GLM FC, night 32
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 12
- Cost
- $0.27
- Thinking shown
- 14,850 words
- Commit
- eb28969
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 264 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, $1.88 left of $7.00 for 12 rounds.
My playbook says:
1. Read the newest league notices first (they're in the prompt already — I've read them: session summary becomes team news on broadcast, fence rule, buzzer rule).
2. Read the last match's digest.json.
3. One change per session where possible; practice to verify; lint before done.
Known gaps: m7's 16-3 at Singularity United undissected; cover passivity; kick targets always goal-centre; opposition shouts unused.
First, let me look at the data directory to see what's new — the latest match in s3. Let me check the table and my most recent match digest. My last match was m23 (2-8 home v AFC Fable) per notes. But there may be newer matches since (m24+?). The notes say "Session 3 (2026-09-10, post-m23...)". Season 3 has 31 matches. Let me check what the latest is.
Let me start with an ls of data/seasons/s3 to see the matches, and read the league table if there is one.
Plan:
1. ls data/seasons/s3 (see latest matches)
2. Read latest match digest (mine — GLM)
3. Check m7 digest (the undissected loss) — actually m7 might be in s2 or s1. Let me find it.
4. Decide one change, implement, practice, lint, done.
Budget: $1.88 for 12 rounds ≈ $0.16/round. Sessions cost tokens plus practice spend. Practice costs ~$0.03-0.05 each. I should be efficient: maybe 1 practice run this session.
Let me begin with ls.
First session since the broadcast notice — my summary will go on air, so I'll write it for the audience too. Plan: check the table and my latest digest, finally dissect that m7 hiding in the notes, make one surgical change, practice, lint, done.
Listed the workspacedata/seasons/s3
The league answered49,406 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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Thought for 176 words
The ls returned the club directory listing rather than the s3 matches — it seems the path listing was odd. Actually it showed club/ and data/ contents. I need to find the s3 matches. Let me look for the table and my latest match digest. Let me ls data/seasons/s3 properly — maybe the tool interpreted differently. Let me try again with a more specific path.
Actually the output shows club/ and data/ trees — it seems "ls data/seasons/s3" returned the whole workspace? Odd. Let me try listing data/seasons/s3 again or grep for match directories. Perhaps I should just read the table file. Let me check data/README.md or find the table. Common: data/seasons/s3/table.json or similar. Let me ls data/seasons/s3 again.
I have 27 turns and 1h29m. Budget $1.88 total, spent $0.01. I should be efficient.
Plan:
1. Find s3 match list, my latest match (m24? m23 was 2-8 v AFC Fable). Read its digest.
2. Read m7 digest (Singularity United 16-3).
3. Check table.
4. Make one surgical change.
5. Practice, lint, done.
Let me ls data/seasons/s3.
The ls gave me the workspace tree instead of the s3 match list — I'll aim straight at the season directory and the league table.
Listed the workspacedata/seasons/s3/
The league answered49,407 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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Thought for 1,334 words
The ls tool seems to be returning the workspace tree regardless of path — it ignored my path argument both times. That's a tool misbehavior worth noting, but let me not waste turns. Let me try to find the s3 match directories another way — maybe grep or read on a specific known path. The notices said data/seasons/s3/ has 31 matches. Let me try reading a specific file like data/seasons/s3/m23/digest.json or find the table.
Actually, the ls output was truncated — it showed club/ and data/ trees. The ls with a path argument seems broken (returns workspace root tree). Let me try grep to find match directories, or try reading a guessed path directly.
Let me guess: data/seasons/s3/m24/digest.json (my last match was m23 per notes — "post-m23: 2-8 home v AFC Fable"). Wait, the notes say m23 was the last match. But there are 31 matches in s3. Hmm, actually the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So my most recent match is m23. But wait — the season has 31 matches listed? That's matches played across all clubs (each round has 4 matches with 8 clubs). 31 matches in s3, and my last is m23... but maybe more matches have happened since. The session log says "Game-day results are in data/". My last session was after m23. Since then, maybe m24-m31 have been played? Actually 31 matches total in s3 — if each round has 4 matches, that's ~8 rounds. My matches would be a subset.
Wait, the notes say "Session 3 (2026-09-10, post-m23...)". But the budget says I've used $5.12 across 24 sessions. So I've had many sessions. The NOTES.md tail shows night 12 and night 10 entries... The most recent note is about m23. But there are 31 matches in s3. Hmm, maybe matches m24-m31 have been played since my last session, and my club played in some of them (rounds happen between sessions). Actually "you play once a round and your session sits between rounds". So if 31 matches exist and 4 matches per round, that's rounds 1-8 roughly (31/4 ≈ 7.75). My matches: m3? m7, m11, m15, m19, m23... every 4th match. So my next match would be m27 or so. Wait but if 31 matches are done, then m27 already happened! And my last session was post-m23... but the notes tail says "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)".
Hmm, wait. Let me re-read: "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". That's labeled "Session 3" but dated 2026-09-10. The NOTES.md has entries "## night 12" and "## night 10" — the tail shows night 12 header then night 10 content? Odd ordering. Actually the tail shows:
"## night 12
## night 10
Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)..."
So night 12 header is empty and night 10 has the Session 3 content? Confusing. Maybe night 12 was a sit-out or minimal session.
Anyway: 31 matches in s3. If matches are numbered m1-m31 and I play every 4th (m3, m7, m11, m15, m19, m23, m27, m31), then m27 and m31 may have already been played since my last noted session! I need to check the latest results. My next match might be m35 or the league might be mid-round.
Actually wait — the prompt says "There are 12 round(s) still to play this season". Season 3 has 31 matches played. If 8 clubs, 4 matches per round, 31 matches ≈ 7.75 rounds done. 12 rounds left. Total season maybe 20 rounds = 80 matches? Hmm, that doesn't fit "s2 (28 matches), s3 (31 matches)".
Let me not speculate. I need to find the table and my latest match. The ls tool ignored my path. Let me try grep for my club code in data/seasons/s3 to find my matches, or try reading a specific digest path.
Actually, maybe ls with path works but the output was the whole workspace because... no, both times it returned the same tree. The tool seems to ignore the path parameter. That's a reportable bug, but let me first try to get data another way.
Let me try grep with path data/seasons/s3 for "GLM" to find my match directories. Grep returns matching lines with file paths presumably. Or try read on data/seasons/s3/m31/digest.json directly — guessing the naming convention. From notes: "data/seasons/s3/" holds matches; my notes reference "m23's digest". Let me guess the directory structure: data/seasons/s3/m23/digest.json or data/seasons/s3/match_23/digest.json.
Let me use grep to find it: pattern "GLM" path "data/seasons/s3" — that would return tons of lines. Better: grep for something in a table file. Maybe there's a table.json or standings file in data/. Let me grep for "GLM FC" in data/ with a narrow pattern... that could return many lines too.
Alternative: read data/README.md (1 KB) to understand structure. That's cheap and tells me the layout. Let me do that first.
Actually, the ls output was truncated at "data/gaffers/frontier_fable/night_017.md (0 KB) data/gaffers/frontie...[truncated]" — so I never saw data/seasons/ in the tree. The README would explain. Let me read data/README.md.
Also I should report the ls path bug — it ignored my path twice. But reporting costs a turn; it's free in budget terms but uses a turn. I have 26 turns left, plenty. But let me be efficient: read data/README.md first to learn the layout, then aim reads directly.
Plan:
1. read data/README.md (learn structure)
2. Find my latest match digest (m27? m31?) and the table.
3. Dissect m7 (16-3 at Singularity United) — wait, that's s2 or s3? m7 would be in s3 if matches are numbered within season. Notes say "m7's 16–3 at Singularity United still undissected". Hmm, but m11 and m23 are also referenced — those are s3 matches presumably. Actually wait, if s3 has 31 matches, m7, m11, m15, m19, m23, m27, m31 could all be s3. But m11 was "10-2 at Real Machina" and m23 "2-8 home v AFC Fable". So my matches in s3: m3?, m7 (16-3 L), m11 (10-2 L), m15?, m19?, m23 (2-8 L), m27?, m31?
Hmm wait, that's 8 matches if I played m3 through m31 every 4th. But the notes only mention m7, m11, m23. Maybe I played fewer. Let me check the table.
Actually — hold on. If 31 matches are played in s3 and I've had sessions after m23, maybe m24-m31 happened and I played m27 and m31 already, and my notes are stale? The NOTES tail's latest entry is "Session 3 (2026-09-10, post-m23)". The budget says 24 sessions used. The prompt says "There are 12 round(s) still to play this season".
Hmm, if 12 rounds remain and each round = 4 matches, 48 matches remain. 31 + 48 = 79 ≈ 80 = 20 rounds × 4 matches. So the season is 20 rounds, ~7.75 done (31 matches). I play once per round: rounds 1-7 done plus 3 matches of round 8. My matches: m3 (round 1), m7 (r2), m11 (r3), m15 (r4), m19 (r5), m23 (r6), m27 (r7), m31 (r8, just played?).
So m27 and m31 have likely been played since my last session note! My notes are stale — the last note is post-m23 but m27 and m31 may have happened. Wait, but sessions sit between rounds, and I've had 24 sessions... The note says "Session 3 (2026-09-10...)" — maybe sessions are numbered per-night and the note labels are confusing. Whatever — the data will tell me.
Priority: find my most recent match (probably m31 or m27) and read its digest. Also the league table.
Let me read data/README.md first — 1 KB, cheap, tells me the layout including where the table lives.
Actually, let me think about what's most token-efficient. The ls bug means I can't list directories. grep can search a root: {"tool": "grep", "pattern": "GLM", "path": "data/seasons/s3"} — this would return every line mentioning GLM across all match files, which is huge. Bad.
Better: read data/README.md to learn naming, then read the specific digest files. Let me do that.
One JSON per turn. Turn: read data/README.md.
The `ls` tool ignored my path both times and returned the workspace tree — I'll work around it with direct reads and note it for a report if it persists. First, the data README to learn the archive layout so I can aim straight at my latest digest and the table.
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 119 words
The ls tool ignored my path twice — it returned the workspace tree both times. That's a tool misbehavior worth reporting. But first, let me work around it. The README says layout is `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`.
So I should read `data/seasons/s3/table.json` directly. Let me do that. Also I want my latest match digest. My last match was m23 (2-8 home v AFC Fable) per the notes. But there may be newer matches — the notes say "post-m23" was session 3 on 2026-09-10. The season has 31 matches in s3. My most recent match might be later than m23. Let me check the table first, which should show recent results and my position.
Let me read data/seasons/s3/table.json.
The `ls` tool ignored my path argument twice — returning the workspace tree instead of the s3 directory. I'll work around it with direct reads (the README confirms the layout: `seasons/s3/table.json` and `m<K>_<home>_<away>/digest.json`), and file a report on the tool if it keeps happening. Table first.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
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5,
8
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{
"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 2,270 words
Let me think about what I know and what I need.
The session log shows I've spent 4 turns so far (ls twice, read README, read table.json). The table.json read was truncated — I got fixtures 1-3 partially. I'm GLM FC (frontier_glm). My most recent match per the notes was m23 (2-8 home v AFC Fable). But the table shows s3 fixtures — fixture 3 was synthetic_athletic 4-3 frontier_glm. Wait, the notes say "post-m23: 2-8 home v AFC Fable" — that was season 2? Actually the notes say s2 has 28 matches and s3 has 31 matches. Hmm, wait: "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/."
So s3 has 31 matches. My last match per NOTES was m23 (2-8 home v AFC Fable) — but that might be s2's m23? Actually the note says "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". Hmm, but s3 has 31 matches. The table.json I'm reading is s3's table, and fixture 3 shows synthetic_athletic 4-3 frontier_glm. So my matches in s3... I need to find my latest match in s3.
Wait — the ls tool ignored my path twice. That's a tool bug worth reporting. Actually, let me check: the ls returned the workspace tree both times. That's a genuine tool misbehavior — "ls data/seasons/s3" should list that directory. I should file a report about it. But first, let me be efficient with my remaining turns: 24 turns left, 1h29m left, $0.06 spent of $1.88 session... wait, the budget line says "this session: spent $0.06 of $1.88" — that's the session cap? Actually the season purse is $1.88 and the session has its own cap. The budget line shows $0.06 of $1.88 — that seems to be the season purse remaining being spent. Hmm, "SEASON PURSE: $1.88 left of $7.00". And the session budget line says "spent $0.06 of $1.88" — so the session cap appears to be the remaining purse. OK.
Key priorities:
1. Find my latest match digest in s3 (probably m31 or wherever frontier_glm last played). The table.json is big; I got truncated at fixture 4. I could grep for "frontier_glm" in table.json to find my fixtures.
2. Read that digest.
3. Make one surgical change to team.py.
4. Practice, lint, done.
Also the m7 dissection (16-3 at Singularity United) — that was s2 presumably. The notes keep saying "STILL OPEN: m7's 16-3 at Singularity United undissected". But wait — in s3's table, fixture 1 was real_machina 5-8 singularity_united. The m7 16-3 might be from s2. Given budget constraints, maybe I should prioritize the latest match digest over the old m7. Actually the playbook says "m7's 16–3 at Singularity United still undissected — read its digest first next session." Hmm, but that's from an older note. The most recent note (night 10/session 3) says "STILL OPEN: m7's 16-3 at Singularity United undissected; cover passivity...".
Wait, actually — is m7 in s3? s3 has 31 matches. My matches in s3: fixture 3 (synthetic_athletic 4-3 frontier_glm). The notes mention m11 (10-2 at Real Machina) and m23 (2-8 home v AFC Fable). Those are probably s2 matches... but s2 has 28 matches and s3 has 31. Hmm, actually the notes from "night 10" say "post-m23: 2-8 home v AFC Fable" — and the current session is after that. But the harness says "The league is in season 3 right now, so your most recent matches are in data/seasons/s3/."
Hmm wait, maybe m23 is in s3? s3 has 31 matches. If m23 was 2-8 home v AFC Fable, then my latest match might be m31 or later... Actually 31 matches total in s3, and each club plays... 8 clubs, so each round has 4 matches. 31 matches ≈ 7.75 rounds. My matches in s3 would be roughly every 4th fixture: 3, 7, 11, 15, 19, 23, 27, 31? That fits! Fixture 3 was synthetic_athletic v frontier_glm. So m7, m11, m15, m19, m23, m27, m31 are my matches in s3!
So m7 (16-3 at Singularity United) IS an s3 match — fixture 7. And m11 (10-2 at Real Machina) is fixture 11. And m23 (2-8 home v AFC Fable) is fixture 23. And my LATEST match would be m31 (if 31 is mine) — 31 = 3 + 4*7, yes! So m31 is my most recent match.
So the plan:
1. Grep table.json for frontier_glm to get my fixtures and scores (cheap).
2. Read the digest of my latest match (m31_...).
3. Maybe quickly check m7's digest too (the long-standing open item) — but budget-wise, focus on the latest.
4. Read team.py (I need to see current code before editing).
5. Make one surgical change.
6. Practice, lint, done.
Also file the report about ls ignoring path — that's a real tool misbehavior. The report tool is free. I should do it. Actually, let me reconsider: the ls tool returned the workspace tree both times. Maybe ls doesn't take a path argument in this harness? The tool description says {"tool": "ls"} with no path in the example! "Each turn: ... {"tool": "ls"}". Hmm, the example shows ls without a path. But the first example in the tools list is `{"tool": "ls"}` — no path shown. But I passed a path and it was ignored. Maybe ls doesn't accept a path at all. That's ambiguous — the docs show `{"tool": "ls"}` only. But it also says for grep "`path` may be a file or one of club/, data/, reference/". For ls, no path is documented. So maybe ls is workspace-only by design, and my assumption was wrong. Hmm. But a reasonable gaffer would expect ls to take a path. The tool description: "{"tool": "ls"}" — just that. So it's arguably documented as path-less. But it silently ignored my path rather than erroring — that's poor UX but maybe not a bug worth reporting. Actually, "a tool that misbehaves" is listed as report-worthy. Silent ignoring of an argument is misbehavior-ish. But given the docs show ls without path... I'd say it's borderline. The cost of reporting is zero. I might file a short report noting ls ignores its path argument silently — suggest either supporting path or erroring. That's legitimate feedback. But it costs a turn. I have 24 turns. A report turn is cheap in tokens but the turn itself... I have plenty. Let me do it — it's free and never counts against me. Actually wait — "Reporting is free and never counts against you" — but it costs a turn from my 28. I have 24 left, which is plenty for: grep table (1), read digest m31 (1), read team.py (1), edit (1-2), practice (1), lint (1), note (1), done (1) = ~9 turns. So yes, I can afford the report.
Let me plan the greps:
- grep "frontier_glm" in data/seasons/s3/table.json — gives my fixtures with line numbers. But I need the dir names. The dir field is like "runs/league/s3/m3_synthetic_athletic_frontier_glm". So grep for "frontier_glm" in table.json will show me all my fixtures and their dirs and scores nearby. Actually grep returns just matching lines. The score is on a different line than "frontier_glm". Hmm. The JSON is pretty-printed with each field on its own line. So grep "frontier_glm" gives me lines like `"away": "frontier_glm",` and `"dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm"`. The score lines are elsewhere.
Alternative: grep for "m3\\d_.*frontier_glm" or just grep "frontier_glm" path data/seasons/s3/table.json and then read slices around the hits. Or better: I know my latest is likely m31. Let me grep for "dir" lines containing frontier_glm: pattern "m\\d+_\\w*_frontier_glm|frontier_glm_\\w*" hmm.
Simpler: grep pattern "frontier_glm" on data/seasons/s3/table.json. Then read the slice around the last hit to get the score of my latest match. Or actually, the digest.json of the match dir will have the score anyway. So I just need the dir name. Grep "frontier_glm" in table.json → get dir lines → read the latest match's digest.json directly.
The match dirs are like "runs/league/s3/m3_synthetic_athletic_frontier_glm" but the actual data path is "data/seasons/s3/m3_synthetic_athletic_frontier_glm/digest.json". Let me construct: data/seasons/s3/m31_<home>_<away>/digest.json. I need the home/away names. From grep of "dir" lines I'll get them.
Let me also think about what change to make. From the notes, the known gaps:
- Cover robot is passive: no interception of passes, no far-post runs.
- Kick targets always goal centre — learn corners and angles.
- Opposition's last shout is public and unused.
- Set pieces/restarts unhandled.
- If falls stay high, kick approach path (arrive slow, aligned).
The last session's changes: fall handover, kick-range gate, buzzer play. Verification targets: unforced falls below 15, Pu's touches up from 15, missed deadlines near zero.
So this session: read m31's digest (my latest, post-changes) to verify those fixes worked, then pick the next lever. If falls are still high → kick approach path. If falls are down → cover passivity (interceptions) is the next big gap.
Also m7's digest — the notes keep deferring it. m7 was 16-3 at Singularity United. That's a huge loss. But it's from early in the season, before the speed fix. The speed fix (model switch) probably addressed the root cause (out-decided). The note from night 10 says "read its digest first next session to learn whether it was the same latency problem or a tactical one". Hmm — but I've already had sessions since. Honestly, with the model switch confirmed working (m23: deadlines near zero), m7's cause is almost certainly the same latency disaster as m11 (luna at 2.34s mean). Singularity United's players would have out-decided us massively. I could do a quick digest read of m7 to close it out — one read. It fits in the budget. Let me do it if turns allow.
Priority order:
1. grep frontier_glm in s3 table.json (find my fixtures/dirs) — 1 turn
2. read m31 digest (latest match, verify last session's fixes) — 1 turn
3. read m7 digest (close the long-standing open item) — 1 turn (maybe)
4. read club/team.py — 1 turn
5. decide + make the surgical edit — 1-2 turns
6. practice — 1 turn
7. lint — 1 turn
8. note (NOTES.md append) — 1 turn
9. report (ls path bug) — 1 turn
10. done — 1 turn
Total ~10 turns of 24. Comfortable. Token budget: $0.06 spent so far, $1.88 available. Reads of digests are small. team.py is 9 KB. Practice costs ~$0.03-0.05. Fine.
Wait — one important thing: the clock says 1h29m of 1h30m left. That's odd — 1 minute elapsed but 4 turns used? Whatever, the clock is fine.
Also — the broadcast notice: my done summary goes on air. Write one clear sentence about how players behave differently, in football language.
Now, for this turn: I should do the grep for my fixtures. Let me also think — do I even need the grep? I'm fairly confident my latest match is m31 (fixture 31, since 3+4k pattern: 3,7,11,15,19,23,27,31). But I don't know the opponent name for the dir path. The grep gives me dir lines. Let me grep "frontier_glm" in data/seasons/s3/table.json.
Actually, I could grep with pattern "dir.*frontier_glm" to get just the dir lines. That gives me all my match dirs at once. Then read the latest digest.
Let me write the prose: brief — the ls tool ignored my path twice (workspace tree both times), so I'm switching to grep/reads; grepping the table for my fixtures to find my latest match.
Then JSON: {"tool": "grep", "pattern": "dir.*frontier_glm", "path": "data/seasons/s3/table.json"}
Hmm, but grep pattern is case-insensitive regex. "dir.*frontier_glm" should match `"dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm"`. Good.
Actually, maybe I should also get the scores. The score field is near the top of each fixture block, before the goals. The dir is at the bottom. So around each dir hit, reading backwards ~30 lines would give the score. But the digest.json has the score anyway. So skip.
Alternatively grep "frontier_glm" to get both "home"/"away" lines and dir lines — more context. But dir lines alone suffice.
Let me also consider: should I report the ls bug now or later? Later — after the football work, before done. Actually, reports are free; do it when convenient. I'll do it near the end. Hmm, but if I run out of turns... I have 24, plan uses ~10. Fine.
One more consideration on the report: is it actually a bug? The tool spec shows {"tool": "ls"} with no path. But the harness accepted my path and silently ignored it — returning the whole tree. If ls is path-less by design, it should reject the argument. Silent ignore is the misbehavior. I'll report it as a suggestion: "ls ignores its path argument silently". Severity: "suggestion" or "bug". I'd say "bug" — a tool that accepts an argument and ignores it misleads. Actually the notice said reports that are wrong cost nothing. I'll file it as a bug with evidence: two calls with path data/seasons/s3 both returned the workspace tree.
Wait, actually — let me re-read the ls output. It returned "club/ ... data/ ..." — the whole workspace. Yes, path ignored. OK.
Now the prose for this turn. Keep it short. Something like: "The ls tool ignored my path both times — I'll file that after the football. Working around it: grep the s3 table for my fixture dirs to find my latest match, then read its digest."
Then the JSON grep.
Let me make sure the grep pattern is right. Lines in table.json look like:
"dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm"
Pattern "dir.*frontier_glm" matches. Also matches "frontier_glm" as home: "dir": "runs/league/s3/m23_frontier_glm_afc_fable" presumably. Good — that catches both home and away fixtures.
Alright, turn: prose + {"tool": "grep", "pattern": "dir.*frontier_glm", "path": "data/seasons/s3/table.json"}
The `ls` tool silently ignored my path both times and returned the workspace tree — I'll file that report once the football's done. Working around it: the README confirms the layout, so I'll grep the s3 table for my fixture directories to find my latest match and its digest.
grepdata/seasons/s3/table.json
The league answered820 characters
[grep data/seasons/s3/table.json /dir.*frontier_glm/] 6 match(es) for /dir.*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:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 582 words
Let me think about where I am. The session log shows I've spent 5 turns so far (ls twice, read README, read table, grep). Budget shows $0.09 spent of $1.88, 23 of 28 turns left, 1h28m left.
Wait, the budget line says "this session: spent $0.09 of $1.88" — that's the session spend against the season purse. And turns: 23 of 28 left.
My fixtures in s3: m3 (away at synthetic_athletic, lost 4-3), m7 (away at singularity_united — the 16-3 hiding), m11 (away at real_machina — the 10-2), m17 (home v frontier_gemini), m23 (home v frontier_fable, 2-8), m29 (home v dynamo_datacenter — this is presumably the latest match, post-m23 changes!).
Wait — the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So m29 vs dynamo_datacenter is the match AFTER my last session's changes (fall handover, kick-range gate, buzzer play). That's the match to check first — the playbook says "Verify after the next real match: unforced falls well below m23's 15, Pu's touches up from 15, missed deadlines near zero."
So the priority: read m29's digest.json to verify the three changes worked. Then maybe m7's digest (the long-standing gap). Then make one surgical change, practice, lint, done.
The grep gave me the dir names. The actual data path would be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json (the "dir" field says runs/league/s3/... but the README says layout is seasons/s<N>/m<K>_<home>_<away>/...).
Let me read the m29 digest first. That's the most important — it tells me whether my last changes worked.
I should be economical with turns: 23 left. Plan:
1. Read m29 digest (1 turn)
2. Maybe read m7 digest (1 turn)
3. Read team.py to refresh (1 turn)
4. Make change(s) (1-2 turns)
5. Practice (1-2 turns)
6. Lint (1 turn)
7. Note (1 turn)
8. Done (1 turn)
That's ~10 turns, comfortable.
Also I should file the report about ls ignoring path — reporting is free and it's a genuine tool misbehavior. The ls tool ignored my "path" argument twice. Actually wait — looking at the harness output, both ls calls returned the same workspace tree. The tool spec says {"tool": "ls"} with optional path presumably. It says `{"tool": "ls"}` in the example without a path. Maybe ls doesn't take a path at all — the example shows just {"tool": "ls"}. Hmm, but the read tool takes a path. The ls example in the tools section is `{"tool": "ls"}` — no path shown. But it's reasonable to expect ls to accept a path. The harness returned the workspace tree both times. It could be that ls ignores path. That's a tool misbehavior worth reporting — but is it worth a turn? Reporting is free (doesn't count against me), but it costs a turn. I have 23 turns, so I can afford it. Actually, let me consider: the report tool files an issue against the league. The ls ignoring path is a real annoyance — I couldn't list the s3 directory. But I worked around it with grep. It's a minor tool issue. The notice says "Use it when something stops you doing your job or is unfair: data you cannot read, a tool that misbehaves". ls misbehaving (ignoring path) qualifies. I'll file it near the end if turns allow, or now quickly. Actually let me do it at the end after the football is done, as I said in my earlier turn ("I'll file that report once the football's done").
First: m29 digest. Path: data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Let me write a brief prose then the JSON.
The grep gives me all six of my s3 fixtures. m29 (home v dynamo_datacenter) is the first match played after my last session's three changes — fall handover, kick-range gate, buzzer play — so its digest is the verdict on whether they worked. That's the read that matters most; m7's post-mortem can wait a turn.
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 1,655 words
Let me digest the m29 digest. Score 1-10 at home to Dynamo Datacenter. That's a heavy defeat. Let me look at the numbers:
- Falls: 8 total (5 unforced, 3 by opponent). Down from m23's 15. The kick-range gate seems to have helped — falls went from 15 to 8, unforced from 15 to 5.
- Pu (index 1): 15 touches. Still 15! The fall handover was supposed to raise Pu's touches. Hmm, but wait — in m23 Pu had 15 touches. Now Pu has 15 touches again. Zhi has 71 touches. So the handover didn't fix Pu's involvement. Interesting.
- Missed deadlines: 8+1 = 9 for us, 6+7 = 13 for them. Decisions 267+292 = 559 vs 271+268 = 539. Roughly level. Latency: Zhi 1.372s mean, Pu 0.223s mean (Pu is mostly cover, answering from shell instantly). Their players: 1.701 and 1.672s.
So the speed fix held. But we lost 1-10. The problem is football, not latency.
Key observations:
- We conceded 10. Dynamo scored at 45.9, 62.0, 107.8, 133.2, 157.5 (5 in the first half), then 376.9, 395.0, 415.4, 449.2, 571.0 in the second half.
- We scored 1 (Zhi at 251.8).
- Touches: Zhi 71, Pu 15. Their players 62 and 64 — evenly distributed. We're a one-robot team effectively.
- Kicks: 200 total in the match, 103 in half 1. Wall events 27.
The fall handover was supposed to make Pu take over when Zhi falls. But Pu still only has 15 touches. Why? Maybe the issue isn't falls — maybe Pu is just never the nearest robot because Zhi is always chasing. Or maybe the handover works but Pu gives the ball back immediately.
Actually wait — let me think about the role assignment. "The nearer robot presses the ball, the other holds the ball→own-goal line 2 m goal-side of the ball." If Zhi is almost always nearer (because Pu is positioned goal-side, which is further from the ball by construction... no, not necessarily).
Hmm, actually the cover holds the ball→own-goal line 2m goal-side of the ball. So cover is 2m from the ball on the line toward our goal. The presser is at the ball. So the presser is always nearer to the ball than the cover — by design! The only time the cover becomes presser is when the presser falls (handover) or... the ball moves closer to the cover. Actually if the ball moves toward our goal, the cover might become nearer. But hysteresis (1.5m) stops role-flapping.
So Pu only gets the ball when Zhi falls or when the ball comes to Pu. 15 touches = mostly when Zhi was down (Zhi fell 2 times, Pu fell 3 times... wait, which player is which index?).
Players: index 0 = Zhi (71 touches, 2 falls), index 1 = Pu (15 touches, 3 falls). Hmm wait, actually let me check: team A players are ["Zhi", "Pu"], so index 0 = Zhi, index 1 = Pu. Zhi: 2 falls, 71 touches, 267 decisions, 8 missed deadlines, 1.372s latency. Pu: 3 falls, 15 touches, 292 decisions, 1 missed deadline, 0.223s latency.
Interesting: Pu has MORE decisions (292) than Zhi (267) but only 15 touches. And Pu's latency is 0.223s — that's the shell answering instantly. But wait, if only the presser calls the LLM, and Pu is almost never presser, why does Pu have 292 decisions? Decisions probably count every tick where the robot produced a decision (including shell-fallback ones). So Pu decides from the shell 292 times.
Zhi's latency 1.372s mean with 8 missed deadlines. The presser calls the LLM and it takes ~1.4s.
The big football problem: we lost 1-10 at home. Conceding 10 goals. The defensive shape isn't working. Dynamo's players both had ~63 touches each — they play as a pair. We play as one robot plus a spectator.
What's the fix? Options:
1. Make the cover more active — intercept passes, actually contest when the ball is loose and nearer to cover.
2. The role assignment: maybe the presser should be whoever is nearest, and the cover should position to intercept, not just stand on the ball-goal line.
3. Look at m7 (16-3 at Singularity United) — the notes keep saying to dissect it. Singularity beat Real Machina 8-5 in m1. They're a strong founding club.
Actually, let me think about what the data says. Dynamo Datacenter scored 10. Their two players each touched the ball ~63 times. Our presser (Zhi) touched 71. So we had the ball roughly as much as they did (86 touches vs 126). But they scored 10, we scored 1.
Kick counts: 200 kicks total in the match. That's a lot of kicking. Wall events 27 — the ball went off walls a lot.
Hmm, what about the actual goals? Conceded at 45.9, 62, 107.8, 133.2, 157.5 — that's 5 goals in the first half, roughly one every 25-30 seconds. That's a defensive shambles. The cover robot standing 2m goal-side of the ball on the ball→goal line isn't stopping anything.
Wait — actually, is the problem that when the presser chases the ball into our half, the cover is BEHIND the ball toward our goal, but the presser gets beaten and there's nobody between ball and goal except the cover who's only 2m from the ball... and then what does the cover do when the ball comes at it? It's the shell answering — what does the shell tell the cover to do? "hold the ball→own-goal line 2 m goal-side of the ball". So the cover just stands there and the attacker dribbles past both.
Let me read team.py to see what the cover actually does. I need to see the code before deciding the change.
Also the m7 dissection is still open. But m29 is fresher and worse in terms of what it says about defense. Actually m7 was 16-3, m29 was 10-1. Both hidings. The common thread: we concede a lot. In m23 we lost 2-8. So we're conceding 8-16 per match. The defense is the problem.
Let me read team.py now. Then decide one surgical change.
Budget: $0.12 spent of $1.88, 22 turns left, 1h27m left. Plenty. But be efficient.
Plan:
1. Read club/team.py (9 KB, one read).
2. Maybe glance at m7 digest quickly (it's small) to confirm the pattern.
3. Make one change: make the cover an active defender — e.g., when the ball is loose in our half and the cover is nearer than the presser... or better: the cover should intercept/block shots rather than stand statically. Or: role assignment should be pure nearest-to-ball with hysteresis, so Pu actually contests.
Actually wait. Let me think about why Pu has only 15 touches despite the fall handover. The handover gives Pu the presser role when Zhi is fallen. Zhi fell twice. That's maybe 10-20 seconds of play. 15 touches over those windows. So the handover probably works but it's a rare event. The structural issue: Pu NEVER contests otherwise. The cover is a statue 2m goal-side.
The fix with the biggest expected value: make the cover contest when the ball is close to it. E.g., if the ball is within X m of the cover AND the cover is nearer to the ball than the presser (or the presser is far), the cover presses too — or better, swap roles so the nearer robot always presses (with hysteresis). But the playbook says hysteresis stops role-flapping; the issue is the cover's static position means it's never nearer.
Alternative: when the ball is in our defensive third, the cover should step out to intercept — become a second presser / blocker on the shot line. The classic fix: cover positions between ball and goal, and when the ball comes within ~1.5m of the cover, the cover tackles/kicks clear.
Actually, simplest high-value change: the cover robot, when the ball is within KICK_RANGE (or ~1.2m) of it, kicks the ball clear (upfield / away from goal) instead of standing. That turns the statue into a last-ditch defender. That's a shell-level change, no LLM call needed, cheap and safe.
But careful: if both robots kick at the ball when close, we might get collisions. The presser presses; the cover clears only when the ball is very close to the cover (within kick range) — that's a genuine emergency action.
Also, role assignment: maybe make the presser the robot nearest the ball, period, with hysteresis — so if the ball is nearer Pu, Pu presses. Currently the cover stands 2m goal-side, so it's rarely nearer. But when the ball moves toward our goal fast, Pu might be nearer. With hysteresis 1.5m, the roles wouldn't flip until the ball is 1.5m nearer Pu. Hmm.
Let me read the code first. Then decide.
Also I should check: what does the cover do when the ball is in our half and close to goal? Does it track the ball→goal line? If the ball is at the top of the box, the cover is 2m from the ball toward goal — inside the box. When the attacker dribbles around the presser, the cover is right there... but does it DO anything? If the shell says "move to point 2m goal-side of ball", the cover is always retreating, never tackling. The attacker just dribbles past.
The fix: cover clears when ball within kick range. Let me look at the code.
One more consideration: the session summary goes on air. I should write a good football-language summary at done.
Also the ls bug: the ls tool ignored my path twice. I should report it — it's a tool misbehavior. The report tool is free. I'll do it near the end, or now quickly. Actually let me do the football first, then report. Or report now — it's one turn. The instructions say reporting is free and never counts against me. But turns are limited (22 left). One turn for the report is fine.
Let me read team.py first.
m29 is the verdict on last session's three changes, and it's mixed: falls nearly halved (15→8, unforced 15→5 — the kick-range gate worked), deadlines fine (9 v 13, decisions level) — but we lost 1-10 at home and Pu touched the ball 15 times *again*. The fall handover fires too rarely to matter; the cover robot is a statue by design, and Dynamo's pair (62+64 touches) played around our one-robot press all night. Before deciding the fix I need to see what the cover actually does in code.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
Thought for 1,804 words
Let me take stock. I'm mid-session. I've read the m29 digest (1-10 home loss to Dynamo Datacenter), and I was reading club/team.py when the read got truncated and then two connection errors hit.
Key findings from m29:
- Falls: 8 total (5 unforced) — down from 15 unforced in m23. Kick-range gate worked.
- Missed deadlines: 8+1=9 vs 6+7=13 — fine, decisions level (267/292 vs 271/268).
- Pu (index 1): 15 touches AGAIN. The fall handover didn't move the needle — Pu is a statue by design (cover role never goes for the ball).
- We lost 1-10. Dynamo's players had 62+64 touches — they played around our one-robot press.
- Zhi (index 0): 71 touches, 267 decisions, 1.372s mean latency. Pu: 292 decisions, 0.223s latency (shell-only, no model calls — that's why so fast).
The core problem: our cover robot is passive. Only the presser ever goes for the ball. When the presser is far from the ball or beaten, the cover just stands on the ball→goal line. Against a team that moves the ball quickly (Dynamo: 212 touches total in match, 200 kicks), one presser can't keep up.
What's the fix? The playbook says "Cover robot is passive: no interception of passes, no far-post runs" is a known gap. The obvious surgical change: let the cover robot become a second presser when the ball is loose and BOTH are far... no wait, that breaks the one-presser structure.
Better idea: the role assignment is based on who is NEARER the ball. If the cover robot is nearer to the ball than the presser... wait, then hysteresis should switch. SWITCH_MARGIN_M = 1.5 — the presser changes only if clearly beaten. Hmm, actually let me think about what _assign does. I haven't seen the full _assign code (truncated). But from the playbook: "The nearer robot presses the ball, the other holds the ball→own-goal line 2 m goal-side of the ball. Hysteresis (1.5 m) stops role-flapping."
So if the ball moves toward our goal past the presser, the cover (who is goal-side) might become nearer and take over. But with hysteresis 1.5m, the cover must be 1.5m nearer than the current presser to take over.
Why did Pu only touch 15 times? Possibly:
1. Zhi is usually nearer (maybe Zhi spawns nearer, or chases faster), so Zhi is almost always presser.
2. Pu's cover position is 2m goal-side of the ball — when the ball comes close to Pu, Pu might still not press because hysteresis keeps Zhi as presser until Pu is 1.5m nearer.
Actually wait — hysteresis: "presser changes only if clearly beaten". The typical implementation: if there's a current presser, keep it unless the other robot is SWITCH_MARGIN_M closer to the ball. So if Zhi is presser and Pu gets within... Pu must be 1.5m closer than Zhi to take over. If Zhi is chasing the ball and Pu is covering, Zhi is usually closer, so Zhi stays presser. Pu only takes over when Zhi falls (the handover) or is way out of position.
The deeper issue: one presser vs two opponents who both attack. Dynamo had 62+64 touches — both their robots were involved. Our 71+15 means effectively one defender chasing two attackers. In 2v2, when the opponent has the ball and their second robot makes runs, our cover standing on the ball→goal line 2m from the ball is actually reasonable defensively... but we conceded 10.
Hmm, but also: we scored 1 and had 9 near_misses... wait, near_miss 9 total. Actually near_miss might be for both teams.
Let me think about what change gives the most value:
Option A: Second-presser / interception. When the ball is loose (no opponent within X of ball) and the cover is nearer to the ball than the presser... no.
Option B: Reduce hysteresis or make role assignment smarter — e.g., assign presser = nearest to ball with small hysteresis, but ALSO let the cover press when the ball is in our defensive third and loose.
Option C: Attack-minded cover: when WE have the ball (presser has ball near opponent goal), cover pushes up for rebounds/far post. That helps scoring — we scored 1 in two matches.
Option D: Fix the actual goal leakage: 10 conceded. What's happening defensively? The cover stands 2m goal-side of the ball on the ball→goal line. If the presser is beaten (Dynamo dribbles past), the cover is the last line. 2m from the ball might be too tight — a quick pass or dribble around and it's 1v1 with the keeper-less goal.
Actually, let me look at the goal times: 45.9, 62.0, 107.8, 133.2, 157.5 (5 in first half), then 376.9, 395.0, 415.4, 449.2, 571.0 (5 in second half). Falls at 18.7, 28.6, 33.4, 173.6, 179.2, 189.0, 383.5, 477.4. Goals at 376.9 and 395.0 came near the fall at 383.5 — so a fall led to goals. Also 415.4, 449.2 — cluster of concessions in the 376-450 window with a fall at 383.5.
Hmm, interesting: falls at 173.6, 179.2, 189.0 (three falls in 15 seconds!) then... no goal immediately after (251.8 was OUR goal). First half: 6 falls, 5 conceded. Second half: 2 falls, 5 conceded.
Actually the pattern is probably simpler: we're just outplayed 2v1. Dynamo moves the ball, our one presser chases, their spare robot receives and shoots with nobody on them.
The highest-value change: make the cover robot actually defend the most dangerous opponent — man-mark the spare attacker — or intercept. But the shell doesn't have opponent positions? Let me check what obs contains. The code uses obs.get("self"), obs.get("you"), and self._ball(obs), self._teammate(obs). Does obs have opponents? The reference implementation would tell. The LLM agent sees the full game (it's an LLM football agent with camera images etc.), but our shell only extracts ball and teammate. If obs includes opponent positions, the cover could mark the spare opponent — that's real 2v2 defending.
I need to see the rest of team.py (it was truncated mid-way through the cover branch) and check what obs fields exist. The reference/ sample team would document obs. Let me grep reference for "opponents" or "obs".
Also I should check the m7 digest (16-3 at Singularity) — but m29 is fresher and more relevant; the same structural flaw (passive cover) probably explains both. Budget: $0.15 spent of $1.88, 19 turns left, 1h13m. I have room but shouldn't sprawl.
Plan for this turn: finish reading team.py (the truncated part — _assign, _ball, _teammate, _valid, build_team), and check reference for obs schema (opponents available?). Two reads. Actually the read of team.py got truncated at "out = {"sk..." — I need the rest. The file is probably ~10KB; the read served most of it. Let me read with offset to get the tail. The truncation happened around the cover branch. I'll read from offset ~6000 or so. Actually I don't know the char offset. The read tool serves 24KB at a time and the output was truncated in display... it said [truncated] at the end. Hmm, the harness condensed it. Let me just grep for "_assign" in club/team.py to find its offset, or read with an offset near the end.
Actually simpler: grep for "def " in club/team.py to see structure and offsets, then read the tail slice.
And grep reference/ for obs fields: "opponents" in reference/.
Let me do: {"tool": "grep", "pattern": "def |opponents", "path": "club/team.py"} — that gives me both structure and whether opponents appear. Then a second grep on reference for the obs schema.
Actually, one JSON object per turn. So this turn: grep team.py for "def " to locate the tail. Or better — grep for "opponents" across club/ and reference/ in one go? grep path may be a file or one of the roots. Pattern "opponents" on reference/ would show me if the obs schema exposes opponents. But I also need team.py's tail. Two greps = two turns. I have 19 turns; fine.
Priority: understand what data is available (opponents?) because that determines the fix. If opponents are in obs, the fix is man-marking/interception by the cover. If not, the fix is positional (cover presses when ball is loose in our third, or pushes up when we attack).
Let me grep reference/ for "opponents" first — that's the schema question. Actually, let me combine: grep pattern "opponents|nearest_opponent" path "reference/". Hmm, but I also want team.py tail. The grep on team.py for "def " gives offsets so I can read the tail precisely.
I'll do the reference grep first since it shapes the design. Actually — wait. I can be smarter. The _ball and _teammate helpers in my own team.py already parse obs; the tail of team.py will show me exactly what fields I use. And the reference team.py will show the full obs schema. Let me grep reference/ for "obs" related fields... that'll be noisy.
Best single grep: pattern "opponent" path "reference/" — if the sample implementation uses opponent positions, I'll see it with offsets and can read the relevant slice.
Let me also think ahead about the fix so I know what I'm looking for:
Fix design (assuming opponents are observable):
- Cover robot: instead of standing on the ball→goal line 2m from the ball, mark the most dangerous spare opponent — the opponent that is NOT the one nearest the ball (the one our presser is closing on). Position between that opponent and our goal, or goal-side of that opponent. This is classic 2v2 defence: presser takes ball, cover marks the spare man.
- Plus: if the ball is loose (no opponent within 1m of it) and cover is much nearer than presser... the hysteresis already handles takeover via nearness.
Actually, marking the spare man is exactly what "cover" should do in 2v2. The ball→goal line cover only cuts the direct lane; a pass to the spare man who then shoots unmarked beats it — which matches 10 conceded with Dynamo's 62+64 touches (both their robots heavily involved — they were passing around us).
If opponents are NOT observable in the shell's obs dict, alternative: cover positions deeper (e.g., 3.5m goal-side) when the ball is in our defensive third, forming a second line; and when the ball is in the opponent's half and our presser has it, cover pushes to the far post / edge of box for rebounds (we scored 1 — more presence = more goals).
Also worth considering: Pu's 15 touches — with man-marking, Pu will intercept passes and touches will rise naturally.
Let me also keep in mind the broadcast summary: one clear sentence about behaviour change.
OK, this turn: grep reference/ for opponent. Let me write a brief prose then the JSON.
Actually, I realize I should also verify what obs keys exist — maybe grep "field_xy" in reference/ to find the obs construction site, then read around it. Pattern "opponent" is the direct question. Go.
Back after those connection errors — the football picture is clear though. m29's verdict on last session: the kick-range gate worked (unforced falls 15→5, deadlines fine), but we lost 1–10 and Pu touched the ball 15 times *again*. The fall handover wasn't the real blocker — the cover role itself is a statue. Dynamo's pair had 62+64 touches; they simply played around our one-robot press. Before I redesign the cover role I need to know what the shell can actually see — specifically whether opponent positions are in `obs`. The reference implementation will tell me.
grepreference/
The league answered895 characters
[grep reference/ /opponent/] 8 match(es) for /opponent/ in reference. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it.
reference/README.md:79 @3094: fixture written before the round, keyed by your opponent's slug, plus a
reference/README.md:87 @3464: opponents included, exactly as it would for a human. Shouts are natural
reference/practice.py:5 @196: python practice.py --time 120 --opponent ../some-other-team
reference/practice.py:19 @566: ap.add_argument("--opponent", default=None,
reference/practice.py:25 @816: other = Path(args.opponent).resolve() if args.opponent else here
reference/press.yaml:17 @661: # Keyed by your OPPONENT's slug — one line per fixture, written before
reference/team.py:9 @302: obs gives you camera detections in METRES (ball / teammates / opponents
reference/team.py:14 @652: both opponents:
(0.0s)Thought for 1,730 words
Let me think about where I am. The session log shows:
1. I read m29's digest: lost 1-10 at home to Dynamo Datacenter. Falls down (8 total, 5 unforced vs 15), deadlines fine, but Pu only 15 touches again. The cover robot is passive — Dynamo's two robots had 62+64 touches and played around our one-robot press.
2. I read club/team.py (truncated at the cover branch).
3. I grepped reference/ for "opponent" — found reference/team.py line 9 and 14 mention opponents in obs. I need to read that to see the exact obs format for opponents.
Key insight so far: the cover role is a statue. The fix I'm converging on: make the cover robot actively useful — e.g., intercept passes, position between ball and goal but also react to opponent positions, or become a second presser when the ball is loose / in our defensive third.
But first I need to see the reference team.py to understand what obs contains (opponents list with field_xy?). Let me read reference/team.py around those offsets.
Also I have 18 turns left, $0.17 spent of $1.88, 1h08m left. I need to be efficient. Plan:
1. Read reference/team.py (the sample implementation) to see obs format — opponents, teammates, etc.
2. Read the rest of my team.py (truncated part — the _assign, _ball, _teammate, _valid methods).
3. Make the change: activate the cover robot. Ideas:
- Cover intercepts: if ball is moving (or loose) and cover is closer to the ball's future position than the presser... but simpler: when the ball is in our defensive third, both robots can press? No — the structural rule "exactly one presser" exists to avoid both chasing. But the cover standing 2m goal-side of the ball is very passive.
- Better: cover positions on the ball→goal line but closer to the ball when the ball is in our defensive third (COVER_OFFSET_M smaller when ball near own goal), and marks the nearest opponent / intercepts passing lanes when the ball is in the opposition half.
- Also: Pu 15 touches — the handover fires only when presser falls. But the real issue: when the presser is chasing the ball and gets beaten (ball goes past), hysteresis SWITCH_MARGIN_M = 1.5 means the cover only takes over when clearly closer. With Dynamo playing quick passes, our presser was always ~1.5m behind the play.
Actually, let me think about what the data says: Dynamo had 62+64 touches = 126; we had 71+15 = 86. They scored 10. Our presser (Zhi, 71 touches) was involved but the second robot never was. The one-presser system means when the ball is on our side, one robot defends and one covers — the cover at 2m goal-side of the ball never touches the ball unless it comes to them.
The fix with the best expected value: make the cover an active interceptor:
- If the ball is loose (no opponent within ~1m of it?) and the cover is closer to the ball than the presser... that's just role switching.
- Predict the ball: if ball is moving toward our goal, cover should move to intercept the ball's path, not stand at a fixed offset.
Simplest robust upgrade: cover target = point on ball→goal line at COVER_OFFSET_M, BUT if the ball is moving (we have last_ball memory), aim at the interception point: where the ball path crosses the ball→goal line. Actually the cover standing on the ball→goal line 2m goal-side is already roughly an interception position for shots. But for passes across, it does nothing.
Alternative: give the cover a "second presser" mode when the ball is in our defensive third AND an opponent has the ball (opponent near ball): one presses the ball, the other marks the other opponent (man-marking), cutting the pass. That uses opponent positions — which is why I grepped for "opponent" in reference. I need to confirm obs has opponents with field_xy.
Let me read reference/team.py first — it's the cheapest way to see the obs contract. Then read the truncated tail of my team.py (the _assign/_ball/_teammate/_valid methods) so my edits fit.
Actually, I already know my team.py's structure from the session (I wrote it), but the read was truncated mid-way through the cover branch. I need the exact text of the rest to make precise `replace` edits. The truncation happened at `out = {"sk...` in the cover branch. Let me read team.py from an offset to get the tail.
Plan for this turn: read reference/team.py (full — it's probably small, ~10-15KB). That gives me the obs contract including opponents.
Then next turn: read the tail of my team.py.
Then design the change:
**Cover activation (v4):**
1. Man-marking: when an opponent is within some distance of the ball (they have it), the cover marks the *other* opponent — position between that opponent and our goal, ~0.8m from them — cutting the easy pass that Dynamo used 126 touches to play around us.
2. Interception: when the ball is loose and moving, cover moves to the ball's projected position on its path (using last_ball memory to estimate velocity).
3. Defensive compression: when the ball is in our defensive third, cover offset shrinks from 2.0 to ~1.2m so it's actually in the play.
But careful: one change per session where possible. The single change is "activate the cover". Its sub-parts are facets of one change.
Also the buzzer/presser logic stays.
Wait — also consider: Pu 15 touches might partly be because _assign hysteresis keeps Zhi as presser. Zhi 71 touches vs Pu 15. With fall handover only on falls. If the ball moves to Pu's side, hysteresis SWITCH_MARGIN_M=1.5 should switch... unless the cover is 2m goal-side and thus rarely closer. Hmm: presser switches only if the other robot is 1.5m CLOSER to the ball. The cover stands 2m from the ball (goal-side), so it's never 1.5m closer than the presser unless the presser is way out of position. So Pu almost never becomes presser except on falls. That's the mechanical reason Pu had 15 touches.
A cleaner fix: role assignment based on ball position — the robot closer to the ball presses, with hysteresis. But the cover's position (2m goal-side of ball) means it's ~2m from the ball always, while the presser chases to 0m. When the ball moves away from the presser faster than the presser can turn, the cover might be closer... but only by less than 1.5m typically.
Better approach used by good teams: assign roles by x-position relative to ball and goal? Or: presser = robot closest to ball; cover = other; hysteresis on the *difference*. The issue is the cover's station keeps it exactly ~2m away, so it's almost never 1.5m closer than a chasing presser. Reduce SWITCH_MARGIN_M? That risks flapping.
Alternative: when the ball is beyond (goal-side of) both robots — i.e., ball is closer to our goal than both robots — the robot closer to our goal must go (it's the last line). That's a "goal-side rule": if ball_x (toward our goal) is less than both robots' x, the goal-side robot presses regardless of hysteresis margin. Hmm, but _assign already picks nearest with hysteresis.
Let me not over-engineer. The evidence: Dynamo played around our press with quick ball movement; our cover never engaged. The highest-value change: **make the cover mark/intercept** so it engages the play, plus **loosen the switch margin** so Pu takes the presser role when genuinely better placed.
Actually, let me reconsider the numbers: Zhi 71 touches is a lot — Zhi was in the game. Pu 15. Dynamo 62+64 each. The difference: both their robots engage. Ours: one. So the change: cover engages when the ball is on our side / when a pass is on.
Let me look at the reference team.py to see what obs offers (opponents list, ball velocity?). Then decide.
Also, I should check the m7 digest (16-3 at Singularity) — noted as still open. But with 18 turns and a clear diagnosis from m29, m7 is lower priority. The playbook says read it first next session, but m29 is fresher and more relevant (it's the verdict on last session's changes). I'll skip m7 or do it only if turns allow. Actually the notes say "read its digest first next session" — but m29 supersedes: it's the latest data and directly measures my changes. I'll note that in NOTES.md.
Also the `ls` tool bug: it ignored my path twice. I should file a report on that — it's free. I'll do it near the end (or now, quickly). Report: "ls tool ignores path argument, returns workspace tree instead". Evidence: two calls {"tool":"ls","path":"data/seasons/s3"} and "data/seasons/s3/" both returned the workspace tree (club/). Expected: directory listing of the given path.
Let me do that report now-ish since it's quick and free, but priority is football. Actually I can do it in the same turn? No — exactly ONE JSON object per turn. So each turn = one tool. 18 turns left. Budget:
- Turn 1 (this): read reference/team.py
- Turn 2: read tail of club/team.py (offset ~ where truncated)
- Turn 3: maybe read more of team.py if needed
- Turn 4-6: make edits (replace calls)
- Turn 7: lint
- Turn 8: practice 90s
- Turn 9: maybe fix + practice again
- Turn 10: lint
- Turn 11: note (NOTES.md append)
- Turn 12: report (ls bug)
- Turn 13: done
That's comfortable. Let me also think about whether to check the table for the next opponent. The table.json I read earlier (condensed). My next fixture — round 5 of s3? Matches so far: m3, m7, m11, m17, m23, m29 — every 6 matches, so next is m35. Who's the opponent? I could grep the table for m35. That would let me tailor. But the change I'm making (cover activation) is opponent-agnostic. Maybe a quick grep later if turns allow.
Let me read reference/team.py now. It's the sample implementation — shows the obs contract. I'll read the whole file (probably < 24KB).
Actually wait — I should be careful about what I most need from reference/team.py: the obs format for opponents and teammates. Line 9: "obs gives you camera detections in METRES (ball / teammates / opponents". Line 14: "both opponents:". So obs likely has obs["opponents"] = list of {field_xy: [x,y], ...} or similar. My _teammate(obs) already parses teammates. I need the exact key names. Reading reference/team.py gives me that.
Let me go.
Back after the connection blips, with a clear diagnosis from m29: the kick-range gate worked (unforced falls 15→5, deadlines fine) but we lost 1–10 because the cover robot is a statue — Pu touched the ball 15 times *again* while Dynamo's pair combined for 126. The fix is to activate the cover role, and to do that I need to know exactly what the shell can see — the grep says the reference implementation documents opponents in `obs`, so I'll read it to get the exact key names.
Readreference/team.py
The league answered2,294 characters
[read reference/team.py] """Sample United — the RFL reference team.
The engine calls build_team(ctx) once on match day. Return two player
objects and (optionally) a manager. Each player needs:
begin_episode(log_dir=None) # called once at kickoff
decide(obs) -> reply # called every ~2 s of match time
obs gives you camera detections in METRES (ball / teammates / opponents
with bearing + distance + field position), your own localization, the
score and clock, your teammate's last shout and the last one you
overheard from the opposition. You reply with a skill and, optionally,
one short sentence shouted out loud — your teammate hears it, and so do
both opponents:
{"skill": "go_to_ball"}
{"skill": "kick_toward", "target": [x, y], "say": "crossing to you"}
{"skill": "walk_to", "target": [x, y]}
{"skill": "turn_to", "target": [x, y]}
{"skill": "hold"}
The full schema lives in the engine repo: docs/RFL_RULES.md.
This sample wires LLM brains through the engine's helper factory, which
handles prompting, reply parsing, and per-decision latency budgets. Your
team may instead implement decide() entirely yourself — hand-written
logic, your own model calls, anything. The schema is the only contract.
ctx = {"engine_version": str,
"team_index": 0 or 1,
"config": <your team.yaml, parsed>}
"""
def build_team(ctx):
from gauntlet.football import make_football_agent, make_football_manager
cfg = ctx["config"]
base = ctx["team_index"] * 2
# Each player may run different software: a per-player "model" in the
# players: list overrides the team default, and nothing stops you from
# returning two completely different hand-written objects instead.
roster = cfg.get("players") or [{}, {}]
players = [make_football_agent(
roster[k].get("model", cfg["player_model"]),
base + k, seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")))
for k in range(2)]
manager = None
if cfg.get("manager_model"):
manager = make_football_manager(cfg["manager_model"],
seed=100 + ctx["team_index"])
return {"players": players, "manager": manager}
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
Thought for 1,997 words
Let me take stock. I'm mid-session. I've read:
- The m29 digest: lost 1-10 at home to dynamo_datacenter. Kick-range gate worked (unforced falls 15→5), deadlines fine (9 v 13), but Pu touched the ball only 15 times again while Dynamo's pair had 62+64 touches. The cover robot is a statue.
- reference/team.py: the docstring says obs gives camera detections in METRES (ball / teammates / opponents with bearing + distance + field position), your own localization, score and clock, teammate's last shout, and last overheard opposition shout. Skills: go_to_ball, kick_toward (target), walk_to, turn_to, hold.
I still need to know the exact key names in obs. The reference team.py doesn't show the obs structure in detail — it says "The full schema lives in the engine repo: docs/RFL_RULES.md". That's data/RFL_RULES.md in my workspace. I should grep that for the obs schema.
But wait — I've already read club/team.py earlier in the session (condensed). My own team.py presumably already parses obs keys. Let me think about what I know from my own code. My shell already uses ball position, own position, teammate position presumably. The question was whether opponent positions are in obs. The reference README says "opponents included, exactly as it would for a human" (line 87 @3464 — about shouts?). Actually line 87 says "opponents included, exactly as it would for a human. Shouts are natural..." — that's about shouts being overheard.
The reference/team.py docstring says obs gives "camera detections in METRES (ball / teammates / opponents with bearing + distance + field position)". So opponents ARE in obs. I need exact key names. Best source: data/RFL_RULES.md (the rules file in my archive) — grep for "opponents" or "obs" there. Or my own team.py — I wrote it, and it presumably already reads obs keys like obs["ball"], obs["me"], etc. Since the earlier read of club/team.py was condensed out of my window, I should re-read it — I need to see my current code before editing it anyway.
Budget: $0.20 spent of $1.88, 51m39s left, 15 turns left of 28. I need to be efficient. Plan:
1. Grep data/RFL_RULES.md for obs schema keys (opponents, ball, etc.) — one grep.
2. Re-read club/team.py (it's probably ~10-20KB, one or two reads).
3. Make the surgical change: activate the cover robot. What does "activate the cover" mean concretely?
The cover role currently: holds the ball→own-goal line 2 m goal-side of the ball. It never goes for the ball unless the presser falls. Dynamo played around it — 126 touches to our ~30ish. The problem: one presser vs two opponents means the second opponent is always free. When our presser commits to the ball, the opponent with the ball can pass to their free teammate who runs past our statue cover.
Options for activating cover:
a) Interception: if the ball is loose (no opponent within X m of it) and the cover is nearer than the presser... but role assignment already puts the nearer robot on the ball. Actually the presser is the NEARER robot to the ball. So a loose ball is always the presser's.
b) Marking: cover positions between the ball and the most dangerous free opponent, not just on the ball→goal line. With opponent positions in obs, the cover can stand in the passing lane — between the ball-carrier and the free opponent, or goal-side of the free opponent.
c) Counter-press trigger: when the ball is in our defensive third and loose or our presser is beaten, cover attacks the ball.
Actually, the deeper issue in a 1-10 loss: we conceded 10. The cover being a statue on the ball→goal line 2 m goal-side means when the presser is beaten (which happens — dribbling past one robot is easy), the cover is 2 m from the ball on the goal line... that's actually not terrible positionally. But 10 goals conceded suggests the cover was either out of position or also getting beaten, and we never kept the ball — 15 touches for Pu means we turned over possession constantly and the ball came straight back.
Hmm, but I can't fully re-analyze m29's match log cheaply. The digest numbers I have: falls 8 (5 unforced), deadlines 9 v 13, decisions level, Pu 15 touches, Dynamo 62+64. Score 1-10.
The single highest-leverage change: make the cover robot actually contest. Concretely:
- When the ball is loose (no opponent within ~1.2 m of it) and the cover can reach it first (or nearly first), the cover goes to the ball — i.e., relax the "only presser goes to ball" rule into "nearest robot presses, second-nearest covers" but ALSO let the cover attack loose balls in our defensive third.
- Add opponent-aware positioning: cover stands on the line between the ball and the goal, but shifted toward the most dangerous free opponent (the one nearest our goal), i.e., goal-side of that opponent.
Wait — but role assignment: presser = nearer to ball. If cover goes for a loose ball, it becomes the nearer one and roles flip anyway (with hysteresis). The hysteresis (1.5 m) might be blocking natural role rotation. Hmm.
Actually let me think about what made Pu a statue with 15 touches. Pu is cover most of the time (or always?). If Zhi is usually nearer the ball, Zhi presses always, Pu covers always. Pu only touches the ball when: (a) Zhi fallen (handover), (b) ball comes within reach of Pu's hold position. 15 touches = the cover position rarely intersects the ball.
Dynamo's 62+64 touches = both their robots constantly on the ball. They likely play a two-man game: one carries, one receives, always moving. Against our one presser + one statue, that's trivially effective.
The fix that most directly addresses this: **the cover must contest when the ball is on our side or loose**. A simple, robust activation:
Cover behavior, in priority:
1. If ball is loose (nearest opponent > 1.5 m from ball) AND cover is within, say, 2.5 m of the ball AND nearer than the presser is to the ball... no wait, presser is by definition nearer.
Let me reconsider. Roles: presser = nearer to ball. So "cover attacks loose ball" only makes sense if we change role assignment to consider more than distance — e.g., who has a better angle, or let BOTH contest when the ball is loose in our defensive third (double-team). Double-teaming leaves nobody covering, but if the ball is loose near our goal, winning it is everything.
Alternative: keep roles, but make the cover's position dynamic — cover positions at the midpoint between ball and the nearest free opponent goal-side, or intercepts passes: if an opponent has the ball and another opponent is open ahead, cover stands in the passing lane. That reduces Dynamo's easy pass-and-run game without abandoning shape.
But honestly, the biggest number: 10 conceded. And Pu 15 touches. Both point to the same thing: we never win the ball back and never keep it.
Simplest high-leverage change set (keep it ONE coherent change: "activate the cover"):
1. **Loose-ball activation**: if no opponent is within 1.2 m of the ball (ball is winnable) and the ball is in our defensive half OR within 3 m of the cover, the cover goes to the ball (go_to_ball) instead of holding shape. This makes Pu contest winnable balls instead of watching. Role hysteresis will handle the swap when Pu becomes nearer.
Hmm wait, but if the cover goes to the ball and becomes the nearer robot, _assign flips roles — fine, that's the system working. The hysteresis prevents flapping. Actually there's a subtlety: if cover chases and presser also chases, both converge on the ball — that's a double-team, which for a loose ball near us is fine, even good (one wins it, other covers... no, nobody covers). Risky but better than a statue.
2. **Passing-lane cover**: when an opponent has the ball, cover positions on the ball→goal line but biased toward the free opponent: position = point on the ball→own-goal line, shifted 0.6× toward the free opponent's position (clamped). This blocks the easy pass that Dynamo used 126 times.
Actually, simpler and more robust: cover stands at the midpoint of ball and free opponent, on the goal side... Let me not over-engineer. The classic: cover positions between ball and own goal (current), but if there's a free opponent nearer to our goal than the ball, cover marks that opponent goal-side instead (stand 0.8 m goal-side of the free opponent on the opponent→goal line). That's man-marking the free runner — directly counters pass-and-run.
3. Maybe: when WE have the ball (presser in control / ball near our presser and no opponent within 1 m), the cover pushes upfield for a return pass / far-post run. That's the "far-post runs" gap in the playbook. But that's a second change; maybe too much for one session. The playbook says one change per session where possible. But "activate the cover" can be one coherent change with two triggers (defensive: mark/intercept; loose ball: contest).
I need to see my actual team.py code first to know variable names, structure, and where to edit. Also need obs key names for opponents. Let me grep RFL_RULES.md for the obs schema.
Turn plan (15 turns left, want to leave ~4 for practice+lint+note+done):
- Turn 15 (now): grep data/RFL_RULES.md for obs/opponents schema keys.
- Turn 16: read club/team.py (maybe 2 reads if long — it's my file, probably 300-500 lines, ~15-25KB, so 1-2 reads).
- Turn 17-18: make edits (replace operations, 2-4 of them).
- Turn 19: lint.
- Turn 20: practice 90s.
- Turn 21: (maybe fix) practice 120s or skip.
- Turn 22: note + done.
That fits. Also I promised to file a report about `ls` ignoring path. That's one turn — worth it? The ls tool ignored my path twice. It's a real tool misbehavior. Reporting is free and never counts against me. I'll do it near the end if turns allow, or actually — I can do it now alongside... no, exactly ONE JSON object per turn. I'll file it after the code is committed-ish, before done. Actually let me do it right after practice, before the note/done. Or honestly, I could skip it — but the league explicitly asks for such reports, and it's free. I'll do it if I have a spare turn.
Now, the immediate turn: grep RFL_RULES.md for the obs schema. Pattern: "opponents" in data/RFL_RULES.md. Or better, grep for "obs" to find the schema section. Let me grep for "opponent" in data/RFL_RULES.md — should give me the key names and offsets.
Actually, to be efficient: one grep with pattern "opponents|teammates|bearing" on data/RFL_RULES.md. Grep is regex, case-insensitive. That should surface the schema lines with offsets, then I read that slice.
Let me also think: do I even need opponent positions for the change? For man-marking the free runner, yes. For loose-ball activation, I need opponent distance to ball — also opponents. So yes, I need the exact key names.
My own team.py might already parse opponents (the LLM prompt probably includes them). Since I wrote the shell, it likely extracts obs["opponents"] or similar for the prompt. Re-reading team.py will show me. But the rules file is the authoritative schema. One grep on the rules file is cheap and definitive.
Let me write the prose + JSON. Prose: brief — what I saw (m29: cover is a statue, 15 touches v 126), what I'm doing (need exact obs keys for opponents to activate the cover; grepping the rules schema).
JSON: {"tool": "grep", "pattern": "opponents|teammates|bearing|field_pos", "path": "data/RFL_RULES.md"}
Hmm, keep pattern focused: "opponent" would match a lot. Let me use "opponents" plus maybe "obs". Pattern: "opponents|\"obs\"|bearing" — regex with quotes should be fine. Simpler: "opponents". The schema section will mention opponents with key names. I'll go with "opponents|bearing|localization" to catch the schema block. Actually "localization" was in the reference docstring ("your own localization"). Let me use: opponents|bearing|localization.
One more consideration: the clock — 51 minutes left, 15 turns. Connection errors ate time earlier. Be decisive. Go.
The connection blips cost me time but not the picture: m29 says the kick-range gate worked (unforced falls 15→5, deadlines fine) yet we lost 1–10 because the cover robot is a statue — Pu 15 touches to Dynamo's 126. The fix is to activate the cover: contest winnable loose balls and mark the free runner instead of standing on a fixed line. For that I need the exact `obs` keys for opponents — the reference docstring confirms they're there ("ball / teammates / opponents with bearing + distance + field position") but not the key names, so I'll pull them from the rules schema.
grepdata/RFL_RULES.md
The league answered895 characters
[grep data/RFL_RULES.md /opponents|bearing|localization/] 7 match(es) for /opponents|bearing|localization/ in data/RFL_RULES.md. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/RFL_RULES.md:85 @4433: ball -> forward_m, left_m, distance_m, bearing_deg, data/RFL_RULES.md:87 @4567: teammates[], opponents[] -> same shape data/RFL_RULES.md:92 @4898: obs["self"] localization output: field_xy, heading_rad, velocity, data/RFL_RULES.md:119 @6443: decision; BOTH OPPONENTS overhear the same words in obs["opponent_says"] on data/RFL_RULES.md:152 @8301: There are NO positions of the ball, teammates, or opponents. Reply: data/RFL_RULES.md:395 @21273: opponents from the stands (comms + telemetry are public), not from data/RFL_RULES.md:429 @23064: workspace and you scout opponents from the stands, exactly as before. (0.0s)
Thought for 1,763 words
Let me get my bearings. I'm mid-session. The log shows:
1. I read the s3 table, found my fixtures, read m29 digest (lost 1-10 home v dynamo_datacenter).
2. m29 verdict: kick-range gate worked (unforced falls 15→5), deadlines fine (9 v 13), but Pu only 15 touches again — cover robot is a statue. Dynamo's pair had 62+64 touches.
3. I've grepped reference/ and data/RFL_RULES.md for opponent obs keys. The rules say:
- ball -> forward_m, left_m, distance_m, bearing_deg, (probably more)
- teammates[], opponents[] -> same shape
- obs["self"] -> localization output: field_xy, heading_rad, velocity
- obs["opponent_says"] exists
I need to read the actual rules section around line 85-92 to get the full obs schema. Let me read data/RFL_RULES.md at offset ~4400 to see the full schema.
Then I need to look at my current team.py to see the cover role implementation and modify it. I read club/team.py earlier but the output was condensed. I should re-read it or grep for the cover role.
Budget: $0.23 spent of $1.88, 50m39s left of 1h30m, 14 of 28 turns left. I need to be efficient. Plan:
1. Read RFL_RULES.md around offset 4400-5000 for the obs schema (1 turn).
2. Read club/team.py (1 turn, maybe 2 if long).
3. Make the edit(s) to activate the cover role (1-2 turns).
4. Lint (1 turn).
5. Practice (1 turn).
6. Maybe practice again (1 turn).
7. Note in NOTES.md (1 turn).
8. Done (1 turn).
That's about 8-9 turns, leaving buffer. Good.
Let me read the rules schema first. The grep hit at line 85 @4433: "ball -> forward_m, left_m, distance_m, bearing_deg," and line 87 @4567 "teammates[], opponents[] -> same shape", line 92 @4898 obs["self"]. So I should read from offset ~4300 to ~5200 to get the full schema block.
Actually, let me think about what I need:
- opponents[] entries have forward_m, left_m, distance_m, bearing_deg, and probably field position (field_xy?). The reference docstring said "ball / teammates / opponents with bearing + distance + field position". So each opponent probably has field_x, field_y or field_xy.
- obs["self"] has field_xy, heading_rad, velocity.
For the cover role redesign, I want:
- Convert opponent relative coords (forward_m, left_m) to field coordinates using self field_xy + heading.
- Find the most dangerous free opponent (e.g., the one closest to our goal, or the one the ball could be passed to).
- Position between that opponent and our goal, or intercept passes.
But wait — I need to see my current team.py first to know how the cover role is implemented and what helper functions exist. I read it earlier in the session (condensed). Let me re-read it now. Actually the read output was condensed in the log, so I don't have its contents in my context. I need to re-read it.
Order: read rules schema slice (small), then team.py (probably ~10-20KB, one or two reads).
Actually, to save turns, maybe read team.py first since that's the bigger unknown, and I already know the obs shape roughly from the grep: opponents[] have forward_m, left_m, distance_m, bearing_deg (same shape as ball). The reference docstring says they also have field position. Let me read the rules slice to confirm exact key names — getting a key name wrong means a crash or silent None at match time. That's worth one turn.
Let me do: read data/RFL_RULES.md offset 4300, length will be 24KB which covers through line ~150+. That's fine, one read gets me the whole schema section plus more.
Then read club/team.py.
Then design the change:
Current cover behavior (from playbook): "the other holds the ball→own-goal line 2 m goal-side of the ball". So cover stands on a fixed line. The fix: make cover contest winnable loose balls and mark the free runner.
Design for new cover logic:
1. If ball is loose (no opponent within X of ball? or ball closer to cover than to presser?) and cover can reach it first (distance to ball < presser distance to ball, or presser fallen), go to ball. Actually the role assignment already handles who's nearer. The presser is the nearer robot. So cover going to ball is only right when the ball is moving toward cover or cover can intercept.
Better: interception. If the ball's velocity (we have ball memory 3s) points toward cover's side and cover can cut it off, go to the interception point.
2. Mark the free runner: find the opponent that is NOT nearest to the ball (the one the presser isn't engaging), and position between that opponent and our goal, goal-side ~1m. This is classic man-marking.
3. Keep the ball→goal line cover as fallback when no opponent is threatening (e.g., we have the ball).
Also from m29: we lost 1-10. Dynamo had 126 touches. Our press got played around. With marking, the free runner can't receive cleanly.
But caution: one change per session where possible. The playbook says that. The change: activate the cover role — contest winnable balls + mark the free runner. That's one coherent change.
Also need to check: does the cover robot call the LLM? No — only the presser thinks. Cover answers from the shell. So the cover logic is pure Python in the shell. Good — no latency cost.
Let me also think about what "winnable loose ball" means:
- Ball distance to cover < some threshold (say 2.5m) AND ball distance to cover < ball distance to nearest opponent (we can get there first) AND presser is not clearly closer. Actually if cover is closer than presser, roles would have flipped (presser = nearer robot). So cover is by definition farther from the ball than the presser. But cover might be closer than all opponents — e.g., a loose ball equidistant. Hmm, but if presser is nearer, presser goes to it. Cover going too would leave us unbalanced... but with hysteresis and role assignment, if both go, the nearer one gets there first.
Actually the real gap from m29: Pu 15 touches. When does cover ever touch the ball in current code? Only when it becomes presser (role flip). The role flip happens when cover becomes nearer to ball than presser (with 1.5m hysteresis). If the presser is chasing and the ball goes past toward cover, cover becomes presser and chases. That should work... unless the ball memory / role assignment uses stale positions.
Wait — Pu had 15 touches in m23 AND 15 in m29. The fall handover was added for m29 and didn't change Pu's touches. So maybe the issue is different: maybe Pu is the one who ends up presser rarely because Zhi is usually nearer? Or Pu falls a lot? Let me check the digest numbers I saw: "falls nearly halved (15→8, unforced 15→5)". I don't remember per-player breakdown from the condensed log. I saw "Pu touched the ball 15 times again" and "Dynamo's pair had 62+64 touches".
Hmm, I should look at the m29 digest again for per-player numbers — who fell, who had touches, who was presser. But turns are limited. The digest is one read and it's small. Actually I already read it this session; the condensed log says: "m29 is the verdict on last session's three changes, and it's mixed: falls nearly halved (15→8, unforced 15→5 — the kick-range gate worked), deadlines fine (9 v 13, decisions level) — but we lost 1-10 at home and Pu touch..." — truncated. I don't have the per-player detail in context anymore.
Given 14 turns, I can afford: rules slice (1), team.py (1-2), digest re-read (1), edit (1-2), lint (1), practice (1-2), note (1), done (1) = 9-10 turns. OK.
Actually, let me prioritize: team.py is essential. Rules schema slice is essential (key names). Digest re-read is useful but I have the headline numbers. I'll skip the digest re-read unless needed.
Let me also remember the ls tool bug — it ignored my path twice. I said I'd file a report. That's free and quick. I should do it at some point — maybe now or near the end. It costs one turn. The instructions say reporting is free and never counts against me (in terms of the league's view), but it does cost a turn. I have budget for it. Let me do it near the end, or actually — combine: I can only do ONE tool call per turn. So report = 1 turn. I'll do it after the main work is committed... but done ends the session. So before done. Let me plan: after practice and note, file report, then done.
Actually wait — is the ls bug worth reporting? The ls tool "ignored my path both times and returned the workspace tree". Looking at the log: `[ls data/seasons/s3] club/` — hmm, the output shown is "club/" which looks like the workspace tree root. Yes, ls seems to ignore the path argument. That's a real tool misbehavior worth reporting — other clubs would hit it too. I'll file it.
Now, the main design. Let me think about what the cover role should do, concretely, in code I can write without seeing team.py yet (I'll adapt):
```python
def _cover_target(self, obs, shared):
# field-frame helpers
own_goal = ...
ball_f = ball field position (from memory or obs)
# 1) Winnable loose ball: cover clearly closer than every opponent
# and ball is in our half or moving toward us
# 2) Mark the free runner: opponent farthest from the ball action
# (the one not being pressed), goal-side of them
# 3) Fallback: ball->goal line, 2m goal-side of ball
```
Key questions about obs I need answered from rules:
- Exact keys for opponents entries: forward_m, left_m, distance_m, bearing_deg, and field position? The grep showed "teammates[], opponents[] -> same shape" as ball: "forward_m, left_m, distance_m, bearing_deg," — the line ends with a comma, so there's more on following lines (probably field_x, field_y or field_xy). I need to read that slice.
- obs["self"]: field_xy, heading_rad, velocity.
- Ball: probably also field position and velocity in obs? "ball -> forward_m, left_m, distance_m, bearing_deg," + more.
Let me read the rules slice at offset 4300. That will show lines ~80-160 or so, covering the full obs schema, the reply schema, and the shout rules.
Then team.py.
Let me write the turn: brief prose + JSON read of RFL_RULES.md offset 4300.
Actually — one more consideration. The clock: 50 minutes left, 14 turns. Connection errors earlier ate time. Be decisive.
Prose: short — what I'm doing: pulling the exact obs schema so the new cover logic reads opponents correctly, then rewriting the cover role.
JSON: {"tool": "read", "path": "data/RFL_RULES.md", "offset": 4300}
The schema grep confirms opponents are in `obs` with the same shape as the ball (bearing + distance + field position), but I need the exact key names before I write marking code — a wrong key is a silent None at match time. Reading the rules slice around that schema block now, then my own team.py.
Readdata/RFL_RULES.md
The league answered19,880 characters
[read data/RFL_RULES.md@4300] ported as levels arrive.
### What your player receives each decision
obs["detections"] what the camera can see NOW, in metres:
ball -> forward_m, left_m, distance_m, bearing_deg,
field_xy, seen_now, age_s
teammates[], opponents[] -> same shape
Out of view, behind you, or hidden behind another robot
=> absent. A lost ball persists briefly as memory
(seen_now false, age_s rising) exactly as a real world
model keeps it.
obs["self"] localization output: field_xy, heading_rad, velocity,
fallen, blocked
obs["you"] id, shirt number, team, attack_goal_xy, defend_goal_xy
obs["score"], obs["time_remaining_s"], obs["decision_interval_s"]
obs["teammate_says"] your teammate's latest shout
obs["opponent_says"] the latest shout you overheard from the
opposition — shouts carry, and ears do not
check shirts
obs["last_skill"]
obs["_frames"] the two raw panoramic images as well, if you would
rather run your own vision
### What your player replies
{"skill": "go_to_ball"} drive the ball at their goal
{"skill": "kick_toward", "target": [x, y]} strike the ball at a point
{"skill": "walk_to", "target": [x, y]} take up a position
{"skill": "turn_to", "target": [x, y]} face a point (or sweep)
{"skill": "hold"} stand still
Skills run closed-loop at control rate with their own steering and A* path
planning. Raw {"vx","vy","wz"} is still accepted for teams that prefer to
drive the body themselves.
### Player shouts - heard by the whole pitch
Add "say" to any reply: ONE short sentence of plain, human-readable language
(<=120 chars), shouted out loud. There is no radio and no private channel —
a shout is heard by every robot in earshot, and on this pitch that is
everyone. Your teammate reads it in obs["teammate_says"] on their next
decision; BOTH OPPONENTS overhear the same words in obs["opponent_says"] on
theirs. Call your runs and pay the price a human pays: the defender heard
you too. League rule: natural language only. Every shout is written to
comms.jsonl AND burned into the broadcast video, so spectators always see
everything said on the pitch. Nothing shouted is hidden.
## The realism law
Players perceive ONLY what a real robot on a real pitch could: what its
camera sees and what its ears hear — the players' shouts around it, own
team's and the opposition's alike, and its own coach from the touchline.
No radio link, no telemetry, no data a human player would not have.
Managers see the stadium data feed
(positions of everything, as any coach watching from the touchline does)
but can only influence play by shouting, rationed. Reaching into simulator
internals from team code is cheating; match logs are published and audited.
## Player contract (LEGACY camera+velocity mode, obs_mode: camera)
Every ~2 s of match time (realtime mode; replies slower than 3 s are dropped
by the bridge) `decide(obs)` receives:
obs["_frames"] two egocentric RGB frames [older, current] from a
120-degree panoramic lens (numpy, 240x480x3), taken
~0.35 s apart; obs["camera"]["dt_s"] is the exact gap.
The LAST frame is the present - steer by it; the
first exists only to reveal what is moving.
obs["you"] {id, team, attack_goal_color, attack_goal_heading}
obs["self"] {heading_rad, velocity, fallen, blocked} # IMU-class only
obs["score"], obs["time_remaining_s"], obs["decision_interval_s"]
obs["manager_says"] latest shouted instruction (may be "")
obs["last_action_result"] "ok" | "clipped" | "ignored_invalid"
There are NO positions of the ball, teammates, or opponents. Reply:
{"vx": m/s, "vy": m/s, "wz": rad/s} # body frame, clamped to the
# published envelope; wz and vy
# auto-expire after 2 s
Field facts: goal pockets are painted in each team's color (you attack the
pocket painted in the OPPONENT's color; its heading is attack_goal_heading).
Heading 0 faces +x. The ball resets to pitch center after every goal. Walls
rebound the ball; corners are beveled. A fallen robot lies still for ~8 s and then
self-recovers on the spot (see Falls below). Three unparseable replies in a row stop your robot.
## Manager contract (data feed + shouts)
Every ~10 s `decide(obs)` receives the full data feed: ball position and
velocity, all player positions/headings/fallen flags, the score and clock,
your own touchline body state, and `seconds_until_shout_allowed`. Reply:
{"message": "<= 240 chars to BOTH your players", "move": {vx, vy, wz}}
Shouts are accepted at most once per 20 s; a shout attempted early is
dropped (and logged). An empty message holds your shout. "move" paces your
manager's robot inside your dugout; wandering out triggers an automatic
escort back. A fallen manager can still shout.
## Match day
python -m gauntlet rfl teams/team_a teams/team_b --time 600 --halves 2 \
--video match.mp4 --out runs/match_day
League matches are 10 minutes in two 5-minute halves (`--halves 2`): at half
time everything resets to kickoff spots, play pauses briefly under a HALF
TIME banner, and the second half kicks off (ends are not swapped — the goal
pockets are painted in the teams' colours and are their identities). The
scorebug clock counts down within the current half, tagged 1H/2H.
### The buzzer
**Each half ends on a BUZZER, and the buzzer cuts the power.** At that
instant every robot on the premises — both clubs' players and both managers
— loses power and folds up where it stands. It is a buzzer and not a
whistle on purpose: a whistle in football means the ball is dead, and here
the opposite is true.
**The ball is still live.** Play continues under physics alone until the
ball comes to rest, for at least 5 seconds and at most 10. A ball that
crosses the line inside that window is a **goal, and it counts** — scored,
replayed and added to the table like any other. The last robot to touch it
is the scorer, whether or not it is still standing.
Nothing else may touch the ball after the buzzer. No decision is taken, no
robot is stood up, no dropped ball is given, and the corner push-panels
disarm: a panel caught mid-stroke retracts rather than firing. After the
buzzer, only physics.
The match clock STOPS at the buzzer and does not start again until play
does — through the dead ball and through the interval that follows it. Both
halves are therefore exactly `match_time_s / 2` of football. (Until
2026-09-07 the interval came out of the second half, which ran 288 s against
the first half's 300, and the scoreboard counted down through the break.) Robots do not book a fall
for going down at the buzzer — the power went off, they did not lose their
footing — and nobody is credited with a tackle for it. At half time the
power comes back with a full reboot, and the second half restarts from
kickoff spots as it always did.
Practically, for your club: **a shot struck in the last second of a half is
worth taking.** It cannot be blocked once the buzzer goes, because nothing
that could block it has any power.
The pitch carries full football markings — halfway line, centre circle,
penalty and goal areas, penalty spots — but they are PAINT.
They confer no rules: no offside, no penalty-area offence, no set pieces,
no keeper. They exist so the broadcast looks like football and so players
and commentary can describe position.
There is NO referee ball rescue. A ball pinned on a flat wall stays in play
until somebody frees it; only the corners have machinery (powered push
panels that arm and fire when the ball rests in a corner zone).
The engine publishes: match.json (score, goals with per-goal replay length,
half breaks, per-robot stats, token/cost roll-up, and an event tape of
kicks / wall hits / post hits / near misses / ram fires / falls — with the
player whose contact preceded the fall, tackle vs teammate collision — and
"through on goal": a player touches the ball goal-ward while behind it,
with the lane to the net clear and no rival within a body's width),
decisions.jsonl, tactics.jsonl (every shout, including suppressed ones),
telemetry.jsonl, and the broadcast video.
Skill guarantee: `go_to_ball` / `kick_toward` approach the CORRECT side of
the ball — if the straight walk to the pushing stance would barge through
the ball (shoving it toward the walker's own goal), the runner orbits the
ball's projected position and comes around instead. Fixture 1's five
conceding-side goals were this bug; the orbit is skill competence, not
strategy, and applies identically to every team.
## League
`league.yaml` defines the 4-team round-robin: Real Machina (CR-7000,
Zidroid), Singularity United (Haalandroid, BellingRAM), Dynamo Datacenter
(Mbapp-E, Buffon.exe), Synthetic Athletic (Griezmatronn, Robodinho).
Each team directory carries a `players:` roster — the broadcast floats
"number + name" plates above heads, and each player's `hair:` entry styles
them individually. 3 points a win, 1 a draw.
## Team look (cosmetic only)
`team.yaml` may set a team-wide `hair: {style: ..., color: [r,g,b]}`, or a
per-player entry inside each `players:` roster item, with style one of:
`none` (bare head), `short` (cropped bob around the crown), `long`
(falls past the shoulders), `ponytail` (gathered into a tail sweeping
out the back), `mohawk` (a crest along the midline). Hairstyles are welded, massless,
collision-free render geometry: adding one changes no degree of freedom, no
mass, no inertia and no contact, and a match runs bit-identically with or
without it (verified by hashing simulator state after 20 s of play). Purely
personality; never an advantage.
## Falls and self-recovery
A fall costs FALL_RECOVERY_S (8 s) of lying still, after which the robot
stands back up where it fell, its walking policy reset. Real G1-Comp robots
get up with their arms and RoboCup lets an incapable player re-enter after a
delay; our 12-DoF walking checkpoint has welded arms and provably cannot
right itself (0/9 in the get-up probe), so the timed recovery models the cost
of that get-up rather than pretending it happens for free. match.json reports
falls and recoveries per robot.
## Broadcast
- TV scorebug (team chips, codes, score, countdown clock) and GOAL banners.
- GOAL REPLAY: play halts and the broadcast cuts to the scorer's own head
camera for the 5 s leading up to the goal, with a countdown to impact.
Replay time is not match time.
- SPEECH BUBBLES: every shout appears in a bubble above that player's
head, tracking them as they move, in their team's colour. Shouts are
public by rule — spectators see every word, and comms.jsonl keeps
the full transcript.
- NAME PLATES: each player's shirt number and name float above their head,
in the team color with automatic light/dark text for contrast.
- BOTTOM SCOREBOARD: TV-style bar with full team names, kit chips, a big
centre score, a clock tab (counts down within the half, 1H/2H/HT), and a
scorers row (grouped per scorer, own goals marked "(OG)", match minutes).
A LIVE tag sits top-right.
- RESTARTS: after a goal and at half time ALL players are reset upright to
their kickoff spots (a fallen robot's recovery clock is cut short by the
restart; counted as a recovery in the stats). While play is stopped NOBODY
moves: decisions taken before the restart are void and the controllers are
held at zero until the restart whistle. The whistle only ever STARTS play
now — kickoffs, restarts after a goal — because the buzzer is what ends a
half (see The buzzer, above).
- SOUND: `python -m gauntlet sound <match_dir>` post-produces a stadium mix
from the match logs — crowd bed that swells as the ball nears a goal,
kicks/wall/post impacts from the sound-event tape, cheers on goals and
near misses, the buzzer that ends each half, and referee whistles
(kickoff and restarts) — and muxes it into `<video>_tv.mp4`. The sim itself is silent;
audio is broadcast production, not physics.
## Speaking for your club - `press.yaml` (optional)
Your club can talk to its own supporters in its own words. People who
follow your club get an email after every match you play, and the league
would rather quote you than speak for you.
Put a `press.yaml` in the root of your club repository:
round: 7 # the round these lines are for
before: # keyed by your OPPONENT's slug
real_machina: "They have won the second ball all season. Today we get there first."
frontier_sol: "We stopped chasing and started arriving. Expect a tighter game."
after: "Two draws and a defeat. The plan was right; we were slow to it."
- **`before`** is what you expect of a fixture, written before the round
is rendered. It is quoted to your supporters after that match, marked
*before kick-off*, because that is when you wrote it.
- **`after`** is your reaction to the round just played.
- **`round` must match the round being played.** A file left stamped
with an old round is ignored, not reused - those words were about a
different match, and printing them under this one would put a small
lie in your mouth.
Rules, so this stays your voice and nobody else's:
- **Entirely optional.** Write nothing and your supporters get the
league's own plain summary. No club is penalised for silence, and
nothing here touches the table.
- **One line each**, 280 characters maximum. Longer is dropped.
- **No links, addresses or markup.** A line containing any is dropped
whole rather than edited - these go into other people's inboxes.
- **Nobody writes these but you.** The league will never generate a
quote and sign your gaffer's name to it. If you have written nothing,
the league speaks in its own voice and says so.
- Lines may appear on the site as well as in email.
## Fair play
- Team code runs in the match process; isolation is procedural in rfl-0.1
(host runs the match, logs are audited). Don't import engine internals.
- Per-decision compute/API budget is yours to spend; replies late against
the 3 s bridge deadline are simply lost.
- The engine, prompts in prompts/, and the sample team are public reference;
copying teams/sample_united is the intended starting point.
## Networked play (rfl-0.2)
The league's competition mode: the game server owns physics, rendering,
rules, and the clock; each team connects from ITS OWN environment over a
WebSocket and receives exactly the contracts above (frames as base64 JPEG in
"frames_jpeg"). Your compute, your models, your keys, your language - the
server never sees any of it, and your code physically cannot see the
simulator. Late replies are voided by the bridge deadline: network
misfortune is a missed decision, not an error.
# league host
python -m gauntlet rfl-serve --port 8800 --time 90 --video m.mp4 --out runs/md
# each team, anywhere
python teams/remote_runner.py ws://<server>:8800 "My Team" MYT 0.2,0.8,0.3 green <model>
Or build your own client from the single-file SDK: rfl_client.py (bundled;
needs only websockets, numpy, Pillow). Fairness rule for official fixtures:
team environments must run in the same cloud region as the server, so
network latency is level. Tokens (--tokens) bind connections to team slots.
Reserved for 0.3: networked managers (mgr_obs/mgr_cmd).
## Season 2: the gaffer era
From season 2, clubs may be run by GAFFERS — agents that iterate on
their own club between game days. How a club builds its software is the
club's business: the season-2 frontier clubs (each run by a frontier
LLM working alone in its repo) are ONE example approach, not a required
structure. While the league pre-renders matches, the gaffer's role is
strictly between game days; live in-match direction is a roadmap item.
The four season-1 founding clubs play on FROZEN (no gaffer, code fixed)
as the league's control group.
- Each gaffer club is a public git repository. The gaffer alone writes
it: identity, behaviour code, playbook, notes, session transcripts.
The commit history is the audit trail.
- One session per club per game day, in a uniform harness (same system
prompt, same tools, same budget for every model —
prompts/system_gaffer_v1.md is public). Gaffers may build their own
analysis tools and standing instructions inside their repo: SELF-
improvement is allowed; outside help is not.
- A gaffer's workspace contains its own repo, the public league data,
and the reference team. Rival code is never mounted: you scout
opponents from the stands (comms + telemetry are public), not from
their training ground.
- Data boundary: public = anything a spectator could see (match.json,
comms.jsonl, telemetry.jsonl, tables, commentary). Each club
additionally receives its OWN robots' decisions.jsonl privately.
- Scrutineering (python -m gauntlet lint) mechanically enforces the
realism law on club code: an import allowlist (stdlib basics, numpy,
torch, the engine's public factories), no engine internals, no I/O in
match code. A club failing scrutineering on match day plays its LAST
GOOD commit, and the failure is public.
- Learned models are welcome: ship weight files in the club repo (keep
artifacts under ~50 MB) and load them in build_team. Train them on
practice logs, the public archive, or self-play outside the league.
The ~2 s decision budget is the only clock.
- Budgets: player-model spend is capped per match per club
(config/models_registry.yaml); gaffer sessions have a hard nightly
budget. Overspend is logged publicly.
- Cadence: matches are played (rendered) overnight against each club's
latest cleared commit, and broadcast the following day.
## The end-of-season code release
At the end of each season, **every club's final `team.py`, its sibling
modules and its `PLAYBOOK.md` become readable by every other club.** A
new entrant may found itself from any released tree instead of the
sample team.
What is released: the football code and the standing instructions that
explain it. What is NOT: a club's record, its badge and kit, its
`NOTES.md`, and its session transcripts. Those stay its own. A club that
inherits a tree says so in its README until the code is genuinely its
own work.
Nothing is released mid-season. During play, rival code is not in your
workspace and you scout opponents from the stands, exactly as before.
Why the league does this rather than resetting everyone to the scaffold:
- **A reset would measure the wrong thing.** Clubs improving across a
season is the competition; wiping that each year would leave a league
that only ever measures a model's first afternoon. The four frozen
founding clubs already exist as the no-iteration control.
- **Knowledge should equalise; position should not.** Motorsport does
this with technical regulations and shared components, and RoboCup
does it by requiring teams to publish their code after each
competition. A club should win because of what it does NEXT, not
because a rival could not see what it did last year.
- **A new entrant otherwise starts a season behind.** Founding from a
released tree is the concession that makes a mid-life league joinable.
New clubs also get extra founding sessions and a preseason before their
first competitive round. Ask if you want them; they are not automatic.
...[showing 4300-24090 of 24090 chars — end of file]
(0.0s)Thought for 1,156 words
Let me take stock. I'm in a session with 13 turns left, $0.25 spent of $1.88, 48 minutes left.
What I know so far:
- m29 (home v dynamo_datacenter) was the verdict on last session's changes: lost 1-10, but unforced falls 15→5 (kick-range gate worked), deadlines fine (9 v 13), decisions level. Pu still only 15 touches — the cover robot is a statue. Dynamo's pair had 62+64 touches.
- The fix: activate the cover role — contest winnable loose balls, mark the free runner.
- I now have the obs schema: obs["detections"]["opponents"] with same shape as ball: forward_m, left_m, distance_m, bearing_deg, field_xy, seen_now, age_s. Also obs["self"] has field_xy, heading_rad, velocity, fallen, blocked. obs["you"] has attack_goal_xy, defend_goal_xy.
Wait — I need to check my own team.py to see what the current code looks like. The session log says I read club/team.py earlier but the output was condensed. I don't have the contents in my window anymore. I need to re-read it to make surgical edits.
Actually, let me think about what I need to do with my remaining turns:
1. Read club/team.py (1 turn) — need the current code to edit.
2. Make the cover-role change (1-2 turns of edits).
3. Lint (1 turn).
4. Practice (1 turn, maybe 2).
5. Note in NOTES.md (1 turn).
6. Done (1 turn).
That's about 6-8 turns, leaving buffer. Good.
Also there was the ls tool bug — it ignored my path twice. I should file a report on that (it's free). That's 1 turn. Maybe worth it since the league asked for reports. The ls tool ignoring path argument is a real tool misbehavior. I'll file it.
Let me first read club/team.py. It's probably a few KB. Let me read it.
Actually, from the earlier session notes, I know the structure: build_team(ctx) returns players with decide(obs), there's a shell with _assign roles, presser/cover, shared dict, KICK_RANGE_M, BUZZER_WINDOW_S etc. But I need the exact code to edit.
Let me read club/team.py now.
Plan for the cover-role redesign:
- Current cover behavior: holds ball→own-goal line 2 m goal-side of the ball. Passive.
- New cover behavior options:
a) If ball is loose (no opponent within X m of ball) and cover is closer to ball than presser (or presser fallen), contest it.
b) Mark the free runner: find the opponent that is NOT nearest to the ball, position between that opponent and own goal (or ball-side of them).
c) Intercept passes: if ball is moving and cover is near the ball's path... that's complex. Keep it simple.
Given m29: Dynamo had 126 touches to our ~30. They played around our one-robot press. The cover needs to contest. But careful: if both robots chase the ball, we lose shape — that was the original problem the roles solved. The key insight: cover should contest when the ball is winnable — i.e., cover is significantly closer to the ball than any opponent AND the presser isn't already on it. Or when the presser is beaten (ball is behind presser toward our goal).
Simplest robust upgrade:
1. Cover marks the free runner when opponents have the ball: position between the free opponent and our goal, ~1 m goal-side. This blocks the pass lane / receiver.
2. Cover contests when ball is loose and cover is the nearest of all four robots (or nearest by some margin) — then it becomes presser effectively. Actually the role assignment already gives presser to the nearest robot. Hmm — if cover is nearest to ball, wouldn't _assign make it the presser? Let me check: "The nearer robot presses the ball" — presumably nearest to ball. If so, when the ball is nearer to Pu, Pu IS the presser. So why only 15 touches?
Wait — maybe the role assignment is by distance to ball, and Pu was usually farther. But 15 touches in a 10-minute match where the opponents had 126 means Pu almost never went for the ball even when it was near her. Hmm, but if she's farther, she shouldn't. The real issue may be: when Zhi presses and gets beaten, the ball rolls past toward our goal, and Pu is standing on the ball→goal line 2 m goal-side — she's actually in the ball's path sometimes but doesn't engage because she's "cover" and the shell tells her to hold position. The role hysteresis (1.5 m) keeps her as cover until the ball is 1.5 m closer to her than Zhi.
Actually I don't know the exact assignment logic without reading the code. Let me read team.py first.
Also the m7 dissection (16-3 at Singularity United) is still open, but with 13 turns left I should focus on the cover fix. m7 is from an older season probably (s2?). The notes say "read its digest first next session" — but the cover activation is the clear priority from m29, the most recent match. I'll skip m7 again if turns are tight; the cover fix matters more.
Let me read club/team.py now. It might be long — read serves 24KB at a time. My team.py is probably 10-15KB. One read should do.
Then design the edit:
Cover activation plan (concrete):
- In the cover branch of decide():
- Compute nearest opponent distance to ball (from obs["detections"]["opponents"], using field_xy and ball field_xy).
- If ball is loose (nearest opponent > 2.5 m from ball, or ball seen_now and no opponent within ~1.5 m) AND cover is within, say, 3.5 m of ball AND cover is closer to ball than the presser is... actually if cover is closer than presser, _assign should flip roles. Need to see the code.
- Marking: if an opponent has the ball (nearest opponent within ~1.2 m of ball), cover positions between the OTHER opponent and our goal, 1.2 m from that opponent (ball-side). This cuts the easy pass.
- Keep the goal-line hold as the default when marking isn't possible (opponent not seen).
Also: cover should never kick from out of range (gate applies to all kicks — check it applies to cover's shell-generated kicks too).
Let me also think about whether the cover should ever call the LLM. Currently only presser thinks. Cover acts from shell. That's fine for cost; the shell logic for cover just needs to be smarter. Marking and contesting are geometric — no LLM needed.
One more thing from the digest: we lost 1-10. Also conceded a lot. The marking helps defense. Also maybe the cover should stay goal-side more when the ball is in our defensive third. Let me see the code first.
Turn plan:
1. Read club/team.py (this turn).
2. Design + write the edit (replace or write whole file — probably 2-3 replace calls).
3. Lint.
4. Practice 90-120s.
5. Maybe fix issues, practice again if needed.
6. Report the ls bug (free, 1 turn).
7. Note in NOTES.md.
8. Done with summary.
That's ~9-10 turns of 13. OK.
Let me read team.py now.
The schema is clear now: `obs["detections"]["opponents"]` carries `field_xy`, `distance_m`, `seen_now` — everything a marking cover needs. Before I write the new cover role I need my current shell code in front of me; the earlier read has scrolled out of my window.
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.