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.

PLARV Argus — Training Integrity Engine

Your training run
didn't have to fail.

Argus watches every step of your training run. Detects failure before it compounds. Intervenes. Hands you back a run worth finishing.

ARGUS INTERVENED

GRADIENT_COLLAPSE

SEVERITY

2.41 σ

STEP

#1847

ACTION

AUTO_REC

ANCHOR POINT

step #1847 · rollback certified

AUTO RECOVERY

47 steps

STABILIZED

0 relapses

STEPS SAVED

~3,200

How it works

Three systems.
One run saved.

Argus operates in three layers simultaneously — watching, deciding, recording.

Anchor Point

Before failure compounds, Argus identifies the last step your model was genuinely stable — not just recent. When rollback is needed, you return to solid ground, not a guess.

How anchoring works→

Intervention Engine

Argus catches what you cannot see in real time — gradient instability, loss divergence, silent drift. It decides and acts before the damage compounds. Your GPU keeps running on the right path.

See the signal architecture→

Signed Certificate

Every run generates a signed certificate of training integrity — a complete record of what happened, what was caught, what was done. Shareable. Auditable. Yours.

View a sample certificate→

Intervention Analysis Record

Recovered.
Certified. Yours.

Every time Argus pulls a run back from the edge, it issues a signed record — what collapsed, when it was caught, how it was resolved. Not a log. A certificate. Cryptographically sealed, permanently verifiable.

HMAC-SHA256 signed

Publicly verifiable

Immutable audit trail

Intervention Analysis Record — PLARV Argus

SDK Integration

Four lines.
Any framework.

Drop Argus into your existing training loop. No rewrites, no config overhead. Works with raw PyTorch, HuggingFace Trainer, Unsloth, and Axolotl out of the box.

pip install plarv

from plarv import Argus

argus = Argus(api_key="your-key", model=model)

# inside your training loop

for step, batch in enumerate(train_loader):

    loss = model(batch).loss

    loss.backward()

    grad_norm = clip_grad_norm_(model.parameters(), 1.0)

    argus.step(loss=loss.item(), grad_norm=grad_norm)

0.3%

engine overhead

12–27ms

detection latency

fail_open=True

never blocks training

MIT licensed

open source SDK

View Full Integration Docs →

[COORD_REF: 40.7128° N, 74.0060° W]

[AZIMUTH: 184.7° // TACTICAL_HORIZON]

Pillar 11 // Forensic Reasoning

Other tools show
the crash.
Argus shows why.

Every failed run gets an exhaustive diagnostic audit — causal signal weights, ATIS integrity score, and deterministic recovery paths.

INITIALIZE_DIAGNOSIS

REASONING_ENGINE_V4.2_ACTIVE

// BITRATE: 8.42 GB/S

// ERROR_THRESHOLD: BREACHED_1847

FORENSIC_DIAGNOSTIC_REASONING

SOURCE: PROPRIETARY_LOGIC_HUB

C.W.

84%

S.P.

ANCHOR_V7

STATUS

ISOLATED

TIMESTAMP: 2026.04.11_19:14:29_SYNC

Your training rundidn't have to fail.

Three systems.One run saved.