RPCS-1

AI-human collaboration

Mismatch Is Destructive Many-To-One Collapse

Humans and AIs are both compressing reality. The collaboration problem is not always that either side is wrong. Often, each side preserves different distinctions. RPCS-1 treats that as a receiver/environment matching problem.

The Shift

Narrow product question

"What temperature should I use?"

Larger collaboration question

"Why are the human and AI misunderstanding each other?"

Core Frame

IMM says observers do not access reality directly. They compress many possible environmental states into receiver states. Human-AI mismatch occurs when that compression destroys a distinction one side needed preserved.

Mismatch = destructive many-to-one collapse

Collaboration improves when:
relevant distinctions preserved / compression cost increases

Different Distinctions Preserved

Human may preserve

Human preserves emotional context

AI may preserve

AI preserves logical structure

Human may preserve

Human says one thing and implies another

AI may preserve

AI preserves literal meaning

Human may preserve

AI explains

AI may preserve

Human hears criticism

Human may preserve

Human wants recognition first

AI may preserve

AI jumps to optimization

Working Matching Score

This is not a finished theorem. It is a practical score for inspecting a conversation: did the interaction preserve the distinctions needed for the next successful move?

M(f) = Relevant Distinctions Preserved / Compression Cost

Architecture For Collaboration

1. Observation

Capture the interaction before judging it.

  • - Prompt
  • - Response
  • - Context
  • - User corrections

2. Collapse Analysis

Ask which distinctions were removed by compression.

  • - What distinctions did the AI remove?
  • - What distinctions did the human expect preserved?
  • - Which missing distinction changed the meaning?

3. Matching Analysis

Estimate whether the compression preserved what mattered.

  • - Relevant distinctions preserved
  • - Compression cost
  • - Mismatch severity

4. Adaptation

Adjust the receiver gates in operational order.

  • - FT
  • - TI
  • - AR
  • - SG
  • - UE

5. Shared Representation

Translate the mismatch into a form both sides can use.

  • - The AI interpreted X.
  • - The human intended Y.
  • - The mismatch occurred because distinction Z was collapsed.

Adaptation Gate Order

FT → TI → AR → SG → UE

Filter noise first. Integrate over time. Resolve ambiguity. Amplify only the interpreted signal. Then update or act.

Where This Leads

The tuner remains useful, but it becomes one layer inside a larger interface theory: understand the match between minds, identify destructive collapse, and generate a shared representation both human and AI can act from.

Read the IMM primerRun a support example