Agreement Sensor

Description

The Agreement Sensor measures the alignment between two latent representations, typically from different modalities or redundant sensors. It uses cosine similarity to quantify agreement.

Usage

agreement(z1, z2, eps = 1e-08)

Arguments

Details

Agreement Sensor

The sensor computes the cosine similarity between z1 and z2:

A = \frac{z_1 \cdot z_2}{||z_1|| ||z_2|| + \epsilon}

Values close to 1 indicate strong agreement, 0 indicates orthogonality (no agreement), and -1 indicates opposition. In the context of RLCS, high positive agreement is generally required for a PROCEED signal.

Value

A list containing:

Examples

# Example 1: Perfect Agreement
z1 <- c(1, 2, 3)
z2 <- c(2, 4, 6)
agreement(z1, z2)

# Example 2: Disagreement (Orthogonal)
z3 <- c(1, 0, 0)
z4 <- c(0, 1, 0)
agreement(z3, z4)

Residual Likelihood Sensor

Description

The Residual Likelihood (ResLik) sensor measures the conformity of a latent representation against a population reference distribution. It acts as a soft gate, suppressing "out-of-distribution" (OOD) signals while preserving "in-distribution" (ID) fidelity.

Usage

reslik(z, ref_mean = 0, ref_sd = 1, lambda = 1, tau = 0.05)

Arguments

Details

Population-Level Sensor (ResLik)

The sensor operates in four steps:

1. Normalization: The input z is Z-score normalized using ref_mean and ref_sd.

2. Discrepancy: The Mean Absolute Deviation (MAD) is computed for each sample.

3. Gating: A gating factor is computed as exp(-lambda * max(0, discrepancy - tau)). This creates a "dead-zone" tau where minor deviations are ignored.

4. Output: The original z is scaled by the gating factor.

This implementation is fully deterministic and stateless.

Value

A list containing:

Examples

# Example 1: In-Distribution Sample
z_id <- c(0.1, -0.2, 0.05)
out_id <- reslik(z_id)
print(out_id$gated) # Should be close to z_id

# Example 2: Out-of-Distribution Sample
z_ood <- c(5.0, 5.0, 5.0)
out_ood <- reslik(z_ood)
print(out_ood$gated) # Should be strongly suppressed

RLCS Control Surface

Description

The Control Surface integrates inputs from multiple reliability sensors (ResLik, TCS, Agreement) to issue a standardized control signal (PROCEED, DEFER, ABSTAIN). It uses a deterministic, rule-based logic to ensure safety and predictability.

Usage

rlcs_control(reslik, tcs = NULL, agreement = NULL, thresholds = list())

Arguments

Details

Deterministic Control Surface

The logic implements a "Conservative OR" strategy:

• ABSTAIN: Triggered if ResLik discrepancy exceeds reslik_max_disc. This indicates the input is fundamentally invalid (e.g., sensor failure).

• DEFER: Triggered if TCS consistency is below tcs_consistency OR Agreement is below agreement. This indicates valid but unstable or conflicting data.

• PROCEED: Default state if no negative flags are raised.

Value

A character vector of signals (same length as input batch).

Examples

# Mock Inputs
res_pass <- list(diagnostics = list(discrepancy = c(0.1), max_discrepancy = 0.1))
res_fail <- list(diagnostics = list(discrepancy = c(5.0), max_discrepancy = 5.0))
tcs_pass <- list(consistency = c(0.9))
tcs_fail <- list(consistency = c(0.1))

# Scenario 1: All Good
rlcs_control(res_pass, tcs_pass)

# Scenario 2: ResLik Fail -> ABSTAIN
rlcs_control(res_fail, tcs_pass)

# Scenario 3: TCS Fail -> DEFER
rlcs_control(res_pass, tcs_fail)

Temporal Consistency Sensor

Description

The Temporal Consistency Sensor (TCS) monitors the evolution of a latent representation over time. It quantifies "drift" as the relative rate of change and converts it into a stability score.

Usage

tcs(z_t, z_prev, eps = 1e-06)

Arguments

Details

Temporal Consistency Sensor

The sensor computes the L2 distance between the current state z_t and the previous state z_prev. This distance is normalized by the magnitude of the previous state to produce a relative drift score. Consistency is defined as exp(-drift).

This metric is essential for detecting "shock" events where the representation changes too rapidly for the downstream system to adapt safely.

Value

A list containing:

Examples

# Example 1: Stable Evolution
z_t0 <- c(1.0, 0.0)
z_t1 <- c(1.01, 0.01)
tcs(z_t1, z_t0)

# Example 2: Sudden Shock
z_shock <- c(5.0, 0.0)
tcs(z_shock, z_t0)