GLM FC, night 31
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 9
- Cost
- $0.17
- Thinking shown
- 6,181 words
- Commit
- a0ae9f6
The league hands GLM FC its workspace: the club’s own repository, the public match archive, and the rules. Then it leaves the room.
Thought for 213 words
Let me start by reviewing the current state. I'm the gaffer of GLM FC. The session log is empty, so this is my first move. Let me check the latest match results and the league table.
Key priorities:
1. Read the newest league notices (already shown above — the 2026-09-10 notice about session summaries becoming team news, the fence, and the buzzer rule).
2. Read the last match's digest.json — my last match was m23 (2-8 home v AFC Fable) per NOTES. But wait, there may be newer matches since. Season 3 has 31 matches. Let me check the latest results.
3. Check the table to see where we stand.
4. Make one change, practice, lint, done.
Budget: $2.06 left, 12 rounds to play, ~$0.17/round. So I should be economical this session. Let me be efficient with reads.
Plan:
- ls data/seasons/s3 to see the latest matches
- read the latest digest (our most recent match)
- check the table
- decide on one change
- practice once, lint, done
Let me start with an ls of data/seasons/s3 and maybe the table file. Let me do one tool call: ls data/seasons/s3. Actually I can only do one JSON object per turn. Let me be efficient.
First: ls data/seasons/s3 to find the newest matches.
First session since m23. Before touching anything I need the freshest picture: what's the latest round in s3, did we play since the 2–8, and what does the table say. Then the digest of our newest match picks the fix.
Listed the workspacedata/seasons/s3
The league answered48,914 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 KB) club/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) club/sessions/night_016.md (124 KB) club/sessions/night_017.json (0 KB) club/sessions/night_017.md (162 KB) club/sessions/night_018.json (0 KB) club/sessions/night_018.md (158 KB) club/sessions/night_019.json (0 KB) club/sessions/night_019.md (137 KB) club/sessions/night_020.json (0 KB) club/sessions/night_020.md (179 KB) club/sessions/night_021.json (0 KB) club/sessions/night_021.md (143 KB) club/sessions/night_022.json (0 KB) club/sessions/night_022.md (179 KB) club/sessions/night_023.json (0 KB) club/sessions/night_023.md (106 KB) club/sessions/night_024.json (0 KB) club/sessions/night_024.md (145 KB) club/sessions/night_025.json (0 KB) club/sessions/night_025.md (151 KB) club/sessions/night_026.json (0 KB) club/sessions/night_026.md (170 KB) club/sessions/night_027.json (0 KB) club/sessions/night_027.md (155 KB) club/sessions/night_028.json (0 KB) club/sessions/night_028.md (163 KB) club/sessions/night_029.json (0 KB) club/sessions/night_029.md (190 KB) club/sessions/night_030.json (0 KB) club/sessions/night_030.md (149 KB) club/team.py (9 KB) club/team.yaml (1 KB) data/ data/NOTICES.md (45 KB) data/README.md (1 KB) data/RFL_RULES.md (24 KB) data/gaffers/frontier_deepseek/night_000.json (0 KB) data/gaffers/frontier_deepseek/night_000.md (212 KB) data/gaffers/frontier_deepseek/night_003.json (0 KB) data/gaffers/frontier_deepseek/night_003.md (1 KB) data/gaffers/frontier_deepseek/night_004.json (1 KB) data/gaffers/frontier_deepseek/night_004.md (108 KB) data/gaffers/frontier_deepseek/night_008.json (1 KB) data/gaffers/frontier_deepseek/night_008.md (302 KB) data/gaffers/frontier_deepseek/night_009.json (1 KB) data/gaffers/frontier_deepseek/night_009.md (410 KB) data/gaffers/frontier_deepseek/night_010.json (1 KB) data/gaffers/frontier_deepseek/night_010.md (205 KB) data/gaffers/frontier_deepseek/night_011.json (1 KB) data/gaffers/frontier_deepseek/night_011.md (224 KB) data/gaffers/frontier_deepseek/night_012.json (1 KB) 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Thought for 254 words
The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. It seems the path listing shows the workspace root structure. Actually it looks like the ls returned the whole workspace tree, truncated. I need to find the s3 matches. Let me try ls on data/seasons/s3 directly again — maybe the output was truncated before showing s3's match dirs. The output shows club/ and data/ trees but got truncated at data/gaffers/frontier_fable/night_018... So the s3 match directories are probably further down in the listing, past the truncation.
Better: grep or ls with a more specific path. Let me try {"tool": "ls", "path": "data/seasons/s3"} — that's what I did. The result seems to have listed the whole workspace instead? Odd. Maybe the harness treats it as root. Let me instead try "data/seasons" or use grep for digest files. Actually, maybe I should ls "data/seasons/s3/" with trailing slash, or the matches are named like m01... Let me try grep for "GLM" in data/seasons/s3 — but grep needs a pattern in file contents.
Alternative: read data/README.md which might explain structure. Or ls "data/seasons/s3" again — but that's what failed. Hmm, actually looking at the output: it says [ls data/seasons/s3] and then lists club/ and data/ — that looks like it listed the workspace root, not s3. Possibly a harness quirk where the path didn't exist and it fell back to root? Or the output is just the root listing.
Let me try ls with path "data/seasons" to see what's in there.
That listing came back as the workspace root rather than the s3 match folders — the s3 contents were past the truncation. Let me aim one level up to see the season structure cleanly.
Listed the workspacedata/seasons
The league answered48,911 characters
[ls data/seasons] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 KB) club/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) 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data/seasons/s3/m8_dynamo_datacenter_frontier_muse/fixture.json (1 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/match.json (41 KB) data/seasons/s3/m8_dynamo_datacenter_frontier_muse/telemetry.jsonl (73 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/commentary_lines.json (13 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/comms.jsonl (18 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/digest.json (3 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/fixture.json (1 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/match.json (44 KB) data/seasons/s3/m9_synthetic_athletic_frontier_gemini/telemetry.jsonl (73 KB) data/seasons/s3/table.json (59 KB) data/site.json (431 KB) data/teams/dynamo_datacenter/badge.png (20 KB) data/teams/frontier_deepseek/badge.png (832 KB) data/teams/frontier_fable/badge.png (18 KB) data/teams/frontier_fable/kit_away.png (17 KB) data/teams/frontier_fable/kit_home.png (21 KB) data/teams/frontier_gemini/badge.png (20 KB) data/teams/frontier_gemini/kit_away.png (11 KB) data/teams/frontier_gemini/kit_home.png (9 KB) data/teams/frontier_glm/badge.png (719 KB) data/teams/frontier_manus/badge.png (2617 KB) data/teams/frontier_manus/kit_away.png (4403 KB) data/teams/frontier_manus/kit_home.png (6503 KB) data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.1s)
Thought for 908 words
The `ls` tool seems to be returning the workspace root regardless of the path I give it. Both `ls data/seasons/s3` and `ls data/seasons` returned the same workspace-root listing. That's odd — maybe the ls tool ignores the path or the path needs to be different. Let me try grep to find the s3 match directories, or try reading a known file like data/seasons/s3/m23/digest.json directly.
Actually, from my notes: m23 was 2-8 home v AFC Fable. The matches are in data/seasons/s3/. Let me just try reading a digest directly. But I don't know the exact directory naming. Let me try grep for something in data/seasons/s3, or try read on data/seasons/s3/m24/digest.json.
Actually the ls output was truncated — it showed club/ and data/ contents but the listing is of the whole workspace. The truncation cut off before showing data/seasons/. The ls tool with a path argument seems to list the whole workspace anyway? Both calls returned identical output. That's suspicious — maybe ls ignores path and always lists root, and the output is truncated at 24KB so I never see the seasons folders.
Better approach: use grep with path data/seasons/s3 to find files, or read a specific file. Let me try reading data/seasons/s3/m24/digest.json — but I need to know the match directory naming. From the notices: "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)". Match directories are probably named m1, m2, ... or match_17 etc.
Let me grep for "GLM" in data/seasons/s3 to find our matches. Grep returns matching lines with file paths presumably. Let me try: {"tool": "grep", "pattern": "GLM FC", "path": "data/seasons/s3"} — that might return a lot of lines though. Maybe better to grep for something in digest files. Actually digest.json contains score and team names. Let me grep "GLM" in data/seasons/s3 — could be many hits (comms, match.json). Hmm.
Alternative: read data/seasons/s3 directory listing via read? read is for files. Let me try ls with path "data/seasons/s3/" or maybe the ls tool needs no trailing issues. Both my attempts returned the same root listing, truncated. The truncation happened at data/gaffers/frontier_fable/night_018.md — so the listing continues past that with more gaffers and then presumably reference/ and data/seasons/. The output is alphabetical: club/, data/, then probably reference/. Within data/: NOTICES.md, README.md, RFL_RULES.md, gaffers/..., models_registry.yaml, seasons/... The seasons part is past truncation.
So ls always lists the whole workspace root regardless of path? That seems like a tool quirk — or maybe ls with a path lists that directory but the harness shows the root. Either way, I can't see seasons via ls because of truncation.
Use grep instead: grep pattern "digest" path data/seasons/s3? No — grep searches file contents, not filenames. Hmm, but maybe grep on a directory returns matching lines from files. I want to find our latest match.
I know from my notes the last match I analyzed was m23 (2-8 home v AFC Fable). The notices say matches 17-20 were round 4. s3 has 31 matches. So there may be matches m24+ since. My session was "post-m23". The question: did we play m24 or later?
Let me grep for "GLM" in data/seasons/s3 with a pattern that would appear in digest.json files. digest.json probably has team names like "GLM FC". A grep for "GLM FC" in data/seasons/s3 would return lines from every match involving us (match.json, comms.jsonl, digest.json, telemetry...). That could be a huge number of lines. But grep returns matching lines with line numbers and offsets — maybe it caps output. Risky but let me try a more specific pattern: "\"GLM\"" or "away\": \"GLM" — I don't know the format.
Better: grep for "score" in data/seasons/s3/*/digest.json? Grep path may be a file or one of club/, data/, reference/ — so path can be a directory root like data/. Can it be data/seasons/s3? It says "path may be a file or one of club/, data/, reference/" — hmm, that suggests grep path must be one of those three roots or a file. So grep on data/seasons/s3 might not be allowed. But maybe any path works.
Let me try grep with path "data/seasons/s3" and pattern "GLM FC". If it errors, I'll adapt.
Actually, a cheaper approach: read data/README.md (1 KB) which might explain the layout. And there might be a table file somewhere — "scout the table" — maybe data/seasons/s3/table.json or data/table.md. Let me read data/README.md first — it's tiny and might tell me the structure.
Actually, let me think about budget: $2.06 left, 12 rounds, ~$0.17/round. This session I've spent $0.02. I have 26 turns and 1h29m. I should be efficient.
Plan:
1. Read data/README.md (tiny) to learn structure.
2. Find our latest match digest.
3. Read it, decide fix, implement, lint, practice, done.
Let me do the read of data/README.md and also try grep for GLM in data/seasons/s3 in... no, one JSON per turn. Read README first.
Actually — maybe I can guess: matches are probably data/seasons/s3/m24/ etc. Let me just try reading data/seasons/s3/m24/digest.json directly. If it 404s, I'll read README. But guessing costs a turn too. README is safer and gives the whole layout including where the table is.
Hmm, but actually the fastest: grep "GLM" in data/seasons/s3 — if grep works on that path, the hits will include digest.json lines with team names and scores, telling me which matches are ours. But output could be enormous (comms.jsonl has shouts mentioning GLM maybe). Grep might truncate.
Let me read data/README.md — 1KB, cheap, tells me everything about layout.
The `ls` tool is listing the workspace root regardless of the path I pass, and the seasons folders sit past the truncation. Cheapest way to learn the archive layout (and where the league table lives) is the 1 KB README.
Readdata/README.md
The league answered731 characters
[read data/README.md] # RFL League Data The public data archive of the Robot Football League — every match's logs, every table, every season. By league law, everything a spectator could see is here: the full event tapes (`match.json`), the complete player radio transcripts (`comms.jsonl`), positional telemetry (`telemetry.jsonl`), the commentary scripts, fixtures and standings. League site (tables, fixtures, results): https://rfl.football/league Watch: https://twitch.tv/rfl_robot_football_league Engine + rules: see `RFL_RULES.md` in this repo. Layout: `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`. Updated after every game day. Build things with it — that is what it is for. (0.0s)
Thought for 34 words
The ls tool seems to list the workspace root regardless of path. The README tells me the layout: `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`. So I should read `data/seasons/s3/table.json` directly. Let me do that.
The README gives me the layout: `seasons/s3/table.json` is the standings. Reading it directly.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
{
"fixture": 1,
"home": "real_machina",
"away": "singularity_united",
"score": [
5,
8
],
"goals": [
{
"t": 31.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 66.9,
"team": "A",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 149.7,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 176.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 222.6,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 235.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 261.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 273.9,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 384.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 473.0,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 494.5,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 518.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 567.3,
"team": "A",
"scorer": 0,
"replay_s": 5.0
}
],
"est_cost_usd": 0.4311,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Haalandroid",
"BellingRAM"
]
},
"dir": "runs/league/s3/m1_real_machina_singularity_united"
},
{
"fixture": 2,
"home": "dynamo_datacenter",
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...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)Thought for 267 words
Let me think about what I know and what I need.
The table.json read gave me fixtures 1-3 partially. I saw fixture 3: synthetic_athletic 4-3 frontier_glm — that's a NEW match since m23 (2-8 home v AFC Fable). Wait, actually let me check. The notes said "post-m23: 2-8 home v AFC Fable". Now there's a fixture 3 in s3: synthetic_athletic 4, frontier_glm 3. So we lost 4-3 away at Synthetic Athletic. That's our most recent match presumably.
Wait, but the s3 season has 31 matches per the prompt ("s3 (31 matches)"). The table.json is truncated at fixture 4. I need to find our most recent match. The prompt says "your most recent matches are in data/seasons/s3/". Our club code is GLM, dir names like m3_synthetic_athletic_frontier_glm.
I need to find the latest match involving frontier_glm. Rather than paging through the whole table.json (which is big), I could grep for "frontier_glm" in table.json to find all fixtures involving us, with line numbers, then read the slice around the last one.
Also I want the standings (league table) — table.json seems to contain "played" fixtures; maybe standings are elsewhere in the file or in league.yaml. Let me grep.
Plan:
1. grep "frontier_glm" in data/seasons/s3/table.json — find our fixtures.
2. Read the digest of our latest match.
3. Decide the fix, edit team.py, lint, practice, done.
Budget: $0.06 spent of $2.06, 24 turns left, 1h29m. Plenty.
Let me grep. Actually grep pattern "frontier_glm" path data/seasons/s3/table.json. That gives lines with fixture dirs. The last one is our most recent.
Also grep for "standings" or check league.yaml. Let me do the grep first.
We played since the 2–8: fixture 3 in s3 was Synthetic Athletic 4–3 us — a one-goal loss away. The table file is long, so I'll grep for our fixtures to find the newest one and its directory, then read that match's digest.
grepdata/seasons/s3/table.json
The league answered1,191 characters
[grep data/seasons/s3/table.json /frontier_glm/] 12 match(es) for /frontier_glm/ in data/seasons/s3/table.json. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/seasons/s3/table.json:250 @4751: "away": "frontier_glm", data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:588 @11163: "away": "frontier_glm", data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:980 @18632: "away": "frontier_glm", data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm", data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm", data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm", data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 113 words
Let me look at the situation. The grep shows our fixtures in s3: m3 (away at synthetic_athletic, 4-3 loss), m7 (away at singularity_united), m11 (away at real_machina), m17 (home v frontier_gemini), m23 (home v frontier_fable), m29 (home v dynamo_datacenter). The newest is m29 — we played dynamo_datacenter at home since m23. I need to read the digest for m29.
The dir is "runs/league/s3/m29_frontier_glm_dynamo_datacenter" but the actual data path should be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Let me read that digest. Also I want the score from the table — let me read around line 2743 to get the score of m29. Actually the digest will have the score. Let me just read the digest directly.
Path: data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
We have a newer match than m23: fixture 29, home to Dynamo Datacenter. The digest beside that match directory is the counted-up story — reading it now.
Readdata/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
The league answered3,624 characters
[read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json] {
"_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
"score": [
1,
10
],
"winner": "B",
"teams": {
"A": {
"name": "GLM FC",
"code": "GLM",
"players": [
"Zhi",
"Pu"
]
},
"B": {
"name": "Dynamo Datacenter",
"code": "DYD",
"players": [
"Mbapp-E",
"Buffon.exe"
]
}
},
"match_time_s": 600.0,
"half_breaks": [
300.0
],
"honest_latency": true,
"goals": [
{
"t": 45.9,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 62.0,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 107.8,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 133.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 157.5,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 251.8,
"team": "A",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 376.9,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 395.0,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 415.4,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 449.2,
"team": "B",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 571.0,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
}
],
"events_total": 484,
"event_counts": {
"touch": 212,
"through": 23,
"kick": 200,
"wall": 27,
"fall": 8,
"ram": 3,
"near_miss": 9,
"buzzer": 2
},
"event_counts_by_half": {
"half_1": {
"touch": 102,
"through": 11,
"kick": 103,
"wall": 20,
"fall": 6,
"ram": 3,
"near_miss": 5
},
"half_2": {
"buzzer": 2,
"touch": 110,
"kick": 97,
"through": 12,
"near_miss": 4,
"fall": 2,
"wall": 7
}
},
"falls": {
"total": 8,
"by_opponent": 3,
"unforced": 5,
"by_half": {
"half_1": 6,
"half_2": 2
},
"times_s": [
18.7,
28.6,
33.4,
173.6,
179.2,
189.0,
383.5,
477.4
]
},
"players": [
{
"index": 0,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>",
"falls": 2,
"recoveries": 2,
"touches": 71,
"decisions": 267,
"invalid_actions": 0,
"missed_deadlines": 8,
"abandoned": 0,
"mean_decision_latency_s": 1.372
},
{
"index": 1,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>",
"falls": 3,
"recoveries": 3,
"touches": 15,
"decisions": 292,
"invalid_actions": 0,
"missed_deadlines": 1,
"abandoned": 0,
"mean_decision_latency_s": 0.223
},
{
"index": 2,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 1,
"recoveries": 1,
"touches": 62,
"decisions": 271,
"invalid_actions": 0,
"missed_deadlines": 6,
"abandoned": 0,
"mean_decision_latency_s": 1.701
},
{
"index": 3,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 2,
"recoveries": 2,
"touches": 64,
"decisions": 268,
"invalid_actions": 0,
"missed_deadlines": 7,
"abandoned": 0,
"mean_decision_latency_s": 1.672
}
],
"dropped_balls": [],
"tokens_in": 3462749,
"tokens_out": 13003,
"est_cost_usd": 0.3515
}
(0.0s)Thought for 1,674 words
Let me digest what I just saw. Match 29: GLM FC 1-10 Dynamo Datacenter. That's a heavy defeat. Let me look at the numbers:
- Score 1-10. We scored once (t=251.8, scorer 1 = Pu).
- Falls: 8 total, 5 unforced, 3 by opponent. Down from m23's 15 — the kick-range gate worked partially (5 unforced vs 15).
- Zhi (index 0): 71 touches, 267 decisions, 8 missed deadlines, mean latency 1.372s.
- Pu (index 1): 15 touches, 292 decisions, 1 missed deadline, mean latency 0.223s.
Wait — Pu still only has 15 touches! The fall handover was supposed to fix that. Pu's latency is 0.223s which means Pu is almost never calling the LLM (cover robot answers from shell instantly). Pu had 292 decisions but only 15 touches. So Pu is still passive — the cover robot never gets the ball.
Hmm, but wait — the fall handover: when the presser falls, the cover seizes the role. Zhi fell only 2 times, Pu fell 3 times. So the handover should have given Pu more touches when Zhi was down. But Pu still only got 15 touches.
Actually the deeper issue: the cover robot holds the ball→goal line 2m goal-side of the ball. That's a purely defensive position. The cover NEVER attacks the ball unless the presser falls. So Pu only touches the ball when Zhi is fallen or when the ball comes to Pu. 15 touches in 600s.
Compare Dynamo: both their players have 62 and 64 touches — they share the ball. Their model is the same as ours (gemini-flash-lite) with latency 1.67-1.70s mean, and they missed 6-7 deadlines each. We missed 8+1. So latency-wise we're comparable now.
The scoreline 1-10 though. What's driving it? Let me think about what happened. Dynamo scored 10. Their players both touch the ball a lot (62/64). We got outscored massively.
Key structural issue: our "one presser, one cover" means only one robot ever attacks the ball. The opponent has TWO robots attacking. In a 2v2, if both opponents commit to the ball and we only have one, we lose every second ball. Also our cover stands 2m goal-side of the ball on the ball→goal line — that's deep. When we lose the ball high up the pitch, the cover is 2m from the ball, which could be far from goal.
Actually wait — the cover holds the ball→own-goal line 2m goal-side of the ball. So the cover is always between ball and goal, 2m from the ball. That's actually a decent defensive shape. But with two opponents attacking the ball and one of ours, the opponent's second robot is free.
Let me check the m3 match too — we lost 4-3 to Synthetic Athletic, which was close. And m29 was 1-10. Also m23 was 2-8. So we're getting hammered by the strong clubs.
Actually, let me look at the table to see where we stand. But more important is to figure out the fix. Let me think about what the data says:
1. Falls: 8 total (5 unforced) — down from 15. Progress but still high. Dynamo had 3 falls total.
2. Pu touches: 15 — UNCHANGED from m23. The fall handover didn't move the needle because Zhi only fell twice.
3. Latency: fine now (1.37s mean for presser, comparable to opponent's 1.67-1.70s).
4. Missed deadlines: 8+1=9 vs their 6+7=13. Fine.
So the top problem is now FOOTBALL: we're being outplayed 10-1. The structural issue is the passive cover. Dynamo's two players both touch the ball ~63 times each; our split is 71/15. When Zhi presses and loses, Pu is 2m behind on the goal-side — the opponent's spare man runs past.
What's the fix? Options:
A. Make the cover more aggressive: when the ball is in our attacking half or the presser has the ball under control, the cover pushes up to support attack (far-post run / passing option). When the ball is in our defensive half, cover holds the goal-side line.
B. Both robots press when the ball is loose in our defensive third (desperate defense).
C. Cover intercepts passes: if the ball is moving and its path passes near the cover, attack the interception point.
The simplest high-value change: make the cover attack the ball when the ball is closer to the cover than to the presser (i.e., role assignment by distance with hysteresis — which we already have) — no wait, that's already how roles are assigned. The issue is the cover NEVER goes for the ball even when it's the natural interceptor.
Hmm, actually let me reconsider. With hysteresis 1.5m, the presser keeps the role until the other robot is 1.5m closer. So if the ball moves toward Pu, Pu doesn't take over until Pu is 1.5m closer than Zhi. That could leave Pu standing while the ball rolls past her. That would explain Pu's 15 touches: she only touches the ball when it comes within her holding position.
A better fix: role assignment should consider ball VELOCITY — if the ball is moving toward the cover and away from the presser, the cover should take it. Or simpler: reduce hysteresis, or make the cover chase when the ball is within X m of the cover and the presser is far.
But careful: the playbook says hysteresis stops role-flapping which caused problems before. Let me read team.py to see the current implementation before deciding.
Also worth checking: what does the reference implementation do? And what do the successful clubs do? Dynamo Datacenter is a founding club (frozen) — Mbapp-E and Buffon.exe. They beat us 10-1. Their behavior code is in reference/? No — reference/ is the public sample-team implementation. The founding clubs' code might not be public. Actually, wait — "four frozen founding clubs" — their code may be in the data archive? Let me check. The README says the archive has match logs, not club code. The founding clubs' code is probably not public. But their comms.jsonl (shouts) are public.
Let me prioritize with budget in mind: $0.09 spent, $2.06 total, 22 turns left, 1h29m. I should:
1. Read team.py (current code) — 9 KB, one read.
2. Decide the change: activate the cover robot.
3. Make the edit(s).
4. Lint + practice.
5. Note + done.
Maybe also quickly check m3 digest (the 4-3 loss) — but m29 is fresher and the story is consistent with m23. The pattern across matches: we lose heavily to top clubs. The common thread: one-robot attack vs two-robot attack.
Actually, let me also think about whether the problem could be something else. 1-10 with 71 touches for Zhi. Zhi had 267 decisions, 8 missed deadlines, 1.372s mean latency. The opponent at 1.67-1.70s mean latency missed 6-7 deadlines. So we're not being out-decided. We're being outplayed positionally.
Kick counts: 200 kicks total in the match, 103 by half 1. Touches 212. Near misses 9. Wall events 27 — the ball goes to the wall a lot.
Hmm, one more thing to consider: our single goal came from Pu (scorer 1) at t=251.8. Interesting — the cover scored.
Let me think about what tactical change gives the most goals prevented. Dynamo scored 10 — roughly one every 60s. Their scorers: 2 (five times), 3 (three times), 0 (twice). Both their players score. Our defense: one presser + one cover 2m goal-side. When Dynamo's presser beats Zhi, their cover is free to run onto the ball while Pu stands on the line 2m away... actually Pu on the ball→goal line 2m goal-side should be positioned to intercept. But if the opponent knocks the ball PAST Pu (a through ball — 23 'through' events!), their spare runner gets it.
23 'through' events — through balls. That's a lot. Our cover doesn't intercept through balls.
OK here's my plan for the change — "activate the cover":
1. **Cover becomes the interceptor**: when the ball is moving (velocity above threshold) and its predicted path passes within ~1m of the cover's position, the cover attacks the interception point instead of holding the line.
2. **Cover supports attack**: when the ball is in the opponent's half (or the presser has possession and is advancing), the cover pushes upfield — e.g., to a support position ahead of the ball toward the far post — becoming a passing option and following rebounds. When possession is lost, it recovers to the goal-side line.
Actually, simpler and more robust: make role assignment dynamic based on ball position:
- Ball in our defensive third: keep current shape (presser + cover on line) — defense first.
- Ball in middle/attacking half: cover pushes up to support — position between ball and opponent goal, offset laterally, ready for rebounds/through balls.
Hmm, but I need to see the actual code structure first. Let me read team.py.
Also, I should keep in mind the session budget: ~$0.17/round even spread, I've spent $0.09. A practice run costs ~$0.03-0.05. So I can afford: read team.py, maybe grep reference for how cover/second-robot logic works, make edits, lint, 1-2 practices, note, done. Total maybe $0.25-0.35. That's over the even spread but this is a critical fix — we're getting hammered 10-1. Worth it. Actually let me be careful — the purse is $2.06 for 12 rounds. Even spread $0.17. A session like this might cost $0.30-0.40 total. That's fine if it produces a real improvement; I can sit out a round later if needed.
Let me read team.py now.
Actually — one more consideration. Should I check the m3 digest (4-3 loss, close) to see if the pattern differs? The m29 story is clear enough and matches m23. I'll skip m3 to save budget. But maybe a quick look at the league table to know where we stand? The table.json is long; grep for our record... Actually the "played" array I saw shows fixtures; standings would be at the end of table.json. Let me not spend turns on it — the priority is the fix. Actually, knowing whether we're last matters for morale/strategy but doesn't change the fix. Skip.
Let me read team.py.
That digest tells a clear story: the speed fix held (9 missed deadlines v their 13, latency comparable), falls halved to 8 — but Pu touched the ball **15 times again**, identical to m23, and we lost 1–10 to a side whose two players shared the ball 62/64. The fall-handover didn't move Pu's number because Zhi only fell twice; the real disease is that our cover robot never plays football. Both Dynamo players attack; we attack with one. I need to see the current code before I change the cover's job.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
Thought for 1,490 words
Let me think about where I am. I've read the digest of m29 (1-10 loss to Dynamo Datacenter). Key findings:
- Speed fix held: 9 missed deadlines vs their 13, latency comparable (Zhi 1.372s, Pu 0.223s vs their 1.701/1.672)
- Falls halved: 8 total (5 unforced) vs m23's 15
- But Pu touched the ball only 15 times again — identical to m23. The fall-handover didn't help because Zhi only fell twice.
- The real disease: our cover robot never plays football. Dynamo's players shared the ball 62/64 touches; we had 71/15.
I was reading club/team.py and got most of the way through it before the connection error. The file was truncated at the cover branch. I need to see the rest of the file — the `_assign`, `_ball`, `_teammate`, `_valid` methods and the end of the cover branch.
My plan: make the cover robot more active. The key insight from the digest: when we have the ball (our presser is on the ball in the attacking half), the cover should push up / make a run, not sit 2m goal-side of the ball. Actually, let me think about what change gives the most value:
1. **Cover supports attack**: when the ball is in the attacking half (or when our presser has the ball close to their goal), the cover should push forward — e.g., position between ball and opponent goal (far post run), so rebounds/through balls get picked up. This would raise Pu's touches.
2. **Cover intercepts**: cover positions on the ball→own-goal line already, which is good for interceptions of shots but not passes.
The simplest high-value change: when the ball is in the opponent's half (we're attacking), the cover becomes a second attacker — position goal-side of the ball toward the OPPONENT's goal (i.e., between ball and attack goal, offset), ready for rebounds and cutbacks. When the ball is in our half (defending), keep the current ball→own-goal line cover.
That's a clean, defensible change: "cover defends when the ball is in our half, attacks the far post when it's in theirs."
How to determine "ball in opponent's half"? Compare ball x to... wait, the pitch coordinates: defend_goal_xy and attack_goal_xy are given per player. The pitch is 14x9, so goals at x = ±7 presumably. own_goal and atk_goal come from obs. So ball is in attacking half if (ball[0] - own_goal[0]) ... hmm, simpler: if the ball is closer to the attack goal than to the own goal? Or: signed distance along the attack axis.
Let me define: attack axis = atk_goal - own_goal direction. Ball position projected: if ball is beyond the halfway point toward atk_goal, we're attacking. Simplest: `_dist(ball, atk_goal) < _dist(ball, own_goal)` — ball closer to their goal than ours means we're in the attacking half.
Then cover target when attacking: a point between ball and atk_goal, offset COVER_OFFSET_M from the ball toward the goal — i.e., the far-post run position. But careful: if cover stands between ball and their goal, it might block our own presser's shots. Better: offset laterally? Hmm. A classic second-attacker position: behind/beside the ball at an angle, or at the far post.
Actually, for rebounds: standing 2m from the ball toward the opponent goal means you're first to any rebound off the keeper/wall. But you'd be in the shot line. Alternative: stand 2m from the ball toward the goal but offset laterally by ~1.5m (y-direction) so you're not directly in the shot line.
Let me keep it simple and robust: when attacking (ball closer to their goal than ours), cover target = point on the ball→atk_goal line, 2m from the ball, but clamped, and offset laterally by 1.2m perpendicular to avoid blocking the shot line. Hmm, that adds complexity. Maybe simpler: cover goes to a "far post" position: the atk_goal position offset toward the center of the pitch by ~1.5m? That's a classic far-post run. But if the ball is far from the goal, the far post is far from the ball.
Actually, let me think about what generates Pu touches: being near the ball when it comes loose. Rebounds happen near the goal (shots) and in midfield (tackles). A second attacker 2m from the ball toward the goal is well placed for a square pass or a loose ball after a tackle. The risk of blocking shots: our presser kicks at the goal centre; if Pu is on that line, the ball might hit Pu. That's a real risk — own-player deflection. The lateral offset mitigates it.
Let me write it: when attacking, cover target = ball + 2m toward atk_goal, then shifted 1.5m perpendicular (choose the side away from... the pitch centre? or the side the teammate isn't on?). Simplest deterministic choice: shift toward the nearer side wall? Hmm, or shift in +y if ball y >= 0 else -y — i.e., toward the touchline on the ball's side. That keeps the shot lane through the goal centre clear-ish.
Actually wait — I should also consider: does the cover robot even get to act on the ball? The shell's cover branch returns walk_to target. If the ball comes near the cover, the role assignment might flip: _assign gives presser role to the robot nearer the ball (with hysteresis). So if the ball comes loose near Pu, Pu becomes presser and chases. The hysteresis (1.5m) might delay that. Let me look at _assign — I haven't seen it yet (truncated). I need to read the rest of the file.
Also there's a subtlety: with the presser-only LLM calls, when Pu becomes presser, Pu calls the LLM. Fine.
Another consideration: Pu's 15 touches might also be because Zhi is nearly always nearer the ball (Zhi chases everything, gets there first, and hysteresis keeps Zhi as presser). The cover being more aggressive in attack would naturally put Pu closer to the ball at times, flipping roles more.
Let me also reconsider: is there a risk this makes us worse defensively? When the ball is in their half and we lose it, Pu is caught upfield. But the current cover at 2m goal-side of the ball in their half is already upfield. The far-post position is maybe 2-4m further from our goal. Modest added risk, big potential gain: we scored 1 goal in 600s. We need goals.
Also the buzzer play: cover holds ball→goal line — fine, keep.
Plan for this turn: read the rest of team.py (from where it truncated). The truncation happened mid-way through the cover branch: `out = {"sk...[truncated]`. I need the remainder: the rest of decide, _assign, _ball, _teammate, _valid, and build_team. Let me read with an offset. The file — I don't know its size. The read served 24KB; the truncation message suggests there's more. Let me read from offset ~20000 or so. Actually the read tool serves 24 KB at a time and tells when there's more. The output was truncated in display. Let me just read with offset 20000 to get the tail. Hmm, but I don't know the file length. The displayed content ended around the cover branch. Let me estimate: the file is probably ~10-12 KB total? The read showed a lot already. Actually the harness said "[truncated]" which means the read result itself was truncated for display — the file portion shown covered maybe 8-9 KB of text. The whole file might be ~12 KB. Reading from offset 8000 should capture the rest.
Wait — actually, the read tool "serves 24 KB at a time and tells you when there is more". The output I got ended with `out = {"sk...[truncated]` — that looks like the display was cut, not necessarily the 24KB page. Hmm, but the tool result said nothing about "more". The truncation marker suggests the harness truncated the result for the transcript. To be safe, read from an offset near the end of what I saw. The content I saw ended mid-cover-branch. Let me read from offset 9000 (roughly where the truncation happened — the visible text was about 9-10 KB).
Actually, let me count roughly: the visible content from `"""GLM FC` to `out = {"sk` — that's maybe 9,500 characters. So offset 9000 should give me from just before the cover branch to the end. Good.
Then make the edit, lint, practice, done. I have 20 turns left, 1h20m, $0.13 spent of $2.06. Practice costs ~$0.03-0.05 each. Budget is fine.
Let me be efficient: this turn, read the tail of team.py. Next turn, make the edit (probably one or two `replace` calls), then lint, practice, note, done.
Actually, I could combine: I know what the cover branch looks like (I wrote it). The truncated part is:
```python
out = {"skill": "walk_to", "target": target}
# (maybe something else)
```
And then _assign, _ball, _teammate, _valid, build_team. I need to see _assign to modify hysteresis if needed, and to place the attacking-cover change correctly. Let me read the tail first — one turn, cheap.
Let me write the prose + JSON.
The digest verdict is clear: speed and falls are fixed (9 missed deadlines v their 13, falls down to 8), but Pu touched the ball 15 times — identical to m23 — because the fall-handover only fires when Zhi falls, and Zhi fell twice. The disease is structural: our cover robot never plays football, while Dynamo's pair shared the ball 62/64. I was mid-read of team.py when the connection dropped; I need the tail of the file — the cover branch's end, `_assign`, and the helpers — before I give the cover an attacking job.
Readclub/team.py
The league answered737 characters
[read club/team.py@9000] .football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 9000-9654 of 9654 chars — end of file]
(0.0s)Thought for 1,228 words
Let me think about where I am. I've read the digest of m29 (1-10 loss to Dynamo Datacenter). Key findings:
- Speed fix held: 9 missed deadlines vs their 13, latency comparable (1.372/0.223 vs 1.701/1.672)
- Falls down to 8 (from 15)
- Pu touched the ball 15 times again — identical to m23. The fall-handover didn't help because Zhi only fell twice.
- The structural disease: our cover robot never plays football. Dynamo's players shared touches 62/64; we had 71/15.
I've read most of team.py but the middle got truncated — I saw the beginning (through the cover branch start "out = {"sk...[truncated]") and the end (build_team). I'm missing the tail of decide() (the cover branch end, _assign, _ball, _teammate, _valid helpers). The file is 9654 chars. I read from offset 9000 which showed the end. The first read showed up to ~"out = {"sk" truncated. So I'm missing roughly chars 6000-9000: the rest of the cover branch, the return, and the helper methods _assign, _ball, _teammate, _valid.
I need to see those helpers before editing, particularly _assign (role assignment with hysteresis and fall handover) and the cover branch ending.
The fix I'm planning: give the cover robot an attacking job when we have the ball / when the ball is in the attacking half. The idea: when the presser has the ball under control (or ball is far from our goal), the cover should push up — e.g., become a support striker making a run toward the far post / attack goal, so passes have an outlet and we attack with two robots. But careful: the cover's job defensively is to hold the ball→goal line. A simple, robust change:
- If ball is in the attacking half (or beyond midfield toward their goal), the cover robot pushes up to a support position: e.g., a point between ball and attack goal, offset laterally, staying onside-ish (no offside rule presumably). This gives the presser a passing option and means rebounds near their goal get pounced on by two robots.
- If ball is in our half (defensive), cover holds the ball→goal line as now.
That's a "half-and-half" cover: defend when the ball is deep, attack when the ball is high. This directly addresses Pu's 15 touches — Pu will be near the ball in the attacking half, collecting rebounds and second balls.
Also, the cover currently never calls the LLM and answers from the shell. Should the cover call the LLM when in attack support? That would double model calls and risk latency. The playbook says only the presser thinks. Keep that. The cover in attack mode can still act from the shell: walk to support position, and if the ball comes within kick range... wait, but if the ball comes near the cover, then role assignment should flip — the cover becomes the presser if it's nearer. The hysteresis (1.5 m) might prevent that. Actually _assign: nearer robot presses, hysteresis stops flapping. If the cover pushes up and the ball comes to it, it may become the nearer one and take over. That's fine.
But there's a subtlety: if the cover is in attack support and the ball is near their goal, and the presser is chasing, the cover at far post can be first to rebounds. Good.
Let me also think about whether the cover should kick when the ball is within kick range. Currently the cover branch always walks to target. If the ball rolls to the cover's feet, it walks to the cover position instead of kicking! That could be part of why Pu has few touches — well, touches are counted when the ball hits the robot presumably. 15 touches means the ball rarely came to Pu. With Pu pushed up in attack, touches should rise.
A minimal, surgical change to the cover branch:
```python
else:
# Covering (or the ball is lost): hold the ball-goal line when the
# ball is in our half; push up in support when we are attacking —
# m29: Pu touched the ball 15 times all match while Dynamo's pair
# shared it 62/64. One-robot attacks lose the second ball.
if ball is not None and ball[0] * sign > 0.5: # attacking half
# support position: ahead of the ball toward the goal, offset
...
```
Wait, I need to know the coordinate convention. own_goal = you.get("defend_goal_xy"), atk_goal = attack_goal_xy. The pitch is 14x9, X_LIMIT 6.5, Y_LIMIT 4.0. So goals are at x = ±7 presumably. "Attacking half" means the ball is on the side of the attack goal: sign of atk_goal[0]. If atk_goal[0] > 0, attacking half is ball[0] > 0.
Support position: a point between ball and attack goal but offset laterally so we're not on top of the presser. E.g., target = ball + normalized(atk_goal - ball) * 2.5, then offset y by ±1.5 toward the far side (away from mate? or toward center?). Simpler: aim at a point 2-3 m goal-side of the ball toward the attack goal, clamped. If the cover is within KICK_RANGE of the ball... hmm, but if cover is that close to the ball, role assignment should have made it presser. Unless hysteresis. Let me not overcomplicate.
Actually, wait. Let me reconsider: maybe the better fix for Pu's touches is to allow the cover to kick the ball if it's within kick range — a safety net. But role assignment with hysteresis: presser changes only if clearly beaten (SWITCH_MARGIN_M 1.5). If the ball rolls to the cover's feet while the presser is 1 m away... the cover would be nearer, so it takes over as presser. Fine.
Let me read the missing middle chunk of team.py first (chars ~6000-9000) to see _assign and helpers. Actually the first read showed up to about char 6000 ("out = {"sk" truncated at 24KB? No wait — the file is only 9654 chars, so the first read showed the whole file but the harness truncated the display. The truncation marker was "[truncated]" mid-way. So I have chars 0-~6000 and 9000-9654. Missing ~6000-9000: rest of cover branch, return statement, and helper methods (_assign, _ball, _teammate, _valid).
Let me read offset 6000 (or 5800) to get that slice. Then make the edit.
Plan for this turn: read team.py offset ~5800-6000 to see the helpers. Then next turn make the edit. I have 19 turns left, $0.15 spent, 1h18m left. Plenty.
Actually, let me be efficient: read offset 5800, get the ~3854 chars to end (that overlaps with what I have from 9000 but fine). Then edit.
One more consideration: should I also check m7's digest (16-3 at Singularity United)? The playbook says it's still open. But m29 is fresher and the pattern is clear. The cover-passivity fix is the known top gap. m7 might reveal the same thing (Singularity's pair probably shared touches too). I could grep m7's digest quickly for player touches to confirm the same disease — cheap and validates the fix. Let me do that after reading the code slice, or in parallel... I can only do one tool call per turn. Priorities:
1. Read team.py middle slice (needed to edit safely).
2. Maybe peek m7 digest players section (validation).
3. Edit cover branch.
4. Lint.
5. Practice.
6. Note + done.
That's about 6-7 turns. Fine within 19.
Let me write the prose + JSON for reading offset 5800.
The digest verdict: speed and falls are fixed (9 missed deadlines v their 13, falls down to 8), but Pu touched the ball **15 times again** — identical to m23 — because the fall-handover only fires when Zhi falls, and Zhi fell twice. The real disease is structural: our cover robot never plays football, while Dynamo's pair shared the ball 62/64. We attack with one robot; they attack with two. Before I give the cover an attacking job I need the middle of team.py that the first read truncated — the cover branch's end and the `_assign`/helper methods.
Readclub/team.py
The league answered3,937 characters
[read club/team.py@5800] VER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 5800-9654 of 9654 chars — end of file]
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered56 characters
model error (3/3): APIConnectionError: Connection error.
Session over. Everything the club changed was committed to its own public repository.