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# Data-science refit (outer loop) — guide

Reference notes, design rationale, and negative results (15) for data-science refit (outer loop). Read when working in this area.

#### 1. CvC throughput levers: capture cadence first, raise mining deposit threshold, beware even role splits
`loop` · ⚠ _session-derived, unverified_

Diagnosing CvC score gaps: capture cadence (cycle time) is the primary lever, ranked above scrambling -- compute ticks-per-cycle from hearts gained (6 hearts/agent over 10k steps ~ 1667 ticks/cycle, only ~5 cycles) and compare per-agent tick-junctions (~55k vs reference ~70k) since fixed role headcount makes per-agent throughput the only lever. The single largest gain in project history was raising miner_deposit_cargo_threshold off its default of 1 ('deposit on any cargo', ~10 of 44 cargo per trip) to ~40 ('bursty mining'): +2.59 mean at 10k steps (9W/0T/3L); a prior experiment missed it by assuming cargo capacity was 4 not 44. Rank-76-vs-rank-1 gaps were 4x worse mining, ~48x fewer scrambler removals, ~3.3x slower expansion, and late gear-up. In live tournament data the 4+4 even role split underperformed asymmetric splits badly (~6.08 vs ~23-25), driven by weak-partner matches.
  <sub>sources: opencode:ses_1ffba12c8ffegXTM7TLJ7oigMW, opencode:ses_1fc6f16cfffe03SKAcP78Ua0IK, opencode:ses_206bfd18dffe1hUfJBOccuaLDC</sub>

#### 2. Verify game mechanics from installed engine source, not replay inference or rulebooks
`crewrift` · ⚠ _session-derived, unverified_

Confirm mechanics from the installed engine source (cogsguard/cogames packages), not replay inference, the bare initializer, or prose rulebooks. Confirmed for CvC machina_1: base energy 20, initial 100, move cost 4; HP limit 100, initial 50, passive drain -1/tick; day cycle 200 ticks, day solar 3 / night solar 1. Energy regen is SET not added (each tick energy snaps to the current solar value via SetGameValueMutation, so it does not accumulate -- policies assuming additive regen mis-budget). Day/night init is a trap: solar inits to night_solar(1) then the tick-0 day event bumps it to 3, so net behavior is day=3/night=1 and night actually starts at day_length//2=100. Death does NOT respawn at base: hp_death (hp<1) clears hearts and gear but leaves the agent in place keeping non-destroyed inventory; the 'respawns near hub' assumption was wrong and had leaked into design docs.
  <sub>sources: codex:019e1355-fd7e-77b2-8af2-3f93c0ae623a, codex:019e1357-835f-7442-bd46-7e911617621f, opencode:ses_1f6f88f62ffe6S4x2gevW9UGvE</sub>

#### 3. Keep starter graders small and fail-soft; know what the generic grader actually rewards
`loop` · ⚠ _session-derived, unverified_

A starter grader should be small and deterministic: cut to 2-3 core signals (a score-divergence signal, optionally one resource/throughput signal) and drop redundant/fragile ones (a reward signal duplicating score; brittle game-specific heart/role signals) -- six weighted signals with fallback chains contradicts the dependency-light goal. When a grader reads the replay only for a normalization constant (e.g. PaintArena max final-score diff = width*height), fall back to a default board size with a warning rather than failing hard, so it stays operational if bundle packaging changes. Critically, the starter bitworld-score grader rewards score divergence and activity, NOT skilled play (0.40*score-spread + 0.25*top-margin + 0.25*activity + 0.10*outcome, where activity = max of whatever numeric fields exist), so a high generic-grader score is NOT evidence the player wins well.
  <sub>sources: auggie:79765dce-4e98-40f6-8606-5cd33f51d4c5, codex:019e74bf-7764-7fd2-82e5-8f66c5ff9e1d</sub>

#### 4. Enforce runtime-admissibility: fit two AUCs and never ship features the live agent cannot compute
`loop` · **negative result**

Every candidate feature must be computable from the live agent's own runtime perception plus meeting observations -- this is the hard line between the shippable 'runtime' feature set and the research-only 'full' set, and a feature that improves offline fit but cannot be reconstructed at inference cannot ship regardless of predictiveness. Always report both numbers: full-feature CV AUC (research ceiling) and runtime-subset AUC; in one fit these were 0.811 full vs 0.739 runtime, exposing that the strongest exculpatory cue (watched task completions, weight ~-9.0) needs a new runtime detector first. Negative result: loading the full model and ZEROING a missing exculpatory feature biased every crew posterior UP (the pull-down never fired), causing systematic mis-votes -- a missing feature is NOT '0 evidence'; refit on the runtime-feasible subset instead. Ship only the runtime model; re-fit and re-vendor each time a new runtime detector lands.
  <sub>sources: personal_labs/crewrift_lab/WORKING_CONTEXT.md, personal_labs/crewrift_lab/suspicion_lab/README.md, personal_labs/crewrift_lab/lessons_archive/TENTATIVE_LESSONS-20260612-, claude-code:0e7b14ca-ffd5-4137-9456-47485d3c6f87</sub>

#### 5. Prevent leakage when training on game replays: group-CV, no look-ahead, visibility-clip, raw counts
`crewrift`

Rows within one game are correlated (cumulative observer-suspect snapshots grow monotonically across meetings), so use game-grouped cross-validation (sklearn GroupKFold, group=game; split by game never by row) or correlated rows leak into train and test and inflate AUC. Build per-(observer,suspect,meeting) rows where each row is one belief state: accumulate positional evidence cumulatively from tick 0 to the decision tick (never reset), but draw public/meeting evidence only from PRIOR meetings so a row never contains the outcome of the decision it predicts. Visibility-clip every positional cue using the replay expander's exact per-(observer,target) rendered-view intervals (never a modelled approximation) so offline features equal what a player in that seat could have seen; use edge-triggered detection for discrete events (count the transition INTO a vent, not every tick inside); include an exposure feature (observed_samples) to distinguish 'no evidence because innocent' from 'never observed'; and emit RAW counts/durations, deferring all binning to fit time.
  <sub>sources: personal_labs/crewrift_lab/suspicion_lab/tools/features.py, personal_labs/crewrift_lab/suspicion_lab/tools/build_dataset.py, personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/suspicion-le, personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/suspicion.md</sub>

#### 6. Benchmark before vectorizing and write idiomatic target-language code from the start
`generic` · **negative result**

Vectorization is not automatically faster -- benchmark first. Scoring ~289 candidate camera positions against a 128x128 frame by building (N,128,128) intermediate tensors was 2-3x SLOWER than a single-candidate score in a Python loop, because the large intermediates blow out L2 cache while the per-candidate form stays in L1 and the per-candidate numpy work ~ the Python call overhead. Conversely, replace O(n^2) nested-loop spatial dedup with a boolean occupancy mask (each kept hit stamps its (2r+1)-square neighborhood) and vectorise per-pixel matches with boolean-mask arithmetic plus an in-band out-of-bounds sentinel. When porting languages, write the idiomatic target form (numpy bincount/argmax/where/sliding_window_view) from the start -- a value-parity oracle catches algorithmic drift but never un-idiomatic encoding, so 'transliterate now, idiomatize later' cost a full second rewrite; but skip a numpy rewrite when it would worsen clarity for no real perf need (a branchy role-update state machine was left in loop form deliberately).
  <sub>sources: claude-code:1c7117ad-9bee-4658-8937-e92adbc1a6ae, players_checkouts/players/archive/players/among_them/coborg/PLAN.md</sub>

#### 7. Make hidden-attribute identification an LLM-free classification problem; spend the question budget
`loop` · ⚠ _session-derived, unverified_

When a game's hidden categorical attribute is drawn from a known finite list (e.g. the 61 style concepts in Cue-n-Woo's data file), make identifying it an LLM-FREE classification problem -- cheaply classify the label from observed evidence with offline text features, handing the label to an LLM only for downstream generation. Featurizer choice dominates accuracy: a word-only lowercased TF-IDF dropping <=2-char tokens scored only ~31% top-1 (it discarded case, punctuation, and function words where style lives) and matching to literal style-descriptor text scored ~21%; switching to char n-grams (word_lower / char_3_5 / combined TF) over ALL private interview questions lifts top-1 to ~95-96%, with no sklearn or sentence_transformers needed. Spend the full per-player question budget before guessing: top-1 was ~43-48% with one private question, ~85-87% with two, ~95-97% (top-3 98-100%) with three.
  <sub>sources: claude-code:b952ba45-d32f-4ce0-a227-c691de8c588f, claude-code:77e158b0-bd16-41f8-8473-d1b1522d451b</sub>

#### 8. Structure a lab notebook as one-file-per-entry with validated YAML frontmatter; do not use RAG
`loop` · **negative result** · ⚠ _session-derived, unverified_

For a player-optimization lab notebook at ~56-experiment scale, do NOT reach for a graph DB plus RAG: the graph DB is overkill and RAG actively HURTS because the notebook's value is multi-hop cross-references (reading several experiments together reveals horizon-scaling or 'byte-identical output means the flag is not firing' patterns) that embedding retrieval fragments, plus a chicken-and-egg failure (the agent must know what to ask to retrieve it). Prefer one-entry-per-file with structured YAML frontmatter (id, title, hypothesis IDs, flags, seeds, verdict/status, scores, per-seed win-loss-tie) plus a searchable prose body, a script-generated committed index, and a curated AGENTS.md heuristics list solving discovery. Institutionalize the schema: document a field reference table in protocol.md and add a validate() in the index generator (required fields, enum validity, cross-field constraints like ships_version implies verdict=ship, type checks like win-loss-tie is exactly 3 ints, cross-ref integrity) that surfaces warnings WITHOUT blocking (in stdout and a validation_warnings field) so one malformed entry doesn't poison the rest; expect prose-vs-status drift on migration and re-run until grep is clean.
  <sub>sources: opencode:ses_20b14e3aaffe6TJnfnmV5DdVmT</sub>

#### 9. Emit per-slot survival and summary metrics: each is free anomaly coverage and fills the missing fitness signal
`crewrift`

Crewrift fitness scoring has no survival term -- survivalRate is hard-coded null because the cross-game results contract gives no alive/dead signal -- so emitting per-slot death tick and survival_ticks in the event log fills the single most-wanted missing fitness signal. Precompute debugging signals as summary.* rows at the final tick: survival_ticks = death_tick - gameStartTick (or maxTick - start if never killed), time_to_first_task, vote_accuracy (fraction of a crew slot's votes targeting a real imposter, -1 for imposters), votes_received. Each new summary metric is also free anomaly coverage: the optimizer's detector z-scores and IQR-fences every numeric key in each episode's flat scores map, so a new per-episode numeric summary metric automatically becomes a new anomaly dimension at zero extra parsing cost.
  <sub>sources: optimizers/docs/events-parquet-spec.md</sub>

#### 10. alpha_cog shipped wins: deficit-weighted mining (K=2, dynamic-K), opportunistic scramble, avoid-enemy-territory
`crewrift`

Shipped alpha_cog CvC wins (machina_1, 12 seeds x 2000 steps unless noted). Deficit-weighted miner extractor selection picks the extractor for the element the hub is most short of; K=2 (the two most-depleted, in AlphaPolicyFull) is the local optimum (vs substrate 10W/0T/2L mean +0.37, subsuming the earlier K=1 ship +0.34/+22%), while K=3 was REJECTED (vs K=2 4W/1T/7L -0.33) because with only 4 elements K=3 excludes just one and dilutes the signal -- further gains need a richer rule, not larger K. Dynamic-K (V4) collapses K 2->1 only when the deficit is sharply unbalanced (min/second-min <= 0.3), giving the lowest stdev of any ship. aligner_opportunistic_scramble (V3) is additive (+0.406 with K=2): an aligner holding gear+heart with no friendly-frontier junction pursues an enemy junction before exploring, keeping all 4 aligners and converting idle ticks to scramble pressure. avoid-enemy-territory (V5) makes miners and unhearted aligners skip targets within 10 Manhattan cells of a remembered team:clips cell (the clip variant deals -1 HP/tick, killing a 50-HP miner in 30-50 ticks); nearly inert at 2000 steps but +0.863 mean at 10000.
  <sub>sources: opencode:ses_2204f83deffedZP7TPbuAnQIhs, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md, archive/cogames_playground/alpha_cog/docs/experiment-axes.md</sub>

#### 11. alpha_cog rejected experiments: don't break mining concentration, drop aligners, or retreat from valuable actions
`crewrift` · **negative result**

Rejected alpha_cog CvC experiments and their mechanisms. Hub-affordability gating ('don't camp stations you can't afford', E001/E002, -0.16) failed because camping a currently-unaffordable station is PRODUCTIVE -- other miners are delivering resources to that same hub. Mining concentration is load-bearing: per-miner distinct-element assignment (E026) lost vs V5 5W/0T/7L mean -1.089 because spreading miners across distinct elements dilutes throughput; vibe-based deconfliction (skip same-vibe peers within radius 4) failed at every hardness (hard -0.12, soft inert with 11/12 byte-identical seeds) because such peers are too rare on the 88x88 map and a return needs a policy that CLUSTERS same-vibe agents, not a better score rule. Any role-slot substitution must preserve 4 aligners: 3M/3A/1Sc/1S (E005) -0.58 and 4M/3A/1Sc (E014) -0.57, because the all-in 4M/4A mix keeps all 8 agents in the territory loop while scramblers need gear+heart and scouts have no objective -- don't add roles without supporting infrastructure. HP-aware aligner retreat (E033, -0.17 to -0.20) lost because completing the alignment is worth more than the agent abandoning it.
  <sub>sources: opencode:ses_2204f83deffedZP7TPbuAnQIhs, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md, archive/cogames_playground/alpha_cog/docs/experiment-axes.md</sub>

#### 12. Test at the deployment horizon; per-tick signals saturate at steady state, so prefer windowed-fraction over streak counters
`loop` · **negative result**

Short test episodes mislead in two linked ways, so always test at the deployment horizon (e.g. 10000 steps, not 2000) before accepting or rejecting a heuristic. (1) A defensive or compounding heuristic whose payoff only materializes late looks worthless on short episodes: the alpha_cog avoid-enemy-territory rule was nearly inert at 2000 steps but worth +0.863 mean at 10000. (2) Per-tick instantaneous signals cannot distinguish a persistent map-geometry property from a transient steady-state pattern, and that distinction itself depends on horizon: deficit experiments E026-E029 plus a deficit-streak attempt all converged on this limit -- a signal discriminates at 2000 steps but by 10000 every seed looks 'persistent'. This is the motivating reason for multi-tick temporal memory over reacting to the current tick, and when you add memory prefer a windowed-fraction signal ('fraction of the last N ticks where element X was the deficit') over a consecutive-streak counter: the streak discriminated at 2000 but became useless at 10000 because every seed develops a persistent deficit at steady state, whereas the windowed fraction separates a genuinely persistent scarce element from one that merely rotates. Two related prioritization notes: danger penalties are structurally dead within a tiny candidate set (aligner candidate sets are 0-1 junctions, so no sort-key change matters, E038/E039) -- the safety axis requires expanding the candidate set first; and deprioritize a fix whose benefit is structurally cancelled by another load-bearing behavior -- pre-gear wandering affects only 1-2 seeds and a hub-biased-exploration fix works mechanically but its throughput gain is cancelled by mining concentration, so it is low priority until temporal memory can precisely detect the stuck-agent pattern.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/experiment-axes.md, archive/cogames_playground/alpha_cog/docs/temporal-memory-design.md, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md</sub>

#### 13. Before blaming the learner, check whether a scripted oracle policy can hit the target at all
`crewrift` · **negative result**

Before treating a PPO/learning scoring gap as a reward-shaping or learning problem, check whether a scripted oracle policy can achieve the target at all. In CoGsGuard the aligned.junction.held gap (~300 observed vs ~30,000 expected, ~100x) was NOT a reward-shaping problem: even the scripted Nim and Python policies aligned zero junctions to cogs (cogs.aligned.junction.held = 0 while clips' stayed ~135,000), so the gap was a game/environment problem, not a learning problem.
  <sub>sources: players_checkouts/players/players/cogsguard/role/aligned_junction_held</sub>

#### 14. Optimizer leaderboard rows are metadata-only; accumulate opponent scouting by policy identity not episode
`loop`

In the Coworld/Crewrift optimizer, imported leaderboard competitor entries are metadata only: the library sync upserts three importer sources into policyLibrary keyed by gameId+source+externalRef -- coworld-baseline (fieldable when a real non-img_ docker tag exists), local-policy (fieldable when an image resolves), and softmax-leaderboard (always fieldable=false, playerImage=null, carrying only rank/rating/division metadata). For crewrift after a sync there are roughly 1 baseline, ~1 local-policy, and ~116 leaderboard metadata rows. When scouting opponents, group observations by the opponent's policy/library identity (policyId/libraryId/label), NOT by episode, so the same opponent across seeds accumulates into one shared dossier: read the existing dossier and merge (increment episodes_observed, fold in evidence ticks, raise confidence for repeated patterns, retire contradicted ones) rather than overwriting accumulated scouting with a single-episode view.
  <sub>sources: coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/downloa, coworld-source-repos.crewrift-upload/optimizers/.cursor/skills/downloa, coworld-source-repos.crewrift-upload/optimizers/instructions/mvp/explo</sub>

#### 15. Underexplored CvC optimization edges beyond saturated miner/navigation axes
`loop` · ⚠ _session-derived, unverified_

Concrete underexplored Cogs-vs-Clips optimization edges versus the saturated miner/navigation axes: (1) temporal-memory deficit control tracking deficit identity over a rolling window/EMA plus deposit throughput to decide miner concentrate-vs-spread; (2) aligner candidate-set expansion, since danger penalties are inert with only 0-1 candidate junctions -- build richer sets (threatened junctions, preemptive Clips-frontier targets, bridge/gap-fill) before scoring; (3) connectivity-value junction scoring that scores how much a junction repairs/extends the connected network rather than nearest-frontier-plus-hub-return; (4) night/energy-aware target selection, since movement outside friendly territory is worse at night, so phase-based hub-return weighting may compound at 10k steps.
  <sub>sources: codex:019df4c6-160f-7740-a002-fbec282812eb</sub>
