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# Data-science refit (outer loop) — guide (loop tier)

Reference notes, design rationale, and negative results (11).

#### 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
`loop` · ⚠ _session-derived, unverified_ · _see related: S004 (other tier)_

Confirm game/engine mechanics from the installed engine SOURCE (the packages your runtime actually loads), not from replay inference, the bare initializer, or prose rulebooks. Verified CvC machina_1 facts that contradicted assumptions: energy regen is SET each tick to the current solar value via a mutation, so it does NOT accumulate (policies assuming additive regen mis-budget); day/night init is a trap (solar inits to night then a tick-0 event bumps it, so day=3/night=1 and night starts at day_length//2); and death does NOT respawn at base -- hp_death clears hearts/gear but leaves the agent in place. The respawn assumption 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**

For any coworld policy with a learned component, draw a hard line between the shippable 'runtime' feature set (computable from the live agent's own runtime perception plus its legal observations) and the research-only 'full' set: a feature that improves offline fit but cannot be reconstructed at inference cannot ship regardless of predictiveness. Always report BOTH the full-feature CV metric (research ceiling) and the runtime-subset metric (e.g. 0.811 full vs 0.739 runtime AUC) to expose the gap. NEGATIVE: loading the full model and ZEROING a missing feature is wrong -- a missing feature is not '0 evidence'; zeroing a strong exculpatory cue let the pull-down never fire and biased every posterior, causing systematic mis-decisions. Refit on the runtime-feasible subset; re-fit and re-vendor whenever 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. 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>

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

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>

#### 7. alpha_cog rejected experiments: don't break mining concentration, drop aligners, or retreat from valuable actions
`loop` · **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>

#### 8. 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 game-policy heuristic. (1) A defensive or compounding heuristic whose payoff only materializes late looks worthless on short episodes (an 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 -- a signal can discriminate at 2000 steps yet by 10000 every seed looks 'persistent'. This motivates multi-tick temporal memory over reacting to the current tick, and when adding memory prefer a windowed-fraction signal ('fraction of the last N ticks where condition X held') over a consecutive-streak counter, which becomes useless once every seed develops a persistent steady state. Two prioritization corollaries: a danger/safety penalty is structurally dead within a tiny candidate set (no sort-key change matters when there are 0-1 candidates) -- expand the candidate set first; and deprioritize a fix whose benefit is structurally cancelled by another load-bearing behavior.
  <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>

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

Before treating a learning/PPO scoring gap as a reward-shaping or learning problem, check whether a scripted oracle policy can achieve the target at all. In one case a held-metric gap (~300 observed vs ~30,000 expected, ~100x) was NOT a reward-shaping problem: even scripted policies aligned zero of the target objects while the opponent's stayed high, so the gap was a game/environment problem, not a learning problem.
  <sub>sources: players_checkouts/players/players/cogsguard/role/aligned_junction_held</sub>

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

In the Coworld optimizer, imported leaderboard competitor entries are metadata only: the library sync upserts importer sources into policyLibrary keyed by gameId+source+externalRef -- a baseline source (fieldable when a real docker tag exists), a local-policy source (fieldable when an image resolves), and a leaderboard source (always fieldable=false, playerImage=null, carrying only rank/rating/division metadata). 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>

#### 11. 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>
