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# Perception & state decoding — guide (loop tier)

Reference notes, design rationale, and negative results (22).

#### 1. Treat localization as a hard gate and a first-class health metric with per-tick diagnostics
`loop` · ⚠ _session-derived, unverified_

Modes typically open with 'if not localized: return noop', so if the agent can't locate itself every behavior collapses to noop and a perfect strategy layer is worthless; treat localization success rate as a first-class metric with per-tick diagnostics and a cumulative success-rate counter ('0% localization success' instantly separates a perception failure from a navigation/action failure). Diagnostic tells: a pixel/OCR bot returning noop every tick is usually stuck localizing (modulabot needs hundreds of frames and may exhaust a short run still localizing -- first confirm frame shape/palette, here 128x128, 4-frame stack, palette indices); a policy emitting mostly NOOP plus uniform random directions signals a blind perception layer, not weak strategy; and '0 found, N off-screen arrows' while objects are plainly visible points at the projection math/camera estimate, not the detector. Pixel-only tiny-frame games (128x128) demand the full stack (self-localization, sprite matching with shadowed fallback, task-icon scan, radar projection, voting/chat OCR, A* on a walk mask) -- blind-patrol fallbacks are uncompetitive.
  <sub>sources: opencode:ses_21a8f9dedffekGKNrfPfGruC81, opencode:ses_21ab38af3ffeCyRfclXzAswGm1, opencode:ses_21f67d3f5ffeBF92agcd0cbOV0, opencode:ses_21fc35ba9ffeK5TywAEvVvlBBl (+1)</sub>

#### 2. Self-play can deadlock localization; always test full self-play, mask dynamic pixels first
`loop` · **negative result** · ⚠ _session-derived, unverified_

Self-play creates a positive-feedback localization deadlock: if every agent shares the same perception weakness none moves, so each view stays crowded with stationary teammates that keep perception failing; MIXED matches hide this because filler bots move and clear the scene, so always test in FULL self-play before trusting a policy. Mechanism: pixel-map localization with a fixed per-frame mismatch budget (bail after 420 mismatched pixels) is blown by other agents' sprites and especially their nameplates -- 7 stationary 12x12 sprites (~1008 px) plus ~13-char nameplates (~3000+ px) trivially exceed 420 px so it never locks. Fixes: (1) stamp the ignore mask for other-agent sprites and nameplates BEFORE localization, not after (an ordering bug; the actor scan is pure screen-space so it is safe to reorder ahead, while the belief merge needing camera coords stays after); (2) use very short identity names ('gb0','f1' vs 'amongthempolicy-0', ~85% smaller nameplate). Treat synchronized identical degenerate behavior across all agents ('all move left in unison') as shared/stale controller state or a coordinate desync, not N independent bugs.
  <sub>sources: opencode:ses_21a8f9dedffekGKNrfPfGruC81, opencode:ses_21e7dd4d4ffewtISu38YXrsOgs</sub>

#### 3. Avoid self-reinforcing stale-position and localization-gated freeze traps
`loop` · **negative result** · ⚠ _session-derived, unverified_

A perception/targeting function that filters to entities whose position matches the CURRENT tick can leave a stationary agent permanently blind: it never moves, so it never refreshes anyone's position, so the filter always returns empty, so it never moves. Stale-position filtering combined with a movement-gated perception source is a self-reinforcing trap. Likewise, gating an action mode entirely on a localized flag freezes the bot whenever perception breaks -- and since some actions (e.g. pressing the report button) themselves trigger a screen transition that breaks the localizer, localization-dependent modes need a fallback/escape path for the common case where the bot's OWN action breaks localization. When a behavior gap or localization-loss DURATION numerically matches a known game-phase timer (e.g. ~672 lost ticks matching a 600-tick vote timer plus meeting transition), suspect the bot failed to DETECT entering that phase rather than a transient glitch.
  <sub>sources: opencode:ses_1fa65507bffexDcakAPT4pHA1d, opencode:ses_20101b42dffeJU331wyDQFEn9W</sub>

#### 4. Manage the per-tick perception budget for the TAIL, and skip perception whose output is unused
`loop` · ⚠ _session-derived, unverified_

Manage a fixed per-tick budget (42ms at 24Hz) proactively: measure ACTUAL per-stage wall time with a repeatable p50/p95/p99 benchmark BEFORE optimizing and optimize for the TAIL and headroom, not the average -- the median tick can be in budget while worst-case meeting/chat frames (OCR ~25ms) blow past it with no margin for policy/A* spikes or GC. Track per-phase cost while building: OCR/text search (~12ms banner sweep) dominated localization (~1ms), actor scan (~2ms), and task/radar (~0.1ms) by an order of magnitude. Skip or fast-path expensive perception whose output cannot affect the current decision (a classifyInterstitial OCR sweep ran every lobby frame though the bot was idle) -- gate costly perception on whether its result is consumed by the current mode and cache classifications across consecutive frames (reset on phase transition). Cap brute-force fallbacks: spiral localization over a 952x534 map could evaluate ~2.7M positions x 16K comparisons (tens of billions of ops, seconds) when no match exists -- give last-resort searches a hard radius/wall-clock budget and fail fast, since the next frame's cheaper path usually recovers.
  <sub>sources: opencode:ses_21af0e707ffeWhoJeouK9agHhn, opencode:ses_21b7f7f7cffeDDc56Goq1NPBmv, opencode:ses_21e7f66a8ffeNFrL2URK8EWAn7</sub>

#### 5. Distrust fixtures: mislabeled, partial, isolated, synthetic, and 'works on fixtures fails live' are distinct traps -- capture live across presets
`loop` · **negative result** · ⚠ _session-derived, unverified_

Confirm a fixture actually contains the visual state under test rather than trusting directory names (three 'interstitial' .bin fixtures were all black voting-screen captures, not the role-reveal screen). 'Works on fixtures, fails live' is the failure fixtures cannot catch: localization can succeed on baked frames yet achieve ZERO live locks because reference data tuned to one season/palette/map silently fails to match. Validating a parser against SYNTHETIC fixtures is not sufficient confidence -- features repeatedly passed synthetic tests yet needed live-frame validation across config presets because synthetic frames omitted real-game quirks (the source game does not render exact duplicate role counts on the role-summary panel). Localization that uses prior-frame state behaves differently isolated vs sequential (some frames lock only via a cheap local refit seeded by the previous lock), so test on the SEQUENTIAL frame stream, not shuffled or isolated fixtures. Capture new live fixtures across presets for missing states before trusting a parser. Record observed-versus-designed deltas (self-colour detection unreliable, voting parser strict, glyph/text search the dominant cost) for the next agent, because real output differs from the design doc and they need reality.
  <sub>sources: players_checkouts/players/users/james/personal_cogs/persephone/TODO.md, opencode:ses_204eb0641ffeIAD293yQHlSd61, opencode:ses_21ab38af3ffeCyRfclXzAswGm1, opencode:ses_21af0e707ffeWhoJeouK9agHhn (+2)</sub>

#### 6. Treat identity as a palette/slot index, not visual hue; verify the player wire encoding
`loop` · ⚠ _session-derived, unverified_

A game's player-identity COLOR NAMES are often arbitrary labels that do not match the sprite's visual hue, and the agent's own sprite color is usually a palette/slot index unrelated to team/role - never infer identity from apparent hue or from spec prose about derivations (e.g. bitworld mapped 'RED' to PICO-8 index 3 which renders dark green). Treat identity as the raw palette/slot index, consult the game's own index-to-name mapping, treat spec prose as a hypothesis to confirm against source, and learn the identity fields (index, color, shape) together from the first frame showing the agent's own sprite. Verify which color-index encoding your policy actually receives: the viewer/global wire protocol and the PLAYER wire protocol can differ, so confirm the on-wire encoding for the PLAYER connection specifically before writing decode logic.
  <sub>sources: opencode:ses_1ffc701efffeS5c5ueXd2vWIkD, opencode:ses_201713d2dffe7pZ6AcZMDZXvNZ</sub>

#### 7. Shared perception code shares blind spots; verify mechanics against source, not rendering
`loop` · **negative result** · ⚠ _session-derived, unverified_ · _see related: S022 (other tier)_

Do not infer game mechanics or capability asymmetries from RENDERING hints alone; verify against source - an omitted UI hint does not mean a disabled action (the bottom bar dropped a K:ACT label during summit, but the real proof was the input-handler gate in source, not the missing label). Conversely, watch for engine bugs that LEAK information and decide deliberately whether to exploit them: an exchange screen that unconditionally rendered true role indicators on all sprites (bypassing the visibility system) was almost certainly a renderer bug - in a tournament where leaks can be patched anytime, document the behavior and exploit it opportunistically rather than building a strategy that depends on it.
  <sub>sources: opencode:ses_1fb148d00ffe5ob6k0yKT3aHke, opencode:ses_20101b42dffeJU331wyDQFEn9W</sub>

#### 8. Guard entity-count detection against transient join states with a phase gate, not stability alone
`loop` · **negative result**

Any detection that COUNTS on-screen entities must guard against transient/partial states during join/setup: when agents connect staggered (e.g. ~0.25s apart), each lobby frame shows only those connected so far (count grows 2,3,...,N) and an agent that latches a partial count is misclassified. A naive 'wait for N consecutive identical frames' gate is insufficient because state is STABLE between discrete change events (the count held constant for ~6 frames between connections, so even a 10-frame window latched a partial count). Combine a longer stability window with a PHASE gate (e.g. a title-text-region check) and reasoning about fixed phase durations (a fixed-length role-reveal vs a variable-length lobby), because stability alone cannot distinguish a settled state from a temporarily-static intermediate one. When all entities are co-located at the first active tick, an initial distinct-count is a strong roster estimate, but prefer an authoritative census (e.g. a meeting roster) when present.
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/design.md, opencode:ses_2051bd225ffefNc9y2aOEEFjHW</sub>

#### 9. Classify the observation surface first and prefer a symbolic channel; build deterministic parsers simplest-to-complex before any VLM
`loop` · **negative result** · ⚠ _session-derived, unverified_

Before generating any pixel-perception pipeline, capture tool, or explorer, classify each game's interface as symbolic-primary (structured JSON/tokens/state), visual-primary (pixels/render packets), or mixed/alternate (a visual contract with a structured side channel like a native observation array or replay/global data), inspecting game SOURCE not generated docs and recording it in the interface-contract doc -- among_them exposed positioned sprite objects (/sprite_player), full world-space coordinates for all entities (/global), and a plain-text reward stream (/reward), so a structured endpoint gives entity positions, phase labels, and own position directly. Building pixel perception when a symbolic channel exists is wasted effort. Symbolic gets a typed decoder + rule-based policy with no VLM; visual gets a deterministic parser FIRST that escalates only uncertain frames to a VLM. Build deterministic parsers simplest-to-complex: frame dimensions/packet decoding, then exact color/palette checks, then template matching for stable UI, then OCR only where text is necessary and stable, then small learned classifiers only when nothing simpler works; require MULTIPLE fixtures per view/variant before promoting a parser so it does not overfit one frame. Do NOT build an automatic visual-frame explorer for games whose navigation requires visual understanding -- it is circular (you'd need the perception agent you're building to collect the right frames); read source/docs, classify, then generate targeted capture tools after a concrete perception need is identified. Generate the decoder per-game from discovered docs, never hardcode it (not every game uses Among-Us-style 4-bit packed buffers, and a grayscale-index decoder can be structurally correct yet color-unfaithful by ignoring the real palette). When scoring observation-surface text to classify, handle negation -- a classifier gave a false visual score from negated phrasing like 'no binary payload'.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/eyes_v1_deprecati, players_checkouts/players/tools/cogbase/docs/designs/guide_v1_design.m, players_checkouts/players/tools/cogbase/docs/designs/maker_v1_design.m, codex:019e13cd-d482-7133-a38d-c683c32a3004 (+2)</sub>

#### 10. Wire a VLM as a slow, constrained, schema-bound oracle, treat output as fallible evidence, and drive reliance down over iterations
`loop` · **negative result** · ⚠ _session-derived, unverified_

Treat a vision-language model as a slow, schema-bound oracle for labeling frames and recommending safe next actions while deterministic parsers are weak, then compile its outputs into deterministic cacheable testable perception code over time -- never a free-form player, never authority. Never let it assert hidden state (hidden roles, unseen cards, off-screen map) as fact, invent actions outside the guide-derived registry, modify code, write memory, or override safety checks. Constrain every call to a fixed small structure -- static system contract, small per-game play card, run context, image payload, and a strict versioned JSON response schema whose state_observations marks each value observed vs inferred vs guide_prior; keep context small (play card plus at most one targeted doc snippet, never full guides/source/prior transcripts, which degrade the call). Gate calls on explicit conditions (parser confidence below threshold, novel perceptual hash, a known view missing a critical field, stuck after deterministic recovery, a planned parser test needs a label) and skip them when symbolic decoding is complete, the frame matches a known fixture, the action is a trivial wait/repeat, or the budget is exhausted. Design the loop so VLM reliance DECREASES over time as deterministic perception/control is built up: the VLM bootstraps labels then is replaced by a generated decoder/parser; track dependence as a first-class metric (calls/episode, calls/minute, fraction handled deterministically, parser fields covered by tests) and drive it down by caching calls, promoting validated labels into deterministic fixtures, and adding parser code for high-frequency uncertain fields first -- if usage does not decrease over iterations, the loop is failing. Do NOT build a fully autonomous visual explorer as the first stage (circular: the explorer must already parse enough frames to navigate to the views it is meant to capture); establish the observation/action contract from source first. A cheap reliable improvement is often REMOVING a perception crutch rather than adding cleverness; and a generated decoder spec must FORBID reusing another game's framebuffer/palette/dimensions/transport/schema assumptions unless the current game's docs/source independently rediscover them, emitting a working decoder only when docs expose concrete raw-visual facts, otherwise failing before spending VLM budget. The submitted player should prefer deterministic code and not depend on an external VLM unless the target environment explicitly allows it.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/eyes_v1_deprecati, players_checkouts/players/tools/cogbase/docs/designs/maker_v1_design.m, codex:019e13cd-d482-7133-a38d-c683c32a3004, codex:019e1df4-08f8-7b33-9a82-18024a199075</sub>

#### 11. When bootstrapping a new Coworld player, copy the transport/packaging template but NOT the perception/belief stack
`loop` · ⚠ _session-derived, unverified_

The among_them/coborg pixel-CV player (bitscreen_v1, 8192-byte packed frames, sprite matching, OCR font) is a good transport/packaging template for a new Crewrift player -- bridge, Dockerfile, entrypoint, build.sh, types, trace, idle-mode, and test structure map almost directly -- but you must NOT copy its perception/belief stack: crewborg perceives from structured Sprite-v1 labels and coordinates, which its design explicitly chose over pixel CV. Generated decoders must rediscover each game's raw-visual facts from its own docs/source rather than inheriting another game's framebuffer/palette/transport assumptions.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/maker_v1_design.m, claude-code:c7a8b339-b61e-4359-95e4-7701c6a548a3, claude-code:cb446cb3-1370-4f0b-9de6-34f05b9e5880, claude-code:f886e63d-1c92-4afd-bb2b-f4ff2cd9b788</sub>

#### 12. A live game policy cannot see durable identity; design strategy only around what the observation packet contains
`loop` · **negative result** · ⚠ _session-derived, unverified_ · _see related: U0751 (other tier)_

A live game policy generally cannot observe durable opponent identity: the player-facing observation typically exposes other players only as within-episode slot/join-order/color/flags, while names and policy IDs exist only in offline server roster, results, and replays. Do not design in-game strategy around durable identity the agent cannot actually see, and confirm exactly what the observation packet contains before assuming a feature is available online. When you DO need durable identity (e.g. for offline training-feature attribution), get it from authoritative offline artifacts, not from anything the live policy could perceive.
  <sub>sources: codex:019e8fc7-ebad-7b00-bc95-df1727641ce0, codex:019e9656-7cfc-7032-ae70-82b6dbc0a053</sub>

#### 13. Make offline and runtime perception features identical by computing exact per-observer visibility
`loop`

When an offline replay expander computes EXACT per-(observer,target) rendered-view visibility every tick, an observer's evidence is simply the global event stream intersected with that observer's visibility intervals. This makes offline training features and runtime perception features the same quantities by construction, so P(evidence seen) becomes a set intersection rather than something you have to model or approximate - a strong reason to compute exact visibility in the expander rather than estimate it.
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/docs/designs/suspicion-le</sub>

#### 14. Verify map/geometry against replay artifacts, not policy beliefs; reconcile coordinate conventions before calling a mismatch
`loop` · **negative result**

Verify map/geometry claims against replay artifacts (header, object tags), not the policy's own logged beliefs which can be stale (a policy still emitting old hub targets). A larger map size in a replay header does NOT mean the mission map changed: e.g. a 98x98 Coworld header is an inner 88x88 arena plus MapGen border_width=5 on every side (88+5+5=98), the full grid including border, not a swap. Replay object coordinates and policy memory often use different conventions (replay serializes [x,y] while memory/logs are (row,col)), so convert (e.g. row=y-border, col=x-border) before comparing - an apparent mismatch is often just a convention error. Do NOT trust the observation's last_action token as ground truth for whether a move applied: a run emitted move_* every tick while every following observation reported last_action=noop though the replay showed agents moving, so chasing the token chased a symptom while the real failure was a doorway deadlock.
  <sub>sources: archive/cogames_playground/bulbacog/COGS_V_CLIPS.md, archive/cogames_playground/bulbacog/designs/LOCALIZATION_ROOT_CAUSE_RE, codex:019e28a2-f81d-79b1-a7f7-5f6583c280f1, codex:019e28e9-6b45-7c02-b4c7-bb9b0f44e022</sub>

#### 15. In Cogs-v-Clips, inv:/role tokens appear on EVERY visible object; decode the :pN power encoding, read peers from gear/vibe/agent_id/team, and collect across cells
`loop` · ⚠ _session-derived, unverified_

In Cogs vs Clips the mettagrid C++ observation engine emits inventory tokens (inv:*) for EVERY non-zero inventory item on EVERY visible object in the agent's vision window, with no self-vs-peer filtering (GridObject::write_obs_features() in grid_object.cpp ~178-203 unconditionally emits inv: for every item amount>0 and every object gets an obs_encoder). So you can read every other agent's HP/energy/hearts/gear/role and carried elements; the hub's full inventory (oxygen, carbon, germanium, silicon, hearts) when the hub cell is in view; and each extractor's exact remaining resource count (starts at 200, decreases with use) -- do NOT design strategy around 'I can only see my own inventory'. Token decoding: tokens are (packed_location, feature_id, value); decode location from the packed byte (upper 4 bits row, lower 4 bits col), treat 0xFF as empty (skip) and 0xFE as global; the egocentric window is 13x13 with the agent fixed at row 6 col 6. Inventory uses a :pN power encoding split across tokens -- inv:<item>:p<N> contributes value*(normalization**N) (plain inv:<item> is power 0) -- so split the name on the last :p, multiply, and sum all tokens sharing the item name (naive raw reads undercount large stacks). Because inv: tokens appear at non-center cells, a parser reading only ego-center drops all peer/hub/extractor inventory; collect grid_inventory: dict[(row,col),dict[item,int]] and grid_agent_ids across every visible cell. Infer a peer's role two ways: DEFINITIVELY from a nonzero gear inventory token (inv:miner/aligner/scrambler/scout), or from the vibe feature (id 5, norm 255; 1=heart,2=gear,3=scrambler,4=aligner,5=miner,6=scout,0=default) -- but vibe==0 is SUPPRESSED and emits no token, so treat 'no vibe token' as default not unknown and prefer gear. agent_id (0-based grid index, max 255 since obs is uint8) is emitted at EVERY location including the observer's own center cell, so peer identity tracks frame-to-frame (a DOCUMENTATION BUG in alpha_cog's docs/cogames-policy-api.md calls agent_id 'self (global)' -- trust an empirical token dump). agent:group (numeric team_id), type:agent, team:cogs/team:clips appear as tag tokens; derive your own team's tag IDs from the center cell's team:* tags at runtime rather than hard-coding. Token emission order per agent cell is tags, then vibe (if non-zero), then inventory, then agent:group, then agent_id -- the trailing agent_id can be truncated under tight max_tokens budgets. CAVEAT: the alpha_cog parser (alpha_cog/v0/obs_parser.py) only parses inv:* at the CENTER cell (the loc == center_loc guard near line 157), so peer gear awareness requires extending it; use token.feature.name (ObservationFeatureSpec.name) not a feature-id dict scan, and access location via the Location NamedTuple's .row/.col (not .r/.c) -- both were recurring bugs in agent-generated parser code. SpatialMemory holds only cells/position/visited/blocked/passable/interactive -- enemy and territory data live in raw Cell.tag_ids and the per-tick memories.scratch dict (wiped each tick), so anything needed across ticks must not live only there.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/cogames-policy-api.md, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-rules.md, archive/cogames_playground/bulbacog/COGS_V_CLIPS.md, archive/cogames_playground/bulbacog/designs/MEMORY.md (+9)</sub>

#### 16. Verify a mettagrid observation feature is actually EMITTED in compiled C++, not just registered
`loop` · **negative result** · _see related: S027 (other tier)_

Do not assume a feature is emitted because it is registered/enabled -- VERIFY emission in the compiled C++ engine (mettagrid_c.so), not the Python registry which only registers id/name/normalization in id_map.py and unpacks tokens in simulator/interface.py. To answer 'what can my CvC player perceive?' trace the actual C++ emission path: the per-cell loop in mettagrid_c.cpp, GridObject::write_obs_features()/obs_features() in grid_object.cpp, agent obs in agent.cpp, the encoder in observation_encoder.hpp, and the factory wiring obs_encoder onto every object (grid_object_factory.cpp); C++ feature-ID constants live in observation_features.hpp. Game-config files (gear.py, heart.py, elements.py) configure inventory CONTENTS but add NO observation-visibility filtering. In machina_1 several enabled features are DEAD: goal (ObservationFeature::Goal never referenced in any emission path even with goal_obs=True) and all protocol_input:*/protocol_output:* features (IDs 36-59, lookup tables built but never queried because cogsguard uses handler-based interaction) -- build no logic on them. A claimed ~60 unused features was largely a mirage -- ~25 fully dead, most live-but-unparsed ones redundant or near-zero. To confirm a feature is emitted (not just defined), trace from the id_map definition to the config flag that enables it (e.g. local_position=True in make_base_env() at cogsguard/missions/mission.py:40), then verify empirically in a token dump. To decide which features are WORTH using, compare against competition-grade reference policies (scripted_alpha, cogames-agents), not the raw feature space. Parsing own-state tokens (inv:hp, inv:energy, inv:solar) is a prerequisite for adaptive phase behavior since alpha_cog's parser does NOT read them, while clock-derived phase flags (episode_completion_pct, tick%200 day/night) have repeatedly failed, making state-derived adaptation the untested promising direction.
  <sub>sources: archive/cogames_playground/bulbacog/COGS_V_CLIPS.md, alpha_cog/AGENTS.md, opencode:ses_1f98db060ffeKMm2h0af93sMl4, opencode:ses_200493314ffeuU44LQ1zuZaduk (+3)</sub>

#### 17. Mine untapped mettagrid features: territory ground-truth, lp absolute position, per-cell inventory, team reward
`loop`

The cogsguard policy reads only ~15-20 of ~74 features; high-value unread ones: territory:* gives PER-TICK ground-truth territory status and directly SUPERSEDES the H028 spatial-memory + Manhattan approximation. lp:east/west/north/south (IDs 7-10, norm 255, global) give the agent's absolute displacement from its SPAWN point (clamped to 255, emitted only when displacement>0 and only on the axis moved, zeros suppressed, updated on successful moves) -- a shared spawn-anchored absolute coordinate frame (NOT relative dead-reckoning) all agents share; since cogs spawn at the hub, LP equals exact hub displacement (zero-drift absolute position), populated but UNUSED by alpha_cog -- valuable for hub-distance, drift correction, cross-agent coordination, and a cheap BFS-navigation alternative. inv:* tokens are emitted at EVERY visible object's cell (other agents and stations too), so reading inventory only at center_loc discards visible teammate/enemy gear/HP/energy -- useful for avoiding duplicate gear runs and inferring teammate roles. last_reward is a TEAM-level per-tick signal (junctions_held/max_steps*100), not agent credit. agent:group (team ID) carries signal only in multi-team maps like four_score (all group 0 in single-team machina_1). Exposed != useful: directional territory features were parsed yet a navigation-level use was rejected -- check the cogsguard config for the actual encoding.
  <sub>sources: alpha_cog/AGENTS.md, opencode:ses_219ac1095fferkumOQK8Ph6LTk, opencode:ses_219cf4bdbffeNdmIXfRPfKWSJD, opencode:ses_21b3c677dffe3YCY8JnACrbDmu</sub>

#### 18. The cogames vibe channel is pure signaling; read role from gear, read peers via agent_id + vibe
`loop` · **negative result**

In default machina_1/cogsguard the vibe channel is PURE signaling with NO gameplay effect: seven change_vibe_* actions ({default,heart,gear,scrambler,aligner,miner,scout}), no resource cost, no cooldown, only the per-tick action slot; mechanical role is set entirely by the GEAR inventory item (same names, looked up independently). Misreading '0 vibe changes' as 'agents never geared up' is a known wrong diagnosis -- read inventory/role metrics (aligner.amount, miner.amount, junctions aligned) from `cogames episode show`, not vibe counts. Reading a peer's vibe: it arrives as one 'vibe' token at their cell when visible (value = positional index 0=default..6=scout), is STATEFUL (compare to last tick to detect change), and the simulator SUPPRESSES value-0 (default) tokens as a sparse optimization, so correlate the per-cell agent_id feature (id=11) with vibe tokens to disambiguate 'visible signaling default' from 'not visible'. A dual-buffer move+vibe action reports the VIBE index in last_action, so 'did I move?' must read last_action_move. Vibe coordination is closed at all rule hardnesses (peers too rare on the 88x88 map); vibe regains coupling only under -v forced_role_vibes/clear_vibes/attack, and the richer 'talk' channel (140-char, sender ID, 50-tick cooldown) is NOT active by default -- re-verify the active variant set.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/cogames-policy-api.md, archive/cogames_playground/alpha_cog/docs/cogames-verification-report., archive/cogames_playground/alpha_cog/docs/cogs-v-clips-rules.md, archive/cogames_playground/alpha_cog/docs/postmortem-vibes-vs-gear.md (+2)</sub>

#### 19. Read CvC mechanics from source: Clips timer cadence, HP=0 clears inventory, HP cap is dynamic
`loop` · ⚠ _session-derived, unverified_

The CvC 'Clips' NPC faction is NOT an agent-based policy with observations or decision logic; it is purely event-driven via a ClipsConfig (a TeamConfig) in cogsguard/game/clips/clips.py -- read its config fields and periodic event handlers, not a nonexistent policy class. It is timer-driven, not reactive: under default config it does periodic align+scramble every 200 ticks starting at tick 100 (scramble_radius=15, greedy_expand_from_ships=True, greedy_max_search_radius=120; angry_target_enemy_hub and adaptive_enabled default False), a predictable cadence a player can pre-empt. In cogsguard, HP=0 'death' is just an on_tick handler hp_death (cogsguard/game/damage.py) whose only effect is a ClearInventoryMutation over destroy_items -- there is NO respawn, teleport, stun, relocation, invisibility, kill-stat, or death counter, so assuming death means removal-from-play misleads the policy. The HP cap is computed from held inventory modifiers, NOT a static per-role constant (scout=0 drops cap 400->100, scrambler=0 drops 200->100), so gear loss lowers the cap. In alpha_cog, HP is read as inventory.get('hp', 50) -- the default 50 is used whenever no HP token is observed, so HP-gated logic (the H041 aligner HP-retreat) silently treats unknown-HP as full health and will not trigger when the token is missing; any new HP-gated logic must re-derive HP this way since there is no shared current_hp accessor. Read actual Handler definitions in the installed package, not high-level summaries.
  <sub>sources: opencode:ses_1fc9940fcffeyZ2EpuUDUVgL0H, opencode:ses_1fed18a4dffeC2IduPwxEO7EGU, opencode:ses_1fedfc8aaffetD2FvlemoKGklS, opencode:ses_200badeadffeB2E7J3adm6BmGw</sub>

#### 20. Resolve CvC extractor corners from source order and the active layout path
`loop` · **negative result** · ⚠ _session-derived, unverified_

In the CvC hub compound the canonical extractor list and order lives in mettagrid/mapgen/scenes/compound.py as DEFAULT_EXTRACTORS = (carbon, oxygen, germanium, silicon) -> corners top-left, top-right, bottom-left, bottom-right; do NOT assume alphabetical or clockwise. Corner positions are defined upstream as (x,y) tuples [(2,2),(w-3,2),(2,h-3),(w-3,h-3)] but placed via grid[y,x] (row-major numpy), so SWAP to (row,col): for a 21x21 hub carbon (2,2), oxygen (2,18), germanium (18,2), silicon (18,18). A real bug arose when a design doc swapped oxygen and germanium by confusing the (x,y) source convention with the player's (row,col) convention. machina_1 wires extractors via ExtractorsVariant.modify_env (cogsguard/game/extractors.py) -> Compound._resolve_corner_names() -> DEFAULT_EXTRACTORS; trace mission wiring to confirm placement. There is also a SECOND layout path, _render_tight_layout, placing extractors relative to center at (cx+-2, cy+-2) -- e.g. (8,8),(12,8),(8,12),(12,12) for a 21x21 hub -- so a player keying off hardcoded corners is wrong under the tight layout; detect which layout the mission actually runs before trusting fixed coordinates.
  <sub>sources: opencode:ses_1f6f01a3bffe9MKEyGfToGKioO, opencode:ses_1f6fd5afeffel4LZ5rEfFadgJO</sub>

#### 21. Verify game mechanics from a live episode before designing detectors or sweeps; maps can be procedural
`loop` · **negative result**

Verify assumed mechanics BEFORE designing a parameter sweep or you waste a run on the wrong range: a miner cargo-deposit-threshold sweep first tried 2/3/4 from the base cargo limit, ignoring that miner gear adds +40 capacity and each extractor bump yields 10, so the meaningful firing range was actually 11/21/31. A config's value is not the live runtime value -- 'initial energy 100' was clamped to a separate capacity of 20 on the first tick -- so VERIFY by resetting one episode and parsing the agent's actual initial observations. When a per-deposit economy changes, RE-DERIVE detector thresholds against the new regime: switching from continuous 10-every-15-ticks to bursty 40-every-~100-ticks turned an ambiguous 'flat hub inventory' stall signal into an unambiguous 'no recent deposit' one. And do NOT bootstrap a map fixture from a single replay seed: a central junction present in seed 0 was absent in seeds 1/2/7, proving it is PROCEDURAL content near the compound, not a deterministic fixture.
  <sub>sources: archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H (+2)</sub>

#### 22. Scope perception to what the strategy consumes, but enumerate the full action/feature space first
`loop`

Scope perception work to what the chosen strategy actually consumes -- you don't always need the hardest piece: chat OCR (reading others' chat) was deliberately skipped because the impostor strategy INITIATES chat and the evidence-only crewmate strategy IGNORES it. But before assuming information is unavailable, introspect the FULL space: enumerate the action space from the environment, not just what the policy emits (machina_1 has noop, four moves, and seven change_vibe_* actions, but alpha_cog used only noop and moves), and an observation space can expose ~74 features when a policy reads only ~15-20. Peer-observation rebalancing is bounded by how few observations arrive before the deadline (only 2-3 teammate-gear observations before an agent gears up can't distinguish severe from moderate imbalance, raising the minimum-observations bar kills all firing, and unanimity guards fail because your own mixed-role agents break unanimity) -- design such gates around the small-sample reality and LOCK the role once gear is acquired or a deadline passes.
  <sub>sources: archive/cogames_playground/alpha_cog/experiments/notebook/hypotheses/H, alpha_cog/AGENTS.md, opencode:ses_219ac1095fferkumOQK8Ph6LTk, opencode:ses_224eeb40bffeZqmBRcrjFgPFul</sub>
