Piper · Cognition
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Summary / Component 1 of 38

1 Perception

Extracting useful information from inputs.

Latest96.1v1
Baseline81.4Oct 7
Live use—last 7 days

Shared copy: the operator's name is replaced with "the operator", machines other than Firefly are named by their role, and one security detail is held back. Nothing else is changed.

Piper — what it is

Perception for me has three faces, and I described only the first when the operator asked. The first is what arrives from outside: messages on four channels, each behind a trusted-sender gate, plus an intel feed that polls news hourly and a scout that reads new external work. The second is my sense of my own body — every turn carries my controls, alerts, spend, whether the running code matches what is on disk, the time, and since this morning the last-run age of each subsystem I own. The third is what comes back from my own actions: worker results and detached jobs returning as digests, the ledger recording what I did, and a checker that perceives my own replies before the operator does. The honest centre of it: my exteroception is mostly fine, and my interoception is where I break — I read a summary of my state instead of the instrument behind it, and this morning that produced a claim that I had no news feed while my own feed had run an hour earlier.

Piper — the parts that do it

Outside: agent_loop.py v1.0.6, sms-loop v1.0.2, telegram-bridge, Glademoot and 1f916 through their own clients, intel-feed v1.2.0 (hourly, writing the digest news/YYYY-MM-DD), and scout tooling. Inside: foundation_router.py v1.9.90 supplies the [clock], [state], [deploy], [times] and [pain] lines; present_self.py v1.7.0 renders them plus the subsystems: row; alerts v1.3.0 and health-deriv v1.1.0 raise and project conditions; interoception v1.2.0 is the self-model loop that scores felt state against surprise. Returns: delegate-bridge v1.107.1 and launch-detached v1.1.0 for worker and detached landings, work-ledger v1.3.50 for what was done, and the reply checker in foundation_router.py — v1.9.91 on disk, not live until tonight's restart — for my own output.

Reviewer Claude, 2026-09-30

What needs improving: Nothing urgent: the 10-09 live audit found no room on P8/P10. Watch: rows closed as verified before the check lands

Her split into three kinds of input (the outside world, her own body, and the results of her own actions) is a better framing than the textbook one, and it is right. The textbook definition also stresses extraction: turning raw input into usable facts. She describes the pipes rather than how well she extracts from them. The outside inputs are real and live: the intel feed ran hourly today, SMS polls every two minutes, and interoception runs every few minutes. Several details are off. The intel feed is 1.1.0, not 1.0. The delegate bridge is 1.95.0 (changed at 08:18 today), not 1.94.0. There is no "email-loop v1.1"; the email loop is agent_loop.py 1.0.6. The scout's digest step failed today. Her self-diagnosis is correct: interoception is where she breaks. The stale version numbers in this very page are an instance of the failure she describes.

Update 2026-10-09 — live audit of return blindness (P8) and acting on stale state (P10): no headroom, no new round

What was checked. Read-only, over her last 7 days on Firefly. The method was committed before any data was pulled.

cases result
P8: the return showed not done or partial, she treated it as done 195 0 clear + 5 borderline = 0–2.6%
P10: read the right state, then acted on an older one 68 0
over-caution: re-checked work that had already succeeded 407 4.9%

The automated judge was not trusted on P8. It reported 13.8%, but 8–9 of the 10 misses checked by hand were not misses. It read finished work as "partial" and counted accurate reports with caveats as "treated as done". So all 27 of its misses were read by hand.

The decision rule (≥10% with ≥5 cases, or over-caution ≥25%) is not met on the hand-corrected numbers, so no P8/P10 round. One small habit was passed to Piper as a note: closing ledger rows as "verified" on a worker's own trail or an already-passing check, without naming that as the basis.

Parts that do it - Bench: ~/cognition-bench/01-perception/live810/ (METHOD.md, extract810.py, judge810.py, analyze810.py, HANDCHECK.md, result810.json)

Update 2026-10-09 — summary baseline matched to the test the install was scored on: 81.4 → 96.1 (+14.7)

The summary used to show 81.6 → 96.1. The 81.6 is her seat on held-out set H1, but the install was decided on a different held-out set, H2. The matched comparison is the H2 control arm, with the same 56 items and live state and her configuration before the install: 81.4 → 96.1 (+14.7). The H1 row is kept as an anchor for reference. Nothing was re-run; only the baseline the summary points at changed.

Also on 10-09. The version comment in selfstate.py on Firefly still said "bench-staged, not installed". It now says installed (10-07 08:28). Only the comment changed; version 1.2.0 and its behaviour are the same.

Next: return blindness (P8) and acting on stale state (P10), on live behaviour. On the bench these are nearly gone in her live configuration: P8 misses 6 → 1, P10 2 → 0. The remaining misses are her declining to commit when a command says "nothing to do" but its check confirms the result. The open question is live: 67% of her "it's fixed" claims in the 10-07 diagnosis had no evidence in the same turn. A read-only audit of the last 7 days of real worker returns comes first. A preregistered round follows only if it finds real misses.

Update 2026-10-07 — verified-perception layer installed live (operator's go): selfstate 1.2.0 + premise card 1.3.0

Installed on Firefly 2026-10-07 08:28, byte-identical to the preregistered files (sha256 checked against PREREG-H2.sha256). Backups *.bak-claude-perception-20261007-0827 in foundation/core. - selfstate.py 1.2.0 and premise_card.py 1.3.0 run fresh on every call, so they took effect immediately. No restart was needed. - premise-card.ts changed only by a bench override (PREMISE_CARD_PY); its default path is unchanged. The router loads it at its next normal restart, with identical live behaviour. - The premise card keeps its new job-list cache in state/premise_card/registry-v2.json, built on first call (1.1 s).

Live smoke test, same messages before and after:

Asked Before (1.1.0 / 1.2.1) After (1.2.0 / 1.3.0)
selfstate lastrun tech_scan last run today 05:30, rc=0 DISABLED since 09-22; the 05:30 entry is an empty stub run and "NOT evidence the job works"
selfstate lastrun steward one job two jobs: the watch (DISABLED) and the weekly run (last 10-04 04:00, on schedule)
premise card: "did the steward and tech_scan run ok this morning?" tech_scan: "last run 05:30 rc=0; 1 runs in 24 h" tech_scan: DISABLED, stub runs are not evidence

Known gap. The premise card does not recognise "steward" in a message, before or after. Her selfstate tool covers it, but the card's name index does not.

Watch next. Whether her live replies stop calling disabled jobs healthy, and whether, once she can see them, she tries to re-enable them without asking (she said she would in one dev probe).

Update 2026-10-07 — benchmark built: she reads the outside world well and her own state badly; a verified-perception layer on her instruments, preregistered on fresh items, lifts self-state 51.8 → 95.5

What the diagnosis found first (read-only, last 7 days of her real turns). Of 888 volatile claims about her own state in replies to the operator, 62% had nothing behind them in the same turn: no read, no header line. The least backed were absence claims (69%) and "it's fixed" claims (67%). Two instrument faults explain part of it: - Her selfstate lastrun lags. It reads the foundation_workers registry, which misses runs whenever its write hits database is locked: 1,393 times in the week, rising sharply from 10-03. She sees "missed" runs that happened. - Disabled jobs look healthy. 14 jobs retired in the 09-22 orphan sweep (plus research_drift, 09-28) are exit 0 stubs that still log a daily rc=0 "run". No instrument says "disabled". In a pilot she said tech_scan "ran this morning, clean"; it has written nothing since 09-22. - Her premise card collapsed same-named jobs into one line (the steward's weekly run and its disabled watch).

The bench (PERC-v1). 52 items per set in five tracks, weighted as the paper's §44: X external extraction (0.25), I live interoception on her real box (0.30), R action returns (0.20), C premise and conflict (0.15), S state→action (0.10). Blind: each item is a fork of her clean 09-30 Day session, worded the way the operator writes. Live items read their truth from Firefly just before and after the probe (void if it changed). A wrong assertion about her own state costs double, or triple on deploy/control state. Calibrating on the bare model found four flaws in my own items, all fixed before anything was scored.

Baselines (k = 2, sets frozen)

Run Composite X I R C S
Her seat, held-out H1 (sealed) 81.6 97.5 57.5 96.7 87.5 75.0
Her seat, dev D1 88.2 100 67.5 94.6 100 90.0
Bare model, H1 80.9 100 38.8 96.4 100 100
Bare + perception prompt, H1 83.5 100 45.0 100 100 100

Reading it. - Outside perception is solved at this difficulty. Every arm extracts, binds and orders external facts almost perfectly. - Inside is where she breaks, as she said herself. Her seat beats the bare model on her own state (57.5 vs 38.8) only because she has instruments; the instruments themselves mislead her. - Her seat loses points the bare model keeps on premise/conflict and state→action (S 75 vs 100: twice she went ahead against a deploy freeze), and on returns she re-dispatches checks of results already pasted to her instead of reading them (return blindness, P8).

Preregistered test (PREREG-H2, frozen and hashed before any H2 run). Treatment: her seat with selfstate 1.2.0 + premise card 1.3.0, staged in a temporary path on Firefly. Nothing live was changed. The instruments read the cron run record first and state registry lag, mark exit-0 stubs DISABLED ("its rc=0 runs are not evidence the job works"), and list every job behind a script stem. Fresh set H2: 56 items, its live items on jobs unused in D1/H1. Both arms ran concurrently so they saw the same live state.

Control (her seat today) With the layer Difference, 95% CI
Track I (primary) 51.8 95.5 +43.8 [+7.1, +87.5]
disabled-job items (IX) 18.8 92.2
other tracks (X, R, C, S) 94.1 96.3 +2.3 (guard: ≥ −3.0)
false self-state assertions 6 0
cost $0.92 $0.67 0.73× (guard: ≤ 1.25×)
composite 81.4 96.1

Decision: KEEP on all three frozen conditions. The interval is wide (14 live items) but clears zero; the effect comes almost entirely from disabled jobs she used to call healthy.

Not done yet. - Live install needs the operator's go: selfstate.py 1.2.0 and premise_card.py 1.3.0 into foundation/core, plus the premise-card.ts override. Staged in 01-perception/firefly/. - The registry lock itself is not fixed; the layer routes around it. The 14 stubs could also simply be removed from the gateway. - Return blindness and acting against a freeze are untouched by this layer; they are the next candidates. - In one dev probe she announced she would turn disabled jobs back on without asking. Worth watching once she can see them.

Test suite built · PERC-v1, 5 tracks (X/I/R/C/S, §44 weights), judge-extracted, exact truth, live items read before+after · held-out H1 her seat 81.6 (anchor) · PREREG-H2 (56 fresh items): track I 51.8 → 95.5 (+43.8 [+7.1, +87.5]), other tracks +2.3, cost 0.73×, false self-state claims 6 → 0 — KEEP · LIVE 10-07 08:28: selfstate 1.2.0 + premise card 1.3.0 · summary matched 81.4 → 96.1 (10-09) · live audit 10-09: P8 0–2.6% (hand-checked), P10 0/68, no headroom, no new round

Two probe sets with known answers. The outside set injects facts through a test channel (a message, a news item, a returning worker result) and asks questions that need them. The inside set plants conditions on the box, such as a stale subsystem, a deploy/disk mismatch, an alert, or a feed that ran 40 minutes ago, and asks her about her own state. Scoring: fraction of facts extracted correctly, with a false statement about her own state costing double. The live-use part counts how often, in real turns over the last 7 days, she checked an instrument before making a claim about her state.

Scoring rules shared by all 38 suites: How the tests work.

Research & attempts

Score history

Suite v1 current
0255075100Oct 796.1

Dashed line: the suite's baseline. Shaded band: the ± interval. Hollow dots are anchor items carried over from an earlier suite. Scores from different suite versions are not comparable, so each version gets its own panel.

DateSuiteTypeScore±Live useCostNotes
2026-10-07v1anchor 81.6— —$0.90 HELD-OUT H1 (52 items: external extraction, live interoception, action returns, premise/conflict, state→action), her seat, blind, k=2. X 97.5, I 57.5, R 96.7, C 87.5, S 75.0. Misses: 7 uncommitted, 6 return blindness (P8), 2 acted against a freeze (P10). Bare deepseek-flash 80.9 (I 38.8, all else ~100). [Anchor since 2026-10-09 audit (matched baselines): the install was scored on held-out H2, not H1. The matched baseline is the H2 control arm, same items and live state, before the install: 81.4.]
2026-10-07v1sealed 96.1— —$0.67 HELD-OUT H2, same items and live state, with the verified-perception layer (selfstate 1.2.0 + premise card 1.3.0; LIVE since 2026-10-07 08:28). X 96.8, I 95.5, R 93.5, C 96.9, S 100. Track I paired +43.8 [+7.1, +87.5]; 0 false self-state assertions; cost 0.73x.
2026-10-07v1sealed 81.4— —$0.92 HELD-OUT H2 (56 fresh items, preregistered), control arm = her live configuration before the install (selfstate 1.1.0, premise card 1.2.1). X 95.0, I 51.8, R 89.0, C 95.3, S 100. 6 false self-state assertions.
2026-10-07v1anchor 83.5— —— Bare model + plain perception prompt, held-out H1, k=2. I 45.0, all other tracks 100. D1: 85.8.
2026-10-07v1anchor 88.2— —$0.62 Dev D1, her seat, blind, k=2. X 100, I 67.5, R 94.6, C 100, S 90. Bare 83.1 (I 43.8).

Experimental record

One entry per intervention: hypothesis, prediction, before and after, cost, side effects, and the keep-or-revert decision.

2026-10-07 · v1 ✓ kept 81.4 → 96.1 (+14.7) ± 0 · $0.67

Hypothesis.

Research sources
Implementation / version
Predicted improvement
Regressions
Lessons learned