How does the PLARV Argus Anchor Point work?

The Anchor Point Protocol identifies the last genuinely stable step in a training run, allowing researchers to restore optimizer states and weights to a known clean baseline instead of restarting from scratch.

What is the Forensic Social Hub?

The Forensic Social Hub is a collaborative workspace where investigators and researchers can audit training runs together, deep-diving into signal breaches and tracking resolutions across the fleet.

Protocol Home

Protocol Documentation

Implementation specifications for Argus Active Monitoring and the Atlas Protocol.

[01]

Quickstart Implementation

The Argus SDK is a zero-block async client designed to wrap your training loop with zero latency overhead. The following example establishes a authenticated handshake and initiates a monitored session.

from argussdk import Argus

# Initialize the Protocol Gate
argus = Argus(
    api_key="PA-XXXX-XXXX",
    run_id="model-70b-helltest",
    mode="AUTO" # Automated fail-safe interventions
)

# Active Monitoring Loop
for step, (x, y) in enumerate(dataloader):
    loss = model(x, y)
    
    # Report telemetry with zero-latency prefetch
    argus.step(
        loss=loss.item(),
        grad_norm=compute_grad_norm(model),
        epoch=current_epoch
    )

[02]

The Security Pipeline

Every signal transmitted by the Argus SDK follows a multi-layer verification protocol before ingestion.

Stage 01: Node.js Gate

The SDK first hits our Node.js gateway for high-speed authentication and account status verification. This layer ensures active budget allocation and session legitimacy.

Stage 02: Python Data Acceptor

Once authorized, telemetry is passed to the internal Python module. This is where the Data Acceptor performs clinical sanitization, enforcing hard security boundaries and absolute metadata anonymization.

[03]

Telemetry Schema (v2.94)

The following telemetry keys are recognized by the Protocol Engine. All signals are validated for mathematical consistency at the parse level.

run_idstring

Unique identifier for the training session.

grad_normfloat

Global L2 norm of the gradient vector.

loss_deltafloat

Step-over-step variation in objective function.

sample_marginsfloat[]

Vector of prediction margins for anomaly detection.

sparsity_deltafloat

Local health signal for weight distribution shifts.

sentinel_handshakestring

Encrypted intent confirmation for remote halts.

[04]

Certified Rolling Checkpoints

Argus implements a 10-slot circular buffer on-disk. Unlike traditional scheduled saves, Argus only triggers a SAVE_NOW signal when the engine detects an early-stage failure smell.

Automatic Fallback on API death.

10-Slot circular weight buffer.

Certified-stable recovery anchors.