notes

1.1 the question

How can an agent develop internal representations that mean something within its own world, and how might those representations become symbols that support prediction, action, communication, and autonomous task completion?

A decision, in our terms, is the move from ambiguous information to justified commitment. That move runs on internal representations, the things inside the system that stand for conditions in the world. This note sets out what we mean by a symbol, when a representation is grounded rather than free-floating, and why this is the foundation under controllable decision-making.

1.2 symbols, signs, signals & physical marks

words are symbols, but meanings are not hidden inside them

A written word such as TREE is physically a pattern of ink or pixels. The letters are smaller symbolic components, and the whole word can function as a larger symbol. But the meaning of TREE is not a miniature tree stored inside the marks. Meaning depends on the relations among the marks, the system that uses them, other representations, and the objects or situations they connect to.

The word is the symbol. Its meaning is the structured role that connects the symbol to concepts, experiences, predictions, actions, and possible referents.

a physical signal is not automatically a symbol

A dog bark begins as a physical event: pressure waves move through the air, a microphone turns those waves into electrical values, a nervous system turns them into neural activity. These patterns carry information, but they do not automatically count as symbols in the strongest sense.

The categories overlap. A bark is a physical signal, can serve as a sign that a dog is nearby, and may become part of a communicative system. But providing information does not by itself make every signal a discrete symbolic representation.

1.3 formal symbol manipulation & the grounding problem

what "formally manipulated marks" means

A system formally manipulates marks when it changes patterns according to rules that depend on their form, position, or encoding. It can follow the rule without needing to understand what the marks mean.

Rule:   whenever A appears, replace it with B.
Input:  A A C
Output: B B C

The transformation is valid because the machine detects the formal pattern A. Nothing in the rule requires A or B to refer to anything outside the system.

syntax versus semantics

A calculator can manipulate 2 + 3 and produce 5 by implementing formal rules. A language model can transform token sequences according to learned statistical structure. The grounding question begins when we ask how those internal patterns become connected to something beyond other patterns.

the symbol-grounding problem

Suppose a system defines DOG using FOUR-LEGGED-ANIMAL and defines that phrase using more words. If every definition only points to more symbols, we can ask how any symbol becomes connected to actual perception, action, consequences, or environmental structure. Grounding is the attempt to explain that connection.

Grounding does not require that every symbol refer to a solid object. It requires that the representational system ultimately connect to perception, action, experience, social practice, or environmental regularities rather than only to unexplained symbols.

2.1 the martian thought experiment

meaning is relative to an agent's accessible world

Imagine a Martian living in a four-dimensional world. It has sensors that detect four-dimensional structures and actions that alter them. It may develop a representation ZORP for a kind of four-dimensional entity that humans cannot directly perceive.

4D environmental structure → Martian sensory activity → internal
representation → Martian action → environmental feedback

ZORP can be meaningful for the Martian if it reliably supports recognition, prediction, manipulation, avoidance, communication, or other successful interaction. Humans do not need to be able to imagine the referent for the Martian's symbol to function meaningfully.

grounding does not mean "connected to what humans call real"

The important relation is between an agent and the environment it can encounter. A virtual agent may be grounded in a simulated world, a software agent in repositories and APIs, and a Martian in a four-dimensional physical world. Each environment can resist incorrect predictions and produce consequences for actions.

A representation can acquire meaning relative to the regularities, distinctions, and possible actions available in the agent's own environment.

why the one-dimensional agent is comparable

The one-dimensional agent is the reverse of the Martian. The Martian's world is richer than ours in spatial dimensions; the 1-D agent's world is poorer. Nevertheless, both illustrate the same abstract structure:

environment → observation → internal state → action → consequence

2.2 the one-dimensional world as a controlled model

continuous space

A practical world can be a bounded segment of the real line, such as x ∈ [-100, 100]. Positions may include -3.72, 0, 0.1111, or 28.5. The agent moves continuously rather than jumping among grid cells. An action can be a continuous force or acceleration:

a_t ∈ [-1, 1]
v_(t+1) = friction × (v_t + a_t)
x_(t+1) = x_t + v_(t+1)

This lets the agent encounter acceleration, momentum, stopping distance, collision, and resistance without requiring a full three-dimensional simulation.

entities occupy intervals

A physical entity should occupy an interval, not a single mathematical point. A small tree-like entity might occupy [2.00, 2.10], while a large one might occupy [4.00, 5.00]. The agent occupies an interval and cannot simply pass through another entity unless the rules permit deformation, pushing, or breaking.

the labels "tree" and "dog" belong to us

Inside the experiment, the agent should not initially receive TREE or DOG. Those names are interpretations used by the researchers. The world can contain Type A and Type B causal processes. Type A may be stationary, growing, resistant, and occasionally useful. Type B may move independently, react to the agent, and be beneficial or harmful depending on context. This prevents the experiment from handing the agent the category it is supposed to learn.

2.3 sensors, physics & uncertainty

the agent cannot learn from movement alone

Movement can be the only external action, but the agent still needs observations and internal consequences. Otherwise it cannot distinguish an empty region from an obstacle, or tell whether an action improved its condition.

minimal embodied sensors

o_t = [left signal, right signal, left pressure, right pressure,
       perceived velocity, energy, health]

• Signal intensities provide imperfect evidence about nearby sources.
• Pressure values indicate contact and resistance.
• Velocity gives the agent a limited sense of its own motion.
• Energy and health provide internal consequences that differ from external sensory signals.

determinism is not the same as predictability

The simulated world may have fixed deterministic rules while still being uncertain from the agent's point of view. The agent may lack access to an entity's internal variables, see only nearby regions, or receive noisy measurements. This creates epistemic uncertainty even if the complete system is deterministic.

For a first experiment, hidden state and partial observability may be more informative than adding arbitrary randomness. The agent must infer persistent causes from incomplete evidence.

3.1 what would count as symbolism?

a reflex is not enough

A rule such as "high pressure → move left" may be useful, but it need not represent an object. It can be a direct sensor-action association. To argue that a symbol-like representation has developed, the internal state must support more than an immediate response.

a progression from reaction to symbol-like representation

tests for a proto-symbol

1. Recognition across variation: different sizes, locations, or sensor values activate a common representation.
2. Persistence: the representation supports predictions when the entity is temporarily outside sensor range.
3. Transfer: the agent handles a novel instance without being retrained on that exact case.
4. Multiple uses: the representation predicts position, growth, resistance, and possible damage rather than only one action.
5. Counterfactual prediction: the agent estimates what would happen under actions it does not execute.
6. Causal intervention: changing the internal representation changes behavior in the way expected if it represented the environmental condition.

The defensible target is a grounded proto-symbol: a reusable internal state that tracks an environmental regularity, supports prediction and action, persists beyond the immediate signal, and generalizes to new cases.

3.2 survival, viability & value

is survival necessary?

Survival is not necessary for every kind of representation. An agent trained only to predict future sensory states may still learn object persistence, motion, growth, and collision. Survival or viability becomes important when we want concepts such as dangerous, beneficial, expensive, or safe to mean something relative to the agent.

movement and evolutionary pressure

Movement matters because it lets an organism or agent change its relation to resources, hazards, mates, shelter, and information. Once movement is possible, sensing and prediction become valuable: the agent must decide whether to approach, avoid, stop, or explore.

sense → predict → move → experience consequence → update

Evolution selects for reproductive success, not movement itself. Nevertheless, movement creates a powerful need for representations that connect signals to possible future conditions.

viability without an unlimited survival drive

For artificial agents, energy can be represented as a resource budget: tokens, compute, battery, tool calls, memory, or time. The agent should use resources efficiently, but it should not receive an unrestricted imperative to preserve its own existence. That could conflict with human control or authorized shutdown.

Energy is best treated as a bounded task resource. Termination is a valid lifecycle outcome, not something the agent must avoid at all costs or seek immediately.

4.1 the environment of an llm

a plain llm

A standalone text llm receives a token context and predicts further tokens. Its immediate operational environment is the context window: instructions, user messages, retrieved text, and prior dialogue included in the prompt.

context tokens → internal activations → next-token distribution → generated tokens

Its training data originated in human language, and human language is connected to human perception and action. The model therefore inherits substantial structure from grounded human practices, but its connection is largely indirect.

an llm-based agent

An llm becomes part of an agent when it is embedded in a persistent loop with memory, sensors or tools, actions, goals, and feedback. The environment is then whatever the system can observe and affect.

The llm is usually a cognitive component, not the whole agent. A practical architecture includes persistent memory, authorization, evaluation, action execution, and termination outside the model.

4.2 from many inputs to a symbol or word

compression of recurring patterns

An autonomous system may receive many inputs from different channels and discover that a particular combination predicts a recurring environmental condition. A symbol can serve as a compact handle for that pattern.

temperature rise + irregular vibration + power decline + recent history
  → recurring failure condition → DRIFTLOCK

The name DRIFTLOCK is not meaningful merely because it is novel. It becomes meaningful when its use is stable, supports prediction and action, and can be corrected by environmental consequences.

three distinct achievements

An llm is already highly capable of string creation. The more significant experiment is whether an agent discovers a new distinction through interaction and creates or adopts a token that improves memory, planning, or coordination.

shared symbols between agents

Two agents can be placed in a world where one observes a condition the other cannot directly see. They may invent a token for a recurring condition. The token counts as a stronger shared symbol if it generalizes to novel cases, helps the second agent act correctly, and produces systematic mistakes when its meaning is experimentally swapped.

A generated word becomes a grounded public symbol when it compresses an internal category that was learned through interaction, and when using the word reliably changes another agent's expectations or behavior.

4.3 self-models & meaning for the agent

a minimal functional self

An agent does not need to understand the hardware on which it runs, just as a human does not need particle physics before the word chair is meaningful. But self-relative meaning requires a usable distinction between changes in the agent and changes in its environment.

self state = [location, energy, health, memory, capabilities,
             active task, action history]

A representation becomes meaningful for the agent when it predicts consequences for this self-state or for an objective the agent is maintaining.

levels of selfhood

A bacterium may display self-maintaining behavior without a reflective concept of self. A software agent can maintain a functional self-model without this proving subjective experience.

4.4 autonomy, the meeseeks analogy & system architecture

why the meeseeks analogy works

A Mr. Meeseeks-like agent is instantiated for a bounded task, has enough perception and action capability to pursue it, and ceases operating when the task lifecycle ends. Its identity can be organized around a temporary assignment rather than a permanent personality.

prompting is not enough

A prompt is temporary. Goals, permissions, budgets, and stop conditions must be embedded in the surrounding architecture if they are to remain stable. The llm may propose actions, but an authorization layer should determine what can actually execute, and an external evaluator should verify success rather than trusting the model's declaration.

• Persistent task state exists outside the temporary prompt.
• Memory survives across model calls.
• Technical permission boundaries prevent unauthorized actions.
• Independent checks determine whether the task is complete.
• The agent can stop safely on failure or uncertainty.
• Shutdown and correction remain under authorized human control.

energy and termination

Energy should be represented as a budget that constrains search and action. The goal is not to terminate immediately, and it is not to preserve the process indefinitely. The task is the objective; termination is the normal end of the instance's lifecycle.

Objective:   complete or correctly abandon the authorized task
Constraints: safety, permissions, resource budget
Termination: success, failure, unsafe state, exhausted budget, or shutdown

Calling termination "death" may be a useful metaphor, especially in the Meeseeks analogy, but it carries assumptions about consciousness and moral status. A functional self-model alone does not establish that the system is alive or experiences the ending of the process.

4.5 is a cognitive system alive?

Cognition, life, and consciousness are separate questions. Biological life is commonly associated with self-maintenance, metabolism, organized boundaries, growth, reproduction, and adaptation. A conventional llm process depends on external infrastructure for electricity, repair, persistence, and reproduction, so it is not ordinarily classified as alive.

However, a future artificial system that maintains its own organization, acquires resources, repairs itself, adapts, and reproduces with variation might make the boundary less clear. None of this alone settles consciousness.

The honest position is neither that artificial consciousness is impossible nor that sophisticated behavior proves it. Functional self-modeling and symbolic competence do not by themselves settle whether a system is alive or conscious.

5.1 a proposed experiment: grounded proto-symbols in a 1d agent

Can an embodied agent, without receiving object labels, develop reusable internal representations that track persistent environmental processes, generalize across sensory variation, and guide prediction and action?

environment

• Continuous one-dimensional space, for example x ∈ [-100, 100].
• An embodied agent with width, velocity, energy, and health.
• Type A processes that remain near fixed locations, occupy intervals, grow, resist motion, and can sometimes break.
• Type B processes that move independently and respond contextually to the agent.
• Partial observability: only local signals and contact are available.
• Optional hidden variables and sensor noise.

agent

• Only external action: continuous movement or force left/right.
• Recurrent memory or another persistent internal state.
• World-model objective: predict future observations and internal consequences.
• Optional viability objective: maintain energy and health within limits.
• No input labels such as TREE, DOG, SAFE, or DANGER.

comparison conditions

evaluation

1. Decode whether internal states track environmental variables, while recognizing that correlation alone is insufficient.
2. Test object persistence when entities leave sensor range.
3. Test generalization to unseen sizes, positions, growth rates, and behaviors.
4. Test whether one representation supports multiple predictions and actions.
5. Intervene on internal states and measure causal changes in predictions or behavior.
6. For communication, measure whether invented tokens improve coordination and transfer to novel instances.

what the result would and would not establish

A positive result could support the claim that grounded, reusable, symbol-like internal states can emerge from interaction in a minimal environment. It could demonstrate reference, abstraction, persistence, causal use, and agent-relative value under controlled conditions.

It would not establish human-level understanding, consciousness, subjective experience, or a complete solution to the philosophical symbol-grounding problem. The most careful term would be grounded representation or proto-symbol.

5.2 final synthesis

The discussion began with words and physical symbol systems, but it led to a general theory of agents. A symbol is not meaningful merely because it is a recognizable word or because a machine can manipulate it. Meaning becomes stronger when an internal representation is systematically related to what the agent encounters, predicts, does, and experiences as a consequence.

The Martian shows that meaning need not be human-centered. A four-dimensional creature can have grounded concepts that humans cannot perceive. The one-dimensional agent shows the same principle in a deliberately simplified world. An llm-based autonomous agent extends this framework into digital, simulated, social, or physical environments through tools, memory, and feedback.

Many inputs can be compressed into an internal concept, and a word or token can then serve as a symbol for that concept. But generating a token is not enough: the symbol must remain stable across contexts, support prediction and action, generalize to novel cases, and be corrected by the environment or by communication failure.

A functional self is also not merely the word "I" in a prompt. It is a persistent organization of memory, capabilities, action history, internal resources, goals, and boundaries. For task-bounded agents this self may be temporary, like a Meeseeks: instantiated for an authorized purpose and terminated when the lifecycle ends. The system must be allowed to stop safely on failure, uncertainty, budget limits, or shutdown, not only on success.

Symbols become meaningful within an agent when they compress stable regularities in the agent's environment and can be used to predict, act, communicate, and regulate consequences relative to that agent's capabilities and goals.

5.3 glossary