RPCS-1

The Five Receiver Primitives

Every RPCS-1 recommendation is computed from five receiver primitives. Each primitive is a scalar in [0, 100], derived deterministically from your environment inputs. Together they characterise the receiver profile that is structurally stable in your agent's environment.

TI

Temporal Integration

[0, 100]

Driven by

Environmental entropy (Matching Principle: TI ~ 1/H)

Maps to

max_tokens, context_strategy

Low value

Short attention window — agent processes recent signals only (frequent_grounding strategy)

High value

Long attention window — agent integrates deep history (long_window strategy)

Example

Chaotic environment (H=0.95) → TI ≈ 10. Stable environment (H=0.2) → TI ≈ 77.

SG

Signal Gain

[0, 100]

Driven by

Stakes (primary), environmental predictability (adjustment)

Maps to

temperature (inversely), top_p

Low value

Low amplification — agent is conservative, less responsive to weak signals. Maps to LOWER temperature.

High value

High amplification — agent responds strongly to all signals. Maps to HIGHER temperature.

Example

catastrophic stakes → SG ≈ 20 → temperature ≈ 0.8 (more cautious sampling). low stakes → SG ≈ 75 → temperature ≈ 0.25.

FT

Filtering Threshold

[0, 100]

Driven by

Stakes (primary), commitment style (adjustment)

Maps to

tool_use_strategy, system_prompt_additions

Low value

Low gating — agent acts readily on incoming signals (aggressive tool use).

High value

High gating — agent verifies before acting (explicit_confirmation tool strategy).

Example

catastrophic stakes + cautious style → FT = 95 → explicit_confirmation + high_stakes system prompt.

UE

Update Elasticity

[0, 100]

Driven by

Environmental entropy (primary), context relevance (adjustment)

Maps to

retry_strategy

Low value

Low elasticity — agent resists revising its model (minimal retry).

High value

High elasticity — agent updates readily on new information (aggressive retry).

Example

chaotic entropy + short context → UE = 92 → aggressive retry. stable + long context → UE ≈ 17 → minimal retry.

AR

Ambiguity Resolution

[0, 100]

Driven by

Commitment style (primary), stakes (adjustment)

Maps to

tool_use_strategy (combined with FT)

Low value

Low resolution — agent defers when uncertain, asks for clarification (ambiguity_caution prompt).

High value

High resolution — agent commits under uncertainty, resolves ambiguity aggressively.

Example

cautious + catastrophic → AR = 10. decisive + low → AR = 75.