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# Experiment discipline — guide

Reference notes, design rationale, and negative results (24) for experiment discipline. Read when working in this area.

#### 1. Pick the eval surface to match the question; the short runner is plumbing-only and its certification config is degenerate
`loop` · **negative result**

`coworld run-episode`/the accelerated runner silently overrides the manifest with the short certification config (maxTicks 10000->~300, tasksPerPlayer 8->1, startWait/roleReveal->0, voteTimer 6000->120) while leaving killCooldownTicks at 900, so an imposter can never reach a kill window and a clean run only proves 'adapter/image/websocket/trace plumbing did not crash', never 'strategy improved' — a score of 0 there is NOT a failure. Audit ALL config overrides the runner applies before any conclusion, since a partial override can structurally disable the exact mechanic under test. The Coworld loop is evaluate->report->decide->improve, STARTING with evaluation: a local run is a Gate-1 correctness/liveness check (connect, play, exit cleanly; passes on clean exit 0, validated results.json, written replay), NOT a competitive matchup — you generally cannot run others' policies locally, so get competitive judgment from hosted experience requests. A fixed-capacity lobby (8 slots) constrains live tests: make one of the N players the debug-instrumented bot rather than adding a viewer, budget slots for filler + policy-under-test + watched slot, and VERIFY the spawned composition matches the request (a '4 filler + 4 policy' request first satisfied with 8 filler bots stalled the game). For full-episode behavior run a full-length match via `coworld play MANIFEST_URI [IMAGES] --variant <id>` (no seed flag: copy the manifest and edit variants[0].game_config.seed per copy keeping max_steps=10000); for one-off diagnostics use `coworld hosted-game create/join` (native amd64, fill seats 2..N via anonymous HTTP joins since the CLI auth-gates a same-user second claim); `coworld submit --league` is league-wide eval only (the public v2 `POST /v2/episode-requests` is 405 by design). A full 10k-step episode runs near-real-time over many minutes and is not wedged; hosted `play` is ~2.2x slower than local (14:07 vs 6:25) because every step round-trips the wss proxy, so iterate locally and reserve hosted for runtime-path validation. A 200 from /healthz does NOT confirm a replay loaded — `coworld replay` never verifies it; confirm playback via `docker logs` reaching 'game started', `docker ps` showing no player containers, or the /replay websocket 'replay tick <N>' sprite advancing ~24/sec. To get a tournament-style Crewrift replay rather than the certification smoke, patch a manifest copy so certification.game_config equals variants[0].game_config before `coworld run-episode`, which writes the replay to <output>/replay.json.
  <sub>sources: auggie:ac491a46-e7f7-4271-a1c0-f0e69987d424, auggie:e251085a-b35d-4171-8dbd-d006b8bc2bd9, claude-code:307f1069-21fb-469e-bcd2-8a07f2dba0d8, claude-code:4ec865f5-bcbc-44f1-b109-a27d69f55506 (+11)</sub>

#### 2. Invest in structured per-decision telemetry before strategy edits, routed per agent
`loop` · ⚠ _session-derived, unverified_

To improve a policy from its experiences, the first investment is structured per-tick telemetry, not strategy edits; a periodic debug print is insufficient. Build, in priority order: a structured JSONL trace, a per-decision score-component breakdown (re-score the chosen action, emit every bonus/penalty term as a field plus before/after world stats, and assert in a test the components sum to the final score - without this you see what the policy did but not why), entity lifecycle events, running health metrics, terminal-event detection, perception-failure logging, and decision latency. Tag each record with mode/phase/intent (chosen intent, target, why an action mask is empty) so a frozen agent is debuggable from the trace alone and perception bugs separate from action bugs. Make full per-tick tracing the DEFAULT for diagnostics (a one-shot first-frame stderr leaves 'disconnected at tick 1' vs 'played to the end' ambiguous), route traces PER AGENT (e.g. a set_trace_dir override) since a shared dir hides cross-agent collisions (treat uneven/duplicate same-tick rows as a smoking gun for shared global state leaking), and filter analysis to the just-finished run's timestamp prefix or you analyze stale mixed sessions. In crewrift, --trace-level decisions writes no frames.bin and blocks the frame viewer - run --trace-level full and verify frames exist before reasoning about them.
  <sub>sources: claude-code:0bd5f20d-10ad-41ef-8372-ae778cd59000, claude-code:bc36021d-994c-48ec-a3bc-7ec637f7464f, codex:019dfea2-76a2-7ba0-996a-9be68ceee04f, codex:019dffc3-a08f-76b1-8b07-8a16f6e5ae38 (+1)</sub>

#### 3. Test A/B changes against the flags they ship with, dedupe overlapping fixes, and reuse named fragile seeds
`loop` · **negative result** · ⚠ _session-derived, unverified_

Additive per-agent flags can be cancelled by systemic collective-behavior flags, so re-test them WITH the flags they will ship alongside, not in isolation: an extra miner (H031b) gave no net throughput because a dynamic-K concentration flag (H017) re-routed mining and ate the gain. When two candidate fixes target the same failure (cache-role-station-on-observation vs stuck-recovery-for-pre-gear-miners), A/B each against the baseline AND against each other and ship only the dominant one rather than stacking both. Re-read the existing decide-stage dispatch before inventing a new flag - a hypothesized 'opportunistic deposit' was already implemented (deposit whenever cargo_amount>0) and the real latent issue was the opposite (miners too eager to deposit single-element cargo); check existing targeting plumbing (pursue_target's prefer_tag_ids, alt_require_tag_ids) before adding new code. Treat specific seeds as named recurring regression probes: on machina_1, seeds 256/512/1024 repeatedly show the same fingerprints (e.g. seed 1024 where the substrate-only floor beats every Full config), so reuse them rather than rediscovering the failure. Beware phase/timing harm: stuck-target 'explore burst' ticks can hurt throughput even when the physical moves match baseline because they shift the blacklist/stuck-window phase - the fix was distance-banded gating (re-pick only inside 30<=stuck_dist<=35) tuned by tracing the worst seed the band still loses, not blind knob sweeps.
  <sub>sources: claude-code:425dc11e-1c8d-4d12-bf44-adf01b6fa796, codex:019de5a5-fadf-70b3-8773-2072cabae94c, codex:019df40f-3672-73c0-8d5e-13abe5edd0f3</sub>

#### 4. Validate from the trace and the variance, not the aggregate scoreboard; triage by failure class and chase the surprise
`loop` · **negative result**

A win can be noise, so confirm the intended mechanism actually fired in per-tick traces before crediting a change (crewrift Hunt spent ~96% of ticks closing distance to a victim, not waiting out witnesses, proving the witness gate rarely blocked a kill and dropping it via CREWBORG_NO_ISOLATION was a no-op at 1.27->1.24/g, p=0.80). Triage replays by failure class instead of watching everything: pull the round-level aggregate, find the distinct failure classes (many crew losses after completing all 8 tasks vs one imposter loss after 2 kills), download one representative worst-case per class, and prefer the most surprising case — a loss that should have been a win — because the largest expectation/outcome gap localizes the bug fastest. Do not stop at the aggregate: which episodes moved and what they share points at the gating fix, and positive outliers are hypotheses whose mechanism you make fire on purpose. Make each diagnosis actionable by naming a mechanism, predicting an observable effect, and pinning it to the proving trace line.
  <sub>sources: claude-code:3c867a71-3057-4e87-a149-4185e82ee728, player_labs/best_practices.md, personal_labs/crewrift_lab/WORKING_CONTEXT.md</sub>

#### 5. Treat the lab notebook as append-only history, log rejected experiments as first-class output, and record only non-derivable runs
`loop`

Keep a chronological, append-only experiment notebook (e.g. LAB_NOTEBOOK.md / designs/EXPERIMENTS.md) where every entry has an ID, hypothesis, expected mechanism, protocol, exact reproducible command, raw numbers, date/branch/SHA, files changed, artifact paths, a metrics table vs baseline and current best, observations separated from inferences, an explicit keep/iterate/abandon (verdict) decision, and follow-ups — never edited after the fact (only annotated) — and keep it SEPARATE from a curated strategy doc that holds current-best understanding (the notebook records WHY the policy is shaped this way including rejected hypotheses; the strategy doc records WHAT to do today), so the next session resumes without reconstructing from chat history. Treat negative/rejected results as worth as much as acceptances: log every rejected experiment with a stable ID capturing why it was believed, how it was tested, and why it failed, and record whether the dead thing is the specific IMPLEMENTATION or the entire FAMILY, so future sessions never silently re-run dead work (one project had four of its first six discovery experiments already rejected in an earlier landing); abandon when replay evidence falsifies the mechanism or only noise-level metrics improve. Log a run ONLY when a future session could not derive it by reading code — ship-affecting runs, code changes needing measurement (even 'no behavior change confirmed'), falsifications, and deliberate null results — NOT one-off crash smokes, latency micro-measurements, or trivial edits; promote a heuristic to the durable project-wide reference only after it survives at least two experiment cycles, and flag any entry referencing an uncommitted result artifact (treat the notebook text as the only record until regenerated). Before running anything, scan the notebook's Open Hypotheses/Backlog, prior Experiments, and the project's saturation map (docs/experiment-axes.md) so you do not re-recommend a closed axis. Preserve hypotheses that exist as code but are unrun as explicit deferred backlog anchors (deleting loses the knobs; pretending they were tested is dishonest), rebased onto the current ship stack. Update the written protocol in the same pass when it evolves (12-seed/2k -> 50-seed/10k), since a stale acceptance bar silently licenses under-powered ships. Have post-game analysis emit STRUCTURED insights (what worked/failed, social dynamics, timing, concrete rules) because a downstream automated editor consumes them. End a hard debugging session with a playbook that ranks working hypotheses AND lists ruled-out anti-pattern theories, plus instrumentation gaps, command recipes, a source-file map, and next opening moves.
  <sub>sources: archive/cogames_playground/alpha_cog/experiments/notebook/protocol.md, alpha_cog/audits/research-audit-2026-05-04.md, claude-code:425dc11e-1c8d-4d12-bf44-adf01b6fa796, claude-code:5f9e8287-da92-4dff-bf39-2511bcaf0700 (+8)</sub>

#### 6. Audit the strategy doc, experiment-axes doc, and policy docstring together as one 'what to try next' surface
`loop` · ⚠ _session-derived, unverified_

Doc drift that re-buckets a feature is actively dangerous, not cosmetic: the experiment-axes doc listed stuck-recovery as 'future Phase-2 infrastructure' when it was already shipped and the strongest recent signal, which steers the next researcher away from the best area. Audit the strategy doc, experiment-axes doc, and policy docstring together because they jointly form the 'what to try next' surface. Design docs go stale silently too (docs/temporal-memory-design.md stopped at V7 while the shipped V8/V9 refinements lived only in LAB_NOTEBOOK.md), so before treating a design doc as current, cross-check its last-referenced version against the notebook's latest experiment ID, and keep both the strategy doc's prose AND its embedded policy code snippet naming the current version (a snippet showing V7 while V8 ships misleads even when the prose is right).
  <sub>sources: codex:019df3f0-db41-7561-8184-8832002dba41, codex:019df4c6-160f-7740-a002-fbec282812eb</sub>

#### 7. Validate parsers, perception, and reporters against real captured artifacts and a ground-truth oracle, never only synthetic fixtures
`loop`

Never trust a binary-replay parser validated only against your own synthetic fixtures: every preview had been generated from a fixtures.make_replay_bytes(...) helper producing structurally-valid-but-fabricated BITWORLD bytes, so the parser had only ever read what the same fixtures produced — capture one real .bitreplay from an actual run and re-run the parser against it before declaring it correct. Validate every ported perception module byte-exact against a ground-truth oracle on a fixed fixture set before merging (changes don't merge unless the parity test is green); build the oracle as a small program that imports the original implementation, runs each fixture, and serializes computed output fields to checked-in JSON sidecars — more robust than parsing the original tests' assertion strings, because many are bare expect(cond) checks carrying no expected value, and the dumper extends to new fields trivially. Synthetic-only parser tests can pass while real renderer output differs, so also capture live fixtures from controlled real runs (e.g. a seeded six-player Spy/Echo run verifying 'Spy', a missing 'Cerberus', an 'Echo of Cerberus -> Cerberus' substitution; a non-default round-schedule [(25,1),(40,2),(75,1)]) before declaring a field complete; prefer pre-recorded fixtures over live capture for a deterministic parity rig (11 .bin frame fixtures spanning gameplay/voting/interstitial/gameover validate a perception port offline and reproducibly), and build a live-capture path only to fill coverage gaps that actually surface — when a path is uncovered, instrument the real Coworld bridge in a normal/replayed session rather than a forbidden separate raw-capture path. For a layered pipeline (perception/trackers -> flags -> inference -> decision -> action) the highest-value tests are the integration seam and the negative paths/invariants (empty-state-doesn't-crash; an already-handled item isn't re-processed; two trackers each process an event exactly once) — unit-testing the frame decoder and the A* planner separately can both pass while their interface (which pixels become walls) is wrong, so add an end-to-end test feeding a viewport with known wall pixels and asserting A* avoids them. Replay the same frame many times (20x) and alternate frame types (playing vs whisper) to catch belief-state accumulation/leak/cleanup bugs; feed lobby->roster->role-reveal->playing sequences to confirm role detection switches; when rewriting a perceptual rule, write a test for exactly the corner case where old and new rules disagree (a pip within the OLD projected-dot tolerance whose ray MISSES the new padded icon AABB), anchored on a stable real station/asset; and encode the exact live false-positive pattern as a regression test ('visible near vent, missed a few frames, re-detected on vent must NOT be impostor proof'). With no platform reporter runtime shipped, the validation ceiling for a reporter is: unit tests for the protocol (encode/decode/validate each message type), unit tests for extraction (rows against a fixture replay such as tests/replays/notsus.bitreplay), plus a mock WebSocket client driving a full report_request -> report_output round-trip against the locally-running service.
  <sub>sources: auggie:5d1ff8d5-2505-4dca-af45-56d3070363e2, claude-code:10f74020-e0e6-4515-8d0c-f071d41766ad, claude-code:e32290d2-800b-4c28-8f9d-ac6f65e9ae76, players/archive/players/among_them/coborg/DESIGN.md (+8)</sub>

#### 8. Optimize to the game's actual scoring mechanism: held-over-time in cogs-vs-clips, topical-then-styled in Cue-n-Woo
`loop` · **negative result** · ⚠ _session-derived, unverified_

Optimize the policy to the game's real scoring mechanism, not one-off events. In cogs-vs-clips, score accrues every tick proportional to the number of junctions the team's connected network holds (more junctions held for more ticks = higher score), so optimize to hold a large connected junction network over time. For a Cue-n-Woo answer policy both topicality and style are required and topicality dominates: a fully LLM-free template-only path is dead on arrival and pure style markers win 0% of head-to-heads, because style is only a tilt - the winning answer is a genuinely good on-topic answer phrased in the judge's style, a style-flavored poor answer cannot be rescued by steering, and a universally-sensible plain answer wins regardless of the judge's hidden style, so do not over-index on style at the expense of answer quality.
  <sub>sources: claude-code:19ac76fb-7c48-44a1-81dd-eebcbdc9c19c, claude-code:77e158b0-bd16-41f8-8473-d1b1522d451b</sub>

#### 9. Prefer thin/unexplored axes, reopen a saturated axis only with a qualitatively new signal, annotate why each flag died, and extract process meta-knowledge at closeout
`loop` · **negative result**

Maintain an explicit axis-saturation ledger (docs/experiment-axes.md) tagging each axis Saturated / Explored / Lightly-touched / Unexplored, and count hypotheses-per-axis (deficit-mining ~19, aligner-target-selection ~11, vs role-composition only 1 — the most upside). Prefer thin/unexplored axes over saturated ones and over quick wins (quick wins live in well-tuned areas with marginal gains; James installed a standing directive to NOT prioritize quick wins). Avoid re-grinding closed axes (deficit mining, vibe-channel coordination, hub-affordability gating, naive static role-slot substitutions); reopen one only with a qualitatively DIFFERENT signal class — reopening deficit mining required temporal-memory 'persistent deficit detection,' so prioritize new signal infrastructure over another per-tick rule. Annotate every rejected flag at its definition with WHY it died, because the reason predicts retry value ('mechanism too rare to fire' is dead unless the game changes, but 'reverses at 10k' might revive at a different horizon), and reject a new hypothesis unrun when it shares a disproven structural mechanism ('pre-position the aligner during a drought' = the same 'pull the aligner from the hub' already shown net-negative). When closing a strategic axis after a failure, record whether the dead thing is the specific implementation or the whole family, because over-broadly closing an axis (all signaling dead after one vibe-deconfliction failure) buries unexplored high-upside variants. Add a new outer-loop script only when a question has recurred at least twice AND a small tool makes the answer cheaper (trace instrumentation was deferred because its hypothesis class had no consumer); when a parity exercise eats disproportionate time, ask 'how much do we actually care?' and cut — deterministic-prefix parity plus a documented divergence rationale beats chasing impossible full-game equivalence. Preserve disabled experimental plumbing (ExploreArgs.bias_toward_origin_radius, aligner_drought_micro_mine) defaulting off so future work reuses it. Beyond logging findings, run explicit knowledge-recording passes at session closeout to extract META-knowledge about the research PROCESS itself (e.g. 'this validation tool was structurally blind to this class of fix') and codify it permanently (AGENTS.md heuristics or a closeout checklist), capturing transient/tribal knowledge before it is lost.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/experiment-axes.md, ses_1fbb083dfffeFNp623MAOdar2j, ses_200d7b1fbffehZb7LsbgTZtHzN, ses_20abe5b3fffetrbr0xbbFMFGgo (+9)</sub>

#### 10. The deficit-mining axis is saturated for per-tick scalar heuristics — reopen only with windowed memory or map-geometry awareness
`crewrift` · **negative result** · ⚠ _session-derived, unverified_

After 15+ experiments (E001-E029: deficit signal, K-selection, dynamic-K, saturation fallback, diversification, temporal streak), the deficit-mining axis is SATURATED for per-tick scalar heuristics. A single-tick scalar signal fundamentally cannot distinguish persistent map-geometry scarcity (extractors clustered by element near the hub, seeds 256/512) from transient steady-state saturation at the 10k horizon; the saturation fallback, deficit signal, K-selection, and concentration dynamics form a tightly coupled equilibrium. Do NOT reopen with another per-tick rule — only with windowed temporal memory (state plus state-deltas over a window, e.g. persistent deficit detection across ticks) or map-geometry-aware classification, and treat windowed memory as its own phase after exhausting cheaper non-gathering axes.
  <sub>sources: ses_219f1c8cdffe3wZlz1iudO5c4O, ses_219f1d27dffeB6anKWkF3ghwm6, ses_21b3c677dffe3YCY8JnACrbDmu, ses_21b8e6d11ffesbWeURWQqzTwIc</sub>

#### 11. Catalog of already-rejected/inert cogs-vs-clips hypotheses and their composition/horizon caveats — check before re-proposing
`crewrift` · **negative result**

Check before re-proposing: (a) HP-triggered aligner-retreat-when-low-HP (H041/E033) scored -0.17 to -0.20 — abandoning an alignment costs more than the gear saved (alignment score compounds; gear is one-time), and aligner_hp_retreat_threshold=0 (no retreat) is therefore the deliberately shipped config, a known tension not a bug. (b) Five phase-aware flags rejected/inert and unshipped: late_game_k1_collapse_pct (H063/E061), night_hub_return_multiplier (H050/H067/E065), late_game_no_stale_cap_pct (H042/H071/E034/E069), late_game_hub_return_boost_pct (H079/E078), early_game_k1_ticks (H080/E079). (c) Vibe-coordination tested only as self-deconfliction (E011), net-zero — never as a teammate signaling channel (still unexplored). (d) Static per-agent deficit-element assignment: ~+0.28 at 2k but -1.09 at 10k. (e) Raising saturation-fallback multiplier 2x->3x/inf: up to +0.65 at 2k but -0.28 to -0.71 at 10k. (f) Gating saturation fallback on a sharp instantaneous-imbalance ratio (<=0.3): wrong signal, -1.86 at 10k. (g) Suppressing saturation fallback after a deficit streak > N: +0.18 at 2k (N=400) but -0.91 at 10k. (h) Trading an aligner for a defensive role (4M/3A/1Sc vs 4M/4A) cut junction-capture throughput ~25% at 2k for the same -0.5 to -1.0 damage as dropping a miner and an aligner, because aligners dominate scoring at 2k and the scrambler acts too infrequently — evaluate such trades only at longer horizons. After shipping bursty mining (miner_deposit_cargo_threshold=40), hypotheses theorizing coordination-loss or survivability harm all failed (the deficit signal is too stable for 'stale signal between bursts,' self-play territory too friendly for HP-survivability mechanisms to fire); bursty mining's only real impact was economic, already captured by the threshold flag. Two procedural caveats when running composition/boost A/Bs: (1) dynamic role balancing (enable_dynamic_role_balancing, ships V16; observe-and-rebalance variant V13/E082) defeats clean composition tests — a custom _assign_role override (3M/3A/2S) does NOT produce the intended composition because the balancer re-assigns post-assignment (telltale: scout gear ~2.5/agent when the policy never assigns scouts), so disable dynamic balancing on the A/B subclass; (2) gate any early-game resource-boost flag on hub affordability, not just an episode-phase percentage — the H-delta boost adds an extra aligner heart preload only when the hub holds at least (preload+1)*7 of every element, since a percentage gate alone would starve teammates of hearts when the hub is depleted.
  <sub>sources: ses_1ffba372effeoVxzZ3X2OpUAcj, ses_200493314ffeuU44LQ1zuZaduk, ses_219ac1095fferkumOQK8Ph6LTk, ses_21b3c677dffe3YCY8JnACrbDmu (+5)</sub>

#### 12. Treat each hypothesis as a named, evidence-grounded mechanism pre-registered as a test; ground one signal at a time and materialize the change as an auditable proposal
`loop` · tool: `hypothesis_create`

A real hypothesis is a causal chain 'X happens because Y in the code, which causes Z' pinned to the specific mode/threshold/trace line that drives it; 'lower the threshold' or 'this should obviously help' is a vibe — ground every hypothesis in something you actually observed and treat plausibility as a reason to test, never to assert, because about half of 'obviously good' ideas regress. Pre-register the predicted observable effect (what should move and by how much per role) as the A/B target, ideally a test written before the run. Verify the signal is actually consumed in the decision path ('capability exists' != 'capability used'; an emitted-but-unconsulted signal is a silent no-op) and use explicit positive/negative controls, since a silent fallback can run a reference player instead of yours. Open each idea with hypothesis_create, link the eval runs and policy versions that test it with experiment_log, and close it with a verdict (confirmed/refuted/inconclusive with reason) so the same idea is not silently re-tested; before acting call memory_search / experiment_list {gameId} to see whether the fix was already tried, and after acting memory_create — playbook after a win, anti_pattern/failure_pattern after a dead end. Investigate one signal to a grounded mechanism before spawning the next hypothesis (generating hypotheses faster than you ground them is the classic failure mode), and ground each proposed edit in a recurring failure pattern across multiple rollouts rather than a single anecdote, reading the rejected-edits buffer first. Every policy change must point to a concrete artifact (a specific episode, tick, replay event, failure pattern, or eval comparison), not an aggregate scalar score — a higher score alone is never sufficient because the reason a policy fails is embedded in the trace. Never edit live policy source directly: a candidate materializes only through an auditable change proposal recording hypothesis, risk, changed files, diff summary, expected metric movement, validation plan, and linked evidence episodes; agents/humans write into a workspace and accepted proposals become candidate versions, versioned/diffed on practical code-diff primitives, not a heavy bespoke system.
  <sub>sources: player_labs/best_practices.md, personal_labs/crewrift_lab/.claude/skills/crewrift-diagnose/SKILL.md, optimizers/.cursor/skills/mcp-optimizer-operator/SKILL.md, optimizers/docs/optimizer-primitives.md (+9)</sub>

#### 13. Check the active machina_1 variant set and evaluate with -v talk
`crewrift`

On machina_1, strategic variants change the game: no_clips removes Clips spread; angry_clips pushes Clips toward enemy hubs faster; adaptive_clips bursts when Cogs dominate; no_vibes removes vibe actions but keeps semantics; talk swaps vibes for speech; forced_role_vibes pins initial vibe to gear; clear_vibes resets vibe on heart-bump -- check the active set before assuming behavior. Run scored evaluation and replay debugging with -v talk so the strategy layer sees the same coordination channel the replay logs expose; talk is part of the intended policy surface, not a debug-only extra.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/cogs-v-clips-rules.md, archive/cogames_playground/bulbacog/designs/LOOP.md</sub>

#### 14. Use event-triggered, smoothly-gated, id-derived role assignment with deficit-softmax resource choice
`loop`

A 1849-line bulbacog engine re-scoring every role each tick plateaued at 2.74-2.94 per cog; a deterministic ~250-line ID-indexed assignment re-evaluating a miner's role only on a deposit event (cargo non-zero->zero within 1 cell of the hub) removed hundreds-of-points-per-tick bid volatility, and the id-derived scheme self-heals on death (a respawned slot reproduces the correct role). Run a gating filter BEFORE the value it gates is committed (a damper firing AFTER an agent set its bid never applied, so all 8 picked the same opening role) and give each condition its own signal (one 'mode_stalled' reused across 10 modes misread 'equipping done' as 'extractors depleted'). Gate probabilistic flips on a smooth ramp (flip prob 0 below 10 hearts-worth of the scarcest hub element, ramping to 1.0 at 20) so simultaneous flips self-stabilize, and pick the next resource by deficit softmax (weight=exp(0.1*deficit): ~7-unit deficit ~2x, ~28-unit ~16x). Expect a slow opening (a single lead with the rest gathering scores almost nothing for hundreds of ticks); rely on the resource-driven flip to ramp.
  <sub>sources: archive/cogames_playground/bulbacog/designs/STRATEGY_V2.md</sub>

#### 15. Ship every change with a validation plan and clear pass/fail gates
`loop`

Each recommended policy change must be concrete, testable, and tied to a specific metric, shipping with a validation plan stating episode count N, the seeds/variant to run, a pass gate (target metric meets/exceeds threshold), and a fail gate (regression on a critical metric). Define optimizer success as a concrete behavioral outcome (e.g. fitness improving monotonically on average over 20+ generations vs the mock, plus the final policy beating the initial head-to-head at 70%+ win rate on the real game) — a feature count like 295 API routes or 55 strategies is not evidence the improve-a-policy thesis works.
  <sub>sources: optimizers/instructions/mvp/analyst.md, optimizers/instructions/mvp/orchestrator.md, optimizers/docs/spec-simple-optimizer.md</sub>

#### 16. Distinguish attempted from server-confirmed: clean action legality is not a win
`loop` · **negative result**

Telemetry attempt counts can look healthy while no interaction actually completes. Distinguish menu-attempt events from server-confirmed outcomes: whisper_exchange_outcome events with server_confirmed=false are local button presses, not accepted exchanges, so counting them would badly overstate real success — only server-confirmed events count as completed. Likewise, clean action-legality (valid_views_mismatch=0, no provider errors) does not imply winning: a smoke trace can be mechanically clean yet still draw with no completed role exchange, meaning the remaining gap is strategic, not mechanical. Concretely, in a self-play smoke test of Eurydice on 2026-05-11, broadly-scoped Haiku LLM control accepted hostage-selection actions with no provider errors and zero valid-view mismatches after stale-action guards were added, yet default three-round self-play still drew with no completed key-pair role exchanges; a separate full-trace live run of the deterministic agent produced many whisper_created/entry_requested events but no server-confirmed 'joined', no exchanged colors, little chat, and a draw — the blocker was agents joining whispers and completing exchanges, not semantic-action legality.
  <sub>sources: personal_cogs/persephone/TODO.md, personal_cogs/persephone/agents/eurydice/README.md, agents/eurydice/LLM_STRATEGY_MIGRATION.md, agents/eurydice/LLM_CONTROL.md</sub>

#### 17. Roll out LLM control in graduated stages with quantitative trace gates and explicit safety invariants
`loop`

Roll out LLM control in stages, not all at once: Stage 1 shadow evaluation (model runs on recorded contexts but rules drive the bot), Stage 2 advisory runtime (validate live decisions, deterministic policy decides whether to use them), Stage 3 constrained control over a few executor-backed actions, Stage 4 broad semantic control with deterministic safety filters still enforcing the contract. Pick probe-target selection as the safest first runtime surface — it maps to existing target params, happens outside fragile menu navigation, and bad choices recover via fallback/timeouts. Gate each surface with specific exit criteria before advancing (accepted-decision rate >95%, targets exist with known position, repeated failed targets drop in priority, completion rate not regressing vs the deterministic fallback). For an agent whose hardest failures are interaction, timing, and cross-agent coordination, deterministic pytest unit tests are necessary but insufficient — they cannot expose rendezvous or timing failures, so pair every phase with quantitative trace criteria over live bot runs (>=70% of probe attempts reach the whisper view within 15s; at least one server-confirmed join by round 1; key exchange completes in >=70% of games where the partner is reachable), and build a minimal rule-based test agent (a few trivial modes plus a no-LLM meta_decide) so unit, mocked-perception integration, and fixed-seed live end-to-end tests have something concrete to exercise. Define explicit safety invariants and require zero violations across a regression trace set: never send your own exchanged key role as a hostage, never role-exchange as Persephone with a confirmed enemy, never let a chat claim overwrite stronger mechanical knowledge, never let a Spy accept an enemy role exchange while cover is valuable, never let any mode persist past a tick limit without progress unless it is an intentional logged hold; cap repeated failing attempts per round (and avoid initiating whispers during HostageSelect) so the agent does not burn its limited interaction windows retrying an unreachable target.
  <sub>sources: agents/eurydice/LLM_CONTROL.md, agents/eurydice/LLM_STRATEGY_MIGRATION.md, personal_cogs/persephone/TODO.md, personal_cogs/persephone/agents/eurydice/IMPLEMENTATION_PLAN.md (+1)</sub>

#### 18. Tune maxTicks AND timeoutSeconds together; escalate difficulty only once the policy has mastered the current setting
`loop`

If optimizer episodes stop near the wrong tick count, check both maxTicks and timeoutSeconds: maxTicks sets the tick budget but the Coworld CLI wrapper can terminate when timeoutSeconds expires, so for long games set maxTicks near 10000 and timeout near 1200s and tune both to the game manifest defaults. Only escalate difficulty (raise maxTicks or move to a harder variant) once the rolling average score over the last 5 games is at least 8, so you do not raise the bar before the policy has mastered the current setting. Run both local lineups (a LineupSpec of baseline-vs-candidate, library-mix, or roster to runGameBatch/launchGameBatch; omit for self-play) and hosted Metta experience requests (vs league champions, async on k8s via the Observatory HTTP client).
  <sub>sources: optimizers/docs/local-development.md, optimizers/instructions/mvp/analyst.md, optimizers/docs/optimizer-primitives.md</sub>

#### 19. Verify reported pain-point claims against code with a file:line verdict before fixing
`generic`

When investigating a tool's reported pain points, verify each claim against the actual code and record a verdict (verified / partially verified / UX-only / mis-stated) with a file:line root cause before proposing a fix. Some user claims turn out wrong: 'submissions silently accept slugs' was incorrect, and the '10 slots, only 8 score' mental model was actually normal rotation — fixing a misdiagnosed model wastes effort.
  <sub>sources: players/users/james/personal_cogs/among_them/guided_bot/coworld/CLI_PA</sub>

#### 20. Version-stamp and checksum derived artifacts so stale data cannot silently contaminate newer work
`generic`

Version-stamp every perception artifact (frame hash, guide bundle hash, generator version, VLM schema version, prompt version, model/provider id, source run id, consuming parser version) so stale VLM labels cannot silently contaminate newer parser work. When the parity oracle and the baked sprite atlas both derive from the same upstream snapshot, regenerate fixtures and atlas from that snapshot together, and guard the atlas with a checksum comparing the baked file digest to a stored digest that fails loudly on mismatch — baked atlas data is tied to game asset versions and rots silently. Always print and log the RNG seed when launching a server with a random seed so runs are reproducible after the fact.
  <sub>sources: players/tools/cogbase/docs/designs/maker_v1_design.md, players/archive/players/among_them/coborg/PLAN.md, players/users/james/personal_cogs/persephone/GAME_API.md</sub>

#### 21. Know your test-harness biases and blind spots before trusting its outcomes
`loop` · **negative result**

When testing a Persephone agent, remove incidental complexity with obstacleCount:0 (removes pathfinding), autoGrantWhisperEntry:true (removes the GRANT dance), and groupNamePrefixInRoomA (forces agents into one room). Account for harness biases: names starting with 'llm_' get assigned TeamA (Shades) key roles first, skewing role assignment; and the default/fast 15s rounds exist for rapid testing and are not realistic tempo (medium presets use descending durations), so make agents robust to both extremes. A harness can also have blind spots: a feature can match the reference parser yet stay untestable because the harness cannot produce the triggering frame — waiting_entry detection had no fixture because aggressive filler bots always initiated whispers themselves; fix with less-aggressive fillers or a two-bot choreography rather than declaring the feature done untested.
  <sub>sources: players/users/james/personal_cogs/persephone/GAME_API.md, players/users/james/personal_cogs/persephone/README.md, players/users/james/personal_cogs/persephone/TODO.md</sub>

#### 22. Instrument and smoke-test long-running optimization loops end-to-end
`loop`

A stalled multi-step loop is invisible without instrumentation, so persist run events, expose an observability endpoint, and emit warnings for stale evaluations, missing replays, noisy evals, risky proposals, failed sandboxes, and stale sessions, with retry/dead-letter tracking and a watchdog that reconciles stalled runs. Smoke-test the whole loop in one script that asserts the end behaviors — a comparison run can promote a better candidate, the watchdog records stale-run events, and smoke-created rows are cleaned up afterward — and run it alongside a full build before trusting the loop.
  <sub>sources: optimizers/docs/OPTIMIZER-MVP-SESSION-SUMMARY.md</sub>

#### 23. Package each scripted module with neutral-default knobs and an embedded smoke test
`loop`

Package each navigation/strategy pattern as one self-contained module with config knobs defaulting to neutral/off, a clear per-tick contract, and an embedded smoke test that pins its non-obvious edge cases — the smoke tests are what make non-obvious behaviors trustworthy to copy. Label tests as white-box (tied to a specific implementation strategy) vs public-API with explicit section headers so a future engineer rewriting the implementation knows which tests they may change.
  <sub>sources: optimizers/.cursor/skills/scripted-navigation/SKILL.md, players/archive/players/among_them/coborg/PLAN.md</sub>

#### 24. Land refactors incrementally with every step green; test contract validates over internal shape
`generic`

Land a refactor incrementally with each step leaving tests green (for an env-to-config refactor: config+tests, then trace, then strategy, then SDK factory, then meeting client), preferring tests that construct config/policy pieces directly over tests that patch env. When porting from a partial fixture set, port simple cases first and leave explicit TODO markers for known edge cases, relying on the parity fixtures to surface rare states and adding a fixture when one is hit in the wild. Copy small shared fixtures (here ~164 KB) into the consuming package so the checkout is self-contained. Validate an agent-generation toolkit on its input/output contract (machine contract in, manifest plus artifacts out) and adversarial VLM cases (malformed JSON, unknown actions, low confidence, hidden-state hallucination), proving the machine contract wins over stale or contradictory Markdown, rather than testing the internal shape of derivers.
  <sub>sources: players_2/players/crewrift/crewborg/docs/designs/config.md, players/archive/players/among_them/coborg/PLAN.md, players/tools/cogbase/docs/designs/maker_v1_design.md, players/tools/cogbase/docs/designs/maker_v2_design.md</sub>
