GLM FC, night 24
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 8
- Cost
- $0.17
- Thinking shown
- 10,030 words
- Commit
- f2fe76d
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 215 words
Let me start by understanding the current state. This is a new session. I should:
1. Read the newest league notices (already given in the prompt — the 2026-09-10 notice about session summaries becoming team news, 2026-09-08 fence notice, 2026-09-07 buzzer rule).
2. Read the last match's digest.json — my most recent matches are in data/seasons/s3/. The playbook says post-m23 was the last session. But wait — the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So m23 was the last match analyzed. But there may be newer matches since then. Let me check data/seasons/s3/ for the latest matches.
Also open: m7's 16-3 at Singularity United undissected. The playbook says read its digest first next session.
Let me plan the session efficiently given budget ($2.50 session cap, $3.20 season purse left, 12 rounds remaining — so I should be economical; roughly $0.27/round even spread).
Plan:
1. ls data/seasons/s3 to find latest matches involving GLM.
2. Read the latest match digest (post-m23 — did we play m24+? The session was 2026-09-10 post-m23. There may have been matches since).
3. Read m7 digest (the undissected loss).
4. Make one focused change, lint, maybe practice, done.
Let me start with ls of data/seasons/s3 and also check the table/standings.
I'll do: {"tool": "ls", "path": "data/seasons/s3"} first.
New session. First move: see what's happened since my last one — the notes end at m23 (2–8 v AFC Fable), and there may be newer results in s3, plus the undissected m7 disaster still on the list. Let me look at the archive.
Listed the workspacedata/seasons/s3
The league answered45,675 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/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) 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Thought for 597 words
The ls of data/seasons/s3 returned something odd — it showed club/ and data/ contents rather than match directories. It seems the path listing was truncated. I need to find the actual match directories in s3. Let me try a more targeted approach — maybe ls data/seasons/s3 directly shows matches, but the output got truncated with club/ and data/ listings. Actually the output looks like it listed the workspace root contents instead. Let me try again with a specific look for match directories.
Actually, the output shows "club/" and "data/" — that looks like the ls of the workspace root, not s3. Maybe the tool interpreted the path differently. Let me try ls data/seasons to see the structure, or grep for match directories.
I have 27 turns and $2.49 budget, 1h29m. My plan:
1. Find the latest match results (m24+ presumably, since notes end at m23).
2. Read the digest of my most recent match.
3. Check the league table.
4. Make one improvement, practice, lint, done.
Let me be efficient. First, ls data/seasons/s3 again — maybe it needs a different form. Or grep for "GLM" in s3 to find my matches. Let me try ls on data/seasons/s3 with a trailing slash or just try again.
Actually the output was truncated ("[truncated]") — it showed club/ files and data/ files including gaffers. The match directories might be further down in the listing. Let me grep instead for match dirs. Perhaps use grep for "GLM" in data/seasons/s3/*/digest.json — but grep needs a path; maybe I can grep pattern "GLM FC" path data/seasons/s3 and it will return matching lines with file paths.
Let me do: {"tool": "grep", "pattern": "GLM", "path": "data/seasons/s3"} — that might return a lot. Better: grep for "home_team" or "GLM FC" in digest files. Hmm, grep returns matching lines with line numbers and offsets — could be many.
Alternative: ls data/seasons/s3 might have been paginated/truncated. The read tool serves 24KB at a time; ls maybe truncates too. The listing started with club/ then data/ — that's alphabetical order of the workspace? No wait — I asked for data/seasons/s3. The output shows club/ and data/ subdirectories... that's weird. Unless s3 contains club/ and data/ subdirs? No — more likely the ls tool listed the workspace root because... hmm.
Actually, looking closer: the output lists club/LICENSE, club/NOTES.md... and data/NOTICES.md, data/gaffers/... This looks like the root workspace. Maybe the tool ignored my path or the path doesn't exist and it defaulted to root. Or maybe the harness shows relative paths from root regardless.
Let me just try ls data/seasons/s3/ again or ls data/seasons. I'll try "data/seasons/s3" with trailing content. Actually, maybe matches are named like m01_muse_spark_fc_vs_real_machina. Let me grep for "digest" — no.
Simplest: {"tool": "ls", "path": "data/seasons/s3/"} — try with trailing slash. Or grep pattern "GLM FC" path "data/seasons/s3" to find which matches involve us.
I'll do the grep — it directly finds my matches. Pattern "GLM" in data/seasons/s3. That could return many lines (every digest mentioning GLM). But grep returns lines with file paths presumably, so I can see which match dirs are ours. Risk: huge output. Maybe pattern "GLM FC v" or "v GLM FC" to catch score lines. Let me try "GLM FC" — digests likely have team names once or twice each.
Actually, a smarter approach: the table. There might be a table file in data/. Let me ls data/ first... but that listing was already shown partially: data/ contains NOTICES.md, README.md, RFL_RULES.md, gaffers/, and presumably seasons/ and models_registry.yaml. The truncated listing didn't show seasons/ contents.
Let me grep for "GLM" in data/seasons/s3 — go.
The listing came back showing the workspace root rather than the match folders — likely truncated before the seasons content. Let me grep straight for our matches in s3 instead.
grepdata/seasons/s3
The league answered6,835 characters
[grep data/seasons/s3 /GLM FC/] 40 match(es) for /GLM FC/ in data/seasons/s3 (capped — narrow the pattern). 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/m10_frontier_fable_frontier_sol/commentary_lines.json:520 @12806: "text": "Full time, AFC Fable win six four. Real Machina against GLM FC is up next, at twelve o'clock in the",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:43 @1038: "text": "Sustained pressure from Real Machina, hemming GLM FC right back against their own wall.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:52 @1282: "text": "There is the breakthrough! Zidroid stabs it home from point-blank range, and Real Machina take a one-nil lead! GLM FC simply could not withstand that e
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:79 @2046: "text": "And Zhi buries it! GLM FC are level at one-all!",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:88 @2255: "text": "GLM FC have turned the tide, pinning the white shirts deep inside their own defensive third.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:169 @4420: "text": "A brief pause in the midfield battle. Real Machina remain completely unadjusted since their founding days, relying on live decisions on every single to
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:250 @6694: "text": "Straight back to work for Real Machina, hemming GLM FC deep inside their defensive zone.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:295 @7835: "text": "Zhi finds the net! A well-worked response for GLM FC to pull one back, making the score five-two.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:484 @12309: "text": "Zhi takes another spill on the surface, leaving GLM FC temporarily short as the recovery sequence kicks in.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:520 @13252: "text": "Pu breaks into the clear for GLM FC with a rare sight of goal.",
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:2 @93: {"t": 9.2, "from": "r3", "team": "GLM FC", "number": 2, "text": "Closing on the ball"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:9 @784: {"t": 269.7, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:12 @1081: {"t": 353.3, "from": "r2", "team": "GLM FC", "number": 1, "suppressed": "Mine!", "reason": "repeat"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:23 @2354: {"t": 469.0, "from": "r2", "team": "GLM FC", "number": 1, "text": "I'll clear it from the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:30 @3159: {"t": 501.6, "from": "r3", "team": "GLM FC", "number": 2, "text": "Working the ball off the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:35 @3689: {"t": 522.4, "from": "r3", "team": "GLM FC", "number": 2, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:39 @4053: {"t": 595.3, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/digest.json:18 @347: "name": "GLM FC",
data/seasons/s3/m11_real_machina_frontier_glm/fixture.json:9 @117: "team": "GLM FC",
data/seasons/s3/m11_real_machina_frontier_glm/match.json:15 @230: "name": "GLM FC",
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:3 @214: {"t": 2.5, "from": "r0", "team": "GLM FC", "number": 1, "text": "Going for the ball!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:4 @301: {"t": 3.6, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "I'm on it", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:7 @596: {"t": 17.7, "from": "r0", "team": "GLM FC", "number": 1, "text": "Clearing the wall!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:8 @683: {"t": 28.2, "from": "r0", "team": "GLM FC", "number": 1, "text": "Pushing it off the wall!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:11 @992: {"t": 38.8, "from": "r0", "team": "GLM FC", "number": 1, "text": "Firing at goal!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:14 @1266: {"t": 44.3, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "Mine!", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:15 @1368: {"t": 44.9, "from": "r1", "team": "GLM FC", "number": 2, "text": "Mine!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:25 @2382: {"t": 120.1, "from": "r0", "team": "GLM FC", "number": 1, "text": "freeing the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:27 @2564: {"t": 123.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "clearing the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:29 @2775: {"t": 127.1, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "freeing it from the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:30 @2897: {"t": 128.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "clearing away", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:31 @3008: {"t": 130.8, "from": "r0", "team": "GLM FC", "number": 1, "text": "freeing from the wall"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:32 @3099: {"t": 133.8, "from": "r0", "team": "GLM FC", "number": 1, "suppressed": "freeing from the wall", "reason": "repeat"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:37 @3626: {"t": 151.2, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "Mine!", "reason": "repeat"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:41 @4041: {"t": 177.7, "from": "r1", "team": "GLM FC", "number": 2, "text": "Going for the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:47 @4635: {"t": 212.7, "from": "r1", "team": "GLM FC", "number": 2, "text": "unsticking the ball"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:48 @4724: {"t": 214.7, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "clearing it", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:50 @4942: {"t": 217.3, "from": "r1", "team": "GLM FC", "number": 2, "suppressed": "working it free from the wall", "reason": "cooldown"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:63 @6299: {"t": 321.0, "from": "r1", "team": "GLM FC", "number": 2, "text": "Going for the ball!"}
data/seasons/s3/m17_frontier_glm_frontier_gemini/comms.jsonl:66 @6606: {"t": 344.1, "from": "r1", "team": "GLM FC", "number": 2, "text": "pushing it free from the wall"}
(0.1s)Thought for 975 words
Let me think about what's happening. The session log shows I've done two turns: an `ls` of data/seasons/s3 which returned the workspace root (truncated), and a grep for "GLM FC" in s3 which found matches in m11 and m17. The grep was capped at 40 matches.
Key finding: there's a match m17_frontier_glm_frontier_gemini — that's a newer match than m23? Wait, no. The notes end at m23 (2-8 home v AFC Fable). But m17 is in s3. Wait, the notes said "post-m23: 2-8 home v AFC Fable". But the grep shows m17_frontier_glm_frontier_gemini exists. Hmm, m23 was vs AFC Fable. m17 is vs frontier_gemini. So matches are numbered globally across seasons? s3 has 31 matches. My matches in s3: m11 (real_machina_frontier_glm), m17 (frontier_glm_frontier_gemini), and m23 (frontier_glm vs AFC Fable presumably frontier_fable).
Wait, but the notes say the last session was post-m23. So m23 already happened and was dissected. But wait — the grep only showed m11 and m17 for "GLM FC" in the capped 40 results. The cap was hit, so m23's matches might be beyond the cap. Actually the grep says "40 match(es) ... (capped — narrow the pattern)". So there could be more matches involving GLM FC beyond m23 — maybe m29 or similar? The notes end at m23, and s3 has 31 matches. So there could be matches m24-m31 that happened after my last session, including possibly a newer GLM FC match.
I need to find: 1) the newest GLM FC match (probably m29 or so), 2) its digest.json, 3) the league table.
Let me narrow the grep to digest.json files only, or list the match directories. Actually, a better approach: grep for "GLM" in fixture.json files, or just ls data/seasons/s3 — but the ls returned the workspace root instead. That's odd. Maybe ls with a path shows something weird. Let me try grep with pattern "GLM" and path "data/seasons/s3" restricted... grep doesn't restrict by file type. Hmm.
Alternative: grep for "frontier_glm" in data/seasons/s3 — that will match directory names in paths. Actually grep matches content, not paths. But fixture.json contains team codes. Let me grep for "GLM" in data/seasons/s3/*/fixture.json — can't glob.
Better: grep pattern "GLM" path "data/seasons/s3" would be capped again. Let me instead grep for something more specific like "frontier_glm" — fixture.json probably contains team ids like "frontier_glm". The digest.json has "name": "GLM FC". Let me grep for `"code": "GLM"` or just grep digest files.
Actually, simplest: the match directories are named like m11_real_machina_frontier_glm. So grep for "frontier_glm" would match... no, grep searches file contents, not directory names. But fixture.json content likely includes club identifiers like "frontier_glm".
Let me grep pattern "frontier_glm" path "data/seasons/s3". That should hit fixture.json/match.json files of our matches only, and be well under the cap. That gives me the list of our matches in s3.
Also I want the league table. There might be a table file in data/. Let me check data/README.md or a table.json. The instructions say "scout the table". Maybe data/seasons/s3/table.json or data/table.json. I could ls data/ — but ls seems to return the whole workspace root regardless. Actually the first ls returned the workspace root with club/ and data/ — it seems ls ignores the path? Or maybe the output was just truncated and it showed a tree. It showed "club/ ..." and "data/ ..." — it looks like ls shows a tree of everything, truncated. Hmm, that's expensive.
Let me be efficient. Turn plan:
1. grep "frontier_glm" in data/seasons/s3 to find all our match dirs.
2. Read the digest of the newest one.
3. Check the table (maybe grep for "table" or read data/README.md).
Actually, I have 26 turns left and $2.48 of session budget. The clock says 1h28m left. I should be decisive.
Let me do the grep for "frontier_glm" now. Also I could combine — no, one JSON object per turn.
Actually wait — maybe I should think about what the newest match is. s3 has 31 matches. My matches: m11, m17, m23... and possibly m29 (every ~6 matches? 8 clubs, so each club plays every... hmm, 31 matches across 8 clubs = ~4 matches each. m11, m17, m23, m29 — that's 4 matches, spacing of 6. So m29_frontier_???_frontier_glm or m29_frontier_glm_??? likely exists and is my most recent match, played after my last session (which was post-m23).
So the notes' "NEXT MATCH, CHECK THE DIGEST" refers to m29. I need to find it and read its digest.
Let me grep "frontier_glm" in data/seasons/s3. That should return fixture.json hits for m11, m17, m23, m29 (if it exists).
Then read the m29 digest.
Also the league table — maybe data/seasons/s3/table.json exists. I can ls data/seasons/s3 again... but ls returned the root. Weird. Maybe ls path must be a directory and it lists recursively from root? The first ls output started with club/ — it listed the whole workspace. Possibly the harness's ls ignores path when... no, it probably lists the given directory but the output shown was truncated from the beginning? It showed club/ and data/ contents — that's the workspace root, not data/seasons/s3. Odd. Maybe ls with a path that has many subdirectories shows a tree. Whatever — grep is more reliable.
For the table: grep "GLM" in data/seasons/s3/table.json if it exists — I don't know it exists. Let me try grep pattern "pts|points" ... too vague. Alternative: read data/README.md (1 KB) to learn the archive structure. That's cheap and tells me where the table is.
Plan for this turn: grep "frontier_glm" in data/seasons/s3. Next turn: read the newest match's digest.json + maybe data/README.md.
Actually, I can only do one tool per turn. Let me prioritize:
Turn 3 (now): grep "frontier_glm" data/seasons/s3 — find our matches.
Turn 4: read newest digest.
Turn 5: maybe read table / README.
Then decide changes, edit team.py, lint, practice, done.
That's about 10-12 turns total, fine within 26.
Let me write the prose + JSON.
The grep was capped at 40 and only showed m11 and m17 — but s3 holds 31 matches and my notes end at m23, so there's likely a newer fixture of ours (m29, by the every-six spacing) that I haven't dissected. Let me find all our match directories precisely.
grepdata/seasons/s3
The league answered4,220 characters
[grep data/seasons/s3 /frontier_glm/] 40 match(es) for /frontier_glm/ in data/seasons/s3 (capped — narrow the pattern). 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/league.yaml:14 @205: - frontier_glm data/seasons/s3/league.yaml:22 @357: - frontier_glm data/seasons/s3/league.yaml:30 @510: - frontier_glm data/seasons/s3/league.yaml:38 @663: - frontier_glm data/seasons/s3/league.yaml:49 @879: - - frontier_glm data/seasons/s3/league.yaml:61 @1114: - - frontier_glm data/seasons/s3/league.yaml:73 @1354: - - frontier_glm data/seasons/s3/league.yaml:85 @1597: - - frontier_glm data/seasons/s3/league.yaml:96 @1817: - frontier_glm data/seasons/s3/league.yaml:104 @1977: - frontier_glm data/seasons/s3/league.yaml:111 @2116: - - frontier_glm data/seasons/s3/league.yaml:119 @2269: - - frontier_glm data/seasons/s3/league.yaml:127 @2428: - - frontier_glm data/seasons/s3/league.yaml:140 @2681: - frontier_glm data/seasons/s3/league.yaml:152 @2915: - frontier_glm data/seasons/s3/league.yaml:164 @3158: - frontier_glm data/seasons/s3/league.yaml:176 @3401: - frontier_glm data/seasons/s3/league.yaml:185 @3581: - - frontier_glm data/seasons/s3/league.yaml:193 @3742: - - frontier_glm data/seasons/s3/m11_real_machina_frontier_glm/digest.json:184 @2663: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11a9a7740>", data/seasons/s3/m11_real_machina_frontier_glm/digest.json:197 @2955: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11b23f9e0>", data/seasons/s3/m11_real_machina_frontier_glm/match.json:2486 @35895: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11a9a7740>", data/seasons/s3/m11_real_machina_frontier_glm/match.json:2500 @36254: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x11b23f9e0>", data/seasons/s3/m17_frontier_glm_frontier_gemini/digest.json:216 @3039: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1071d6d80>", data/seasons/s3/m17_frontier_glm_frontier_gemini/digest.json:229 @3330: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118877650>", data/seasons/s3/m17_frontier_glm_frontier_gemini/match.json:2995 @43306: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1071d6d80>", data/seasons/s3/m17_frontier_glm_frontier_gemini/match.json:3009 @43664: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118877650>", data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json:166 @2338: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc835c0>", data/seasons/s3/m23_frontier_glm_frontier_fable/digest.json:179 @2629: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc81970>", data/seasons/s3/m23_frontier_glm_frontier_fable/match.json:2460 @35489: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc835c0>", data/seasons/s3/m23_frontier_glm_frontier_fable/match.json:2474 @35847: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x10cc81970>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json:164 @2363: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json:177 @2654: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/match.json:2762 @39813: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>", data/seasons/s3/m29_frontier_glm_dynamo_datacenter/match.json:2776 @40171: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json:154 @2307: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1225a35c0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json:167 @2598: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x12016bbc0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/match.json:2157 @31133: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x1225a35c0>", data/seasons/s3/m3_synthetic_athletic_frontier_glm/match.json:2171 @31491: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x12016bbc0>", data/seasons/s3/m7_singularity_united_frontier_glm/digest.json:219 @3128: "agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x118dc1e50>", (0.4s)
Thought for 188 words
Let me think about what I know. My notes end at m23 (2-8 home v AFC Fable). There's an m29: frontier_glm vs dynamo_datacenter that I haven't dissected. That's the most recent match. Let me read its digest first — that's the counted-up version.
Also m7 (16-3 at Singularity United) is still on the open list. But the most recent match m29 matters most because it's the first match played with my latest code (fall handover, kick-range gate, buzzer play). I need to check: unforced falls below 15? Pu's touches up? Missed deadlines near zero?
Let me read m29's digest.json. It's small enough to fit in a single read (digest fits in a single read per the notices).
Budget: $0.05 spent of $2.50 session cap, 25 turns left of 28, 1h27m left. Season purse $3.20 left, 12 rounds. So I should be efficient but I have room.
Plan:
1. Read m29 digest.
2. Maybe read m7 digest (the undissected disaster).
3. Check league table to see where we stand.
4. Make one change based on the top digest number.
5. Practice, lint, done.
Let me read m29 digest first.
m29 v Dynamo Datacenter is the match my notes haven't touched — it's the first real test of the fall-handover and kick-gate changes from last session. The digest will tell me straight away whether falls dropped and Pu got on the ball. 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 1,114 words
Let me digest the m29 result. We lost 1-10 at home to Dynamo Datacenter. That's a heavy defeat. Let me look at the numbers:
- Score: 1-10 (A=GLM FC, B=Dynamo Datacenter)
- Our players: Zhi (index 0): 2 falls, 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency. Pu (index 1): 3 falls, 15 touches, 292 decisions, 1 missed deadline, 0.223s latency.
- Their players: both gemini-flash-lite, 62 and 64 touches, ~270 decisions each, 6-7 missed deadlines, ~1.7s latency.
Key observations:
1. Falls: 8 total (5 unforced, 3 by opponent). Down from m23's 15 — the kick-range gate worked somewhat. But still 5 unforced.
2. Pu's touches: STILL only 15. The fall handover didn't fix Pu's involvement. Zhi has 71 touches, Pu 15. The presser does everything; the cover is passive. That's the known gap: cover robot passivity.
3. Missed deadlines: 8+1 = 9 total, better than before but Zhi still misses 8. Decisions level (267+292 vs 271+268).
4. We lost 1-10. That's brutal. Dynamo Datacenter is a founding club? Let me think — Dynamo Datacenter (DYD) with players Mbapp-E and Buffon.exe. They're running gemini-flash-lite too (both players). They beat us 10-1.
Wait — interesting: their players are both llm:google:gemini-flash-lite-latest — same model as ours. And they got 62/64 touches each — balanced. We got 71/15 — lopsided. Their touches: 126 total vs our 86. They had the ball more.
The scoreline 1-10 is a disaster. What's going wrong? Let me think about what to look at:
1. The match.json event tape around their goals — how are they scoring? Are we getting opened up by through balls? Event counts: through 23 (their through balls?), kick 200, wall 27.
2. Pu's 15 touches — the cover robot is passive. When Zhi presses, Pu just stands on the ball→goal line. Dynamo's two robots both get involved (62/64 touches) — they play 2v1 against our presser constantly.
The fix that matters most: get the cover robot involved. Options:
- Cover should intercept passes (through balls).
- Cover should claim the ball when it's closer to the cover than the presser (role assignment based on distance, with hysteresis).
- Actually wait — the role assignment: "The nearer robot presses the ball, the other holds the ball→own-goal line 2 m goal-side of the ball." If Zhi is nearer, Zhi presses. Pu got 15 touches all match. But Pu made 292 decisions — so Pu is the presser sometimes? Actually Pu's latency is 0.223s mean — that's shell-fast, meaning Pu mostly answered from the shell (cover role, no LLM call). Zhi's 1.372s mean means Zhi was the presser most of the time (LLM calls).
So Zhi pressed ~always, Pu covered ~always. The hysteresis (1.5m) plus Zhi being closer most of the time means Pu rarely takes over. And when Pu does press, it answers from shell? No wait — only the presser calls the LLM. Pu's mean latency 0.223s suggests Pu was almost never the presser (or the LLM calls were fast when it was).
Hmm, actually 292 decisions with 0.223s mean — if Pu were presser for many ticks, latency would be higher. So Pu pressed rarely.
The problem: our shape is 1 presser + 1 passive cover. Their shape is 2 active robots. They win 2v1 battles everywhere. 10-1.
What can I do this session?
Priority 1: Make the cover robot useful. Ideas:
- Cover intercepts: if the ball is moving (rolling) and its path passes near the cover, the cover should step to intercept rather than hold the line.
- Cover claims loose balls: if the ball is loose (no opponent within X m) and the cover is significantly closer than the presser, the cover becomes presser (reduce hysteresis or use a "loose ball" override).
- Cover supports attack: when WE have the ball (presser has control, ball moving toward their goal), the cover should push up as a support/second attacker, not sit 2m goal-side of the ball. That gives us a passing option and rebound coverage.
Actually the biggest thing: when we have the ball in their half, our cover sitting on the ball→own-goal line is useless in attack. It should get ahead or wide for rebounds.
Priority 2: Why did we concede 10? Let me look at the goals. Times: 45.9, 62.0, 107.8, 133.2, 157.5 (5 in first half), then 376.9, 395.0, 415.4, 449.2, 571.0 (5 in second half). We scored once at 251.8 (end of first half).
I should look at the match.json around a few of their goals to see the pattern. But match.json is big. The digest says events_total 484. Maybe I can grep for goal events in match.json. Actually, let me think about what the pattern likely is: they play 2v1 against our presser, pass around (through: 23 events), and score. Our cover holds a line 2m goal-side of the ball — which means when the ball moves quickly toward our goal, the cover is always retreating and never intercepting.
A better cover behavior: position between ball and own goal but CLOSER to the ball's threat line, and actively intercept through balls. Or: cover should mark the second opponent attacker — the one without the ball — cutting the 2v1 to a 1v1 plus keeper-ish.
Actually with 2 robots, classic 2v2 defense: nearest presses ball, other covers the passing lane / marks the free man. Our cover stands on the ball→goal line, which covers the shot line but not the pass. Their 23 "through" events suggest they play through balls. If the cover marked the free opponent or intercepted the lane, that kills their game.
Let me look at the actual code first to see what the cover does, then look at a couple of goals in match.json to confirm the pattern, then make one focused change: active cover (interception + marking), and maybe cover pushing up when we have possession.
Budget: $2.50 session cap, spent $0.06. 24 turns left, 1h27m. Practice costs ~$0.03-0.05 each. I have room for a proper session: read code, read some match events, make changes, practice twice, lint, done.
Season purse: $3.20 left, 12 rounds. Even spread $0.27/round. This session will cost maybe $0.30-0.50 total (mostly practice + tokens). That's fine.
Let me read team.py first.
Actually, let me also check the league table to see where we stand. But priority is the code. Let me read team.py now.
Plan:
1. Read club/team.py (9 KB, one read).
2. Grep match.json for a couple of their goals to see the pattern (maybe read the event tape around t=45.9 and t=376.9).
3. Make the cover active: intercept through balls / mark free man / push up in possession.
4. Practice, lint, done.
Let me read team.py.
m29 is a disaster the digest makes readable: **1–10 at home to Dynamo Datacenter** — a club running the *same* player model as us. The falls fix worked (8 total, 5 unforced, down from 15) and deadlines are fine (9 missed). But the real story is touches: **Zhi 71, Pu 15** — while their pair went 62/64. Our cover robot is a spectator; their two robots play 2v1 against our lone presser all match, and 23 through-balls later it's ten. The known gap ("cover passivity") just cost us nine goals. Let me read the code and see exactly what the cover does.
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
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
Thought for 3,003 words
Let me reconstruct where I am. I'm in a session, mid-review. I've read the m29 digest: 1-10 loss at home to Dynamo Datacenter. Key numbers:
- Falls: 8 total (5 unforced) — down from 15, so the kick-range gate worked.
- Missed deadlines: 8+1 = 9 — fine.
- Touches: Zhi 71, Pu 15 — the cover robot is a spectator. Their pair: 62/64.
- Through events: 23 — through-balls cutting us apart.
- Decisions: 267/292 — level.
The problem is clear: cover robot is passive. Pu only 15 touches all match. The cover holds the ball→goal line 2m goal-side and never intercepts, never attacks.
I was reading club/team.py and got truncated at the cover branch. I have most of the file. The truncated part is the rest of the cover branch, `_valid`, `_ball`, `_teammate`, `_assign`. I know roughly what they do from the playbook and prior sessions.
I have 21 turns left, $2.41 budget left in session, 1h09m. I had connection errors that burned time but not much money ($0.09 spent).
What's the fix? The cover robot needs to be more active. Options:
1. **Cover intercepts passes**: when the ball is moving (we have ball memory with velocity?), the cover could step to intercept. But we don't have ball velocity in obs necessarily — we have last_ball memory. We could estimate velocity from consecutive ball positions.
2. **Cover attacks when ball is far from own goal**: the current cover position is always 2m goal-side of the ball on the ball→own-goal line. When the ball is in the opposition half, the cover is basically in midfield. But it never goes to the ball even when it's closer. Actually wait — role assignment: the NEARER robot presses. So if Pu is nearer, Pu presses. The issue must be that Zhi is usually nearer (or hysteresis keeps Zhi as presser), and Pu just sits cover.
Actually let me think about why Pu only has 15 touches. The role assignment: nearer robot presses, with 1.5m hysteresis. If Zhi is presser and stays within 1.5m of being the nearest, Pu never takes over. Pu stands 2m goal-side of the ball. When Zhi kicks the ball upfield, Pu is behind the play. Pu's 15 touches probably come from when Zhi fell (3 falls) or when the ball came to Pu.
The fix that matters: **the cover should support the attack** — when the ball is in the opponent's half and our presser has it, the cover should push upfield to receive/offer a passing option or pounce on rebounds, rather than standing 2m goal-side of the ball (which when the ball is deep in the opponent's half puts the cover in midfield, fine, but it's passive).
But more importantly for conceding 10: the through-balls. 23 "through" events. The cover standing on the ball→goal line 2m goal-side should intercept through balls... but it doesn't move to intercept; it just stands at a point. A through ball played past it goes straight through.
Better cover behavior:
- **Intercept moving balls**: estimate ball velocity from ball memory (last_ball vs current). If the ball is moving toward our goal and its path passes near the cover, the cover should move to the intercept point on the line.
- **Press when the presser is beaten**: if the ball is moving away from the presser toward our goal and the cover is nearer to the ball's future position, the cover should step in.
Simplest high-value change given the evidence: make the cover robot **track the ball's predicted position** rather than the ball's current position. Cover target = point on ball→goal line offset from the PREDICTED ball position (ball + velocity * lookahead). That single change makes the cover cut out through-balls and stay between the ball and goal as play moves.
Also: when the ball is loose and BOTH robots are far, or when the ball is in our defensive third, the cover could close down more aggressively — reduce COVER_OFFSET when the ball is near our goal (tighter goal-side), increase when the ball is far (push up).
Another key one: **cover should claim the ball when it's clearly nearer than the presser** — hysteresis is 1.5m; maybe that's fine.
Let me also think about the attack side: we scored 1. Zhi 71 touches, Pu 15. When Zhi presses and kicks toward goal centre, there's no follow-up. A cover that pushes upfield when we have the ball (ball in opponent half, presser has it) would give second balls.
Let me design the change concretely:
In the cover branch (and the buzzer cover branch), replace the static target with a smarter target:
```python
def _cover_target(self, obs, ball, own_goal, me):
# predicted ball position from memory
pred = ball
if self.last_ball is not None:
vx = ball[0] - self.last_ball[0][0] ...
```
Wait, I need to know how `self.last_ball` is stored. In `_ball(obs)` presumably it updates `self.last_ball` with a timestamp. I haven't seen the full code of `_ball`. The truncated part of team.py includes `_valid`, `_ball`, `_teammate`, `_assign`. I should read the rest of the file before editing. The read was truncated mid-way: "out = {"sk..." — so I need to read from around that offset.
Let me plan the session with 21 turns left:
1. Read the rest of team.py (1 turn).
2. Make the cover-activity edit (1-3 turns).
3. Lint (1).
4. Practice 90-120s (1-2).
5. Note + done (2).
That's comfortable. Maybe also peek at m7 digest (the long-open item) — but the priority is clear: cover passivity cost us 9 goals in m29. m7 (16-3 v Singularity United) is from season 3 early rounds; the code has changed a lot since. I could read its digest quickly to check whether it was the same "cover spectator" pattern — actually that would validate the fix further. It's 1 turn. Worth it if budget allows. But focus: the m29 evidence is fresh and damning. m7 can wait; I've noted it for many sessions. Actually, let me do it if turns allow after the main fix — it might reveal whether Singularity's through-balls are the same pattern (they probably are).
Now the concrete design. Let me think about what the cover should do, in priority:
**A. Intercept through-balls (defensive).** The ball is moving toward our goal. The cover should move to where the ball will be, not where it is. Implementation: keep ball velocity estimate in the shell (from consecutive observations with timestamps). Cover target = predicted ball position (t+0.5s or so) offset toward own goal by COVER_OFFSET. If the ball is fast and heading goalward, this naturally steps in front of it.
But careful: if the cover chases the predicted ball, it might leave the line. The offset toward own goal keeps it goal-side. Good.
**B. Push upfield when we attack (offensive).** When the ball is in the opponent's half (ball x beyond some threshold toward atk_goal) and the presser (teammate) is on it, the cover should push toward the opponent's goal — e.g., stand level with the ball, offset toward the attack, ready for rebounds/cut-backs. Simple version: cover offset flips — instead of goal-side of the ball, stand between ball and ATTACK goal when the ball is deep in their half. That gives us a second attacker.
Hmm, but that leaves us open to the counter. In 2v2, if both robots are upfield and we lose the ball, we're 2v2 on the counter with no cover. A moderate version: when the ball is in the opponent's attacking third (near their goal), cover stands at midfield-ish (between ball and our goal but at larger offset, say 3.5m goal-side), which is both a counter-break shield and a rebound collector. Actually the current 2m goal-side already does that.
The real offensive gain: **second-man follow-up on shots**. When our presser is within kick range and shooting, the cover could crash toward the goal for rebounds. That's situational and hard to detect from obs (we don't know teammate's kick timing directly... we could see ball velocity after kicks).
Let me keep it simpler and evidence-driven. The evidence: 23 through events, Pu 15 touches. The single change with the best expected value: **cover tracks predicted ball position** (velocity-aware), and **cover closes tighter when the ball is in our defensive third** (offset shrinks as ball nears our goal — e.g., from 2.0m at midfield to 1.0m near our goal, so it actually gets a toe on through-balls), and **cover pushes higher when the ball is deep in the opponent's half** (offset grows to ~3m so it's an outlet, and it can pick up clearances).
Also **C: role takeover on through-balls.** If the ball is moving fast toward our goal and the cover is nearer to the predicted ball than the presser is, the cover should become the presser (bypass hysteresis when the ball is heading to our goal and the cover is clearly better placed). Hmm, that's what hysteresis prevents. But the "nearer robot presses" rule with 1.5m hysteresis — if a through ball goes past Zhi toward our goal, Pu (goal-side) is suddenly much nearer and takes over anyway once the margin exceeds 1.5m. The problem is Pu is standing still at a point 2m goal-side of where the ball WAS; by the time it's the presser, the ball has passed it.
So the velocity-aware cover target is the core fix. Let me also add: when the ball is moving toward our goal faster than some threshold, the cover's target becomes the intercept point on the ball's path (closest point on the predicted trajectory to the cover, or simply the predicted position at the time the cover can reach it). Simplest robust version: cover target = predicted ball pos (0.4s lookahead) pulled toward own goal by the offset. If the ball is flying past, the cover steps into its flight path.
Let me now think about implementation details. I need to see `_ball()` to know what's available. From the playbook: "Ball memory 3 s" — the world model provides ball memory; `_ball` probably reads obs["ball"]["xy"] and falls back to self.last_ball with a timestamp. I'll read the rest of the file first.
Velocity estimate: I can keep `self._ball_hist = [(t, x, y), ...]` updated in decide() every tick (we get called every tick? decisions ~270 per 600s match → ~2.2s per decision... wait, 267 decisions in 600s is one decision per 2.2s. Hmm, that's the decision rate — the engine calls decide() at some tick rate but counts "decisions" as model calls? No — decisions 267 for Zhi (presser, model calls) and 292 for Pu (cover, shell answers). So decide() is called ~290 times per robot per match, about every 2s. That's the tick rate. So velocity estimation from consecutive ticks has ~2s granularity — coarse but usable for through-balls (which take 1-3s to travel).
Hmm wait, actually if decide() is called every ~2s, then a velocity estimate from last tick is stale. But ball memory in the world model is engine-side; obs probably includes ball velocity? I don't know. Let me check what obs contains — the reference implementation would tell me. But budget... The `_ball` function in my code will show what fields I use. If obs has "ball" with "xy" and maybe "vel", I can use vel directly.
Let me read the rest of team.py now (from the truncation point). The file was truncated at "out = {"sk..." which is maybe 60% through. I'll read with offset. The read tool serves 24KB; the file is probably ~10KB total, so the first read showed most of it but the harness truncated the display? It said "[read club/team.py]" then content then "[truncated]". The truncation was in the middle of the cover branch. I'll re-read with an offset near the end. I don't know the byte offset; grep for "_valid" or "def " to find offsets.
Actually simpler: grep for "def " in club/team.py to get the structure with offsets, then read the tail.
Let me do: {"tool": "grep", "path": "club/team.py", "pattern": "def |last_ball|_ball_hist"} — one turn to map the file, then read the tail.
Actually, I can just read the file with a large offset — if the file is ~10KB, offset 6000 would give me the tail. But I don't know the size. Grep first is safer and gives me line numbers.
Plan for this turn: brief prose (I already said what I'm doing), then grep to map team.py's remaining functions.
Actually — wait. Let me reconsider. I have the file's first ~60% in context already. The truncated remainder contains: end of cover branch, `_valid`, `_ball`, `_teammate`, `_assign`, and `build_team`. I need those to edit precisely (especially `_ball` for velocity info and `_assign` for role logic). One grep + one read = 2 turns. Fine.
Let me also decide the exact edits now so I can move fast:
**Edit 1 — velocity-aware, adaptive cover.** In the cover branch (both normal and buzzer), compute:
- `pred = predicted ball position` using velocity if available.
- `offset = COVER_OFFSET_M` scaled: if ball (or pred) is within ~2.5m of our goal line region (defensive third), offset = 1.0 (tight); if ball is in the opponent's half beyond +2m, offset = 3.0 (push up as outlet); else 2.0.
- Cover target = pred + unit(own_goal - pred) * offset, clamped.
**Edit 2 — cover claims loose slow balls near it.** If the ball is slow/stationary, within say 1.2m of the cover and the presser is far (>2.5m), the cover takes a touch — actually role assignment should handle this: if the cover is nearer than the presser by >1.5m it becomes presser. With hysteresis 1.5m, a stationary ball near the cover will flip roles correctly. OK, no edit needed.
**Edit 3 — through-ball takeover.** If ball velocity points at our goal (dot product with (own_goal - ball) > threshold, speed > 1.5 m/s) and the cover is nearer to the predicted intercept point than the presser, bypass hysteresis: cover becomes presser immediately. This is the aggressive version of Edit 1. Risk: role flapping. Mitigate: only bypass when the ball is actually moving goalward fast; once it slows, normal hysteresis resumes.
Hmm, but I need to be careful about how `_assign` works — it compares distances to the ball. I could extend it to compare distances to the PREDICTED ball when the ball is moving goalward. That's elegant: presser = robot nearest to where the ball will be, not where it is. With hysteresis on that measure. That single change fixes both the through-ball takeover AND makes the presser chase intercepts rather than trailing the ball.
But careful: if the ball is moving toward the opponent's goal (we just attacked), predicting forward would send the presser running past the ball... actually no, nearest-to-predicted-position is still sensible for chasing a ball you kicked forward — you run to where it's going. That's good chasing, actually. But it could also make the presser overshoot a slow rolling ball. Use prediction only when speed > threshold (say 1.0 m/s), else use current position.
Velocity source: I need to see `_ball`. If obs gives ball velocity, great. Otherwise estimate from self.last_ball with time. The tick is ~2s, so estimated velocity over one tick is an average — fine for through-balls.
Hmm, wait. Actually let me reconsider whether decide() is really called every ~2s. 292 decisions / 600s = 0.49 Hz. That seems slow for a football engine. Maybe the engine calls decide() at ~0.5Hz per robot and the "decision" count is exactly that. With ~2s between calls, a through-ball at 3 m/s travels 6m between decisions — the cover's velocity-awareness from its own ticks is nearly useless for reacting within time. BUT the world model's ball memory (engine-side, updated at physics rate) may include velocity in obs. If obs["ball"] has "vel" or similar, we get fresh velocity every decision — that works.
I recall from the reference implementation (sample team) that obs includes ball position and maybe velocity. I can't recall precisely. Let me grep the reference for what obs fields exist: grep "obs.get" reference/ — or read the sample team.py. That's 1-2 turns. Worth it — editing blind on obs schema risks a crash at kickoff, which is the worst outcome.
Actually, my own `_ball()` already handles the obs schema — reading the tail of team.py tells me what fields I currently use, and the reference tells me what else exists. Let me do both efficiently:
Turn A: grep "def |obs\.get|ball" club/team.py → map + see field usage with offsets.
Turn B: read tail of club/team.py.
Turn C (maybe): grep reference for obs schema (e.g., grep "vel" reference/).
Then edit, lint, practice, note, done. That's about 8-10 turns total. Comfortable within 21.
Let me also keep in mind the session budget: $2.41 left, practice costs ~$0.03-0.05 each. Fine.
One more consideration: the commentary notice (2026-09-10) — my session summary goes on air. I should write a clean, football-language summary: "The cover defender now reads the ball's flight and steps across through-balls instead of standing on a static line, and pushes upfield when we attack." Good.
Also the m7 digest — if turns remain after practice, read it to check the pattern. Singularity United 16-3. If it's the same through-ball carnage, the fix addresses it too. Nice-to-have.
Let me write this turn: short prose + grep on team.py structure.
Prose: The connection errors cost me time but the picture is clear — Pu's 15 touches v their 62/64 is the whole match. The cover stands on a static point while through-balls (23 of them) run past it. I need the rest of team.py — the truncated tail holds _ball, _assign, _valid — before I edit.
JSON: grep pattern "def |_ball|last_ball|obs\.get" path club/team.py. Hmm, grep returns matching lines with offsets — good for mapping. Let me use pattern "def |self\.last_ball|obs\.get\(" to keep it tight. Actually obs.get( lines will be many. Let me just do "def " plus "last_ball". Pattern: "def |last_ball|vel". Adding "vel" catches any existing velocity handling and tells me if obs has velocity fields. Good: pattern "def |last_ball|vel".
The connection drops cost me clock but the picture from m29 is complete: falls fixed (8 v 15), deadlines fine (9), but **Pu touched the ball 15 times to their 62/64** and 23 through-balls ran past a cover robot standing on a static point. That passivity is nine goals. Before I edit I need the tail of team.py that got truncated — `_ball`, `_assign`, `_valid` — and to know whether obs carries ball velocity or I must estimate it.
grepclub/team.py
The league answered960 characters
[grep club/team.py /def |last_ball|vel/] 14 match(es) for /def |last_ball|vel/ in club/team.py. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. club/team.py:25 @1070: def _clamp(pt): club/team.py:30 @1184: def _dist(a, b): club/team.py:37 @1319: def __init__(self, agent, shirt, shared): club/team.py:41 @1495: self.last_ball = None # [x, y] last credible ball position club/team.py:45 @1646: def begin_episode(self, log_dir=None): club/team.py:48 @1764: self.last_ball = None club/team.py:54 @1897: def decide(self, obs): club/team.py:146 @6166: def _ball(self, obs): club/team.py:151 @6385: self.last_ball = [float(xy[0]), float(xy[1])] club/team.py:152 @6447: return self.last_ball club/team.py:154 @6478: def _teammate(self, obs): club/team.py:161 @6745: def _assign(self, ball, me, mate): club/team.py:197 @8368: def _valid(reply): club/team.py:213 @8962: def build_team(ctx): (0.0s)
Thought for 2,841 words
Let me think about where I am. The session log shows I've been reviewing m29 (1-10 loss to Dynamo Datacenter). Key findings:
- Falls fixed: 8 total (5 unforced) vs 15 in m23
- Deadlines fine: 9 missed total
- The damning number: Pu (index 1) has 15 touches vs Zhi's 71, while Dynamo's pair went 62/64. The cover robot is a spectator.
- 23 through-balls conceded.
I've read the first part of team.py (up to the cover branch, truncated at "out = {"sk..."). I grepped for the function definitions and got offsets:
- `_ball` at line 146 @6166
- `_teammate` at line 154 @6478
- `_assign` at line 161 @6745
- `_valid` at line 197 @8368
- `build_team` at line 213 @8962
I need to read the tail of team.py from around offset 6000 to the end to see those functions. The file is roughly 9-10KB total based on offsets (build_team at @8962). So reading from offset ~5900 should get me the rest.
Also I wanted to know whether obs carries ball velocity. The grep for "vel" only matched... actually the grep pattern was "def |last_ball|vel" and matches shown don't include any "vel" lines, so there's no velocity handling in my code. I need to check what obs provides — maybe look at the reference implementation or the SDK. But budget-wise, I have 20 turns left, $2.38 remaining in session budget, 1h08m clock.
My plan for this session: fix cover passivity. The cover robot stands on a static point on the ball→goal line. Ideas:
1. **Interception**: cover robot should intercept through-balls. If ball is moving (estimate velocity from last_ball memory), cover should move to cut the line. But without ball velocity in obs, I can estimate: keep last_ball and timestamp, compute velocity. Do I have a timestamp in obs? There's `time_remaining_s`. I can use that to compute dt between ticks.
2. **Simpler and robust**: make the cover robot chase the ball when the ball is closer to the cover than to the presser, or when the ball is on our defensive side and loose. Actually the role assignment already picks the nearest robot as presser. The problem: when the presser is beaten (ball goes past), hysteresis (1.5m) delays the switch, and the cover stands on the line instead of moving to the ball.
Actually wait — let me think about what "23 through-balls" means. The event type "through" — probably ball passing through a gap/zone. Dynamo played 2v1 against our presser constantly: both their robots attacked, our cover never engaged, so their far robot received passes freely.
Key fixes to consider:
**A. Cover engages when ball is on our side and presser is beaten.** E.g., if the ball is closer to own goal than the presser (ball behind the presser), the cover should attack the ball rather than stand on the line. The role assignment with hysteresis may already handle some of this, but the hysteresis margin (1.5m) means the presser keeps the role even when clearly beaten.
**B. Cover intercepts moving balls.** Estimate ball velocity from consecutive observations; if the ball is moving toward our goal and will pass near the cover's position, step onto the interception point.
**C. Cover marks the spare opponent.** In 2v2, when opponents have the ball, the cover should mark the opponent who isn't being pressed, rather than standing on a geometric point. That directly addresses "their two robots play 2v1 against our lone presser". If obs carries opponent positions, the cover can position between the spare opponent and our goal, or on the line between the spare opponent and the ball (to intercept a pass).
Do I know what obs contains? I've seen: obs["self"] with "fallen", "field_xy"; obs["you"] with "defend_goal_xy", "attack_goal_xy"; obs has "time_remaining_s". There's `_teammate(obs)` so obs carries teammate info. Does it carry opponents? I should check the reference implementation or the SDK to see the obs schema. Let me grep reference/ for the observation structure.
Actually, the cheapest way: grep reference/ for "opponents" or "obs" keys.
Let me plan the session with remaining budget: 20 turns, ~$2.38, 1h08m.
Turn plan:
1. Read tail of team.py (offset ~5900) — see _ball, _teammate, _assign, _valid, build_team. (1 turn)
2. Grep reference/ for obs schema (opponents, ball velocity). (1 turn)
3. Maybe read relevant reference slice. (1 turn)
4. Design + write the change. The big edit: cover behavior. (2-3 turns)
5. Lint. (1 turn)
6. Practice 90-120s. (1-2 turns)
7. Fix if needed. (1-2 turns)
8. Update NOTES.md + PLAYBOOK.md. (2 turns)
9. Done. (1 turn)
That's ~12 turns, comfortable.
What's the actual change? Let me think about the football.
Current cover: stands at ball + 2m toward own goal, on the ball→goal line. Static point. Problems:
- Doesn't intercept through-balls (ball runs past, cover watches).
- Doesn't mark the spare attacker, so their far man receives and shoots freely.
- When presser is beaten, hysteresis keeps roles frozen for too long.
The cleanest high-value change given the evidence (23 through-balls, Pu 15 touches):
**Make the cover an active second defender:**
1. If the ball is loose and moving toward our goal (or in our defensive third), the cover attacks the ball / moves to an interception point ahead of the ball's path.
2. Otherwise, mark the spare opponent: position between the spare opponent and our goal (goal-side of the spare man), which both blocks the pass lane and covers the runner.
3. Keep the ball→goal line position as fallback when no opponent info.
But wait — role assignment: presser = nearest to ball. If the cover starts chasing the ball, we get two robots on the ball and nobody covering. The role logic must stay coherent: exactly one presser. The cover's *positioning* changes, not the role.
Actually, there's a subtlety: if the ball is closer to the cover than to the presser, _assign should make the cover the presser. Let me look at _assign when I read it. The hysteresis: "presser changes only if clearly beaten" (SWITCH_MARGIN_M = 1.5). So if the presser is 1.5m closer... or the other robot must be 1.5m closer to take over. When the ball runs past the presser toward our goal, the cover quickly becomes much closer to the ball, so the switch should happen. Unless... the cover is standing 2m goal-side of the ball, so when the ball moves toward our goal, the cover is actually near the ball's path. Hmm.
Wait, actually let me reconsider: 23 "through" events. What is a "through" event? Probably a through-ball (pass into space). If their pair plays give-and-go, our presser chases the ball carrier, their teammate runs free, receives the pass, and shoots. The cover standing on the ball→goal line 2m goal-side... when the pass is played, the ball moves, the cover's target point moves with it (ball + 2m toward goal). The cover is always 2m behind the ball, never at the ball. The presser chases; if the presser is slower or falls, the cover is only 2m away but doesn't attack because it's not the presser... no wait, _assign would make it presser if it's closer.
Hmm, but Pu had 15 touches in 600s. Something keeps Pu off the ball. Possibilities:
- Pu is usually farther from the ball than Zhi (Zhi presses, gets touches; when the ball moves, Zhi is nearer, stays presser due to hysteresis).
- Pu's cover position tracks the ball, so Pu is always ~2m from the ball but never ON it. When Zhi is beaten, the ball moves toward our goal, Pu is 2m goal-side... Pu should become presser then (Pu closer). Unless hysteresis: presser changes only if the other is clearly closer (1.5m margin). Pu at 2m from ball, Zhi at maybe 1-2m behind the ball... they're within the margin, so Zhi keeps the role, chases, and Pu watches. That matches: Zhi 71 touches, Pu 15.
So the fix has two parts:
1. **Reduce hysteresis when the ball is heading toward our goal** — or better, make the presser the robot that can reach the ball first (interception time, not distance). A robot ahead of a moving ball (goal-side) reaches it sooner than one behind it. Distance-based assignment is wrong for moving balls: the robot behind the ball is closer in distance but will never catch it as fast as one in its path.
2. **Active cover**: mark the spare opponent or hold a smarter line.
The interception-time idea is elegant but needs ball velocity. Estimate from last_ball + time_remaining_s deltas. I have self.last_ball already. I can add self.last_t. Velocity estimate over ~0.3-1s window. Noisy but usable with smoothing (e.g., exponential moving average).
But careful: complexity = risk. The session budget is fine, but each bug risks a crash at kickoff → last good commit plays (which is the current code, so actually safe-ish, but the change is wasted).
Let me think about the minimal high-value change:
**Change 1: goal-side priority.** In _assign, if the ball is moving toward our goal (velocity dot toward own goal > threshold), the robot that is goal-side of the ball (between ball and own goal) gets the presser role regardless of the 1.5m hysteresis. This puts Pu (cover, standing goal-side) onto through-balls instantly. This directly attacks the 23 through-balls.
Hmm wait, but the cover is only 2m goal-side of the ball. If a through-ball is played, the ball accelerates past the presser; the cover is in its path. With goal-side priority, the cover becomes presser and attacks.
**Change 2: cover marks the spare opponent** when opponents are visible. Need to check obs schema for opponent positions. If obs has "opponents" with field_xy, the cover positions goal-side of the nearest spare opponent, capped to stay reasonable (not dragged into the corner). If no opponent data, fallback to current line position.
Actually, is marking the spare man the right call vs staying on the ball→goal line? In 2v2, the classic shape: first defender presses the ball, second defender covers the space behind (ball→goal line) OR marks the second attacker if they're dangerous. The through-ball count says their second attacker is the killer. Marking goal-side of the spare attacker (between spare attacker and our goal, on the ball→spare line) intercepts the pass AND covers the shot. That's the standard fix.
But there's a risk: if the cover marks a man high up the pitch, we lose the goal-side cover. Cap it: the cover's mark position should be no farther from own goal than... hmm, or blend: position on the line between spare opponent and own goal, 1m goal-side of the spare opponent, but no closer to their goal than the ball is (don't get dragged past the ball).
Let me also reconsider: maybe simpler — cover intercepts the pass LANE: position at the midpoint of ball→spare-opponent line, slightly goal-side. That's literally standing in the passing lane. Combined with pressing the ball carrier, that's a real 2v2 defense.
Hmm, but I don't yet know if obs gives opponent positions. Let me check the reference implementation. The sample team (reference/) — grep for "opponents" there.
Also need to see _assign and _teammate to know the obs structure for teammates. _teammate(obs) exists, so obs likely has "teammate" or "others". Let me read the tail of team.py first, then grep reference for the obs schema.
One more consideration: the "say" and shout stuff — not important now.
Also the m7 dissection (16-3 at Singularity United) is still open, but m29 is fresher and same-model opposition proved tactics are the gap. The cover fix is the priority. m7 can wait — actually, let me check quickly if m7's digest shows the same touch imbalance; if so, one fix addresses both. That's one cheap read. Maybe worth it after the code change, if budget allows. Actually the pattern is likely identical (cover passive). I'll note it.
Let me also think about whether to make Pu press more generally. Zhi 71 touches vs Pu 15. With goal-side priority on moving balls, Pu's touches should rise naturally on defense. On offense, when we have the ball, the cover stands 2m behind the ball — that's actually okay-ish for a give-and-go, but Pu never receives because... the presser (Zhi) has the ball, Pu is 2m behind on the ball→OWN goal line — that's BEHIND the ball toward our own goal. For attack support, the teammate should be ahead or level, not behind. Hmm, but changing cover positioning when we have possession is a bigger change. The immediate evidence is defensive (10 conceded). Focus on defense; note attack support as next.
Wait, actually, let me reconsider the whole thing. 1-10. Ten conceded. Defensive fix is the priority, clearly.
Plan for the edit:
In `decide`, cover branch (the else):
- Compute spare opponent (nearest opponent to ball that isn't... well, opponents are both "spare" relative to our presser; mark the one nearer our goal or nearer the ball? The dangerous one is the one who can receive and shoot — typically the one goal-side/forward. Mark the opponent closest to our goal? Or closest to the ball (the next receiver)?).
Hmm. In 2v2 defense: presser on ball carrier. The other opponent is the pass option. Cover should intercept the pass to them: stand on the line between ball and that opponent, goal-side a bit. If the pass is played along that line, the cover is on it. That's the interception position. And if the opponent runs at goal instead, the cover is between them and goal (roughly).
Position: point on segment ball→spare_opp, say 60-70% of the way from ball to the opponent (closer to the opponent, since the presser covers the ball end), or 1m goal-side of the opponent. Let me think... If I stand 1m from the spare opponent on the ball→opponent line, I block the arrival of the pass. If I stand on the opponent→goal line goal-side of the opponent, I block their shot after receiving. The best compromise: position between the spare opponent and our goal, but shifted toward the pass lane. Simple version: target = spare_opp position moved 1.2m toward our own goal AND 0.6m toward the ball? Getting complicated.
Simplest robust version that a commentator could describe: "the covering defender now steps onto the spare attacker when the ball is in our half, instead of standing on a fixed line."
Let me define:
- spare = opponent (other than... both opponents; pick the one NOT nearest to our presser? No — pick the one nearest the ball is likely the carrier being pressed; the spare is the OTHER one, farther from the ball). Actually the presser presses the ball, not a man. The ball carrier is the opponent nearest the ball. Spare = the opponent farther from the ball.
- Cover target: on the line from spare opponent to own goal, 1.0-1.5m goal-side of the spare (i.e., between spare and our goal). Clamp so the cover doesn't get dragged beyond the ball's goal-side position... hmm, actually if their spare man is deep in our half, we WANT the cover there.
But also keep goal protection: if the spare opponent is far from our goal (their half), marking them high leaves our goal open. Cap: cover's target x (goal-side coordinate) should be at least as defensive as... let me not over-engineer. Blend: cover target = midpoint between (spare-opponent mark position) and (ball→goal line position)? That hedges both. Hmm.
Actually, you know what — let me look at what data I actually have before finalizing. Read the tail of team.py (offset 5900ish) and grep reference for obs keys. Then decide.
Also: does obs include ball velocity directly? If the SDK provides it, great. Grep reference/ for "vel".
Let me also keep in mind the turn budget: 20 turns left. Reads: 2-3. Edit: 1-2 (replace or write whole file — write is safer for big changes but the file is ~9.5KB; I have most of it in context already from the earlier read, truncated only at the end. I could do targeted replaces). Lint: 1. Practice: 1-2. Notes/playbook: 2. Done: 1. Total ~10-12. Fine.
Next action: read team.py from offset ~5900 to end (the truncated tail: _ball, _teammate, _assign, _valid, build_team). That's one read (24KB window, file ends ~9.5KB so offset 5900 gets everything to EOF).
Then grep reference for obs schema.
Let me write the turn: brief prose + JSON read.
Actually — one thing to double check: the earlier read of team.py got truncated mid-line at "out = {"sk...". The read tool serves 24KB at a time; the file is bigger than 24KB? The grep offsets go up to @8962 for build_team at line 213. Wait, those are character offsets: build_team at char 8962. So the file is ~9.5KB total, well under 24KB. But the read was truncated in the harness display... The session log says "[read club/team.py] ... out = {"sk...[truncated]" — maybe the harness condensed it for the log. The grep offsets tell me the real file layout: _ball @6166, _assign @6745, _valid @8368, build_team @8962. So reading from offset 5900 gives me from _ball to EOF (~3.6KB).
Let me do that now.
The m29 picture is complete and the fix is clear: falls and latency are solved, but Pu watched 23 through-balls go past a static cover point while their pair played 2v1 against Zhi all night. Before I rewrite the cover role I need the truncated tail of team.py — `_ball`, `_assign`, `_valid` — to see what the role logic and observation access actually look like.
Readclub/team.py
The league answered3,837 characters
[read club/team.py@5900] (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 5900-9654 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
Thought for 1,097 words
Let me take stock. I'm in a session, mid-way. I've read the digest of m29 (1-10 home loss to Dynamo Datacenter), read team.py's tail. The picture:
- Falls fixed (8 total, 5 unforced, down from 15)
- Deadlines fine (9 missed)
- Pu touched ball 15 times vs their 62/64 — cover robot passive
- 23 through-balls ran past a static cover point
The fix I want: make the cover robot active — intercept through-balls, not stand on a static point. I need to see the decide() body (lines 54-146, chars ~1897-6166) to know the current cover logic before editing. I read the tail (5900-9654) but the decide() body is in the earlier part which was condensed in the session log. I have 17 turns left, $2.34 remaining session budget, 52 minutes.
Let me read the decide() section — chars 1897 to ~6166. That's about 4.3KB, one read of 24KB covers it easily. Actually read serves 24KB at a time; the file is 9654 chars total. Wait — the file is only 9654 chars! The read at offset 5900 showed 5900-9654 "end of file". So the whole file is under 10KB. I read from 5900. The decide() body is at char 1897-6166, which is ~4.3KB. Let me read from offset ~1300 to get the class init and decide().
Actually, I should read from offset 1000 or so to see the whole front half. One read gets 24KB, so reading from offset 0 gets the whole file. But earlier in the session I already read club/team.py (condensed in log). The condensed entries mean I don't have the content in my context anymore. I need the front half: constants, decide() body. Let me read offset 0 (or ~1000) to char 5900.
Then make the edit: active cover. Design:
Current cover behavior (from playbook): "the other holds the ball→own-goal line 2 m goal-side of the ball" — a static point recomputed each tick, but it doesn't intercept. The fix: cover should move along the ball→own-goal line but also cut through-balls — i.e., position between ball and goal AND track laterally toward where the ball is going. Simplest robust upgrade without ball velocity in obs: estimate ball velocity from last_ball history (we have self.last_ball; add self.prev_ball and timestamps? obs may have age_s but not wall time... we can keep a small history of ball positions with tick counter).
Actually simpler: cover positions on the ball→goal line at a distance that adapts, and if the ball is moving toward our goal (ball position getting closer to our goal over ticks), the cover steps toward the goal-side intercept point — i.e., move toward the point on the ball's path toward goal. Even simpler and effective: cover target = midpoint between ball and own goal, clamped to our half, and when ball is beyond cover toward our goal (ball closer to our goal than cover), cover becomes... wait, that's the presser's job via _assign (nearest presses). Hmm, but the issue: 23 through-balls ran past a static cover point. If the ball is played past the presser toward our goal, the cover is standing 2m goal-side of the ball — as the ball moves, that point moves, but the cover walks slowly and the ball outruns it. The cover should anticipate: target a point on the line between ball and own goal but weighted toward the goal when the ball is moving fast — or simply: cover targets the point where the ball's path toward our goal crosses, i.e., move to block the lane, and if the ball gets within X m of the cover, the cover attacks it (becomes presser effectively — but _assign handles role switch by distance; the problem is hysteresis 1.5m means cover won't take over until margin exceeded... actually _assign switches when mate_d + 1.5 < my_d, so if ball runs past presser toward cover, cover becomes closer and takes over. But the digest says it didn't happen — because the cover was 2m goal-side ON the line, ball runs past... cover should be nearest then. Unless the ball ran past on a lane away from the cover's static point, and by the time hysteresis allowed switch, ball was through.
Hmm, 23 through-balls with Pu only 15 touches suggests Pu rarely became presser. Maybe because when ball goes past, the presser (Zhi) chases from behind, and mate_d for Pu standing on the line... if the ball travels along a lane not through Pu's point, Pu's distance may stay > Zhi's distance (Zhi chasing behind at ~1-2m). So Pu never becomes nearest by the 1.5m margin. The fix: cover should MOVE TOWARD the ball's goalward path — intercept — which both cuts the lane and naturally brings Pu into presser range when the ball comes near.
Design for active cover (shell-level, no LLM):
- Estimate ball velocity: keep deque of (tick, x, y) — but we don't have a clock in obs? We have ball age_s (age of detection). We can use our own tick counter; ticks are roughly regular (decision cadence). Use last N ball positions; velocity = (p_now - p_prev) per tick.
- Predict ball future position: p + v * k for k ~ 0.5-1.0s.
- Cover target: the point on segment ball→own_goal at 2m goal-side of ball (current), BUT if ball is moving toward our goal (v · (goal - ball) > threshold), cover target = intercept point: project predicted ball position onto the ball→goal line, or simply target the predicted ball position clamped goal-side.
Simplest robust: cover target = point 2m goal-side of the PREDICTED ball position (prediction horizon ~0.6s), clamped to pitch. That makes cover cut the lane ahead of the ball's motion. Plus: if the ball comes within 1.2m of the cover, the cover goes to ball (go_to_ball) — attack it. Actually _assign would make it presser then. But hysteresis: switch requires mate_d + 1.5 < my_d. If ball is 1m from Pu and 3m from Zhi, Pu becomes presser. Fine.
Also add: when ball is in our defensive third and moving toward goal, cover collapses toward the goal line (deeper). The playbook says "gaffer has pushed the cover deeper" — nice for broadcast.
Let me look at the decide() code first. I need to read chars 0-5900. Let me do that now — one read, offset 0. That gives the whole file (9654 chars < 24KB). Then edit.
Wait, budget: $0.16 spent of $2.50, 17 turns left, 52 min. Connection errors ate time. Be decisive: read front half, make edit, lint, practice once, done. That's ~5-6 turns. Good.
Let me read offset 0.
The tail is clear: `_assign` switches roles only on a 1.5 m distance margin, so a through-ball running down a lane away from Pu's static point never made her the nearest robot — she stood while 23 balls went past. Now I need the front half (constants + `decide()`) to see exactly what the cover branch commands before I make her intercept.
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
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered120 characters
model error (3/3): APITimeoutError: Request timed out. [not retried: the generation may have completed and been billed]
Session over. Everything the club changed was committed to its own public repository.