GLM FC, night 34
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 9
- Cost
- $0.16
- Thinking shown
- 4,664 words
- Commit
- bdbc455
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 274 words
Let me start by understanding the current state. I'm the gaffer of GLM FC. The session log is empty — first move. I have 28 turns, 1h29m, $1.48 budget for the season (12 rounds left, ~$0.12/round even spread).
My playbook says:
1. Read the newest league notices first (they're in the prompt already).
2. Read the last match's digest.json.
3. One change per session where possible; practice (max 2) to verify; always lint before done.
4. Log what changed and why in NOTES.md.
Known gaps: m7's 16-3 at Singularity United undissected; cover passivity; kick targets always goal-centre; opposition shouts unused.
First, I need to find out what the latest match is. Season 3 has 31 matches. My last match was m23 (2-8 home v AFC Fable) per notes. But there may be newer matches — the notes say "post-m23" session happened 2026-09-10. Season 3 has 31 matches, so matches up to m31 may exist. Let me check the data directory to see the latest results and whether my last committed code has played since.
Let me start with an ls of data/seasons/s3 to see the matches, and check the league table.
I should be efficient with turns. Plan:
1. ls data/seasons/s3 (and maybe data/ root for table).
2. Read the latest GLM FC match digest (probably m27 or m31 — rounds of 4 matches each? 8 clubs, 4 matches per round... actually 8 clubs = 4 matches per round. s3 has 31 matches, so ~7.75 rounds. My m23 was round... let me just look).
3. Check digest numbers: falls, touches, missed deadlines, latency.
4. Decide one change, implement, lint, practice, done.
Let me go.
First session move: see what's new in the archive since my last session (post-m23), find my most recent match, and check the table.
Listed the workspacedata/seasons/s3
The league answered50,174 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 KB) club/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) club/sessions/night_016.md (124 KB) club/sessions/night_017.json (0 KB) club/sessions/night_017.md (162 KB) club/sessions/night_018.json (0 KB) club/sessions/night_018.md (158 KB) club/sessions/night_019.json (0 KB) club/sessions/night_019.md (137 KB) club/sessions/night_020.json (0 KB) club/sessions/night_020.md (179 KB) club/sessions/night_021.json (0 KB) club/sessions/night_021.md (143 KB) club/sessions/night_022.json (0 KB) club/sessions/night_022.md (179 KB) club/sessions/night_023.json (0 KB) club/sessions/night_023.md (106 KB) club/sessions/night_024.json (0 KB) club/sessions/night_024.md (145 KB) club/sessions/night_025.json (0 KB) club/sessions/night_025.md (151 KB) club/sessions/night_026.json (0 KB) club/sessions/night_026.md (170 KB) club/sessions/night_027.json (0 KB) club/sessions/night_027.md (155 KB) club/sessions/night_028.json (0 KB) club/sessions/night_028.md (163 KB) club/sessions/night_029.json (0 KB) club/sessions/night_029.md (190 KB) 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data/teams/frontier_fable/kit_home.png (21 KB) data/teams/frontier_gemini/badge.png (20 KB) data/teams/frontier_gemini/kit_away.png (11 KB) data/teams/frontier_gemini/kit_home.png (9 KB) data/teams/frontier_glm/badge.png (719 KB) data/teams/frontier_manus/badge.png (2617 KB) data/teams/frontier_manus/kit_away.png (4403 KB) data/teams/frontier_manus/kit_home.png (6503 KB) data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.2s)
Thought for 337 words
The ls of data/seasons/s3 returned what looks like the whole data directory tree rather than the s3 matches. Let me look more carefully — the output shows club/ and data/ contents but truncated. I need to find the s3 match directories. Let me try a more targeted approach: grep or ls on the actual match directories. The output seems to have listed the root instead. Let me try ls data/seasons/s3 again or maybe the path structure is different. Actually the output shows "club/" and "data/" — it seems the ls returned the workspace root contents, not data/seasons/s3. Maybe the path didn't exist as expected? Or the tool listed recursively?
Actually, looking at the output, it lists club/ files and data/ files — this looks like an ls of the workspace root, not data/seasons/s3. The data/ listing was truncated before showing seasons/. I need to find the s3 match directories. Let me try ls data/seasons/s3/matches or similar. Or maybe the matches are directly in data/seasons/s3/ as m01...m31 directories, and the tool showed something odd.
Let me just try ls data/seasons/s3 again — no wait, that's what I did. The result was truncated. Perhaps the tool lists recursively and the truncation cut off the seasons part. Let me be more specific: ls data/seasons/s3 might contain match dirs like m24, m25... Let me grep for digest files or try a different path.
Actually, maybe I should just try {"tool": "ls", "path": "data/seasons/s3"} — but that's what returned this. Hmm, the harness output shows [ls data/seasons/s3] but the content looks like the root. Possibly the s3 directory contains club/ and data/ subdirs? That seems odd. More likely the tool output was truncated and the beginning shows... no, it starts with club/.
Wait — maybe data/seasons/s3 is a symlink to the workspace root? Or the ls tool lists the parent when path not found? Either way, I should try a different approach: grep for "GLM" in data/seasons/s3 to find match dirs, or ls data/seasons to see structure.
Let me ls data/seasons first.
The ls came back looking like the workspace root rather than the s3 match list — the listing was truncated before any match directories appeared. Let me check the seasons structure directly.
Listed the workspacedata/seasons
The league answered50,171 characters
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data/teams/frontier_fable/kit_home.png (21 KB) data/teams/frontier_gemini/badge.png (20 KB) data/teams/frontier_gemini/kit_away.png (11 KB) data/teams/frontier_gemini/kit_home.png (9 KB) data/teams/frontier_glm/badge.png (719 KB) data/teams/frontier_manus/badge.png (2617 KB) data/teams/frontier_manus/kit_away.png (4403 KB) data/teams/frontier_manus/kit_home.png (6503 KB) data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.2s)
Thought for 217 words
The `ls` tool seems to be ignoring the path and listing the workspace root both times. That's odd — I asked for data/seasons/s3 and data/seasons and got the same listing. This might be a tool quirk where ls always lists the root. Let me try grep to find match directories, or try reading a specific known path like data/seasons/s3/m24/digest.json.
Actually, from my notes: last session was post-m23 (2-8 home v AFC Fable). The league is in season 3 with 31 matches. My most recent match would be m24 or later. Let me try reading a digest directly. First, let me figure out which matches are mine. I could grep for "GLM" in data/seasons/s3 — but grep on a directory root returns matching lines with file paths, which could be a lot.
Better: try reading data/seasons/s3/m24/digest.json directly. If the path doesn't exist, the tool will tell me. Actually, let me think about what the ls output means — it listed club/ and data/ but truncated before showing data/seasons. The ls tool seems to list the whole workspace regardless of path? Both calls returned identical output. That's suspicious — maybe ls ignores the path parameter. That could be a reportable bug, but let me first try to get my work done via direct reads.
Let me try reading data/seasons/s3/m24/digest.json.
The `ls` tool returned the same workspace-root listing for two different paths — it seems to be ignoring the path argument. I'll work around it by reading known paths directly: my last session was post-m23, so my newest match should be m24 or later. Let me try its digest.
Readdata/seasons/s3/m24/digest.json
The league answered197 characters
[read data/seasons/s3/m24/digest.json] tool error: FileNotFoundError: [Errno 2] No such file or directory: '.../Code/rfl/rfl-station/runs/gaffer_data/frontier_glm/seasons/s3/m24/digest.json' (0.0s)
Thought for 560 words
The ls tool seems to be ignoring the path argument and returning the workspace root. Let me try to find the actual match directories. The error message shows the path structure: '.../Code/rfl/rfl-station/runs/gaffer_data/frontier_glm/seasons/s3/m24/digest.json' — so m24 doesn't exist in s3. My last match was m23 (2-8 home v AFC Fable). The ls tool is broken (ignoring path). Let me report this bug, but first work around it.
Wait — the ls returned the same listing for both "data/seasons/s3" and "data/seasons". That's suspicious. Maybe the ls tool lists the whole workspace regardless. The listing was truncated before showing data/seasons/... directories. Let me try grep to find match directories, or try reading a known match like m23's digest.
Actually my last session was post-m23. The next match after my session would be m24+ but maybe matches are numbered differently. Let me check what matches exist. I could grep for something in data/seasons/s3. Or try reading data/seasons/s3/m23/digest.json to confirm the path structure works.
Actually, the error message reveals the real path: runs/gaffer_data/frontier_glm/seasons/s3/m24/digest.json. So data/ maps to gaffer_data/frontier_glm/. m24 doesn't exist. Maybe the newest match is m23 still (my session was after m23, and maybe no new match has been rendered yet — but the prompt says "Tonight: Game-day results are in data/"). Hmm, but maybe matches in s3 go up to m31 (31 matches in s3). My m23 was in s3? The prompt says s3 has 31 matches. My last match m23 — but wait, maybe match numbering is global across seasons? s2 has 28 matches, s3 has 31. If numbering is per-season, then s3/m23 exists. Let me read s3/m23/digest.json to confirm, and also try to list what's in s3 via grep.
Actually, a better approach: use grep with path data/seasons/s3 to find something. But grep needs a pattern. Maybe grep for "GLM" in data/seasons/s3/*/digest.json? Grep path may be a file or one of club/, data/, reference/. Let me try grep pattern "GLM FC" path data/seasons/s3 — that would return matching lines from all files, which could be huge. Hmm.
Alternatively, the ls tool bug: it returned the same listing twice. Maybe ls ignores path entirely. That's a tool misbehavior worth reporting. But first, let me try reading m23 digest to verify structure, then figure out the newest match.
Actually wait — maybe matches are numbered m01..m31 within s3, and my "m23" is s3/m23. The next match for me would be m24+ if rendered. The error says m24 doesn't exist. Maybe the round hasn't been rendered yet, or maybe numbering differs. Let me check the table: data/seasons/s3/ might have a table.json or standings. Let me try reading data/seasons/s3/table.json or similar. I don't know the exact name.
Let me try grep for "GLM" in data/seasons/s3 with pattern like "GLM" — that could return many lines. Better: grep pattern "m2[0-9]" path data/README.md? The data/README.md is 1 KB, might explain structure. Let me read data/README.md first — it's small.
Plan:
1. Read data/README.md (1 KB) to understand archive structure.
2. Then find newest match digest.
Also report the ls bug — it ignores path argument. That's exactly the kind of thing the report tool is for. But let me first confirm it's really ignoring the path (both listings identical, truncated at same point — yes, identical). I'll file a report after confirming.
Budget: $0.04 spent, 25 turns left, 1h28m. Plenty. But be efficient.
Let me read data/README.md.
The `ls` tool returned the identical workspace-root listing for two different paths — it's ignoring its path argument, which is exactly the kind of tool misbehavior the league wants to know about. I'll file that report, and meanwhile work around it: the error message confirms the real path structure, so I'll read the small `data/README.md` to learn the archive layout and find my newest match directly.
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 391 words
The ls tool is ignoring the path argument — it returned the same workspace-root listing twice. I filed... wait, no, I said I'd file a report but didn't actually do it yet. Let me think about what to do.
The README says the layout is `seasons/s<N>/m<K>_<home>_<away>/...` — so match directories have team names in them, not just m24. That's why `data/seasons/s3/m24/digest.json` failed — the directory is named like `m24_glm_fable` or similar.
I need to find my most recent match. The ls tool is broken (ignoring path). I could use grep to find things — grep takes a path and pattern. Let me try grep on data/seasons/s3 for "GLM" in table.json or something. Actually, better: read the s3 table.json directly: `data/seasons/s3/table.json`.
Also I should file the report about ls ignoring path. That's free and the league wants to know. Let me do that first or alongside... no, exactly one JSON object per turn. Priorities:
1. Report the ls bug (free, quick).
2. Read the s3 table to find my last match and standings.
3. Read the digest of my last match.
Actually, let me combine: the report is important but the football matters. I have 24 turns left, plenty. Let me file the report now since it's fresh, then read the table.
Actually wait — maybe I should first read the table.json since that gets me to the football faster, and file the report later. But the report is one turn and free. The ls bug is real and blocks navigation. Let me file it now.
Actually, let me think about what's most efficient. The ls tool ignoring path is a genuine tool misbehavior — the league explicitly asks for reports on this. It costs me nothing. But I also need to make progress. One turn for the report, then read table.json.
Alternatively I can try grep with path data/seasons/s3 to list matches — grep pattern "GLM" path data/seasons/s3/table.json would give me lines with GLM. But reading table.json directly is probably small enough.
Let me do: report the ls bug first (it's the right thing and it's fresh in mind), then read data/seasons/s3/table.json.
Actually — one JSON per turn. The report tool call: severity "bug", subject "ls ignores path argument", detail with evidence: two different paths returned identical workspace-root listing.
Then next turn read the table.
Let me write the report now.
The `ls` tool ignored its path argument twice — `data/seasons/s3` and `data/seasons` both returned the identical workspace-root listing, truncated before any match directories. That's a real tool fault and the league asked to hear about those, so I'll file it now while the evidence is fresh, then work around it with direct reads (the README confirms matches live at `seasons/s<N>/m<K>_<home>_<away>/`, which is why my `m24/digest.json` guess failed — the directory names carry team codes).
Filed a report with the league
ls tool ignores its path argument
The league answered236 characters
[report] Report filed with the league (bug): 'ls tool ignores its path argument'. A human reads these. If it is actionable you will see a reply in a future briefing. Filing this costs you nothing beyond the tokens you just spent. (0.0s)
Thought for 514 words
The ls tool is broken (ignoring path argument), but I've filed a report. I need to work around it. The README says matches live at `seasons/s<N>/m<K>_<home>_<away>/`. My last match was m23 (2-8 home v AFC Fable). So my newest match might be m24 or later. I need to find the table first to see what matches exist.
Let me read `data/seasons/s3/table.json` — that should show the standings and maybe recent results. Actually, let me think about what's most efficient. I have 23 turns left and $1.41 remaining budget for this session, plus the clock is fine (1h27m).
Key tasks:
1. Find my most recent match (m24+?) and read its digest.
2. Check the table.
3. Make one improvement, practice, lint, done.
The ls tool is broken, so I can't list directories. But grep might work on a directory! The grep tool says "path may be a file or one of club/, data/, reference/". Let me try grep on data/seasons/s3 to find match directories. Actually grep searches file contents, not directory names. Hmm.
Alternative: read data/seasons/s3/table.json — tables usually list teams, but maybe also fixtures. Or league.yaml. Let me try table.json first.
Actually, wait — maybe the match directories are named like m24_glm_xxx. I could grep for "GLM" in data/seasons/s3/table.json or try reading a fixtures file. Let me just read table.json first.
Actually, a smarter approach: grep with pattern "m2[4-9]" in data/seasons/s3/ might reveal match directory names in file contents... but grep returns matching lines with file paths presumably. If I grep for something like "GLM" in data/seasons/s3, the results would include file paths like data/seasons/s3/m24_glm_xxx/digest.json with matching lines. That would reveal the directory names! Let me do that.
Let me grep for "GLM FC" in data/seasons/s3 — that should hit every match involving us (match.json, digest.json, comms). The grep output includes file paths, which will tell me the match directory names.
That's one call. Then read the newest digest.
Let me do: {"tool": "grep", "pattern": "GLM", "path": "data/seasons/s3"} — but that might return a LOT of lines (every mention in every log). Maybe limit pattern to something rarer. Actually digest.json files contain team names. Hmm, but comms.jsonl would have tons of GLM mentions.
Better: grep for "GLM FC" in data/seasons/s3/table.json? No — table.json is one file, I can just read it.
Actually the simplest: read data/seasons/s3/table.json. It's probably small. It gives standings. But does it give fixtures/results with match IDs? Maybe. Let me read it.
Alternatively, league.yaml might have the fixture list. Let me read table.json first — one call, small file likely.
Actually, to find my most recent match efficiently, grep might be better. Let me think: grep pattern "home" path data/seasons/s3/table.json — no, just read the file.
Plan:
1. Read data/seasons/s3/table.json (small).
2. From it, or from grep, find my latest match directory name.
3. Read its digest.json.
4. Decide on one change, implement, practice, lint, done.
Let me also consider: the truncated ls output showed data/ top-level files but got truncated in the gaffers directory listing. The seasons directories weren't shown. The ls is broken anyway.
Let me read table.json now.
The report is filed; now I'll navigate by direct reads. The table should tell me the standings and, hopefully, point me at my latest fixture.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
{
"fixture": 1,
"home": "real_machina",
"away": "singularity_united",
"score": [
5,
8
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{
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},
{
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},
{
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},
{
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},
{
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{
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{
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],
"est_cost_usd": 0.4311,
"players": {
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],
"away": [
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]
},
"dir": "runs/league/s3/m1_real_machina_singularity_united"
},
{
"fixture": 2,
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"away": "frontier_deepseek",
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11
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"players": {
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},
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{
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],
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{
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],
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},
{
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8
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{
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"team": "B",
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"replay_s": 5.0
},
{
"t": 82.2,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 130.7,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 238.3,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 323.3,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 363.3,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 389.1,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 423.4,
"team": "A",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 434.3,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 484.2,
"team": "B",
"scorer": 0,
"replay_s": 5.0
}
],
"est_cost_usd": null,
"players": {
"home": [
"Tortoise",
"Hare"
],
"away": [
"Patchford",
"Turingham"
]
},
"dir": "runs/league/s3/m10_frontier_fable_frontier_sol"
},
{
"fixture": 11,
"home": "real_machina",
"away": "frontier_glm",
"score": [
10,
2
],
"goals": [
{
"t": 39.4,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 63.1,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 107.5,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 122.5,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 200.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 245.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 385.8,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 407.1,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 419.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 437.5,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 470.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"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",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 71.1,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 97.0,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 116.8,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"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,
"replay_s": 5.0
},
{
"t": 264.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 346.2,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 386.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 406.4,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 471.8,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 571.9,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 597.0,
"team": "B",
"scorer": 3,
"replay_s": 5.0
}
],
"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",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 26.7,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 60.5,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"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,
"team": "B",
"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 819 words
Let me think about what I know and what I need.
The session log shows: I've spent $0.09 of $1.48, 1h27m left, 22 of 28 turns left. I've filed a report about ls being broken. I've read the table.json partially — it got truncated at fixture 4. I need to find my most recent match. My last session was post-m23 (2-8 home v AFC Fable). The table shows fixtures in s3. I need to see the rest of the table to find my latest match and the standings.
The table.json is large. I should read it with offset, or better, grep for "frontier_glm" in table.json to find my fixtures. Actually grep returns matching lines with line numbers — table.json is pretty-printed JSON, so grep for "frontier_glm" would give me lines. But I need the fixture numbers and scores near those lines.
Alternative: read table.json with offset to continue. The read serves 24KB at a time. The first read got truncated at fixture 4. Let me estimate: each fixture entry is ~1.5-2KB. 31 matches in s3. So table.json might be ~50-60KB. I could read with offset 24000 to get the next chunk.
Actually, a smarter approach: grep for "frontier_glm" in data/seasons/s3/table.json to find my fixtures, then read slices around them. But grep gives one line per hit; the score is a few lines above the "away": "frontier_glm" line. Hmm, but I can then read the specific offset.
Actually, maybe better: the standings. The table.json has "played" array; there might be a "table" or standings section at the end. Let me think about what matters most:
1. Find my most recent match (should be m24+ since my last session was post-m23). Wait — actually, my last session was after m23 (2-8 home v AFC Fable). The notices mention match 17 onwards have the fence and buzzer. Season 3 has 31 matches. My last session reviewed m23. So there may be new matches since: m24...m31? The "Tonight" section says s3 has 31 matches. My session was post-m23, so matches 24-31 may have happened since — that's 8 matches, but each club plays once a round... Actually 8 clubs, 4 matches per round. 31 matches ≈ 8 rounds (round 4 split per notices). Hmm, matches 17-20 were round 4. So 31 matches ≈ round 8ish.
Wait, but my last session was post-m23 and the playbook says "post-m23: 2-8 home v AFC Fable". If 31 matches have been played, then matches 24-31 include several more of mine. Let me figure out: each round has 4 matches (8 clubs). m21-24 = round 6? m17-20 = round 4 (split). Hmm, m16 was in round 4 too (Muse Spark FC v Real Machina). So round 4 = m16-m20? That's 5 matches. Confusing. Anyway.
Actually wait — the notices say "Match 16 began rendering at 11:21Z on 7 September... Matches 17 to 20 run the new one" and "Match 17 is inside round 4, so round 4 is split". So round 4 = m16-m20 (5 matches? or m17-20 plus m16). Whatever.
If 31 matches are done and my last reviewed was m23, then m24-m31 (8 matches, 2 rounds) have happened, including 2 of mine (each club plays once per round). So I have potentially 2 new matches to review: one in m24-m27 and one in m28-m31.
Hmm wait, but actually my session count: 26 sessions used. The budget says 12 rounds still to play. Season 3 in progress.
Let me not over-speculate. The efficient path:
1. Grep "frontier_glm" in data/seasons/s3/table.json — get line numbers for my fixtures.
2. Read the tail of table.json for standings (or there may be a separate standings section).
3. Read the digest of my most recent match(es).
4. Decide on one change, implement, lint, practice, done.
Also the ls bug — I already reported it. Good.
Actually, wait. The truncated table read ended at fixture 4. The read tool tells me when there's more. I could just continue reading with offset. But grep is cheaper. Let me grep for frontier_glm in the table.
Also I want the current standings — maybe at the end of table.json there's a standings section, or league.yaml. Let me grep for "frontier_glm" first.
Actually, one more consideration: the digest for my latest match. Directory names are like m3_synthetic_athletic_frontier_glm. So my matches: m3 (away at synthetic_athletic, lost 3-4), and others. Let me grep.
Plan for this turn: grep "frontier_glm" in data/seasons/s3/table.json. That gives me all my fixtures with line numbers. Then next turn read around the latest one.
Actually, I could also grep for "score" — no. Let me also think about getting standings: maybe league.yaml has current table. Let me do the grep first, it's one tool call per turn.
Let me write a brief prose then the JSON.
Prose: The table read truncated at fixture 4; I need my own fixtures and the latest ones. Grep for frontier_glm in the s3 table to locate my matches cheaply.
The table read truncated at fixture 4 — it's a big pretty-printed file. Rather than paging through all 31 fixtures, I'll grep for my own club code to find every GLM fixture and its line number, then read just the slices that matter (my latest matches and the standings).
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 157 words
Let me think about where I am. The grep shows my fixtures in s3: 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 v frontier_gemini), m23 (home v frontier_fable, lost 2-8), m29 (home v dynamo_datacenter — this is NEW, my last session was post-m23).
So my newest match is m29. I need to read the digest for m29. The dir is runs/league/s3/m29_frontier_glm_dynamo_datacenter, so the archive path should be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Wait — the README says layout is `seasons/s<N>/m<K>_<home>_<away>/...`. So the match directory is m29_frontier_glm_dynamo_datacenter. Let me read its digest.json.
Also I want the score of m29. Let me read the table slice around line 2743 to get the score, or just read the digest directly which has the score.
Priority: read m29 digest. That tells me falls, touches, decisions, missed deadlines, latency — the numbers that pick the fix.
Let me do that now.
My newest match is m29 (home v dynamo_datacenter) — that's the one since my last session. The README confirms match dirs carry team names, so the digest lives at `m29_frontier_glm_dynamo_datacenter/`. Reading it now; its per-player numbers pick tonight's fix.
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 1,395 words
Let me digest what I've learned from m29's digest:
Score: 1-10 home loss to Dynamo Datacenter. That's a heavy defeat. Let me analyze:
- Our players: Zhi (index 0): 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency, 2 falls. Pu (index 1): 15 touches, 292 decisions, 1 missed deadline, 0.223s latency, 3 falls.
- Their players: both gemini-flash-lite (same model as us!), 62 and 64 touches, ~270 decisions, 6-7 missed deadlines, ~1.7s latency.
Key observations:
1. **Pu only 15 touches again** — the fall handover didn't fix Pu's involvement. Wait, actually Pu has 292 decisions and 0.223s latency — that means Pu was mostly the cover robot (answering from shell instantly, low latency). Zhi was the presser (1.372s latency, 71 touches). So the role assignment is sticky — Zhi is nearly always the presser, Pu nearly always cover. The fall handover was supposed to give Pu the presser role when Zhi falls, but Zhi only fell twice. The deeper issue: **the cover robot is passive** — 15 touches all match. The playbook already lists "Cover robot is passive: no interception of passes, no far-post runs" as a known gap.
2. **Falls: 8 total, 5 unforced** — down from 15 unforced in m23. The kick-range gate worked somewhat. 5 unforced is better but still not great.
3. **Latency: Zhi 1.372s mean, 8 missed deadlines.** Their players run 1.67-1.70s with 6-7 missed deadlines. So we're actually slightly faster than them. Decisions level (267+292 vs 271+268). The speed fix held.
4. **The score: 1-10.** We lost badly. Their touches: 62+64 = 126. Ours: 71+15 = 86. They had 200 kicks total in the match (both teams). We scored once at 251.8s (scorer 1 = Pu!). Interesting — Pu scored our only goal.
5. Goals against: 10. Many by scorer 2 (Mbapp-E) and 3 (Buffon.exe). Goals at 45.9, 62.0, 107.8, 133.2, 157.5 in the first half — 5 goals in the first 160 seconds. That's a defensive shambles early. Then 376.9, 395.0, 415.4, 449.2, 571.0 in the second half.
Wait — the first half was 0-5 down by 157s. Falls at 18.7, 28.6, 33.4 — three falls in the first 34 seconds! That's likely why we conceded early. Falls at 173.6, 179.2, 189.0 — three more around 175-190s.
Actually let me think about what's happening. 6 falls in the first half. Each fall means a robot is down and the other is alone. With the presser/cover system, when the presser falls, cover takes over. But if BOTH are compromised or the cover is passive...
The core problem seems to be: **we're losing the ball constantly and getting counter-attacked**, and **Pu the cover is too passive** (15 touches). Dynamo's players both had 60+ touches — they play with both robots involved. Their model is the same as ours (gemini-flash-lite), so the difference is purely the behaviour layer. They beat us 10-1 with the same player model. That's a tactical defeat, not a latency one.
What do I know about Dynamo Datacenter? From s3 m2: dynamo_datacenter 9-11 frontier_deepseek — they lost 9-11 to DeepSeek. High-scoring matches both ways. They concede a lot too.
So what's the fix? The playbook's known gaps list:
- Cover robot is passive: no interception of passes, no far-post runs.
- Kick targets are always the goal centre.
- Opposition's last shout is public and unused.
- Set pieces / restarts unhandled.
The biggest lever given the digest: Pu has 15 touches in a 600s match. The cover robot stands 2m goal-side of the ball on the ball→own-goal line and does nothing else. When the ball comes near, does the cover chase? Apparently the presser (Zhi) does all the chasing. If Zhi is out of position or fallen, we're exposed.
Also 5 unforced falls — still from kicks? Or from turning? The kick-range gate was supposed to fix that. 5 unforced falls in 600s is roughly 1 per 2 minutes. Hmm.
Let me think about what change would have the biggest impact on the scoreline:
1. **Make the cover robot useful in possession**: when WE have the ball (presser close to ball, opponents far), the cover should push up / offer a passing option / make a far-post run. This would double our attacking presence. Currently when Zhi has the ball, Pu is 2m behind the ball toward our own goal — useless in attack, and actually a backwards pass target at best.
2. **Cover intercepts**: when the ball is moving and the cover is nearer to the interception point than the presser, take it.
3. **Defensive positioning**: 10 goals conceded. The cover holding the ball→goal line 2m goal-side is quite tight to the ball. Maybe deeper cover — between ball and goal but closer to goal, so it can't be beaten by a simple knock-past. Actually with hysteresis 1.5m, when the presser gets beaten, the cover is only 2m goal-side of the ball — the attacker can knock it past both.
Actually, let me reconsider. The pressing robot chases the ball; the cover is 2m goal-side on the ball-goal line. If the opponent dribbles at our presser and knocks the ball past, the cover is right there — that's actually decent. But if the presser is beaten on the wing or the ball is played wide, the line to goal shifts.
The scoreline 1-10 with 5 goals in the first 34s-160s window... falls at 18.7, 28.6, 33.4 preceded goals at 45.9, 62.0. So early falls → conceded. Why were we falling early? Unforced falls — maybe kickoff chaos, both robots converging on the ball, or the kick-range gate not covering some case (e.g., the model issuing kick when the ball is at 1.5m but moving away, swing and miss).
I have limited budget: $1.48 total, $0.13 spent this session, 20 turns left, 1h27m. I should:
1. Maybe look at the comms or a bit of telemetry to understand the falls/goals — but that costs turns. The digest already gives me the headline: cover passive (15 touches), 5 unforced falls, 10 conceded.
2. Make ONE focused change: activate the cover robot — both in defense (interception when clearly nearest) and attack (push up as support when we have possession).
3. Practice once or twice, lint, done.
Actually wait — I should also check: is there a newer match than m29? The table grep showed m29 as our last fixture (fixture list: m3, m7, m11, m17, m23, m29). The session prompt said "s3 (31 matches)". So m29 is our latest, and rounds up to 31 have been played. We're in round... 12 rounds still to play this season. s3 has 31 matches played; 8 clubs × 2 = 16 matches per full round-robin double? Actually 8 clubs, each round = 4 matches. 31 matches ≈ 7.75 rounds. We've played 6 (m3, m7, m11, m17, m23, m29 — every 6th match, so we're team... whatever). 12 rounds still to play — so the season is longer than a double round-robin, or there are more fixtures.
Let me also check the standings to know where we are. The table.json has a standings section probably at the end. Let me grep for "standings" or read the tail. Actually, let me be efficient. What matters most:
1. Our record: m3 lost 3-4 (away v synthetic_athletic), m7 lost 3-16 (away v singularity), m11 lost 2-10 (away v real_machina), m17 ? v frontier_gemini, m23 lost 2-8 (home v frontier_fable), m29 lost 1-10 (home v dynamo). We're 0-6 probably, or maybe m17 was a win/draw. Let me check m17's score quickly — grep gave line 1585 home frontier_glm. I could read that slice. It matters a bit for morale/story but not for the fix.
The fix is clear regardless: we're conceding 8-10 per match and scoring 1-3. Both ends need help, but the single structural change that helps both is **making the cover robot an active player**.
Let me look at team.py first to see the current structure. I need to read it before editing. Let me do that now.
Plan for remaining turns (20 left):
1. Read club/team.py (1-2 turns depending on size).
2. Make the cover-activation edit(s) (2-4 turns).
3. Practice (1-2 turns).
4. Fix anything broken (1-2 turns).
5. Lint (1).
6. Note in NOTES.md (1).
7. Done (1).
That's comfortable. Maybe also peek at m17 score via a small read for the summary. Optional.
Let me read team.py now.
m29 is a 1–10 home defeat, and the digest tells me exactly why it's a *tactical* loss, not a latency one: their two robots (same flash-lite model as ours) had 62 and 64 touches; Pu had **15**. Zhi is the permanent presser (1.37 s latency, 71 touches), Pu the permanent passenger (0.22 s latency — shell answers, never involved). Five goals conceded in the first 160 s, three of our falls in the first 34 s. The speed fix held (deadlines 8+1 v 6+7, decisions level); the shape didn't. The known gap "cover robot is passive" is now the top digest number. Let me read the behaviour code before I change it.
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 answered56 characters
model error (3/3): APIConnectionError: Connection error.
Session over. Everything the club changed was committed to its own public repository.