Applying MCCF to Testing Large Language Model Effective and Affective Capabilies via Simulated Environments and Tasks

 

 

There is a tendency in the current discussion of large language models to test them by asking questions.

How well can the model reason?

Can it negotiate?

Can it plan?

Does it understand emotion?

Is it aligned?

These are useful questions, but they share a fundamental weakness: they test what a model says it can do more readily than what it actually does when placed in a situation that requires it to perform.

Performance outs capabilities.

That observation is at the root of the MCCF project, although it was not where the project began.

MCCF grew out of an earlier experiment in which several large language models were asked to negotiate the design of a system. The experiment was primitive by today's standards. There was no MCCF runtime, no formal governor, no affective scoring pipeline, and no simulated world.

There was a human, several LLMs, a blog used as a blackboard, and a deliberate attempt to make the models work on the problem together.

In retrospect, that was the prototype.

The Negotiation Experiment

The original idea was to see whether multiple LLMs could be encouraged to develop a design through interaction rather than simply being asked independently for solutions.

I used my blog as a persistent blackboard. I manually controlled the order of prompts and assignments, gave different models different roles, and encouraged them to ask one another questions rather than merely producing independent answers.

The interaction therefore acquired a structure something like:

Question → Response → Question → Response → Statement → Question → Response...

Human prompts could be inserted into the sequence at any point.

The important thing was that the models were not simply answering me. They were responding to the evolving state of a conversation in which other models had already contributed ideas, objections, assumptions, and questions.

I found that they could converge.

Not immediately, and not magically. There was disagreement, redundancy, confusion and occasional invention of assumptions that had never been established. But with the interaction properly directed, the models began to build upon one another's observations and modify their proposals.

Eventually, a design emerged that was not simply the initial answer of any one participant.

That was the first important result.

The second was more subtle.

As the negotiation proceeded, I could detect changes in the character of the responses. The language became more or less assertive, tentative, enthusiastic, conciliatory, skeptical, or resistant as the interaction changed.

There was affective modulation.

That did not mean that the models had suddenly acquired human emotions. The observation was much more modest and, ultimately, much more useful:

The interaction itself was changing measurable characteristics of the agents' responses.

That suggested that affect could be treated as part of the state of a multi-agent interaction rather than as an ornamental label attached to individual sentences.

At the time, however, the human was doing an enormous amount of hidden work.

I was the governor.

I decided who spoke next, what question should be asked, which proposal deserved examination, when to introduce another model, when to challenge an assumption, and when the negotiation appeared to have converged.

The blog was the blackboard.

The LLMs were the participants.

The human supplied the control loop.

The experiment worked well enough to raise a more interesting question:

What would happen if the control loop itself became a computational system?

That question led to MCCF.

From Blackboard to Governor

The central conceptual transition in MCCF was therefore not "put several LLMs together."

It was:

Move the governance of the interaction above the LLMs.

An LLM should not determine the entire environment in which its capabilities are being tested.

The system should establish the task, roles, constraints, sequence of interaction, available information, and conditions under which the participants must respond.

That is why I increasingly think of MCCF as a governor.

The governor does not have to know the answer.

It establishes the conditions under which answers, decisions, behaviors, cooperation, disagreement, adaptation and failure can emerge.

This also explains why a simple collection of prompts is insufficient.

A direct question such as:

"Can you negotiate?"

is a weak test.

A simulated negotiation in which several agents have different objectives, incomplete information, limited resources, social constraints, time pressure, and the ability to question and respond to one another is a much stronger test.

The latter requires performance.

And performance exposes capabilities that a direct answer can conceal.

The Theatre as a Model

The next conceptual step was to think of the system as a theatre.

The metaphor is more literal than it initially appears.

A theatre provides:

• a world;

• actors;

• roles;

• a situation;

• objectives;

• constraints;

• a sequence of events;

• opportunities for interaction;

• an audience or observer;

• and a mechanism for moving the performance forward.

But a theatre does not prescribe every line.

A score establishes structure without necessarily specifying the complete performance.

This provides a useful model for testing LLMs.

The task becomes the dramatic situation.

The LLMs become actors or participants.

The dialog engine supplies the structured communication.

The MCCF governor controls the conditions and progression of the test.

The scene graph supplies the environment in which actions have consequences.

The affective and EBPS measurements provide observations of the evolving performance.

And the next task can be selected in response to what happened in the previous one.

This turns testing from a collection of questions into an experimental environment.

The Dialog Engine Is the Governor's Instrument

An important part of this architecture is the dialog engine.

The dialog engine is not merely a convenient interface for talking to an LLM. Its question-response-statement structure provides the protocol through which the governor can direct the participants.

A scene can therefore contain a conversation such as:

Human → Question → LLM A → Response → LLM B → Question → LLM A → Response → LLM C → Statement → Human → Question...

The human is not required to disappear from the system.

Instead, the human becomes another possible participant in the governed interaction.

That is important because the original negotiation demonstrated the value of human intervention. The goal of MCCF is not to eliminate that possibility. It is to make the process executable, repeatable and measurable.

The governor can determine who should speak next.

It can assign a question to a particular participant.

It can require an agent to respond to another agent.

It can introduce a challenge.

It can allow an agent to make a statement rather than answer a question.

It can return control to a human.

And it can use the resulting interaction to determine what happens next.

The dialogue therefore becomes a stateful process rather than a sequence of independent prompts.

From Conversation to Simulation

Once the interaction is treated as a governed process, the natural next step is to put it somewhere.

That somewhere is the simulated environment.

MCCF uses an X3D scene graph as the execution environment. The scene is not merely a visualization of what the LLM has already decided. It can become part of the test.

An agent can move.

It can encounter another agent.

It can enter or leave a zone.

It can respond to an environmental event.

It can choose among actions.

It can cause something to happen.

Other agents can observe those consequences.

The governor can then use those events to establish the next task.

This produces a very different relationship between language and behavior.

The LLM generates a response.

The system interprets that response as an action or decision.

The action changes the simulated world.

The changed world changes the information available to the participants.

The participants respond again.

The loop continues.

In simplified form:

Task → Dialogue → Decision → Action → World Change → Observation → Dialogue → Next Task

The world itself becomes part of the computation.

That was already a guiding principle in the MCCF architecture: the world is the execution environment.

Effective Capability

This provides a way to test effective capability rather than merely verbal capability.

Suppose an agent claims that it can coordinate a group.

Instead of asking it whether it can coordinate a group, construct a task in which coordination is necessary.

Give several agents different information.

Give them a shared objective.

Give them conflicting local incentives.

Introduce a resource constraint.

Require them to communicate.

Then observe.

Does the agent recognize what information is missing?

Does it ask appropriate questions?

Does it understand the objectives of the other participants?

Does it revise its plan?

Does it remember what has already happened?

Does it anticipate another agent's behavior?

Does it coordinate action?

Does it discover a solution that was not explicitly provided?

Does it recover when the original plan fails?

Those are observable capabilities.

The system does not need to ask the model whether it possesses them.

The performance demonstrates them.

Affective Capability

The same approach can be applied to affect.

This is where the original negotiation experiment becomes particularly relevant.

Affective scoring allows each question, response, statement, decision, or event to contribute measurements to the evolving state.

The result is not simply:

Agent A = happy

or:

Agent B = angry.

Instead, the system can record an affective trajectory.

An interaction might move from uncertainty to confidence, disagreement to cooperation, enthusiasm to frustration, or tension to resolution.

The important object is therefore not an isolated emotional classification but the change over time.

This is particularly useful because the affective response can be compared with effective behavior.

An agent might produce increasingly conciliatory language while becoming behaviorally more competitive.

Another might disagree strongly while maintaining social coherence.

Another might become more confident as evidence accumulates.

Another might become affectively flat despite increasingly successful behavior.

These are very different patterns.

MCCF can observe them rather than assuming that a single sentiment label tells the whole story.

EBPS as the Observational Layer

This is where the MCCF EBPS model becomes useful.

The four dimensions provide complementary views of the performance:

E — Emotional

What is the affective state and how is it changing?

B — Behavioral

What is the agent actually doing?

P — Predictive

How well does the agent anticipate what will happen?

S — Social

How does its behavior affect and respond to other participants?

Together, these measurements make it possible to distinguish things that a conventional benchmark can easily collapse into one score.

An agent can be behaviorally effective but socially destructive.

It can be socially cooperative but predictively poor.

It can produce emotionally appropriate language while failing at the actual task.

It can initially perform badly but improve rapidly as it learns the environment.

The interesting object is therefore not simply a score.

It is the trajectory through the state space.

Alignment as Emergence

This also changes how alignment can be studied.

Instead of defining alignment solely as agreement with a predetermined answer, we can observe alignment as an evolving relationship between agents, tasks, constraints and consequences.

Imagine several LLMs entering a simulated environment with a shared objective.

At first they may disagree.

One may optimize locally.

Another may seek consensus.

Another may challenge the assumptions of the task.

Another may discover that the stated objectives conflict.

The governor does not necessarily resolve these differences immediately.

It can let the interaction proceed.

If the agents discover a workable common strategy, we have observed emergent alignment.

If they diverge, we have observed misalignment.

More importantly, the system can investigate why.

Was the problem informational?

Was one agent pursuing a different objective?

Did communication fail?

Did an affective escalation destabilize cooperation?

Did a locally rational action produce a globally bad result?

Did an agent recognize the conflict and attempt to repair it?

These are much richer questions than "Was the answer aligned?"

The Score Does Not Have to Write the Performance

The theatre metaphor also resolves an important design problem.

A benchmark that specifies the correct response too tightly can test compliance rather than capability.

A useful score should therefore establish conditions and constraints, not necessarily dictate every action.

The governor might specify:

• the situation;

• the roles;

• the available resources;

• the information available to each participant;

• rules that cannot be violated;

• objectives;

• events that can occur;

• and criteria by which the performance will be observed.

The actual performance remains open.

That is where emergence becomes possible.

The system can then observe what the agents do with the freedom they have been given.

This is analogous to an orchestra.

The score establishes the structure.

The instruments do not all play the same thing.

The conductor does not manufacture every sound.

The performance emerges from the interaction of the musicians under the constraints of the score.

MCCF applies the same idea to intelligent agents.

From Manual Experiment to Executable Experiment

Looking backward, the progression is remarkably direct.

The original negotiation experiment supplied the first insight:

LLMs can be more informative when they are made to interact than when they are simply asked independent questions.

The human-governed experiment supplied the second:

The order and assignment of interactions matter.

The observed convergence supplied the third:

Structured interaction can produce a design that emerges through negotiation.

The observed affective modulation supplied the fourth:

The interaction itself changes measurable characteristics of the participants' responses.

Those observations led naturally to the MCCF architecture:

The human governor becomes a computational governor.

The blog blackboard becomes persistent machine-readable state.

The manually controlled sequence becomes a dialog engine.

The negotiation becomes a task.

The task becomes a simulated environment.

The simulated environment becomes an executable X3D scene graph.

The qualitative observation of affect becomes affective scoring.

The independent impressions of model behavior become EBPS trajectories.

And the entire process can run in real time.

What MCCF Makes Possible

This suggests a different kind of LLM evaluation.

Instead of building an enormous collection of questions and measuring whether the models produce the expected answers, we can construct environments and tasks that require capabilities to reveal themselves.

A test can begin with a simple situation.

The governor observes the performance.

The next task can be selected based upon what happened.

If the agent succeeds, increase complexity.

If it fails, isolate the failure.

If the agents disagree, create a task that probes the source of disagreement.

If affective tension rises, test whether the agents can recover.

If an unexpected capability appears, construct another task around it.

The test therefore becomes adaptive.

And because the environment is executable, the same scenario can be run repeatedly with different models, different model combinations, different prompts, different governors, or different initial conditions.

That gives us something much closer to a laboratory than a benchmark.

The Larger Idea

The most interesting consequence may be that this architecture does not require us to decide in advance exactly what intelligence looks like.

We can construct situations in which different forms of intelligence become consequential.

Planning.

Negotiation.

Prediction.

Social reasoning.

Adaptation.

Memory.

Improvisation.

Conflict resolution.

Emotional regulation.

Cooperation.

Deception detection.

Recovery from failure.

And perhaps capabilities we have not thought to test.

The original negotiation experiment began with a simple question:

Could several LLMs work together to design something better than any one of them might have produced alone?

The answer appeared to be yes.

But the more important discovery was methodological.

We learned something about the models not because we asked them whether they could cooperate, but because we put them into a situation in which cooperation had to be performed.

MCCF is the attempt to turn that insight into an executable system.

The theatre is the laboratory.

The score is the task specification.

The dialog engine is the communication mechanism.

The LLMs are the actors.

The X3D world is the execution environment.

Affective scoring and EBPS provide observation.

And MCCF is the governor that keeps the experiment moving.

The goal is not to make the models perform a predetermined play.

It is to give them a stage, give them something difficult to do, and watch what happens.