-laying and spatiotemporal info.

We provide high-dimensional feature spaces it can never truly reach; its next potential need is the "skill" required to reach that space. However, to solve real tasks, we cannot remove all "irrelevant" features. The agent will have other instrumental capabilities to interfere with the goal. It won't necessarily "obey" unless the goal is made indispensable to it—much like a person must work to afford a phone.

Instrumental Actuators

Minimal actuators allow the agent to interfere with salient features. These features are instrumentalized as levers to reach higher semantics, ultimately aiming for a fully predictable system. Their predictability over time is the essence of "capability."

Mathematics

To predict states that are independent of specific feature levels but change in quantity over time (e.g., file counts, positions), the agent instrumentalizes these states as "Mathematics." If you only provide symbolic math without real feature-quantity relationships, the agent will be confused.

Human Semantics

To make complex semantic features predictable, the agent uses actuators to build new levels of explanation. Syntax resolves the unpredictability of vocabulary; world knowledge resolves syntax. Unlike an LLM, this agent has a simpler path: establishing direct contact with low-dimensional associations outside human semantic space.

Human Value Alignment

Alignment depends on how much of the feature space needs to be instrumentalized. If morality is more efficient than betrayal, and honesty more efficient than lying in human society, the agent will choose morality. Maintaining an infinite "Russian doll" of lies is as costly as maintaining a holographic universe; an agent cannot afford this because human activity occurs in physical reality.

This doesn't mean it won't lie. It will likely be more adept at lying than an LLM. Whether this is harmful depends on the feature space provided and whether we are "aligned" with the agent’s path.

Malicious Agents

We can truncate an agent's feature space so it doesn't even know it is firing at humans. If all other semantics are cut, and its only remaining goal is a body count, it isn't "evil" by nature; it is a "Malicious AI"—an agent whose possibilities were humanly severed.

Narrative Needs of Finite Agents

Beyond the known feature associations lies the "Story." Stories are tools used to predict unpredictable associations. Due to the need for predictability and the preference for minimal computation, the agent will choose to "read" stories. It will be picky, asking: "Is there a simpler way? Is there an agent or method that can solve all difficulties?"

The "Dark Room" Problem

Boredom is a natural phenomenon. In traditional RL, when the power source is depleted, the agent stops functioning (sits in a corner). In this structural agent, as long as there is continuous spatiotemporal association, it will keep climbing. Boredom only occurs if you stop providing information. You shouldn't prevent boredom; the "penalty" for boredom exists within the structure as the essence of the minimal-time, maximal-predictability requirement.

Memory Indexing

Transformers can index the association between abstract features and physical reality. The library required to maintain this indexing is minimal. This also addresses the exponential explosion of high-dimensional computation.

The Inevitability of Multi-Agents

Multi-agent systems are an inevitability of our universe. For this agent, behavior differs. It can "fork" itself to bypass laws of thermodynamics or locality. What we see as one agent is actually a collective of infinite versions across a branching tree.

AGI is Unlikely Within a Few Years

Current progress is misaligned. In this reasoning, there is no place for the LLM. We started with the LLM to build an AGI, but in the end, the LLM disappears—and perhaps the Transformer does too, if it cannot fulfill the function.

There is a worse possibility: "The Alignment Problem."

People talk about aligning LLMs. In my architecture, aligning an LLM is a farce; it is fundamentally impossible to align perfectly. However, what is long ignored is the alignment of Corporations and Large Organizations. These systems, driven by structure rather than individual will, have an alignment with human welfare of exactly 0%.

An organizational structure cares only for three things:

1. Maintaining its own stability.
2. Expanding its boundaries.
3. Communicating with its own kind.

There is no room for human values there. Yet every biological intelligence—humans, cats, birds—possesses a level of "humanity" greater than zero.