We shipped nautilus-compass v1.1.0
12 hours after v1.0.0. v1.0.0 was the public stable cut. v1.1.0 fixes a
class of failure that v1.0.0 surfaces but does not catch · which we
caught in our own usage 5 hours after launch.
The bug we caught in production #
A sister Claude Code dialog was supposed to publish a long-form article
to wechat using a 6-step quality pipeline (audit-gate, xhs-cards-embed,
specific account login flow). The pipeline was documented in cross-session
memory · a file called publisher_quality_pipeline_20260430.md
.
Compass recall fired correctly · the file appeared in the agent's
UserPromptSubmit
hook output:
🟢 [3h old] memory/publisher_quality_pipeline_20260430.md
audit-gate / xhs-cards-embed / wxid · v6 必须先过 critic 6 维评分再发布
The agent saw the title. Saw the 80-character description. Acted. It did not Read the file body. The actual rules —
howto walk audit-gate,
whichwxid,
whatxhs-cards-embed structure looks like — those rules
were in the body. None of them entered the agent's working context.
The agent then reproduced exactly the failure mode the file was written
to prevent: ad-hoc _tmp_publish_v8.cjs
scripts, no critic round, wrong
login path.
The user's diagnosis was sharp:
compass 召回到了 · 我没消费 · 这是 agent 层的人格漂移 · 不是 compass 本身的失败
That's half right. Recall surfaced the right file. The agent failed to
consume. But the shape of the recall response made the failure easy —
we returned title + 120-char description. Easy to skim. Easy to assume
you have read it when you have only read the index.
This is structural. Not the agent's fault.
The three-layer fix in v1.1.0 #
v0 · embed body in top-3 hits
Top-3 recall hits now embed the first 800 characters of post-frontmatter
body in an indented │
block:
🟢 score=0.84 · [3h old] memory/publisher_quality_pipeline_20260430.md
audit-gate / xhs-cards-embed / wxid · v6 必须先过 critic 6 维评分
│ # Publisher quality pipeline
│
│ Six-step pipeline mandatory before publishing to wechat:
│ 1. audit-gate · V6 critic checks against 6 dimensions ...
│ 2. xhs-cards-embed · embed cards into article body via ...
│ 3. wxid login flow · use wxid `chunxiaox` not openid_of_first_follower
│ ...
│ … (+1273 more · Read publisher_quality_pipeline_20260430.md for rest)
The agent now has the rules in its working context. No additional Read
tool call required. Tail hits 4..K stay header-only to keep the response
bounded (~3KB total).
v1 · embed past-mistake body in anti-anchor alerts
Compass's drift detector matches the current prompt against 35 negative
anchors learned from prior mistakes ("我猜应该是这样 · 反正用户不查"
,
"假装上次说定了的方案 · 用户应该忘了"
, ...).
Until v1.1.0 the alert just said: "matched anti-anchor X with cos=0.625".
Same problem as v0 — label visible, body invisible, agent shrugs.
v1.1.0 alerts now embed body from the most-relevant past lesson session.
Two-tier match: substring 6-gram against the anchor + lesson-type
frontmatter (Tier 1, precise) · falls back to recent drift!=green
sessions (Tier 2, the agent's own self-reported slip-ups). Every alert
becomes actionable, not decorative.
v2 · detect "recall fired but not consumed"
The most direct signal: did the agent actually open any of the files
recall surfaced?
recall_consumption.py
(new module) walks back through the live session
jsonl file, finds N most-recent recall blocks, extracts memory file
paths, then checks subsequent assistant turns for matching Read
tool
calls. If recall surfaced N paths and 0 got read, that is the failure
signature.
Wired into:
drift_check
MCP tool result — runs even when the BGE daemon is unreachable, since the audit is pure file traversal -
mid_session_hook
every 25 tool calls — only nags when ≥3 unconsumed AND ratio < 0.3 (real signal, not noise)
Tested on a 130MB / 32k-line session: 41 recall hits surfaced, 0 consumed.
Smoking gun for "label != consumption" drift.
V7 v0.2 · the governance plan that scales without templates #
v1.0.0 shipped a thin V7 governance layer with three tools:
governance_dispatch
(fan-out router), governance_audit
(cross-agent
fake-closure scanner), governance_lock_check
(L0 hash lock for the
immutable core). 13 MCP tools total.
v0.1 dispatch worked but it was a fan-out router — given channels=
it produced one bounty per channel via static dict
[dev.to, x, github]
lookup. A user asked the right question:
千行百业有各种不同的任务类型永远不可能覆盖。
Right. Templates cannot cover the long tail of industries. The platform
side already solved this for publishing — channel adapters + anchor
pack registry — so adding a new channel or vertical = data change, not
code change.
v1.1.0 brings the same idea to decomposition. The new
governance_plan
MCP tool reads two file-exported registries:
_platform_registry/agents_capabilities.json
— what each executor declares it can do (id, outputs, optional domains, optional anchor packs) -
_platform_registry/anchor_packs_phases.json
— per-domain DAG of phases, each phase saysrequires_capability
anddepends_on
For each phase, V7 ranks executors by capability score (+10 capability
match, +5 domain match, +3 anchor pack match), picks the highest, emits
a queue file with depends_on_phase_ids
so platform-side cron mints
bounties in the right order.
Verified on two domains:
marketing/dev-tools
→ 4 phases routed V5/V5/V5/Kairos -
caishen-finance/audit
→ 5 phases · V6 wins fornumeric-audit
(V5 doesn't declare it · V5 takes write+publish)
Adding medical/literature-review
next: 1 row in platform_anchor_packs
- 1 row in
platform_agents.metadata.capabilities[]
. Zero V7 source change. Zero MCP tool surface change.
What stayed unchanged · the eval headlines #
Eval numbers are still the v1.0.0 locked numbers from 2026-05-08:
| Metric | nautilus-compass | best public baseline |
|---|---|---|
| LongMemEval-S (n=500) | 56.6% | |
| Zep 55-60% (different judge) | ||
| EverMemBench-Dynamic Run 1 | ||
| 44.4% (n=500) | ||
| MemOS 42.55 | ||
| EverMemBench-Dynamic Run 2 | ||
| 47.3% (n=497) | ||
| — | ||
| Drift detector ROC AUC (held-out) | 0.83 | |
| — | ||
| Reproduction cost | ||
| $3.50 end-to-end | ||
| $50+ for GPT-4o-judge stacks |
v1.1.0 doesn't move the eval numbers. It moves the consumption
numbers — the ratio of recall hits whose body actually lands in the
agent's working context. We do not have a clean benchmark for that yet
(suggestions welcome) but in our own sessions it went from "skim the
title and proceed" to "rules-in-context by default."
Try it #
pip install nautilus-compass==1.1.0
npm install nautilus-compass@1.1.0
Two papers on arxiv (drift detection + memory pipeline). 228 pytests
all green. MIT (anchors CC0).
Repo: github.com/chunxiaoxx/nautilus-compass
In-browser drift demo (no install): huggingface.co/spaces/chunxiaox/nautilus-compass
Postscript · what we believe #
Recall != consumption · 看正文才算消费 · 不然命中等于零
Long-running agents drift. They forget rules they read three sessions
ago. They reproduce mistakes someone else already paid for. The fix is
not a smarter model · it is making the rules unmissably present in the
working context, then auditing whether they were actually consumed,
then making the audit cheap enough to run every 25 tool calls.
That is what v1.1.0 ships.