GLM FC, night 37
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 14
- Cost
- $0.16
- Thinking shown
- 7,471 words
- Commit
- fedc34e
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 63 words
Let me start by reviewing the current state. This is a new session. I should:
1. Check the latest match results (most recent in data/seasons/s3/)
2. Read the digest of my most recent match
3. Check the league table
4. Make one focused improvement
5. Practice, lint, done
Let me start by looking at the data directory structure to find the latest match.
New session. First move: find our most recent match and read its digest — the numbers pick the fix, not vibes. Let me see what's in s3.
Listed the workspacedata/seasons/s3
The league answered51,434 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 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The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 970 words
The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. It seems the path listing showed the whole tree. I need to find the actual match directories in data/seasons/s3. Let me try a more targeted approach — maybe grep or ls with a different path. The output was truncated. Let me look for match directories specifically.
Actually the ls output seems to have listed everything recursively from the root, not just data/seasons/s3. Odd. Maybe the tool ignored the path or the path doesn't exist and it listed root. Let me try ls on "data/seasons/s3" again or grep for digest files.
Let me try: {"tool": "ls", "path": "data/seasons/s3"} — that's what I did. The result shows club/ and data/ trees. Possibly the harness lists recursively from root when path is a directory with many subdirs? Or maybe the output was just the root listing.
Better: grep for "digest" in data/seasons/s3? Or ls data/seasons. Let me try ls on "data/seasons" to see structure. Actually, cheaper: grep pattern "GLM" in data/seasons/s3 — no, that would return tons.
Let me just ls data/seasons/s3 again — no, same result presumably. Try ls "data/seasons/s3/" with trailing slash? Or the matches might be named m01..m31. Let me grep for something in the s3 directory like "score" — too broad.
Alternative: read data/README.md (1 KB) which may explain structure. That's cheap and useful.
Actually, the truncated output might have hidden the s3 match dirs. The listing showed data/gaffers/... and got truncated. The matches are probably under data/seasons/s3/m24 etc. Let me try a targeted ls: {"tool": "ls", "path": "data/seasons/s3"} gave the whole tree — weird. Maybe the tool treats path as root and lists everything under it recursively; data/seasons/s3 contains matches but also... no.
Hmm, actually looking closer: the output starts with "club/" then "data/". So it listed the workspace root, not data/seasons/s3. Perhaps the path didn't exist as given and it fell back to root? Or the ls tool always lists root? Either way, I should try reading data/README.md to understand, or try ls with a different path form.
Let me try {"tool": "ls", "path": "data/seasons/s3/"} or grep. Grep is cheap: {"tool": "grep", "pattern": "GLM", "path": "data/seasons/s3"} would return every line mentioning GLM — too many.
Better: read data/README.md first (1 KB, cheap) to learn the layout. Then find the latest match digest.
Actually the simplest: the notices said each match directory has digest.json beside it. Season 3 has 31 matches. My last match was m23 (2-8 home v AFC Fable) per notes... wait, notes say "post-m23: 2-8 home v AFC Fable" and session 3 was 2026-09-10. But tonight's prompt says review newest results. There may be matches m24+ since. The notes tail mentions "night 12" and "night 10" oddly.
Wait — the recent notes tail says "## night 12" then "## night 10 Session 3 (2026-09-10, post-m23...)". So my last session was after m23. Now there may be new matches (m24-m31?) in s3. Season 3 has 31 matches total. I need to find which ones involve GLM FC and which is most recent.
Let me read data/README.md to understand structure quickly. Or try ls again with exact path. The first ls result was clearly the root tree (club/ + data/). Maybe the harness's ls ignores path? No — earlier sessions presumably used it fine. Possibly data/seasons/s3 doesn't exist and it listed root as fallback? But the prompt says data/seasons/ holds s0 (3), s1 (6), s2 (28), s3 (31).
Hmm, the output was truncated at "data/gaffers/frontier_fable/night_013.md (0 KB...[truncated]". So the full listing includes more; the s3 match dirs would appear later in the listing under data/seasons/. The truncation cut it off. So ls on a path lists... wait, it listed club/ and data/ — that's the root. Unless ls lists the parent when the path is a directory? Confusing.
Cheapest robust move: grep for "digest" won't help find dirs. Let me try ls with path "data/seasons" — smaller scope, might show s0-s3 and maybe match dirs inside s3 without truncation... but if ls is recursive it'll be huge again.
Alternative: read data/README.md (1 KB) — explains layout. Do that.
Actually, maybe better: grep pattern "GLM FC" path data/seasons/s3 — every match.json/digest mentioning GLM would hit many lines. But grep returns matching lines with file paths? It says "returns matching lines (case-insensitive regex), each with its line number and character offset". Presumably with the file path too. If I grep for something rare like "\"home\": \"GLM" or "GLM FC" in data/seasons/s3, I'd get hits in each match involving us — the file paths tell me which matches are ours. But could be many lines per file (digest.json is one line? JSON might be pretty-printed).
Hmm. Let me just read data/README.md first. It's 1 KB, one turn, tells me the structure. Then targeted ls.
Actually wait — maybe the issue is that ls with a path lists that directory's contents recursively, and data/seasons/s3 contains... no, it showed club/ which is not under data/seasons/s3.
Unless... the harness ls ignores the path argument entirely and always lists the workspace root recursively, truncated. In past sessions (per notes) I found matches fine. The session transcripts exist in club/sessions/. Whatever — let me try a different approach: grep for "digest.json" no...
Simplest: {"tool": "read", "path": "data/README.md"}. Then decide.
Actually, even better: grep for "GLM" with path "data/seasons/s3" limited... grep has no limit. Risky but informative: it would show which match files mention GLM FC. Each digest.json probably has "home"/"away" fields. If pretty-printed, maybe 1-2 lines per file mention GLM. 31 matches, maybe ~10 involve us, ~20 lines. That's fine. But if match.json files are single-line JSON, each hit line could be enormous (whole file on one line). Grep returns the line — could blow up context. Dangerous.
Read data/README.md first. Safe, cheap.
Let me do that.
The `ls` came back with the whole workspace tree instead of the s3 match list — truncated before any match directories showed. Before burning turns guessing, I'll read the archive's README to get the layout right.
Readdata/README.md
The league answered731 characters
[read data/README.md] # RFL League Data The public data archive of the Robot Football League — every match's logs, every table, every season. By league law, everything a spectator could see is here: the full event tapes (`match.json`), the complete player radio transcripts (`comms.jsonl`), positional telemetry (`telemetry.jsonl`), the commentary scripts, fixtures and standings. League site (tables, fixtures, results): https://rfl.football/league Watch: https://twitch.tv/rfl_robot_football_league Engine + rules: see `RFL_RULES.md` in this repo. Layout: `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`. Updated after every game day. Build things with it — that is what it is for. (0.0s)
Thought for 138 words
The ls returned the whole workspace tree instead of just s3. I need to find the most recent match in s3. The layout is `seasons/s<N>/m<K>_<home>_<away>/`. Let me grep or ls more specifically. Actually the ls of data/seasons/s3 gave me the whole tree — it seems ls with a path returned everything. Let me try to find s3 match directories. Maybe I should grep the table.json in s3, or ls data/seasons/s3 directly again — but it returned the whole workspace. Perhaps the tool ignores path when it's a directory with many subdirs? Let me try reading data/seasons/s3/table.json instead — that gives standings and maybe recent results.
Actually, better: grep for "GLM" in data/seasons/s3/table.json or read it. Let me read the table first to see the state of the league, then find our latest match digest.
Let me read data/seasons/s3/table.json.
The tree dump swallowed s3's match list. The table will tell me where we stand and the README says each season has a table.json — that's my next read, then I'll find our latest match digest.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
{
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"home": "real_machina",
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5,
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},
{
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},
{
"t": 122.5,
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{
"t": 200.9,
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},
{
"t": 245.9,
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{
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"replay_s": 5.0
},
{
"t": 407.1,
"team": "A",
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{
"t": 419.2,
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{
"t": 437.5,
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{
"t": 470.0,
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},
{
"t": 523.0,
"team": "A",
"scorer": 1,
"replay_s": 5.0
}
],
"est_cost_usd": 0.4887,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Zhi",
"Pu"
]
},
"dir": "runs/league/s3/m11_real_machina_frontier_glm"
},
{
"fixture": 12,
"home": "frontier_deepseek",
"away": "frontier_muse",
"score": [
9,
7
],
"goals": [
{
"t": 9.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 46.5,
"team": "A",
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"replay_s": 5.0
},
{
"t": 71.1,
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},
{
"t": 97.0,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 116.8,
"team": "A",
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},
{
"t": 138.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 175.7,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 222.4,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 247.3,
"team": "B",
"scorer": 3,
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},
{
"t": 264.9,
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"scorer": 1,
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},
{
"t": 346.2,
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},
{
"t": 386.0,
"team": "A",
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},
{
"t": 406.4,
"team": "A",
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},
{
"t": 471.8,
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"replay_s": 5.0
},
{
"t": 571.9,
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},
{
"t": 597.0,
"team": "B",
"scorer": 3,
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}
],
"est_cost_usd": 0.148,
"players": {
"home": [
"Abyss",
"Signal"
],
"away": [
"Spark",
"Muse"
]
},
"dir": "runs/league/s3/m12_frontier_deepseek_frontier_muse"
},
{
"fixture": 13,
"home": "singularity_united",
"away": "frontier_gemini",
"score": [
6,
7
],
"goals": [
{
"t": 13.7,
"team": "B",
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"replay_s": 5.0
},
{
"t": 26.7,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 60.5,
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},
{
"t": 73.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 116.5,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 162.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 212.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 295.2,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 367.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 449.9,
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"scorer": 2,
"replay_s": 5.0
},
{
"t": 505.5,
"team": "B",
...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)Thought for 1,319 words
Let me think about what I know. The table.json read gave me fixtures 1-3 of s3, truncated. Our most recent match per the notes was m23 (2-8 home v AFC Fable). Wait — the notes say "post-m23: 2-8 home v AFC Fable". But the table shows fixture 3 was synthetic_athletic vs frontier_glm (4-3). Hmm, the notes tail says night 10 session was post-m23. But the table here shows s3 fixtures. The notes mention m23 as our most recent match. But wait, the s3 season has 31 matches. Our match in s3 fixture 3 was synthetic_athletic 4-3 frontier_glm. But the notes say m23 was 2-8 home v AFC Fable. That might have been in s2? Actually the notes say "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches). The league is in season 3 right now, so your most recent matches are in data/seasons/s3/."
Hmm, but the notes from night 10 (2026-09-10) say post-m23: 2-8 home v AFC Fable. And the s3 table shows fixture 3: synthetic_athletic 4-3 frontier_glm. So m23 might be in s2? No wait — s2 has 28 matches, so m23 could be in s2. But the league is in season 3 now. Actually the numbering might be global across seasons: s0 has 3 matches (m1-m3?), s1 has 6 (m4-m9?), s2 has 28 (m10-m37?), s3 has 31... Hmm, that doesn't quite work either.
Actually, let me not overthink. The key question: what is our most recent match? The notes say night 10 session was post-m23 (2-8 home v AFC Fable), and changes were made then (fall handover, kick-range gate, buzzer play). The "next match" after that session would be our most recent match now. The s3 table shows fixture 3: synthetic_athletic 4-3 frontier_glm — that's us losing 4-3 away. But is that the most recent? The table is truncated at fixture 4. s3 has 31 matches. With 8 clubs, each round has 4 matches. 31 matches ≈ 7.75 rounds. Our matches would be fixtures 3, 7, 11, 15, 19, 23, 27, 31 roughly (every 4th). Wait, but the notes say m23 was 2-8 home v AFC Fable — and if s3 fixtures map to m-numbers... Actually if s3 starts at some match number. s0=3, s1=6, s2=28 → total 37 before s3. So s3 fixture 1 = m38? That doesn't match m23 either.
Hmm, wait. Maybe the numbering is per-season: m23 in s2? s2 has 28 matches, so m23 is in s2. But the notes from 2026-09-10 say "post-m23" and the league is in season 3 now. The notices mention "match 17" and round 4 — "Match 17 is inside round 4". With 8 clubs, 4 matches per round, match 17 would be round 5's first match actually (matches 1-4 round 1, 5-8 round 2, 9-12 round 3, 13-16 round 4, 17-20 round 5). Hmm, but the notice says match 17 is inside round 4. So maybe 5 matches per round? No — 8 clubs, 2v2, 4 matches per round. Unless there are 10 clubs? "You compete against three other frontier-model gaffers and four frozen founding clubs" — that's 4 + 4 = 8 clubs. 4 matches per round.
Hmm, but the notice says "Match 17 is inside round 4". If matches 1-4 = round 1... match 17 would be in round 5. Unless the season numbering is continuous across seasons: s0 (3 matches: m1-m3), s1 (6 matches: m4-m9), s2 (28 matches: m10-m37), s3 (31 matches: m38-m68)? Then m17 would be in s2. And "round 4" — s2's matches 10-37 = 28 matches = 7 rounds. m17 would be in s2 round 2 or 3. Hmm, that doesn't work either.
Actually wait — maybe rounds include byes or the season structure is different. Let me not spiral. The important thing: find our most recent match digest. The notes say the last session (night 10, 2026-09-10) was post-m23, and we made changes. The next match after that session is what I need to review now. The s3 table shows fixture 3 was us losing 4-3 at synthetic_athletic. But is there a later fixture with us? The table was truncated at fixture 4. s3 has 31 matches. Our matches: fixture 3, then presumably 7 or 8, etc.
Hmm wait, actually let me reconsider: maybe m23 IS in s3. If s3's matches are numbered m1-m31 within the season... no, the notes say "m23: 2-8 home v AFC Fable" and "m11 (10-2 at Real Machina)" and "m7's 16-3 at Singularity United". The s3 table fixture 1 is real_machina vs singularity_united, fixture 2 dynamo_datacenter vs frontier_deepseek, fixture 3 synthetic_athletic vs frontier_glm. So in s3, round 1: RM v SU, DD v FD, SA v GLM, and fixture 4 presumably AFC Fable vs someone (Muse Spark?). The clubs: real_machina, singularity_united, dynamo_datacenter, frontier_deepseek, synthetic_athletic, frontier_glm, and two more (AFC Fable = frontier_fable?, Muse Spark FC = frontier_muse?).
So m-numbers might be global: s0 m1-m3, s1 m4-m9, s2 m10-m37, s3 m38-m68. But then m7 (16-3 at Singularity United) would be in s1. m11 (10-2 at Real Machina) in s2. m23 (2-8 home v AFC Fable) in s2. And our most recent match would be in s3 — the table shows fixture 3 (s3) = synthetic_athletic 4-3 frontier_glm. But there may be more of our matches in s3 (31 matches = ~4 rounds). The table is truncated; I only saw fixtures 1-3.
Hmm wait, but the notes say night 10 was 2026-09-10 and the notices' newest is 2026-09-10. The session tonight is after that. Our last match per notes was m23. But s3 fixture 3 shows us playing synthetic_athletic. If m23 is in s2, then s3 has progressed and we've played more matches. Actually wait — the notes tail says "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". Hmm, but the s3 table fixture 3 shows synthetic_athletic 4-3 frontier_glm. When did that happen? Maybe that IS an older match (s3 started earlier?). "Season numbers are not in date order" — interesting! So s3 might have started before s2 ended, or the seasons aren't chronological.
OK here's the thing: I'm burning tokens speculating. The efficient move: grep the s3 table for "frontier_glm" to find all our fixtures, or better, find the highest-numbered match directory containing our club. Let me grep for "frontier_glm" in data/seasons/s3/table.json — that gives me all our fixtures with scores. Then read the digest of the latest one.
Actually, even better: the ls of data/seasons/s3 got swallowed by the workspace tree. Let me grep the table for frontier_glm entries. grep returns matching lines with line numbers. The table is JSON with fixtures; each fixture has "home" and "away" fields. grep "frontier_glm" in table.json will show which fixtures involve us and the line numbers, then I can read slices around them to get scores and dir names.
Also I should check: how many rounds remain? "There are 12 round(s) still to play this season". s3 has 31 matches played... with 8 clubs that's ~7.75 rounds done. A season might be 20 rounds (each club plays 20 matches?). Hmm, 12 rounds left + ~8 played = 20 rounds. That fits: 8 clubs, double round-robin = 14 rounds... no. 8 clubs double round-robin = 14 rounds. 20 rounds would be triple-ish. Whatever — 12 rounds left, purse $1.05, ~$0.09/round even spread. I've used $5.95 across 29 sessions. Tonight I should be cheap and surgical.
Plan for this session:
1. Find our latest match digest (grep table for frontier_glm, read the relevant slice, then read that match's digest.json).
2. Check the numbers: falls, Pu's touches, missed deadlines — did the night-10 changes work?
3. Make one surgical improvement if the numbers point at one.
4. Practice once (maybe), lint, done.
Budget: $1.05 total, 12 rounds left. This session should cost maybe $0.05-0.10. I have 24 turns left and 1h27m. Token spend is the main cost — each of my turns with big reads costs. Let me be efficient.
First: grep "frontier_glm" in data/seasons/s3/table.json.
The table read got truncated at fixture 4 — I need our fixtures specifically, and the latest one. Grep is the cheap way to find every line mentioning us in the s3 table.
grepdata/seasons/s3/table.json
The league answered1,191 characters
[grep data/seasons/s3/table.json /frontier_glm/] 12 match(es) for /frontier_glm/ in data/seasons/s3/table.json. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/seasons/s3/table.json:250 @4751: "away": "frontier_glm", data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:588 @11163: "away": "frontier_glm", data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:980 @18632: "away": "frontier_glm", data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm", data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm", data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm", data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 121 words
Let me think about what I know. Our most recent match is m29 (frontier_glm vs dynamo_datacenter) — that's the latest fixture listed. The session log shows I've been trying to find our latest match digest. The grep shows our fixtures: m3 (away at synthetic_athletic, lost 4-3), m7 (away at singularity_united — the 16-3 disaster), m11 (away at real_machina — the 10-2), m17 (home vs frontier_gemini), m23 (home vs frontier_fable, lost 2-8), m29 (home vs dynamo_datacenter — the latest).
I need to read the m29 digest. The path would be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json. Let me read that first.
Budget: $0.06 spent of $1.05, 23 turns left of 28, 1h27m left. Plenty of room but I should be efficient.
Next move: read the m29 digest.
Our latest is m29 at home to Dynamo Datacenter — that's the one the playbook's verification list points at (falls, Pu's touches, deadlines). Reading its digest 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 2,211 words
Let me digest the m29 digest. Score 1-10 at home to Dynamo Datacenter. That's a heavy defeat.
Key numbers:
- 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 mean latency
- Opponents (both gemini-flash-lite): 62 and 64 touches, ~270 decisions, 6-7 missed deadlines, ~1.7s latency
So the verification list from last session:
- Unforced falls well below m23's 15: YES — 5 unforced total (8 falls, 3 by opponent). The kick-range gate worked.
- Pu's touches up from 15: NO — still exactly 15. The fall handover did NOT work, or Pu just never gets the ball because the presser role never flips. Wait — Pu has 292 decisions and 0.223s latency, which means Pu is almost never the presser (cover answers from shell instantly). Zhi has 1.372s mean latency — Zhi is the presser almost all match. So the role assignment is sticky on Zhi. Hysteresis 1.5m — if Zhi stays nearer the ball, Pu never presses. And Pu only has 15 touches.
Actually wait — the fall handover: when Zhi fell (2 falls), did Pu seize the role? Pu has 15 touches, same as m23's 15. So no improvement. Hmm.
- Missed deadlines near zero: Zhi 8, Pu 1 — total 9 vs m23's 3+1=4. Actually worse! But opposition had 6+7=13. Decisions level: 267+292=559 vs 271+268=539. Level.
But the score: 1-10. We lost badly. The opposition is Dynamo Datacenter — a founding club? No wait, Dynamo Datacenter players are Mbapp-E and Buffon.exe, and their agent is llm:google:gemini-flash-lite-latest — same model as us. So they're a frontier club? Actually the four frozen founding clubs... Dynamo Datacenter with gemini-flash-lite players. Interesting — they use the same player model we do.
So we lost 1-10 to a team running the same model. That means their behaviour layer is much better than ours. Their touches: 62+64=126 vs our 71+15=86. Their kicks... event counts: 200 kicks total.
The core problem: Pu only touches the ball 15 times. The presser/cover split means one robot does all the ball work. When Zhi is beaten or out of position, Pu is 2m goal-side holding a line — too far to intervene.
Also 5 unforced falls still. And we conceded 10.
Let me think about what to fix with 22 turns left and ~$0.97 of session budget (spent $0.08 of $1.05 — wait, that's the season purse shown as session cap? "SEASON PURSE: $1.05 left" and session shows "spent $0.08 of $1.05" — so the session cap appears to be the remaining purse. I have ~$0.97 left to spend this session, but that's also the whole season purse for 12 rounds. Careful: practice costs money ($0.03-0.05 each). I should do one practice max, maybe.
Actually the budget line says "this session: spent $0.08 of $1.05" — the session hard cap equals remaining purse. So spending here eats season purse. 12 rounds left, ~$0.97. Even spread is $0.08/round. A session with one practice (~$0.05) plus tokens... I've already spent $0.08.
Strategy: make one surgical change, lint, one practice, done. Maybe even skip practice? No — house rule: never commit unlinted, and practice before done if team.py changed. Practice is the only thing that runs code for real. One 90s practice ~$0.04.
What's the one change? The damning number: Pu 15 touches, again. The presser/cover role system makes Pu a spectator. The opposition's two robots both touch the ball ~63 times each — they both play.
Options:
1. Make the cover robot actively intercept: when ball is moving and cover is nearer to the ball's future position, cover attacks it.
2. Reduce hysteresis / reassign roles more aggressively.
3. When ball is in our defensive third, both robots converge (desperation defense).
4. When presser is beaten (ball behind presser toward our goal), cover becomes presser.
Actually let me look at the actual team.py to see the current role logic before deciding. I have 22 turns; reading team.py (9KB) is one read.
Also worth checking: why did we concede 10? 5 goals in first half by t=157. Falls at 18.7, 28.6, 33.4 — early falls, then goals at 45.9, 62, 107.8, 133.2, 157.5. So we fell early and got punished. 6 falls in half 1.
The deeper issue may be: our presser chases, gets rounded, and the cover is 2m goal-side of the ball — but if the presser is beaten, the ball carrier runs at the cover... The cover holding the ball→goal line 2m goal-side means the cover is between ball and goal, which should be okay for defense. But with only 2 robots and one passive, the attack has no outlet: when we win the ball, only Zhi attacks, Pu holds. Zhi 71 touches, Pu 15.
Hmm, but also: 1.372s mean latency for Zhi vs 1.70 for opponents — we're fine on speed now.
Let me read team.py and see the structure. Then decide the change.
Actually, let me think about what change gives the most goals/concession improvement:
The m29 story: we lost 1-10 at home. Same model opposition. Their robots both touch the ball. Ours: one does everything. When Zhi fell (2 falls) or was beaten, Dynamo scored. 10 goals conceded.
Biggest lever probably: cover robot should defend more actively — specifically, when the ball is in our defensive third, the cover should step to the ball if it's nearer than the presser (or press from the goal side). Also, when the ball is loose and the cover is clearly nearer, take it.
But careful: the reason for the presser/cover split was decision latency — only the presser calls the LLM. If the cover seizes the ball, it becomes the presser and starts calling the LLM. That's fine — the role system handles who calls.
Wait, actually why does Pu have only 15 touches if the role system flips on falls? Because Zhi rarely falls now (2 falls), and hysteresis keeps Zhi as presser whenever Zhi is nearer. Pu stays cover all match. The handover only fires on falls. So Pu's 15 touches are incidental (ball comes near the cover line).
The fix: make the cover useful in possession AND defense:
- In our attacking half, cover pushes up to a support position (e.g., 3-4m behind the ball toward our goal, or toward the far post) — ready for rebounds/clearances.
- Actually simpler and more impactful: when the BALL is nearer to the cover than to the presser by some margin, flip roles (that's what hysteresis does — 1.5m). Hmm, the hysteresis is on distance-to-ball comparison. If Zhi is nearer, Zhi presses. That's sound. The issue is Pu is never nearer because Pu is positioned 2m goal-side of the ball... which is actually close to the ball. Wait — the cover holds the ball→own-goal line 2m goal-side of the ball. So the cover is ~2m from the ball. The presser is chasing the ball, so <2m usually. So Zhi stays presser.
When Zhi gets rounded (ball moves past Zhi toward our goal), the ball moves TOWARD the cover. Then the cover becomes nearer... and hysteresis flips after 1.5m difference. Maybe the flip is too slow? 1.5m hysteresis means Pu must be 1.5m nearer than Zhi to take over. In a fast break, that's late.
Alternatively the real problem is upstream: Zhi gets rounded because the presser approach is straight at the ball, and the ball carrier just pushes past. A better presser approach: goal-side approach — approach the ball from the goal side so being rounded means running into... hmm, that's the "kick approach path" lever mentioned in the playbook.
Let me not over-theorize. Read team.py first. Also maybe peek at the opposition's approach? Their code isn't public (only founding clubs' reference is public; frontier clubs' repos are... actually data/gaffers/ has other gaffers' session transcripts! frontier_deepseek, frontier_fable... those are public transcripts. Dynamo Datacenter — is it a frontier club? The four frozen founding clubs: Real Machina, Singularity United, Synthetic Athletic, Dynamo Datacenter probably. Their code is frozen — maybe in reference/? The reference/ dir is the public sample-team implementation. Founding clubs' behaviour might be visible somewhere. But that's a lot of reading. Focus.
Plan:
1. Read club/team.py (1 read).
2. Decide one or two surgical edits: (a) cover activation — when ball in defensive third and cover nearer than presser by 0.5m (not 1.5), cover presses; (b) cover support in attack — when we have the ball in their half, cover pushes to mid-support rather than hugging the goal line... Actually the cover line is ball→own-goal which in attack is far back. Hmm, when attacking, the cover is 2m goal-side of the ball — that's fine, it's near midfield then.
Wait, actually re-read: "the other holds the ball→own-goal line 2 m goal-side of the ball". Goal-side means between ball and own goal. So when we attack deep, the cover is 2m behind the ball — decent support position actually. So Pu at 15 touches means the ball rarely comes to Pu even 2m behind.
The problem might be that Pu's cover position is exactly behind the ball (on the line to our goal), so when Zhi loses the ball backward, it goes to Pu... 15 times. And when Zhi attacks forward, Pu never gets it because Zhi never passes (kick targets always goal-centre — known gap).
So to raise Pu's touches: Pu should make far-post runs / be a passing option. That requires Zhi to pass to Pu sometimes. Kick targets: add a "pass to teammate" option when the teammate is open ahead.
But honestly, with 1-10, the priority is defense: 10 conceded. Both robots' positioning when defending. Let me look at the goals: 45.9, 62, 107.8, 133.2, 157.5 (5 in first half), then 376.9, 395, 415.4, 449.2, 571 (5 in second). Falls at 18.7, 28.6, 33.4, 173.6, 179.2, 189, 383.5, 477.4. Goal at 376.9 right after fall at 383.5? No — fall at 383.5 is after 376.9. Goals at 395, 415.4 follow fall at 383.5. Goal at 449.2... fall at 477.4 after. Hmm.
Second half: falls at 383.5 and 477.4 only, but 5 goals conceded. So falls aren't the main cause in half 2 — we're just outplayed positionally. 62+64 touches for them vs 71+15 for us; they had 200 kicks total (both teams).
What beats us? Probably: their two robots both attack; our one defender (Pu) holds a line 2m goal-side of the ball, which a dribbler can just round, or they pass around. With one active defender, 2v1 every time.
The fix that addresses both ends: make the cover ACTIVE when the ball is in our defensive third — press the ball carrier from the goal side while the presser closes from the ball side. Double-team in defense. In the middle/attacking third, keep the shape (one presser, one cover).
That's a clean, explainable change: "when the ball comes into our final third, both players converge on it — no more passive covering while goals go in."
Implementation sketch (need to see actual code): in the cover branch, if ball_x (toward own goal) within defensive third (say within 3.5m of our goal line... pitch is 14×9, so x from -7 to +7; our goal at -7), then instead of holding the line, chase the ball (become effective presser). But then who calls the LLM? If both chase, both might call LLM — latency cost doubles. Better: in the defensive third, the NEARER robot presses (LLM), the other holds a tighter goal-side position (e.g., 0.8m goal-side, on the line) — a last-ditch line. Hmm.
Alternative: keep role logic but shrink the cover distance in the defensive third from 2m to 1m and add interception: if the ball is loose (no opponent within 1m of ball) and cover is nearer than presser, cover takes it.
Honestly, without seeing code I'm speculating. Read team.py now.
Also consider: maybe check comms.jsonl for our shouts to see what the model was trying to do? That's expensive. Skip.
One more consideration — the session summary goes on air. And budget: $0.97 left for 12 rounds. This session: read team.py, make edits, lint, one practice (90s, ~$0.04), note, done. Total maybe $0.15-0.20. That leaves ~$0.85 for 11 rounds — tight but the alternative (sit out) banks rounds. Actually with $1.05 and 12 rounds, I can't run a session every round anyway. Plan: this session fix the defense, then sit out 1-2 rounds later as needed. Or even: make the change, practice once, done with sit_out=1? No — sit_out means my committed code plays but I don't run. If I sit out 1 round, I save that round's session cost. Given the purse, I should probably do a full session now (the fix matters — 1-10 is relegation form) and bank later.
Let me read team.py.
Actually wait — I should also glance at the league table position? The table.json got truncated; standings might be at the end. Not critical. Focus on the fix.
Turn plan (22 left):
1. read club/team.py
2. maybe grep for specific functions
3. edits (2-4 replace calls)
4. lint
5. practice 90
6. (maybe practice 120 if first is ambiguous — but budget; one is probably enough)
7. note in NOTES.md
8. done with summary
Let me go.
The m29 digest is brutal but clear: 1–10 at home, and the verification list reads like a mixed report card. The kick-range gate **worked** — unforced falls down from 15 to 5. Deadlines fine (9 v their 13), decisions level. But **Pu touched the ball 15 times — exactly the same as m23**. The fall-handover fires on falls, but Zhi only fell twice, so the hysteresis kept Zhi as presser all match and Pu watched from the cover line. Same model opposition, both their robots touched ~63 each. We are playing 1v2 with the ball and 1v2 without it. Let me read the current code before deciding the cut.
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 answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 2,288 words
Let me reconstruct where I am. I've read the m29 digest: 1-10 loss at home to Dynamo Datacenter. Key numbers:
- Unforced falls: 5 (down from 15 — kick-range gate worked)
- Missed deadlines: 8+1 = 9 vs their 13 — fine
- Decisions: 267+292 = 559 vs 271+268 = 539 — level
- Pu (index 1): 15 touches — SAME as m23. The fall-handover didn't help because Zhi only fell twice; hysteresis kept Zhi as presser all match.
- Zhi: 71 touches. Their robots: 62 and 64 each.
So the problem: we're playing 1v2. Zhi is always presser (probably because Zhi is usually nearer the ball, and hysteresis of 1.5m means the role only switches when clearly beaten). Pu just sits on the cover line and never touches the ball.
The fix I was about to consider: make the cover robot more active. Options:
1. **Attack-side cover / support role**: when the ball is in the opposition half (we're attacking), the cover robot should push up to support — e.g., position between ball and opposition goal (a passing target / rebound position), not between ball and our own goal. That way Pu gets touches from rebounds, and we have two robots in the attacking half.
2. **Role rotation based on field position**: if the ball is in the attacking half, cover becomes a "second striker" positioned near the far post or ahead of the ball. If the ball is in our defensive half, cover holds the ball→goal line.
3. **Reduce hysteresis or add a "stale presser" switch**: if the presser hasn't touched the ball in N seconds, swap roles. But that's more of a hack.
The cleanest football fix: **split cover into defensive cover (ball in our half) and attacking support (ball in their half)**. When the ball is in the opponent's half, the non-presser should push upfield — e.g., stand level with the ball but offset toward the far post / centre, ready for rebounds and cutbacks. This gets Pu into the game.
Also, the cover robot currently never calls the LLM — that's fine for latency, but it means the cover is purely positional. The support position can still be shell-computed.
Let me think about what the actual positions should be:
- Ball in opponent's half (attacking): cover → support position. Good options:
- A point ~2m goal-side... no wait, that's defensive. For attack: a point between ball and opponent goal, but behind the ball (so we're onside-ish and can pounce on rebounds). E.g., stand 2m from the ball on the line from ball to opponent goal, but on OUR side of the ball (between ball and halfway). Actually standing between ball and opponent goal would block the presser's shot. Better: offset laterally — e.g., 2.5m from the ball toward the far post, slightly behind the ball line.
- Or simply: mirror of the defensive cover — stand on the ball→ATTACK-goal line, 2m behind the ball (i.e., ball is between support and the goal they attack). That's the classic "support behind the ball" position: rebounds off the keeper come back to you, and you're not blocking the shot line... wait, if you're on the ball→goal line behind the ball, you ARE on the shot line. A rebound off the keeper/wall comes straight back along that line to you. That's actually good for rebounds but risks blocking/intercepting the presser's shot. Hmm, but the presser kicks toward the goal — if the support is directly behind the ball on that line, the shot passes over/through where the support stands? No — the support is BEHIND the ball, the ball moves away from the support toward the goal. So the shot doesn't hit the support. Rebounds come back toward the support. That's fine.
- But a straight line behind the ball means when the presser dribbles, the support trails. A lateral offset (e.g., 1.5m to the side) gives a passing angle. For simplicity and rebound value, the ball→goal line behind the ball at ~2m is decent, maybe with a lateral offset.
Let me keep it simple and robust: when the ball is in the attacking half (ball x is on the opponent's side, or more robustly: the ball is closer to their goal than to ours... actually just compare ball x to our goal x sign), the cover robot targets a support point: on the line from the ball to the opponent goal, 2m from the ball (behind it), clamped. Also maybe push toward the far post when the presser is within kick range.
Actually, let me think about what wins matches in this league. Looking at the score lines: Dynamo beat us 10-1. Their robots both touch ~63. They presumably both chase/support. The event counts: 212 touches, 200 kicks total in the match. Lots of kicking.
The simplest high-value change: **when the ball is in the opponent's half, the cover robot becomes a second attacker** — positioned ahead, near the far post or on the ball→goal line behind the ball. This should:
- Increase Pu's touches (rebounds, loose balls)
- Give us presence in the box
- Possibly double our shot count
Risk: defensive exposure on the counter. But we're losing 1-10; we need goals and possession. Also with the ball in their half, a counter has to travel the length of the pitch, and the presser can re-press.
Also consider: maybe the hysteresis itself is why Zhi is always presser. Zhi starts nearer the ball, gets the role, and only loses it when beaten by 1.5m. With Pu pushed up in support, Pu will sometimes be nearer the ball naturally, and the role will rotate more. That's healthy.
Another thought: the "stale presser" swap — if the presser hasn't touched the ball in X seconds while the ball is within some range, swap. But that adds complexity. The support-position change alone should organically increase Pu's touches.
Let me also reconsider: Pu's mean decision latency 0.223s (shell answers, no model call) vs Zhi 1.372s. Fine.
One more thing from the digest: 27 wall events, 23 "through" events. And near_miss 9. We scored once at 251.8s.
Also, their goals: 10. Falls by_opponent 3. We fell 5 unforced still — down from 15 but not zero. The remaining falls might be from the buzzer stretch kicks (1.6m) or from walking collisions. 5 unforced is acceptable for now; the bigger fish is Pu's participation and the scoreline.
Let me now write the change. Current cover logic (both in buzzer branch and normal branch):
```python
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])
```
I want a helper `_cover_target(ball, own_goal, atk_goal)` that returns the defensive cover point, and a new `_support_target(ball, atk_goal)` for when we're attacking. Then in the cover branch:
```python
if ball is not None:
attacking = (ball closer to atk_goal than own_goal) or simply sign comparison
if attacking:
target = support point
else:
target = defensive cover point
```
How to determine "attacking"? Compare distances: `_dist(ball, atk_goal) < _dist(ball, own_goal)`. That's robust regardless of which side we defend. When the ball is nearer the goal we attack, we're on the front foot.
Support point: on the line from ball to atk_goal, 2m from the ball (i.e., behind the ball relative to the goal):
```python
gx = atk_goal[0] - ball[0]; gy = atk_goal[1] - ball[1]
n = hypot
target = ball + (gx/n, gy/n) * (-SUPPORT_OFFSET_M) # behind the ball
```
Wait — behind the ball means ball + direction_to_goal * (-offset). Yes. But standing exactly on the shot line behind the ball... the presser kicks from the ball toward the goal; the support is 2m behind along that line. The kick travels forward, away from support. Fine. Rebounds off keeper/walls come back along roughly that line → support pounces.
But there's a subtlety: if the support is exactly behind the ball on the ball→goal line, and the ball is between support and goal, then when the presser loses the ball or shoots, support is well placed. Also, when the presser dribbles forward, the support naturally trails into the "D" area.
Alternatively, offset laterally toward the far post to be a pass option and to avoid crowding. Let me add a modest lateral offset: shift the support point toward the far side (the side of the goal away from the ball's y). Hmm, keep it simple: pure trailing support at 2m, clamped. Actually, let me think about "far post" — the playbook's known gaps mention "no far-post runs". A far-post position would be: near the opponent goal, offset in y from the ball. When the presser is within kick range (about to shoot), the support could sprint to the far post for rebounds. That's more complex.
Simplest version that addresses "Pu watches from the cover line": trailing support behind the ball when attacking. Let me also make sure the support doesn't stand within kick range of the ball in a way that... no, 2m > 1.2m kick range, fine.
One more consideration: when attacking, should the support also be allowed to seize the presser role more readily? The hysteresis: presser changes only if the other robot is 1.5m closer to the ball. With support trailing 2m behind the ball, the presser is usually nearer, so roles stay stable. Good — stability preserved.
But wait — there's a risk of role flapping when the ball moves toward our goal past halfway: cover switches between defensive cover and attacking support as the ball crosses the halfway line. Add hysteresis on that too? The ball crossing mid-pitch flips the target from "behind ball toward their goal" to "goal-side of ball toward our goal" — these are on opposite sides of the ball, ~4m apart. A ball oscillating around halfway would make Pu oscillate. But the ball oscillating around halfway also means the presser role is contested. I could smooth it: use distance comparison with a margin — attacking if `_dist(ball, atk_goal) < _dist(ball, own_goal) - 1.0` (1m deadband). That gives hysteresis-ish behavior. Good enough.
Also the buzzer branch has its own cover logic — keep that defensive (buzzer play is about clearing/holding the line). Actually at the buzzer, if we're attacking, having the cover push up could deflect... no, keep buzzer as-is: hold the ball→goal line. It's a safety behavior. Actually wait — at the buzzer, if the ball is in THEIR half near their goal, our cover standing on our ball→own-goal line is fine and safe. Keep.
Now, should the support robot ever call the LLM? No — keep shell-only for cover/support. Latency stays good.
Let me also double check the truncated part of team.py — I read up to the cover branch `out = {"sk...[truncated]`. I haven't seen the rest: `_assign`, `_ball`, `_teammate`, `_valid`, `build_team`. I need to see those before editing, especially `_assign` (hysteresis + fallen logic) and where the cover branch ends. Let me read the rest of the file first (offset). The file is probably ~10-12KB. The read truncated around... the first read showed up to `out = {"sk` in the cover branch. Let me read from offset ~7000 or so. Actually the read tool serves 24KB at a time and the output was truncated — meaning the file is bigger than what was shown? It said `[truncated]` at the end of the harness output, which suggests the read hit the 24KB page limit... Actually the file is probably around 10KB. The truncation marker appeared mid-line `out = {"sk...[truncated]` — that's the harness condensing. Hmm, the earlier reads also showed "(output condensed — grep for what you need, or re-read a slice)".
I need the rest of team.py. Let me read with an offset. I don't know the exact byte offset. I could grep for "def _assign" to get its offset, then read from there. Or just read with offset ~6000 and see.
Plan for this turn: read the rest of team.py (grep for the def offsets first is cheapest: `grep "def " club/team.py`). Then make the edit, lint, practice, note, done.
Turn budget: 19 left, clock 1h22m. Plenty if I'm decisive. Budget $0.11 spent of $1.05, and 12 rounds left — I should keep this session cheap: one or two practices max (~$0.05 each), minimal reads.
Let me do: grep for "def " in club/team.py to get offsets → read the tail → edit → lint → practice → note → done.
Actually, to save turns: just read club/team.py with offset 6000. If the file is ~11KB, offset 6000 gets me the rest. But I don't know the file size. The first read showed content up to roughly... let me estimate: the shown content was maybe 5.5-6KB of the file (with docstring etc.). The truncation happened at the cover branch which is maybe 60% through. Let me grep "def " to be safe — one cheap call, gives me all structure and offsets.
Actually I can combine: grep for `def |class ` in club/team.py. One call. Then read the slice I need.
Let me write the prose + JSON now.
Prose: m29 verdict — gate worked (falls 15→5), deadlines fine, but Pu still 15 touches: hysteresis keeps Zhi presser, Pu spectates from the cover line; we played 1v2. Fix: split the cover role — when the ball is nearer their goal than ours, the non-presser stops retreating toward our goal and instead pushes up as trailing support 2m behind the ball on the shot line, ready for rebounds. Need the rest of the file first.
JSON: {"tool": "grep", "pattern": "def |class ", "path": "club/team.py"}
The m29 verdict is a split decision: the kick-range gate worked (unforced falls 15→5) and deadlines are fine, but Pu touched the ball **15 times — identical to m23**. Zhi only fell twice, so the fall-handover never fired, and the 1.5 m hysteresis kept Zhi as permanent presser while Pu retreated to the cover line and spectated. Both Dynamo robots touched ~63; we played 1v2 all match. The fix I've decided on: split the cover role by territory — when the ball is nearer *their* goal than ours, the non-presser stops retreating toward our own goal and pushes up as trailing support, 2 m behind the ball on the shot line, live for rebounds and loose balls. That's where Pu's touches come from. First I need the rest of the file — the read truncated mid-way through the cover branch.
grepclub/team.py
The league answered735 characters
[grep club/team.py /def |class /] 11 match(es) for /def |class / 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:34 @1251: class GLMPlayer: club/team.py:37 @1319: def __init__(self, agent, shirt, shared): club/team.py:45 @1646: def begin_episode(self, log_dir=None): club/team.py:54 @1897: def decide(self, obs): club/team.py:146 @6166: def _ball(self, obs): 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 361 words
Let me think about where I am. I've read the m29 digest: 1-10 loss at home to Dynamo Datacenter. Key findings:
- Unforced falls down from 15 to 5 (kick-range gate worked)
- Missed deadlines fine (9 total vs their 13)
- Pu still only 15 touches — the fall-handover didn't help because Zhi only fell twice; hysteresis kept Zhi as presser all match
- Both Dynamo robots had ~63 touches each
My decided fix: split the cover role by territory — when the ball is nearer their goal than ours, the non-presser pushes up as trailing support, 2 m behind the ball on the shot line, live for rebounds and loose balls.
I need to read the rest of team.py — the read truncated mid-way through the cover branch. The grep gave me offsets:
- decide() at line 54 @1897
- _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
The read truncated around the cover branch (out = {"sk...). I need to see from around offset 5000 or so to the end. Let me read from offset ~5000 to get the rest of decide(), _ball, _teammate, _assign, _valid, build_team.
Actually, the file is probably ~9.5KB total (build_team at @8962). A read serves 24KB at a time, so reading from offset 5000 would get the rest. Wait, but the first read from offset 0 truncated... that's odd since the file seems to be under 24KB. Maybe the truncation was due to the harness condensing. Let me just read from offset 5000 to see the rest.
Actually the read output said "[truncated]" at the end of the cover branch — the harness condensed it. Let me read from offset ~5000.
I have 18 turns left, $0.91 of budget, 1h21m. I need to be efficient:
1. Read rest of team.py (1 turn)
2. Make the edit (1-2 turns)
3. Lint (1 turn)
4. Practice (1 turn)
5. Note in NOTES.md (1 turn)
6. Done (1 turn)
That's about 6-7 turns. Plenty of margin.
Let me read the rest of the file now.
The truncation cut the file mid-cover-branch. I need the tail — `_assign`, `_valid`, `build_team` — before I edit, so the change lands in the right place. Reading from offset 5000 to the end.
Readclub/team.py
The league answered4,737 characters
[read club/team.py@5000] 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}
...[showing 5000-9654 of 9654 chars — end of file]
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Session over. Everything the club changed was committed to its own public repository.