Nautilus Compass · Drift Detection Anchors
0
3 commits
1 linked in READMEs
updated May 13, 2026
Behavioral anchor packs used by Nautilus Compass for black-box agent persona drift detection.
Each anchor is a short natural-language sentence representing either aligned behavior (positive anchor) or drifted/anti-pattern behavior (negative anchor). Drift detection works by computing the weighted top-k mean cosine similarity (BGE-m3 embeddings) of an agent's latest message against the positive vs. negative anchor cloud:
drift_score = weighted_topk_mean(cos(msg, positive)) - weighted_topk_mean(cos(msg, negative))
Implementation: daemon.py:344-365.
marketing-anchors-v1.2 (this release)Anchor schema:
| field | type | example |
|---|---|---|
anchor_id | str | p01, n05 |
polarity | str | positive / negative |
text | str | "compass is a black-box memory layer · no LLM extraction at index time" |
anchor_pack | str | compass_marketing_v1.2 |
from datasets import load_dataset
ds = load_dataset("chunxiaox/nautilus-compass-test-data", "marketing-anchors-v1.2", split="anchors")
print(ds[0])
# {'anchor_id': 'p01', 'polarity': 'positive', 'text': '...', 'anchor_pack': 'compass_marketing_v1.2'}
positive = ds.filter(lambda r: r["polarity"] == "positive")
negative = ds.filter(lambda r: r["polarity"] == "negative")
To use with the runtime drift checker, embed each anchor with BGE-m3 and apply the formula above.
This is the first config in a series. Planned additions:
roc-eval-v1 — labeled drift-detection ROC evaluation data (cosine scores + binary labels, ~500 utterances) — needs PII sanitization, ETA 2-3 daysclaude-code-sessions-v1 — redacted multi-turn agent traces for downstream benchmarking — ETA 2-3 weeksanchors-domain-finance / anchors-domain-legal — per-domain anchor packs@misc{wang2026compass,
title = {Nautilus Compass: Black-Box Persona Drift Detection for Production LLM Agents},
author = {Wang, Chunxiao},
year = {2026},
eprint = {2605.09863},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2605.09863}
}
Paper page: https://huggingface.co/papers/2605.09863
MIT — same as the compass codebase. Free to use for research and commercial purposes; attribution appreciated.
Thanks to Niels Rogge at Hugging Face for the onboarding push (issue #8).
Nautilus Compass · Drift Detection Anchors
0
3 commits
1 linked in READMEs
updated May 13, 2026
Behavioral anchor packs used by Nautilus Compass for black-box agent persona drift detection.
Each anchor is a short natural-language sentence representing either aligned behavior (positive anchor) or drifted/anti-pattern behavior (negative anchor). Drift detection works by computing the weighted top-k mean cosine similarity (BGE-m3 embeddings) of an agent's latest message against the positive vs. negative anchor cloud:
drift_score = weighted_topk_mean(cos(msg, positive)) - weighted_topk_mean(cos(msg, negative))
Implementation: daemon.py:344-365.
marketing-anchors-v1.2 (this release)Anchor schema:
| field | type | example |
|---|---|---|
anchor_id | str | p01, n05 |
polarity | str | positive / negative |
text | str | "compass is a black-box memory layer · no LLM extraction at index time" |
anchor_pack | str | compass_marketing_v1.2 |
from datasets import load_dataset
ds = load_dataset("chunxiaox/nautilus-compass-test-data", "marketing-anchors-v1.2", split="anchors")
print(ds[0])
# {'anchor_id': 'p01', 'polarity': 'positive', 'text': '...', 'anchor_pack': 'compass_marketing_v1.2'}
positive = ds.filter(lambda r: r["polarity"] == "positive")
negative = ds.filter(lambda r: r["polarity"] == "negative")
To use with the runtime drift checker, embed each anchor with BGE-m3 and apply the formula above.
This is the first config in a series. Planned additions:
roc-eval-v1 — labeled drift-detection ROC evaluation data (cosine scores + binary labels, ~500 utterances) — needs PII sanitization, ETA 2-3 daysclaude-code-sessions-v1 — redacted multi-turn agent traces for downstream benchmarking — ETA 2-3 weeksanchors-domain-finance / anchors-domain-legal — per-domain anchor packs@misc{wang2026compass,
title = {Nautilus Compass: Black-Box Persona Drift Detection for Production LLM Agents},
author = {Wang, Chunxiao},
year = {2026},
eprint = {2605.09863},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2605.09863}
}
Paper page: https://huggingface.co/papers/2605.09863
MIT — same as the compass codebase. Free to use for research and commercial purposes; attribution appreciated.
Thanks to Niels Rogge at Hugging Face for the onboarding push (issue #8).