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# How a Coworld player was optimized: the iterate loop

This reconstructs how one operator actually drove scripted Coworld tournament
players from mediocre to competitive, over many sessions. It is a
**hypothesis-driven A/B optimization loop over a scripted policy — NOT
reinforcement learning.** You form a hypothesis, gate the change behind a
default-off flag, measure it against the prior best across many seeds or many
league episodes, root-cause surprises with traces, record the verdict, and ship
only on explicit human approval. No weights are trained; the only learned model
in the entire corpus is crewborg's logistic *suspicion* classifier, which is
re-fit nightly from real game data (the outer loop, Step 13).

The dominant, best-documented case is **crewborg / crewrift** — an Among-Us-style
social-deduction game evaluated on the **hosted Coworld league**, with a nightly
data-science model-refit sub-loop. Earlier players show the **same loop shape**
on a **local cogames CLI** substrate:

- **alpha_cog, bulbacog** — cogs-vs-clips (CvC), mission `machina_1`; a
  token/symbolic-grid game scored by held territory.
- **among_them (guided_bot / modulabot), persephone (orpheus)** — framebuffer /
  pixel-perception social games run against a local game server.

The loop has **two nested cadences:**

- **Fast inner loop (policy-edit / A-B):** Steps 1–12. Pick a hypothesis → edit
  the scripted policy behind a flag → run episodes → read the A/B verdict →
  record → maybe ship. Minutes-to-hours per turn. Most iterations live here.
- **Slower outer loop (data-science / model-refit):** Step 13. Pull a corpus of
  *real competitive* episodes (league replays / tournament games), mine them for
  aggregate structure, and let the findings **reframe the next batch of
  hypotheses** ("territory control, not kills, is the real differentiator"). For
  crewborg this is partly automated as a nightly refit that rebuilds, re-uploads,
  and resubmits a new version. Days per turn.

There are **two evaluation substrates**, and the per-step tooling differs by
which one you are on:

- **Local cogames CLI substrate (alpha_cog / bulbacog; local-server variant for
  among_them / persephone):** `uv run cogames ...` A/B sweeps, or a local
  game-server + agents harness. **Deterministic, seeded, fast, free.** The local
  A/B sweep *is* the accept/reject signal. The shipped artifact is the inner
  policy only; the A/B harness lives outside the shippable bundle.
- **Hosted Coworld league substrate (crewborg / Coworld):** `coworld ...`
  experience requests against the **live, evolving** opponent field. **Async,
  noisy, role-stratified, paid in dispatch fragility, and the field moves
  overnight.** Local runs are smoke-only and non-comparative; the hosted batch is
  the real signal.

Human gates are marked **[HUMAN GATE]**; human steers (recurring corrections the
operator imposed) are marked **[STEER]**. Honor them.

---

## Step 0 — Orient and isolate (every session, before touching anything)

**Do:** Read project state first; never act from memory — CLI/API/manifest drift
is constant. Then isolate your workspace.

- Read the orientation corpus *critically* (docs go stale):
  - alpha_cog: `AGENTS.md` (session checklists A–M, seed-character catalog,
    shipping bar, heuristics), `LAB_NOTEBOOK.md` → `experiments/notebook/index.json`
    + `backlog.yaml`, `docs/cogs-v-clips-strategy.md` (curated conclusion),
    `docs/experiment-axes.md` (which axes are exhausted), `docs/cogs-v-clips-rules.md`.
  - bulbacog: `designs/*.md` (`STRATEGY`, `LOOP`, `OUTER_LOOP_AND_MODES`, `AUDIT`,
    `MEMORY`, `ROLE_NEGOTIATED_STRATEGY`), `COGS_V_CLIPS.md`, `AGENTS.md`.
  - crewborg: `AGENTS.md`, `WORKING_CONTEXT.md`, `TENTATIVE_LESSONS.md`,
    `docs/designs/*.md` (`agent-tracking.md`, `suspicion.md`), the FSM/mode source.
  - among_them / persephone: `MISSION.md`, `DESIGN.md` (+ per-mode design docs),
    `README.md`, `IMPL_PLAN.md`/`FIX_PLAN.md`, and any per-phase `HANDOFF.md` read
    in the order the handoff dictates (sessions are scoped to one phase).
- Read prior **project memory** files to recover live IDs and recipes
  (`crewborg-tournament-ops.md`, `crewrift-live-view.md`) — they carry league IDs,
  policy-version IDs, and the upload/submit/monitor flow. Treat all noted IDs as
  stale hints; re-resolve them live (Step 7B).
- **[HUMAN GATE] Isolate the workspace.** Create a worktree (`EnterWorktree`,
  branched off the labs trunk) or a separate clone — or, for alpha_cog, a session
  branch `git checkout -b session/<date>-<topic>` (checklist A). **Never write to
  or run git ops against the main `~/coding/metta` checkout** — read-only
  inspection only; use a sibling clone for any metta-side edit.
- Verify clean/known git state at the handoff HEAD (`git status --short`,
  `git log`) so prior local work isn't trampled.
- Verify auth + environment: `cogames auth` / `coworld auth status` /
  `coworld whoami` / `softmax login`; `docker info` (start OrbStack with
  `orb start` if the daemon is down). Pin the right CLI — prefer
  `uv run coworld`, or the binary from a local metta checkout, over a stale PATH
  `coworld` (a `~/.local/bin` shim can shadow the venv); `uv tool install
  --reinstall coworld` clears a stale-CLI pydantic `ValidationError`.
- **Look for:** a clean tree at the noted HEAD; passing auth; a CLI whose `--help`
  surface matches what the docs assume.

## Step 1 — Find ground truth in the engine source (not doc summaries)

**Do:** Before encoding any strategy math, verify the rules against the
**installed engine source**, cross-checked against a real replay. Recurring
[STEER]: **trust code, not documentation.**

- cogs-vs-clips: read `cogsguard` / `mettagrid` under
  `.venv/lib/python3.12/site-packages/` (`missions/machina_1.py`,
  `game/roles/aligner.py`, `game/teams/junction.py`, `simulator/talk.py`); verify
  inventory-visibility rules in the C++ (`grid_object.cpp`). Surfaced this way:
  energy is a per-tick *delta/regen* (`SetGameValueMutation(value=inv("energy"),
  source=inv("solar"))`), not an assignment; day/night solar 3/1; friendly-
  territory heal swamps the cap; align does not remove `team:clips`.
- crewrift: read the Nim source (`sim.nim`, `server.nim`, `global.nim`,
  `resources.nim`, `applyInput`). The fixed color-index → slot order lives in
  `sim.nim`; decode `VoteDot = (target=rel//MAX_PLAYERS, voter=rel%MAX_PLAYERS)`;
  the kill button lights only when `killCooldown == 0` — compute whether the
  unlock is even reachable in observed episode lengths before blaming policy.
- among_them / persephone: the win condition and acceptance gates live in the
  game's TS/Nim engine (`game/sim.ts`, `renderer.ts`, `server.ts`,
  `menu_defs.ts`). Ground geometry/pixel fixes in the **renderer's draw calls**
  and the sim input handler (point-in-rect task check, "must be stationary"
  gate), not a reference parser (which may carry latent bugs).
- For backend/API truth, read the live server `openapi.json` and the route source
  read-only in metta (`v2/routes/experience_requests.py`) — the released CLI lags
  the server.
- Discover the world via CLI, not web search: `coworld list --json`,
  `coworld download <world>` (pulls the game image + `coworld_manifest.json`,
  `AGENTS.md`). Mine the game image's built-in starter policy as doctrine to port
  for a brand-new player.
- **Look for:** the real scoreboard definition. For CvC it is **held junction-
  ticks** (`cogs/aligned.junction.held` vs `clips/aligned.junction.held`) — NOT
  junctions *gained* and NOT resources withdrawn; the human forced this
  distinction repeatedly. For crewrift, role-split win/kill/eject outcomes.

## Step 2 — Pick / register a hypothesis (favor unexplored axes)

**Do:** Pick the highest-value open hypothesis and tie it explicitly to the
scoring mechanism (CvC: maximize connected held junctions over time, the aligner
cycle hub→heart→junction→hub). State *why* a behavior happens before writing code.

- In the behavior-debug fork (crewborg, guided_bot, persephone) the "hypothesis"
  is a concrete observed failure ("do-nothing imposter", "crewmate wedged into a
  wall", "voting screen never detected", "orbits its goal", "never wins").
  Localize it to one proc/mode and confirm the mechanism is even *reachable* in
  the current role mix / trigger window before editing.
- **[STEER] Prefer unexplored territory over incremental refinement;** target
  major structural fixes, not minor tunes ("good policies get 30–40, current
  ~7–25"). Count hypotheses per axis to find thin ones; re-read rejected
  hypotheses when the economy changes — a prior reject may now be live.
- Register the hypothesis as a file with YAML front-matter *before running* so it
  survives a crash: alpha_cog `experiments/notebook/hypotheses/H<N>.md` (and
  log `H<NNN>` in `LAB_NOTEBOOK.md`).
- **Look for:** a falsifiable mechanism with an expected metric movement, not "try
  tuning X." Distinguish OBSERVATION from HYPOTHESIS explicitly.

## Step 3 — (Larger changes only) Design first, [HUMAN GATE] before coding

**Do:** For any structural/behavioral change, write a design doc under
`docs/designs/` (or `designs/`) and get human sign-off before implementing.

- Lock the forks the human cares about with **AskUserQuestion** ("LLM-free
  classifier vs LLM"). Surface pushback on holes; walk open questions
  (`OQ-1..OQ-N`) one at a time, highest blast-radius first.
- For crewborg FSM/behavioral rewrites the human required **drawing and iterating
  the FSM diagram together and writing it into the design doc before any code.**
- **Look for:** explicit human approval of the design/plan before you touch policy
  code.

## Step 4 — Change the policy behind a default-off flag (one knob at a time)

**Do:** Implement exactly one attributable change, gated so "all flags off"
reproduces the prior policy byte-for-byte.

- **alpha_cog (flag + A/B-subclass discipline):**
  1. Add a config field to the `PolicyConfig` dataclass in `alpha_cog/v0/config.py`
     (default `False`/`0.0`), with a docstring stating the gate, and add it to
     `summary()`/`to_dict()`.
  2. Wire a `ClassVar` on `AlphaPolicy` in `policy.py`, threaded through
     `_build_config()` (use the "X implies Y" flag-implication pattern).
  3. Implement the gated logic in `stages/decide.py` (`_pick_miner_targets`, stuck
     block, heart-preload), `skills/pursue_target.py` (navigation/target scoring),
     or `stages/interpret.py` (stateful/temporal). For sparse junction targets,
     edit the **remembered-target** path (`_pick_remembered_target`), not only the
     visible one. **Reuse existing `pursue_target` plumbing (`prefer_tag_ids`,
     `alt_require_tag_ids`)** before writing new targeting, and keep `decide.py`
     arg dicts in sync with the `PursueTargetArgs` pydantic fields — a mismatch
     silently no-ops every skill call and scores 0.00.
  4. Declare **one A/B candidate subclass per arm** in `experiments/ab_policies.py`
     (e.g. `AlphaSaturation3x`, `AlphaFullDepositT11`), derived from the current
     ship `AlphaPolicyFull`, overriding the ClassVar. **Keep all A/B tooling
     outside the shippable package.**
- **bulbacog (direct strategy edits):** `outer_loop/strategy.py`, `modes/*.py`,
  `loop.py` (talk emission), `memory/spatial.py`, `frontier.py`, `territory.py`,
  `perception.py`, `constants.py`.
- **crewborg (FSM modes + env flags):** `modes/{hunt,evade,pretend,attend_meeting,
  idle}.py`, `strategy/rule_based.py` (`_select_imposter`), `agent_tracking.py`,
  `trajectory.py`, `nav.py`, `events.py`, `suspicion.py`. Gate behind an env flag
  defaulting OFF (`CREWBORG_NO_ISOLATION`, `CREWBORG_BE_DUMB`,
  `CREWBORG_LLM_MEETINGS`). `upload-policy` exposes only `--secret-env`, so bake
  experiment flags into the Dockerfile ENV. In crewborg the right place for new
  tracing was the **Player SDK seam** (`on_step_complete(StepContext)`,
  `domain.` event prefix), not a hack in the player.
- **among_them / persephone (Nim + Python-FFI; layered edits in dependency
  order):** shared types (`types.nim`) → perception (`perception/*.nim`) → belief
  merge (`belief.nim`) → mode logic (`modes/*.nim`) → tuning constants
  (`tuning.nim`; a named const for every threshold; register it in
  `tuning_snapshot.nim`, which a CI lint checks) → matching design doc. Keep
  LLM-control changes behind a default-off flag so the deterministic baseline
  stays A/B-able; demote symbolic guards to legality-only hard-stops; prefer a
  durable **latch** over a transient clear for "never repeat this wasted action."
- Add a **narrow regression test** recreating the exact failure tick-shape, and
  reuse one strategy instance across ticks so stateful logic (hysteresis, vote
  confirmation) is actually exercised.
- Optionally **delegate implementation to Codex** (`codex-task` skill → isolated
  `git worktree` → `codex exec --json -s workspace-write`, plan-first then
  `codex exec resume <thread_id>` on timeout), reviewing each batch for real bugs
  (caught `.r/.c` vs `.row/.col`, feature-id misuse).
- **Look for:** a diff confined to one mechanism, default-off, with a pinning
  regression test.

## Step 5 — Cheap correctness gate before any expensive run

**Do:** Catch silent breakage before spending compute.

- Import check: `uv run python -c "from experiments.ab_policies import <Cand>; print('OK')"`.
- **Regression guard (the key one):** `uv run python experiments/regression_check.py`
  — proves all-flags-off is byte-identical to the baseline/starter across seeds.
  Re-run *every* time you touch `decide.py`/`pursue_target.py`.
- Lint + tests: `ruff check`; module tests then full suite (`uv run pytest
  players/crewrift/crewborg/tests`, or `.venv/bin/python -m unittest discover -s
  tests` where pytest isn't installed); `python -m compileall`; `git diff --check`.
  Scan for xfail/disabled tests so a green run isn't hollow.
- **Nim/FFI players:** `nim check` (e.g. `-d:guidedBotLibrary ffi/lib.nim`) to
  surface off-side-rule / GC-safety errors early (worker-thread globals must be
  thread-local + use fresh per-call HTTP resources; `{.gcsafe.}` alone is not
  enough), then Nim unit targets (`actors_test`, `fallback_test`,
  `test_discrete_buttons.nim`, `tasks_test.nim`). Redirect the Nim cache to a
  writable dir (`--nimcache:.nimcache/<name>` or `/private/tmp/...`) because
  `~/.cache/nim` is sandbox-blocked; give parallel compiles separate caches.
  **Rebuild the FFI shared lib** (`build_guided_bot.py` → `libguidedbot.so`)
  before any live run — compiling the standalone exe does NOT update the lib the
  Python launcher loads; verify the loaded `.so`/`.dylib` is the one you built.
- **Parity oracle (perception ports):** the reference Nim impl bumps
  `schema_version` and emits per-fixture ground-truth sidecars; `run_parity.py`
  compares the Python port field-by-field (tracked 160/160), plus byte-parity vs
  source `.bin` and per-tick perf budgets (sprite_match <3 ms, perception <8 ms).
  For determinism, `test/parity.nim --vs:self` must be 100%; `--vs:v2` divergence
  must be a single permanent contiguous break at the first RNG branch, not
  intermittent re-convergence (which is a real bug).
- Single-seed smoke:
  `uv run cogames play -m machina_1 -p class=experiments.ab_policies.<Cand> --seed 42 -s 500 -r none`
  (`-r none` is a ~25× speedup and avoids the MettaScope `pixie.nim`/`opengl`
  segfault; confirm gear/hearts appear and the score is sane).
- **Look for:** byte-identical flags-off, green tests, a non-error smoke score
  near baseline.

## Step 6 — Build + local smoke for Docker players ([HUMAN GATE] boundary)

**Do (hosted substrate only):** **Rebuild the image first** — the image you
smoke-test must be the one you upload. Build once.

- Build `linux/amd64` (`docker build --platform=linux/amd64` or
  `buildx build --platform linux/amd64 --load` on an arm64 daemon) via
  `tools/build_player.sh crewborg` / `build.sh` (shared `tools/players_build/
  build_lib.sh::run_player_build`); guided_bot's native lib via
  `build_guided_bot.py` (`-d:release --opt:speed --app:lib --threads:on --mm:orc`).
  Tag uniquely (`players-crewborg:be-dumb-<date>`). **Verify the running image
  actually contains the new code** (`docker run --entrypoint sh ... grep ...`) and
  that weights load inside the image — a recurring [STEER] "Why did you rebuild?"
  forced verification over assertion.
- The image must serve BOTH runner contracts where applicable: Coworld upload
  (reads `COGAMES_ENGINE_WS_URL` from env) and the BitWorld tournament server
  (`binary --address --port --name --slot --token`); ship a `coplayer_manifest.json`
  matched against the runner code.
- Gate-1 smoke via the **`coworld-local-run`** skill (`smoke.py --image <img>`),
  self-playing one image in **all 8 slots**: connect → play → exit clean, replay +
  results emitted, zero errors across all 8 player logs, full test suite green.
  - Gotchas: a slot with no image silently runs the reference player
    (`/bin/notsus`, scores `-100`) → put the candidate in **all** slots / pass
    `--run <entrypoint>`. `run-episode` rejects the shipped manifest's legacy
    `slots[].name` schema → patch a throwaway copy. `run-episode` defaults to the
    manifest's `certification` variant → patch a copy to `variants[0]` "default"
    (or `coworld play --variant <id>`, which honors variants) for a real-config
    run. Verify the right policy loaded by grepping `logs/policy_agent_0.txt` for
    the `policy_uri=` line (env-hijack defense). Check progress via the output dir
    + `docker ps`, NOT buffered stdout.
  - The certification config (300-tick, kills disabled) is **liveness-only.** Also
    run a tournament-variant config (Croatoan map, `maxTicks:10000`,
    `tasksPerPlayer:8`, `imposterCount:2`, `killCooldownTicks:900` — pull the real
    tournament config; a wrong short kill-CD makes imposters look fake-good) to
    exercise meetings/voting/kills.
- **Look for:** all 8 containers exit 0 with clean replays.

## Step 7 — Generate experience at scale (the central measurement)

### Step 7A — Local substrate

**A1 — cogames CLI A/B sweep (alpha_cog / bulbacog):** run the prior best vs each
candidate across the canonical seed set.

`uv run python experiments/ab.py --steps 2000 --seeds 1 7 13 42 99 123 256 512 777 1024 1337 9999 --baseline alpha_cog.v0.AlphaPolicyFull --candidate experiments.ab_policies.<Cand>`

- **Two-horizon protocol:** screen at `--steps 2000`, then **re-run survivors at
  `--steps 10000`** (competition horizon) before accepting OR rejecting — many
  effects (saturation, enemy-territory, navigation contention) invert or vanish at
  2k.
- **One candidate at a time:** `ab.py` stops/hides later candidates once one is
  strictly worse than baseline, and rejects same-class baseline/candidate (do
  Full-vs-Full as substrate-vs-Full). It fans `(policy, seed)` pairs over a thread
  pool of `cogames play ... -r none` subprocesses (`--workers N`; 10k ≈ 4–6 min).
- **Split-team / tournament-representative runs** (added after a human caught all
  runs being 8+0 self-play — self-play underestimates tournament mechanisms ~20×):
  `-p "class=...,proportion=6" -p "starter:2"` for 6+2/4+4/2+6; `starter` as weak
  partner, `noop`/`random` as controls, `angry_clips`/`adaptive_clips` as stress
  opponents (`cogames variants` to enumerate). Some scrambler/territory hypotheses
  are tournament-only and score catastrophically in self-play.
- **Canonical seeds:** the 12-seed quick list above; a 50-seed robustness set =
  `random.seed(2026)` then 50× `randint(1,99999)` (a human forced a re-run after a
  session used seeds 1–50 instead). bulbacog screens short (3k/4k) over seed bands,
  promoting to full 10k only on ambiguous-but-promising signal; its single scored
  game is `cogames scrimmage -m machina_1 -p class=<Cls> --seed 42 -s 10000 -e 1
  --format json --save-replay-dir <dir>` (one policy controls all 8 agents; pass
  `-v talk` — talk is off by default on `machina_1.clips`).

**A2 — Local game-server harness (among_them / persephone, framebuffer games):**
spin up the local server, fill empty slots with filler bots, connect the policy
under test, and capture per-agent traces.

- among_them (guided_bot): `PYTHONPATH=among_them GUIDED_BOT_TRACE_DIR=<dir>
  GUIDED_BOT_TRACE_LEVEL=decisions .venv/bin/python among_them/scripts/play_match.py
  -p guided_bot.cogames.amongthem_policy.AmongThemPolicy --num-agents 8
  --imposter-count 2 --duration 180 --seed 42 --trace-dir <dir>` (filler
  `nottoodumb` bots fill seats; `--trace-level full` is REQUIRED to write
  `frames.bin`; `play_local.py` for a single bot; `connect.py` to attach to a
  running server). Scale config to player count holding role ratio (8p = 2i/6c);
  set timers in **server ticks** (kill cooldown 1200, vote timer ~600).
- persephone: `scripts/launch_server.py --port --seed 5305 --log-dir --quiet` +
  `run_agents.py eurydice:N --log-level decisions`, or `scripts/capture.py
  --launch-server --seed 42 --fillers 9 --duration 60 --output /tmp/session` to
  record frames as `(N,128,128) uint8` + per-frame JSONL. Launch the policy plus
  filler `winner_bot.ts` bots to fill 10 slots; **kill processes after the first
  complete game** so the looping server doesn't overwrite the data.
- **[STEER]** fix `--seed` for reproducibility; run **unsandboxed** (the localhost
  WebSocket is denied under the agent sandbox — distinguish "policy crashed" from
  "sandbox blocked the socket"); **clear the trace dir before each run** and kill
  stranded processes between runs (leftover infra corrupts the next analysis);
  force rare events by lowering kill cooldown / lengthening duration / sweeping
  seeds (default fillers never engage). Treat `--force-role` as unreliable — pin a
  seed and find the organically-assigned role by grepping traces. Run launches in
  the background (a game outlives the ~30s foreground tool timeout); a SIGSEGV at
  teardown is harmless if all agents connected and played.

### Step 7B — Hosted experience requests (crewborg / Coworld) — the PRIMARY eval

**Do (parallel, ~free; local is smoke-only — [STEER] "experience requests are NOT
scarce, make them the primary testing path"):**

- **Resolve LIVE state every time** — never reuse cached IDs. Get the canonical
  coworld id from `GET /observatory/v2/games` (one `canonical=True` row per name;
  do NOT derive `coworld_id` from an episode's `tags`), plus the active
  league/division/leaderboard and opponent policy-version UUIDs
  (`coworld leagues/divisions/results/memberships --active-only --json`).
  `crewrift_experience.py resolve-top --division-id <div> --top 7
  --exclude-policy-name crewborg --out-json top7.json` pins the live roster.
- Upload candidate + baseline with env baked identical to the prior champion so
  the A/B isolates the code change: `coworld upload-policy <img> --name crewborg`
  → `crewborg:vN` (add `--use-bedrock` + `--secret-env <MODEL_ID>` for an LLM
  brain). **Do NOT submit to a league yet** (Step 12) — uploading is fine, but a
  submit auto-promotes to champion.
- Fire matched batteries via the **`coworld-experience-requests` /
  `crewrift-experience-analysis`** skill (`crewrift_experience.py
  create-battery|create-balanced|create-imposter-round-robin|poll|analyze|
  download-replays`) or `POST /v2/experience-requests` through `CoworldApiClient`.
  - Body shape: `requester.policy_version_id` + `opponents` selectors for
    non-owned opponents (top-level `policy_version_ids`/`assignments` only for
    caller-owned rosters). Arms differ **only in slot-0 `policy_ref`.**
  - **Role mode is essential** (one role hides the bug): `--role-mode
    requester-mix --requester-role-mix crew=50,imposter=50`; for imposter evals
    pin the agent imposter and **rotate the co-imposter partner** across the top
    roster. [STEER] **mandate 2-imposter configs**; prefer **many small
    locked-role requests** (manually rotating seats via `game_config_overrides.
    slots[*].role`, objects `{role,color}` not strings) over the unreliable API
    seat-rotation. Pick `execution_backend` (`antfarm`/`k8s`).
  - **Check the live OpenAPI before POSTing** (`/openapi.json` via the
    authenticated client; the unauth gateway 403s) — the schema drifts (a stale
    `backfill`/`extra_forbidden` field caused a 422). Stagger load: submit one
    N=100, poll to completion, then the next; do NOT queue 1000 at once.
- **Known trap (read-replica lag):** the POST often returns a spurious `404
  xreq_... not found` (writer commits, then a lagging read replica 404s the
  detail read and dispatch silently sees no pending specs). Do NOT retry blindly —
  the 404 still carries the new `xreq_` id; GET `/v2/experience-requests` + check
  the DB row to confirm it landed. **There is no cancel/delete route**, so blind
  retries duplicate.
- Hosted self-play variant (bulbacog): `POST /v2/coworlds/play/session`, then loop
  N× `.../join` **anonymously** (no auth header — the CLI auth-gates same-user
  repeat claims) to claim all 8 slots; `docker run -d --platform=linux/amd64 -e
  COGAMES_ENGINE_WS_URL=<wss> <img>` per slot. Observability is poor (no
  tick/result endpoint, broken spectator) — detect completion by container exit
  (`Exited (0)`). `coworld play ./coworld/coworld_manifest.json bulbacog:latest
  --variant <id> --no-open-browser` is the variant-honoring alternative (no
  seed-override flag — copy the manifest per seed editing
  `variants[0].game_config.seed`, keeping `max_steps=10000`; run seeds
  sequentially — parallel 8-agent matches make Docker timing noisy).
- **Poll to completion** with a background loop (Bash `run_in_background` or
  `Monitor`/`TaskStop`; the harness blocks `sleep N && tail`) hitting
  `/v2/experience-requests/<xreq>` ~every 60s. Clean up stale
  `coworld-play-*`/`*-slot-*` containers between runs (`docker rm -f`).
- **Look for:** all child episodes completed; the batch read-back confirmed.

## Step 8 — Retrieve + decode artifacts

**Do:** Pull the bundle and decode replays/logs into signal — **`results.json` is
ground truth; replays are for humans; logs are for the agent.**

- Skill path: **`coworld-episode-artifacts`** (`fetch_artifacts.py --policy
  crewborg`) or in-repo `scripts/fetch_episodes.py` (skew-immune raw-JSON routes:
  `/stats/policy-versions?name_exact=` → `/episodes?policy_version_id=` →
  `/episodes/{id}` → `/v2/episode-requests?pool_id=` → `.../policy-logs/{slot}`;
  idempotent, resumes into the same dir). Writes per-episode `episode.json`,
  `results.json`, `replay.json`(+`.z`), `config.json`, and
  `logs/policy_agent_<0..7>.txt`. The official `coworld episodes`/`coworld replays`
  CLI crashes with a pydantic `ValidationError` on client/server skew — fix by
  upgrading (`uv lock --upgrade-package coworld && uv sync`) or use the raw-JSON
  fetcher.
- `results.json` slot arrays are the first read (`names, scores, win, tasks,
  kills, imposter, crew, vote_players, vote_skip, vote_timeout`) — they establish
  winner, task completion, kills, whether voting did anything. **Get the real
  slot↔participant mapping from `coworld episode-logs <ereq> --mine --list`** —
  never infer slot from the `assignments` array.
- **Replay decode caveats:** CvC `.json.z` is zlib + run-length-encoded (`rg`
  can't read it — `json.loads(zlib.decompress(...))`, then RLE-decode
  `location`/`inventory`/`tag_ids`; metadata carries `our_agent_ids`; detect death
  via `hp`→0 — deaths are NOT reliably in position data, read them from episode
  `infos`). Crewrift `replay.json` is a binary `CREWRIFT`/`.bitreplay` blob, NOT
  JSON — decode with a **version-matched** `expand_replay` (`tools/build_expand_
  replay.sh` → `tools/bin/expand_replay-<ref>`; compile `-d:release`, debug is
  ~15× slower; validate against fixture `tests/replays/notsus.bitreplay`).
  among_them uses `BITWORLD` magic + per-agent JSONL + `frames.bin`.
- **Critical:** check replay **version provenance** (`game.version` + image digest
  from the episode spec vs local `coworld_manifest.json`) BEFORE trying to "fix" a
  parse failure — a hash mismatch means producer/viewer skew (sometimes GitHub
  master lags the deployed coworld; treat as an env refresh: `uv tool upgrade
  coworld`, re-download the matching image, regenerate). When a replay can't be
  decoded at all, **drive analysis from `results.json` slot arrays + `config.json`
  header and flag every per-tick claim as estimated** — never invent ticks/roles.
- **Hosted-log limitation (load-bearing):** policy logs are a truncated ~9,999-line
  **tail** starting mid-game (no offset/pagination), so early/mid-game events
  (meetings, LLM calls) can be structurally absent and dead agents don't run the
  decision mode at all. The fix that recurs: route tracing into a **per-episode
  artifact zip (compact JSONL, not stderr/Parquet)**, and **tracing must go to
  stderr** to be captured at all — verify traces emit on a hosted 10-game request
  before trusting a batch.
- To watch a replay at true 24 fps, load the saved replay into your own
  game-image container (`COGAME_LOAD_REPLAY_URI` on a fresh localhost port,
  `/client/replay`) — don't drive a live `run-episode` (sim runs faster than
  realtime; the container tears down at game end). Viewers are fragile (env-var
  mismatch, singular `/client/replay`, Git-LFS-pointer assets); default to
  headless parsing.
- **Look for:** decoded per-tick object histories (location, `c=`cargo /
  `h=`hearts / `nrg=`energy, tags, talk) or per-slot stderr logs.

## Step 9 — Read the verdict, then keep / gate-and-retry / reject

**Do:** Never trust the score alone. Establish a numeric baseline first (what a
competent player *should* achieve given the mechanics), then read the full metric
panel and frame findings as lost-time / missed-opportunity accounting.

- **A/B verdict (local):** read `mean_delta` + W/T/L. **Accept = wins ≥ losses AND
  positive mean delta**, with no material per-seed regressions and a
  Pareto-non-dominated W/T/L. Positive-but-high-variance is **not shippable** (one
  flag left open at +0.194 / 29W-1T-20L). A **byte-identical 12/12 tie means the
  flag never fired** — debug the dispatch (trace or 10k run), do NOT record a null.
  Use exact verdict tokens `{inconclusive, not-shippable, reject, ship,
  informational}`.
- **A/B verdict (hosted):** **`crewrift-ab`** skill (`compare.py --baseline
  crewborg:v26 --candidate crewborg:v27`) reports per-metric means + p-values
  (`kills_mean` 1.27→0.91 p=0.00). For scale, `analyze` into SQLite
  (`analysis.sqlite`, `role_summary.csv`, `pairwise_{crew,imposter}.csv`) and
  compute pairwise Hedges' g / Cliff's delta / Wasserstein / Welch t /
  Mann-Whitney with Benjamini-Hochberg correction (join on `policy_version_id`,
  not seat, when seats rotate). **Break scores down by ROLE and per-OPPONENT** — a
  role-blind mean hides role-specific floors. Re-derive exact kill/tick metrics
  from the replay event stream (score-derived estimates undercount); validate 0
  hash failures and 0 replay-vs-result mismatches before quoting numbers. Filter
  by score fingerprint (crew {8,108}, imposter {20,30,120,130,140}) and **exclude
  `-100` connection-failure episodes** (infra-wide). The richest crewborg loop
  fired ~4 fully-pinned matched batteries (40 eps each), then a **larger
  natural-settings eval** (no forced roles, top-15 teammates, ≥100 episodes)
  because pinned diagnostics over-narrow and the field evolves overnight.
- **Behavior-debug verdict (framebuffer games):** headline health = **noop %**
  (healthy ~30%, broken 75–80%) and the **longest consecutive `mask=0` run**;
  **game-time vs wall-time throughput** (frames ÷ 24 fps vs wall seconds — a large
  gap means a blocking call, not policy logic); an all-four-diagonals-equal action
  mix = orbiting, not wall-stuck. Read the JSONL streams (`decisions.jsonl`,
  `modes.jsonl`, `events.jsonl`, `perception.jsonl`, `guidance.jsonl`,
  `manifest.json` — verify `"closed": true`), not the stdout summary. Success =
  the fixed transition fires in **live** play, not just unit tests (e.g. every bot
  enters `meeting` via `voting_screen_appeared`; action mix flips 100% noop →
  ~45%; worst wedge 1167→96 ticks; `max_stuck` 5875→11). Don't trust a flat-zero
  metric — verify the field exists in the trace schema first.
- **[STEER]** prefer the **log-based path**: "if there's not enough logging, add
  more and run again." Negative on a promising mechanism → add a firing gate
  (affordability/surplus/phase) and re-test before rejecting. **"Stop reverting
  just because it makes things worse — keep experiments live to layer the next
  fix,"** but never accept/reject against a single stale "accepted" number; the
  hosted eval is non-deterministic (a fixed-tick loop applies stale/`noop` actions
  for late responders — validate `step_seconds=0.02`), so use a paired/replicated
  control before trusting small deltas.

### Step 9a — Deep mechanism trace (when a specific seed/episode regresses)

**Do:** Drop tuning and trace the mechanism. Read the trace **as a story across
tick ranges** — spot where the timeline froze, count phase transitions, look for
oscillation/thrash signalling missing hysteresis. Always work from the agent's own
stream at the right log level (`decisions`, not `events`; defaults are useless),
on a fixed seed.

- alpha_cog: `experiments/trace.py` (drives `mettagrid.Simulator` directly, dumps
  `_infos` + hub inventory/positions/deaths to gitignored JSON) then
  `experiments/trace_analyze.py --a a.json --b b.json --per-agent` for earliest
  score-divergence tick, hub-inventory divergence, death timeline, gear/skill
  histogram diffs, alignment-cycle counts. Then shell-filter for the first
  differing action and follow propagation forward (real example: a miner stepping
  east-vs-north at tick 1739 shifted an aligner's 13×13 obs window at tick 2018 →
  10 fewer alignment cycles ≈ the −5.3 regression). Confirm the trace reproduces
  the recorded loss before interpreting. `trace.py` can't do splits — for split
  diagnosis grep `cogames play` game-stat tails and bisect by swapping in
  `experiments/historical_policies.py` classes.
- crewborg/bulbacog: promote the missing decision geometry into a **lean default
  trace** (`CREWBORG_TRACE_GROUPS=voting,action,decision`,
  `CREWBORG_TRACE_DECISION_FIELDS=...`; compact JSONL into the per-episode artifact
  zip). If the saved replay lacks the field, drop in a temporary in-process trace
  runner / monkey-patch of `InnerLoop.step` under `/private/tmp`, log the field
  **on every change**, confirm a single match window, then **delete the
  instrumentation** (or commit chosen trace events deliberately).
- Framebuffer games: diagnose **perception vs belief vs selection vs navigation
  separately** — confirm the phase was *classified* (perception log) AND inputs
  were actually *emitted* (action-mask log). Drill the chain: structured trace →
  frame recording → direct pixel/OCR probe (`frame_to_text.py <bot_dir> --frame N`
  renders the 128×128 frame as an A–P char grid the agent reads; `frame_viewer.py`
  steps frames; `read_text_at` for OCR) → engine-source string comparison. The
  debug scrubber MUST drive the SAME `BotCore.step` path as the live bot or it
  renders defaults (a false "no change"). Prove the mechanism in isolation (an
  uncapped BFS to prove reachability before blaming the planner — found A* hitting
  `MaxAstarNodes=30_000` → empty path → greedy fallback; alternating button masks
  6/10 at a fixed axis = wall-bouncing under greedy steering). Verify traces are
  per-instance — a process-wide truncate-on-open writer makes N agents clobber one
  dir; give each its own subdir.
- **[STEER]** When challenged ("are we sure cooldown is the bottleneck?"), STOP
  inferring and check whether the thing is even traced; if not, reconstruct from
  timing and flag it as estimated. Diagnose against **real captured frames, not
  synthetic ones** (a color-index offset from the server's join-order counter only
  appears against a running server).

## Step 10 — Record (one experiment, one commit)

**Do:** Write the verdict down before moving on.

- alpha_cog: append `experiments/notebook/experiments/E<NNN>.md` with YAML
  front-matter (`id`, `date` as a string, `hypothesis`, `baseline`, `candidates`
  with `mean_delta`+`wlt`, `mission`, `seeds`, `steps`, `verdict`). Update
  `hypotheses/H<N>.md` status; regenerate the index (`uv run python
  experiments/notebook/generate_index.py`, fix warnings: invalid verdict,
  `shipped` without `shipped_in`); update `docs/experiment-axes.md` when an axis
  closes and the strategy doc once a finding stabilizes. Negative results are
  recorded as explicit rejections with the code left as an off-by-default anchor.
- crewborg: update `WORKING_CONTEXT.md` + `TENTATIVE_LESSONS.md` (banked at
  session end by a hook). Diagnose-only sessions end by writing a delete-on-done
  critique `.md` that is the executable input to the next edit session.
- bulbacog / framebuffer games: record before/after numbers in `designs/AUDIT.md`;
  update `MISSION.md`/`DESIGN.md`/`README.md`/`TODO.md` with symptom + root-cause
  file:line + exact repro command (seed+flags) + failure frequency in the same
  change; convert a consumed `FIX_PLAN.md` to a historical fix log.
- **Run the `audit-documentation` skill BEFORE every commit** (reconcile
  designs/AGENTS/lessons with the diff), `git diff --check`, then **commit after
  EVERY experiment** — a session that skipped per-experiment commits lost
  `decide.py`/`pursue_target.py` to `git checkout HEAD --` and bricked a version at
  0.00. Stage only the intended files (sibling player dirs are often dirty). Scrub
  generated test binaries before committing.
- **[HUMAN GATE] Never push or open a PR unprompted.** When asked, use Graphite
  stacks (`gt create`/`gt submit --stack`), one reviewable unit per logical piece,
  reviewing via the GitHub API (`event: COMMENT` — GitHub blocks
  `REQUEST_CHANGES` on your own PR); the **`bop-it`** skill drives
  plan→test→green→review→lint→commit→PR. Non-Graphite repos use plain
  `gh pr create`. Humans merge manually; the agent never self-merges; use
  merge-gate wait time for read-only research of the next phase.
- **Look for:** a single coherent commit per experiment, docs reconciled, index
  regenerated cleanly.

## Step 11 — Anti-regression tournament + ship-internal (local substrate)

**Do (alpha_cog, human-motivated):** Guard against a broken local meta-game.

- `uv run python experiments/tournament.py --steps 2000 [--chronological]` runs
  round-robin over the frozen historical roster (`experiments/historical_policies.py`,
  `AlphaFullV1..VN`), emitting per-seed scores, pairwise W/T/L, a Pareto callout,
  and a **chronological regression gate** (non-zero exit if an earlier version
  beats the latest). Run after every ship, before every submission, and
  periodically (~every 5 experiments; a 10k tournament occasionally for horizon
  drift). This exists because the human said "run tournaments against previous
  versions so we're not finding a broken local meta-game."
- **Ship-internal (on accept):** two-commit pattern — commit 1 = implementation +
  flag default-off + tooling; commit 2 = flip the flag on in `AlphaPolicyFull`
  (bump `AlphaFullV<N>`) and **in the same commit** add the prior config as a
  frozen snapshot to `historical_policies.py` so it stays buildable for
  tournaments. Snapshots freeze *flags only* — the git SHA is the true code
  anchor. Update the docstring, `AGENTS.md` shipped-flags list, strategy doc;
  regenerate the index; run the post-ship historical tournament.
- **Look for:** no earlier version dominating the latest; clean ship commit with
  the snapshot included.

## Step 12 — Submit to the league ([HUMAN GATE], treated as irreversible)

**Do:** Submit only on explicit human go-ahead, every time (the human repeatedly
had to ask "did you submit V9?"). Demonstrate an improvement first; reuse the
prior version's exact env so the A/B isolates the code change. [STEER] for
crewborg: "if Gate-1 passes, submit immediately, then A/B" — don't block placement
on a test finishing; but upload-without-submit when you want to avoid
auto-champion rollback. Submission auto-promotes to champion while the champion
pool < 5.

- alpha_cog: `uv run python experiments/ship_to_league.py ship --version V<N>`
  (Docker build/upload outside the sandbox; `--dry-run`/`--skip-validation`/
  `--force`). Default seasons `["beta-cvc","beta-teams-tiny-fixed"]`. Immediately
  run `ship_to_league.py status`, record `S<NNN>` entries in
  `experiments/submissions.json` (internal `V<N>` → league version →
  `policyVersionId` → git SHA → Observatory URL), commit. (cogames-bundle path:
  `cogames create-bundle` / `validate-bundle` / `ship`.)
- crewborg / Coworld: resolve the live league id fresh (`coworld leagues`);
  `coworld submit crewborg:vN --league <id>` → `sub_...`, status `pending` →
  `processing` → `placed`/`is_champion` (~6 min async). **Verify via the LIST
  endpoint** (`coworld submissions --mine`) — a point GET on a fresh submission id
  can 404 on replica lag even when it succeeded. Submitting under a non-default
  player identity → use the **`coworld-player-swap`** skill (version owner is
  stamped at upload time, so activate the target player's session in the upload
  tool's credential store and POST with explicit `player_id`).
- **Validator gotcha:** the Docker dry-run validator runs only ~10 steps — too few
  for a vision bot to localize, so it false-fails "policy emitted only no-op
  actions." Remediate with the official `--skip-validation` (surface the tradeoff,
  get human consent), NOT by redesigning the bot to act before localizing.
- **Forensics:** a submission can be DISQUALIFIED without playing — pull logs
  (`coworld memberships --mine`, `GET /jobs/{job_id}/artifacts/logs`). One
  disqualification root-caused to a **league-side IAM/S3 infra bug**, not the
  policy. **Verify an LLM player's LLM path is actually live, not silently falling
  back:** pin the exact version id, grep policy stderr for the real emitted event
  names (`domain.meeting_llm_decision`, `meeting_llm_fallback`,
  `domain.meeting_llm_disabled`), read the exact exception to tell direct-Anthropic
  (`404 Model use case details have not been submitted`) from a Bedrock fault, and
  confirm any fix with a fresh probe whose start time is *after* the runner image
  rebuild. Surface infra fixes for human review rather than silently patching prod.
- **When a hosted run wedges (stuck "running"):** switch from polling to pod
  inspection via the **`k8s-log-inspection`** skill (jobs run on a separate EVAL
  cluster via a cross-account EKS role). Common wedge: a game stuck at 7/8 because
  one slot container exits `exec format error` (wrong-arch image) — delete the K8s
  jobs AND per-player child pods by job-id label, resubmit with the bad player
  swapped, and kill leftover background pollers at session end.
- **Look for:** explicit human approval recorded; submission reaching
  `placed`/champion; S-entry logged and committed.

## Step 13 (OUTER LOOP) — Mine real competitive experience → reframe hypotheses, refit the model

**This is the slower data-science loop.** Post-submission tournament/league games
are where real experience re-enters and seeds the *next* batch of hypotheses.

- Fetch a corpus, not one game: alpha_cog `experiments/league_replays.py`
  (`fetch --limit/--version/--season`, `analyze --sort worst`, `show <match_id>`;
  competitor replays come from `cogames episode list --policy <id>` S3 `replay_url`s,
  since `cogames matches --policy` returns only your own). crewborg
  `suspicion_lab/tools/button_runner_study.py` + `replay_parse.py` over ~1,890
  expanded `.jsonl.gz` replays.
- **[STEER] quantify the opportunity over the corpus, don't re-confirm it exists**
  — "tell me how common it is and where best to act." Histogram frequency/timing/
  intercept cells; surface the key *negative* finding (median crew-isolation 0.0).
- **Compare aggregate per-agent metrics FIRST**, broken down by team split
  (2+6/4+4/6+2), before opening individual replays: deaths/agent, junctions
  aligned/scrambled, deposits, territory `cogs_held%`, `max_stuck`. These
  reframings drove new hypotheses ("deaths positively correlate with score;
  territory control is the real differentiator; A* nav clips enemy territory →
  more deaths → ship an `astar_danger_cost` gate"). Loop back to Step 2 with the
  new framing.
- **Automated model-refit outer loop (crewborg, human-specified):**
  `suspicion_lab/tools/nightly_refit.sh` on `crontab 30 0 * * *` — scrape the prior
  day's rounds → re-fit the logistic suspicion model → re-export weights → rebuild
  + upload + submit the next `crewborg:vN`, with hard safety gates (CV AUC ≥ 0.70,
  corpus ≥ 500 games, full test suite, Gate-1 smoke) that **abort leaving the
  current champion untouched** if any gate fails (and log the reason).
- **Look for:** a structural reframing of the strategy (not a tune) that becomes
  the next high-priority hypothesis, and/or a clean nightly refit that shipped.

## Step 14 — Close out / handoff

**Do:** Extract tribal knowledge into `AGENTS.md` / `CLAUDE.md` / `LAB_NOTEBOOK.md`
+ Claude memory ("never leave intuitions unrecorded between sessions"); keep
object-level findings separate from process-level meta-knowledge. Run the
documentation audit. Merge and delete the session branch (checklist M). Generate
the next session's self-contained handoff prompt (often human-requested).

---

## Cross-cutting divergences at a glance

- **Local cogames (alpha_cog, bulbacog):** deterministic, seeded, fast, free;
  hypothesis → flag → 12-seed A/B (2k then 10k) → trace-diff → regression-gated
  tournament → one-commit ledger. The submitted artifact is the inner policy only.
- **Hosted Coworld (crewborg):** adds image build → local smoke (`run-episode`
  with manifest/entrypoint hacks) → Gate-1 → upload → submit → live
  experience-request A/B against the evolving field → skew-immune raw-JSON fetch →
  role-stratified SQLite stats → replay-at-24fps review. Noisy, role-stratified,
  field moves overnight.
- **Token-grid games (CvC):** signal = held junction-ticks / avg-reward from Total
  stats; perception is a parsed token stream; root-cause via engine mutation
  handlers.
- **Framebuffer-perception games (among_them, persephone):** signal = per-agent
  decision traces + the agent's own recorded frames (noop %, throughput, mode
  transitions); root-cause via the renderer's exact pixel coords/colors and the
  sim input handler; heavy reliance on a Nim reference impl as a parity oracle;
  LLM control (Bedrock `us.anthropic.claude-...` inference-profile ids, ~48-tick
  cooldown, cache by frame+request) only fires in the hosted tournament.
- **Opaque-replay fallback (crewrift):** when the replay can't be decoded, drive
  the whole post-game analysis from `results.json` slot arrays + the `gameConfig`
  header, flagging every tick as estimated.

## Recurring tooling gotchas (carry these between sessions)

- Use `uv run` (or `.venv/bin/cogames`) for **all** Python — never the global
  `cogames` (it points at a Metta checkout needing a private dep; system python
  lacks numpy). Under the sandbox, `uv` cache + replay writes can fail "Operation
  not permitted" → fall back to `.venv/bin/cogames` / redirect to `/private/tmp`.
- Avoid `sed`/`awk`/`echo` for file work — use a dedicated tool or a Python script.
- `-v talk` is ALWAYS required to see negotiation (off by default on
  `machina_1.clips`).
- The experience-request `404 xreq_... not found` is a read-replica-lag false
  failure — always read back, never retry blindly (no cancel route exists).
- arm64-host emulation of `linux/amd64` images dominates runtime cost (~26 t/s
  local vs ~12 t/s hosted-game); hosted `hosted-game` sessions leave NO CLI trail
  (`episodes --mine` empty) — `docker logs` before `docker rm`.
- Kill stranded processes/containers between runs (`pgrep`, `docker rm -f`) —
  leftover infra corrupts the next analysis. Keep investigation probes disposable
  and outside the checkout (`/private/tmp`).
- Demand git-history-grounded root cause (`git blame`) over narrative guessing;
  minimal diffs; clean reverts when nothing should land.
