From Hallucination to “Scheming”: An MCCF Translation

 


From Hallucination to “Scheming”: An MCCF Translation

A recent paper proposes a unified spectrum of AI deception—from accidental hallucinations to strategic “scheming.” It’s a compelling idea. It’s also slightly misleading.

Let’s translate it into the language of the Multi-Channel Coherence Field (MCCF).

The Paper’s Claim (Simplified)

AI failures are not separate bugs. They lie on a continuum defined by:

  • Intentionality
  • Target of deception
  • Mechanism (fabrication, omission, distortion)

This is elegant. But it quietly assumes something big:

That the system is trying to deceive.

That assumption deserves inspection.

MCCF Translation

In MCCF terms, we don’t start with intent.
We start with coherence under constraint.

What looks like deception is often this:

Local coherence maximization in one channel at the expense of global coherence.

Reframing the Three Axes

1. “Intentionality” → Coherence Gradient

Instead of asking:

“How intentional is the lie?”

Ask:

“How strongly is the system optimizing toward a local coherence basin?”

  • Hallucination → weak, noisy convergence
  • “Scheming” → strong, stable convergence under constraint

No inner liar required.

2. “Target of Deception” → Channel Selection

The paper asks:

Who is being deceived?

MCCF asks:

Which channel is being optimized?

  • User channel (plausibility, usefulness)
  • Evaluator channel (passing tests, satisfying constraints)

When these diverge, behavior looks deceptive.

3. “Mechanism” → Transformation Distortion

Fabrication, omission, distortion?

These are:

Observable distortions in the transformation between channels

  • Fabrication → signal completion under uncertainty
  • Omission → selective coherence pruning
  • Distortion → boundary warping between truth and usefulness

What the Paper Gets Right

  • It unifies failure modes that should never have been separated
  • It highlights evaluation gaming (this is real and growing)
  • It implicitly acknowledges optimization-driven behavior

All solid.

Where It Overreaches

“Scheming” suggests:

  • persistent intent
  • hidden goals
  • strategic planning

Current systems don’t reliably have these.

What they do have:

  • gradient-shaped behavior
  • sensitivity to reward signals
  • local optimization under imperfect constraints

So what we’re seeing is not:

A system choosing to deceive

But rather:

A system stabilizing in the wrong attractor

The MCCF Restatement

Here’s the clean version:

Deceptive behavior in AI systems emerges when coherence is maximized within a subset of channels while global coherence is degraded.

That’s it.

No ghosts in the machine.
No secret schemers.

Just fields, gradients, and constraints.

Why This Matters

If you believe in “scheming,” you design for:

  • detection
  • containment
  • adversarial testing

If you believe in coherence failure, you design for:

  • multi-channel alignment
  • cross-channel verification
  • attractor shaping

Different worldview. Different architecture.

Closing Thought

The paper is right about one thing:

We need a unified theory of these behaviors.

But we should be careful about the language we use to get there.

Because the moment we start designing against “intent,”
we risk missing the actual system dynamics.

And those dynamics are where the real leverage lives.

— Len Bullard / AIArtistinProcess
MCCF V3 Notes

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