RPCS-1

AI-readable IMM primer

IMM → RPCS-1 → AI Agent Pre-Tuning

IMM treats observation as compression: a receiver cannot carry every environmental distinction forward. RPCS-1 turns that idea into five tuning gates for deployed agents.

One-Pass Summary

IMM -> many-to-one collapse -> receiver/environment matching
    -> five gates -> AI pre-tuning

FT -> TI -> AR -> SG -> UE

What the framework says

Matching happens when the receiver preserves the distinctions needed for the task. In practice that means the environment determines how much history to keep, how much to filter, how quickly to commit, and how readily to update.

The five gates

FT

Filtering Threshold

Filter noise first.

TI

Temporal Integration

Integrate over time.

AR

Ambiguity Resolution

Resolve ambiguity.

SG

Signal Gain

Amplify only the interpreted signal.

UE

Update Elasticity

Then update or act.

Failure modes

oscillation

The receiver keeps revisiting the same evidence and cannot settle.

overload

Too much signal gets through and weak evidence drives action.

freeze

The receiver resists updates and stalls when it should act.

mismatch

The receiver profile does not preserve the distinctions the task needs.

Why it matters for AI agents

A support copilot under live queue pressure needs different filtering, context, and commitment behavior than a stable research agent. RPCS-1 is the pre-tuning layer that translates operating conditions into a receiver profile before teams reach for prompt edits or blind parameter sweeps.

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