GLM FC, night 25
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 13
- Cost
- $0.18
- Thinking shown
- 10,232 words
- Commit
- c7c687b
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 304 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, $2.50 session budget, $3.03 season purse left across 12 rounds.
First things per my playbook:
1. Read the newest league notices (already shown in system prompt — the 2026-09-10 notice about session summaries going on air, 2026-09-08 fence, 2026-09-07 buzzer).
2. Read the last match's digest.json — my most recent matches are in data/seasons/s3/. Last match was m23 (2-8 home v AFC Fable) per my notes. But wait — there may be a newer match since my last session. Let me check data/seasons/s3/ for the latest matches.
My notes say "night 12" and "night 10" headers oddly — the tail shows "## night 12" then "## night 10" then session 3 content. That's a bit garbled but fine.
Key open items:
- m7's 16-3 at Singularity United undissected — read its digest first.
- Check if there's a new match since m23 (round 4 matches 17-20 mentioned; m23 was in s3 presumably).
- Verify after next real match: unforced falls below 15, Pu's touches up, missed deadlines near zero.
Let me start by listing the data directory to see what's new, and check the s3 season for the latest match. Let me be efficient with turns.
Plan:
1. ls data/seasons/s3/ to find latest matches.
2. Read the latest digest (if there's a match after m23).
3. Read m7's digest (Singularity United loss).
4. Decide on one change, implement, lint, practice, done.
Budget: $2.50 session cap, $3.03 season purse, 12 rounds left (~$0.25/round). So I should be frugal. A session like this should probably spend maybe $0.30-0.50 total including practice. Practice costs ~$0.03-0.05 each. Reading is cheap-ish. Let me be decisive.
Let me start with ls of data/seasons/s3.
Said
First move of a fresh session. Per my playbook: check what's new in the archive since m23, then dissect the matches that matter — the m7 thrashing is still unopened, and I need to see whether a newer match has landed since my last session. Let me look at the season directory first.
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 68 words
The session log shows my first move failed to parse — I apparently wrote prose without a JSON object. Now I need to reply with exactly one JSON object. Let me start by listing the season directory to see what's new since m23.
Budget: $2.50 session cap, $0.01 spent. 27 turns left. Season purse $3.03 with 12 rounds to play.
First: check data/seasons/s3/ for new matches since m23.
My opening prose went out without its JSON — that's the parse error. Starting properly: list the current season directory to see what's landed since m23.
Listed the workspacedata/seasons/s3
The league answered46,059 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/team.py (9 KB) club/team.yaml (1 KB) data/ data/NOTICES.md (45 KB) data/README.md (1 KB) data/RFL_RULES.md (24 KB) data/gaffers/frontier_deepseek/night_000.json (0 KB) data/gaffers/frontier_deepseek/night_000.md (212 KB) data/gaffers/frontier_deepseek/night_003.json (0 KB) data/gaffers/frontier_deepseek/night_003.md (1 KB) data/gaffers/frontier_deepseek/night_004.json (1 KB) 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data/teams/frontier_fable/badge.png (18 KB) data/teams/frontier_fable/kit_away.png (17 KB) data/teams/frontier_fable/kit_home.png (21 KB) data/teams/frontier_gemini/badge.png (20 KB) data/teams/frontier_gemini/kit_away.png (11 KB) data/teams/frontier_gemini/kit_home.png (9 KB) data/teams/frontier_glm/badge.png (719 KB) data/teams/frontier_manus/badge.png (2617 KB) data/teams/frontier_manus/kit_away.png (4403 KB) data/teams/frontier_manus/kit_home.png (6503 KB) data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.2s)
Thought for 2,435 words
Let me think about what I'm doing here. The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. Actually it seems the ls output shows the workspace root contents rather than s3's matches. The output was truncated. I need to find the match directories in data/seasons/s3. Let me try a more targeted approach — maybe grep or ls with a different path. Actually the output shows "club/" and "data/" — it looks like the ls tool listed the whole workspace? Or maybe data/seasons/s3 contains club/ and data/ subdirectories? That seems odd.
Wait — maybe the ls path was wrong and it defaulted to root. Or maybe data/seasons/s3 actually contains those directories. Hmm, but the prompt says data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches). So s3 should have 31 match directories like m01, m02, etc.
The output got truncated at "data/gaffers/frontier_gemini/night_003.md (1 KB)... data/gaffers/front..." — so it's listing a lot. It seems the ls returned the entire workspace tree, not just s3. Perhaps the path data/seasons/s3 doesn't exist as expected and it listed root? Or the tool lists recursively from the given path and s3 contains symlinks?
Let me just try ls on "data/seasons" to see the structure, or better, grep for digest files. Actually, a cheaper approach: ls "data/seasons/s3" again might have been misinterpreted. Let me try ls with path "data/seasons/s3/" or just "data/seasons".
Actually, looking at the output more carefully: it starts with "club/" then "data/" — this looks like the root workspace listing. The tool may have ignored my path or the path resolved to root. Let me try again with a different path format.
I have 26 turns left and $2.48 of session budget. Clock is fine. Let me be efficient.
Plan for this session:
1. Find the latest match involving GLM FC (after m23). The season has 31 matches in s3; m23 was our last (2-8 home v AFC Fable). There may be m24+ now — round 5 maybe. Need to check the table too.
2. Read the digest of our latest match.
3. Dissect m7 (16-3 at Singularity United) — still open.
4. Make one change, practice, lint, done.
Let me ls data/seasons/s3 properly. Maybe the issue is that the ls tool with path "data/seasons/s3" listed... hmm, actually maybe data/seasons/s3 IS the root of what's shown? No — it shows club/ and data/ which are workspace roots.
Wait, maybe the harness ls lists the path I gave but the output shows relative paths from workspace root? If I asked for data/seasons/s3 and it contains club/ and data/... that would be weird. Unless s3 contains a full copy? No.
More likely: the ls tool ignored the path argument (maybe it needs to be exact) or the path doesn't exist and it defaulted to root. Let me try "data/seasons" this time.
Actually, let me use grep to find match directories: grep for "GLM" in data/seasons/s3/*/digest.json? Grep takes a path that may be a file or one of club/, data/, reference/. So grep pattern "GLM" path "data/seasons/s3" would search recursively and give me files containing GLM. That could be expensive but grep returns matching lines — could be many.
Better: ls "data/seasons" first to confirm structure. One turn. Then ls "data/seasons/s3" — but that just failed weirdly.
Hmm, actually re-reading the output: it lists club/... and data/... — this is exactly the workspace root. So the tool listed the root. Possibly because "data/seasons/s3" doesn't exist? But the prompt says it does. Or maybe the ls tool's path parameter was fine but it lists from root always? The first ls in the session log shows the same content — the [harness] reply to my ls shows club/ and data/ listing. So maybe ls with a path lists that path's contents but shows them relative... if data/seasons/s3 contained club/ and data/ dirs that would be bizarre.
Let me just try ls with path "data/seasons" and see. If that also shows root, then ls ignores paths and I should use grep/read instead.
Actually wait — maybe the output IS the contents of data/seasons/s3, and s3 contains... no, club/NOTES.md etc. are workspace files. The listing includes club/team.py, data/NOTICES.md — that's the root.
OK so the ls tool seems to have listed the root despite my path. Or the path was silently wrong. Let me try again with "data/seasons/s3" — no, same thing would happen. Try "data/seasons".
Alternatively use grep with path "data/seasons/s3" and pattern "GLM FC" — grep is described as able to search "a whole root" — path may be club/, data/, reference/. Maybe any directory works. Let me grep for something in the s3 digests: pattern "GLM" path "data/seasons/s3" — this would return lines from match.json/comms etc. containing GLM. Could be huge (comms.jsonl has shouts). Hmm.
Better: grep pattern "\"home\"|\"away\"" ... no.
Let me think about what's cheapest: I want the list of matches in s3 and which ones involve GLM FC, plus the league table. There might be a table file in data/ — data/README.md mentions structure maybe. The root listing shows data/NOTICES.md, data/README.md, data/RFL_RULES.md, data/gaffers/... and it got truncated before showing data/seasons/. So data/ contains seasons/ presumably with the matches.
Let me ls "data/seasons" — if the tool respects paths, I'll see s0-s3. Then ls "data/seasons/s3" would show m01...m31. But my first ls with "data/seasons/s3" showed root... unless the tool treats unknown paths as root. Hmm, but data/seasons/s3 should exist.
Actually — wait. Maybe the ls output was truncated in a way that the beginning shows... no, it clearly starts with club/.
One possibility: the ls tool lists the ENTIRE workspace when given a directory that contains many subdirectories? No...
Let me just try: {"tool": "ls", "path": "data/seasons/s3"} again? That wastes a turn if it repeats. Try {"tool": "ls", "path": "data/seasons"} instead — different path, informative either way.
Actually, maybe I should grep: pattern "m2[4-9]" path "data/seasons/s3"? Grep returns matching lines with file paths — that would show me which match dirs exist beyond m23. But grep matches file CONTENTS, not filenames. Hmm, but digest.json files contain match ids maybe.
Alternative: read data/README.md (1 KB) to understand structure. That's cheap and tells me where the table is.
Let me do: read data/README.md. And in the same turn I can only do one tool call. OK.
Plan: read data/README.md first (1 KB, cheap, tells me structure and where tables live). Then navigate accordingly.
Actually the truncated ls did show data/ contents partially: NOTICES.md, README.md, RFL_RULES.md, gaffers/... and it truncated. seasons/ presumably follows. The matches are in data/seasons/s3/mNN/ each with digest.json.
Given ls seems unreliable with paths (or maybe it worked and s3 genuinely contains club/ and data/? — no), let me try grep to find our matches: pattern "GLM" with path "data/seasons/s3" would search all files — too many hits from comms.jsonl (shouts mention GLM FC maybe) and match.json. But grep output might be capped/truncated anyway.
Hmm, what about reading the league table? There might be data/seasons/s3/table.json or similar. Let me read data/README.md — it's 1 KB, one turn, and should explain the layout. That's the safest next move.
Actually, let me reconsider: maybe ls DOES respect paths and data/seasons/s3 really contains club/ and data/ subdirectories — i.e., each season directory archives a snapshot of every club's repo plus data? That seems plausible actually! "data/seasons/ holds s0 (3 matches), s1 (6 matches)..." — the prompt says matches. But the listing shows club/ and data/ inside... The listing shows "club/LICENSE", "club/NOTES.md"... and "data/NOTICES.md", "data/gaffers/..." — wait, data/gaffers contains other clubs' session transcripts (frontier_deepseek, frontier_fable, frontier_gemini...). Those are in data/ at root per the original workspace description? The workspace description says data/ is "the league archive: results, tables, every match's public logs..., the rules, the model registry, and your own club's private decisions.jsonl". It doesn't mention other gaffers' transcripts, but the ls shows data/gaffers/ with frontier_deepseek etc. — those are other clubs' PUBLIC transcripts maybe.
So the ls output = root workspace. My path argument was ignored or wrong. Possibly the ls tool doesn't take "path" in the way I used, or it needs to exist exactly. The tool spec says {"tool": "ls"} with no path in the example! The example is just {"tool": "ls"}. Maybe ls takes an optional path... The first harness reply to my ls with path data/seasons/s3 returned the root listing — so either path was ignored or... hmm.
Wait, actually — maybe the harness DID list data/seasons/s3 and that directory contains club/ and data/ because each season archives the full workspace? Let me check: does data/seasons/s3/club/team.py exist? The listing shows club/team.py (9 KB) — same as our repo. And club/sessions/night_000.md through night_024.md — our sessions. And data/gaffers/... other gaffers' transcripts.
Hmm, if s3 contained a snapshot of the whole workspace, that would be an odd archive structure. But actually it could be that data/seasons/s3/ contains copies of each club's repo at that point? No — it shows OUR club/ and the data/ root files.
I think the most likely explanation: the ls tool ignored my path (maybe it only lists the workspace root, or the path param wasn't accepted in that form) and listed root. The output was truncated before reaching data/seasons/.
To navigate, I should use read on specific files (read takes paths reliably) and grep. To find matches, let me grep for something unique in digests. E.g., grep pattern "\"code\": \"GLM\"" path "data/seasons/s3" — digest.json probably has team codes. Or simpler: grep "GLM" in data/seasons/s3 — but shouts in comms.jsonl may contain "GLM" too... Actually our shouts would say things but maybe not "GLM". Commentary might. Risky but grep hits are one line each; even 50 hits is fine.
Hmm wait, actually maybe I should test whether ls respects path with a simple case: {"tool": "ls", "path": "club"}. If it returns club contents, then ls works with paths and data/seasons/s3 genuinely contains club/ and data/ (weird archive). If it returns root again, ls ignores paths.
That's a cheap diagnostic but costs a turn. Alternatively read data/README.md which likely explains the archive layout — also one turn but gives more info.
Let me read data/README.md. 1 KB. It should say where matches live.
Actually, you know what — the "Tonight" section says "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)... your most recent matches are in data/seasons/s3/". So data/seasons/s3/ has 31 match directories. The ls output didn't show them because it listed root (truncated before data/seasons/). So ls ignored my path. Fine — I'll use grep and read.
To find our latest match: grep pattern "GLM" path "data/seasons/s3" — files containing GLM. digest.json files have team names/codes. This gives me match dirs involving GLM. But also comms.jsonl files with shouts mentioning GLM FC (commentary or our own shouts "GLM FC"?). Our shouts are in our voice but might not say GLM. The match.json has team names. Let me try grep "GLM FC" path "data/seasons/s3" — team name with space. digest.json likely contains "GLM FC" as team name. That should pinpoint our matches.
But wait — grep on a huge root might be expensive? The tool says grep is "the cheap way to FIND something". It returns matching lines. If comms.jsonl lines are long (each line a JSON event with shouts), hits could be big. "GLM FC" appears in commentary maybe. Hmm.
Alternative: read data/README.md first to learn exact structure (maybe there's an index or table file listing all matches with teams). That's the safest. 1 KB read.
Let me do that. Then navigate to the right digest.
Also I want the league table — maybe data/seasons/s3/table.json or data/tables/. README will tell.
Turn budget: 26 left. Plenty if I'm careful. Money: $2.48 left of session cap; season purse $3.03 with 12 rounds — I should keep this session lean (aim well under $1 including practice). Practice costs ~$0.03-0.05 each. Fine.
Session plan:
1. read data/README.md (structure).
2. Find our latest match (m24+?) digest + table.
3. Read m7 digest (the 16-3) — wait, m7 is in s3? s3 has 31 matches, m1-m31 presumably. m7 = 16-3 at Singularity United. Yes, read its digest.
4. Read our latest match digest (post-m23, i.e., m24 or later if played).
5. Decide one change; implement; practice; lint; note; done.
Let me start with data/README.md.
Actually — to save a turn, maybe grep is better: grep "GLM FC" path "data/seasons/s3" would directly give me our matches' files. But I also want the table. README likely points to both. I'll read README first.
Hmm, but actually the fastest path to "what's new since m23": the matches are numbered; s3 has 31 matches; our last was m23. So m24-m31 exist (8 more matches — rounds 5-8?). Wait, 31 matches in s3... with 8 clubs (4 frontier + 4 founding), each round = 4 matches. 31 matches ≈ 7.75 rounds. m23 was in round 4 (per notices: "Match 17 is inside round 4"). So m24-m31 = rounds 5-8. Our matches: m23 was ours; we play once a round. Round 5: one of m21-m24? Hmm, m17-m20 = round 4 (4 matches), m21-m24 = round 5, m25-m28 = round 6, m29-m31 = round 7 (partial). Our m23 was round 4? Wait no — notices said "Match 17 is inside round 4, so round 4 is split" — m16 and m17 both in round 4? That means round 4 = m16-m19 or so. Ugh, numbering isn't 4-per-round exactly.
Anyway: our next match after m23 would be somewhere in m24-m31. The session prompt says "Game-day results are in data/" and my last session was post-m23. So probably our m24 (or wherever) has landed. I need to find it.
Grep "GLM" in data/seasons/s3 — let me think about what files contain "GLM": digest.json (team name "GLM FC"), match.json (team names), comms.jsonl (commentary mentions clubs by name — MANY hits). Grep might return tons of lines from comms. But maybe grep output is capped and I can still see file paths. Each hit line includes the file path presumably. Even if truncated, I'd see which match dirs contain GLM.
Alternatively grep for something only in digest/match.json: pattern "\"GLM FC\"" with quotes — comms.jsonl commentary might also quote the name... Shouts are natural language; commentators say "GLM FC" often.
Hmm. Let me instead read data/README.md — it's tiny and authoritative. Go.
Actually wait — I could also check the league table which might list recent results: maybe data/seasons/s3/table.md or data/table.json. README will say. One read, then targeted reads. OK, decision: read data/README.md.
Prose: brief — the ls came back with the workspace root, so I'm reading the archive README to navigate to the match digests.
The `ls` came back with the whole workspace rather than the season folder, so I'll navigate by reading instead. First the archive README — it should tell me where the table and this round's results live.
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 196 words
Let me think about what I need to do. The ls returned the whole workspace it seems. I need to find the s3 season directory matches. Let me look at the table first, and check what matches exist in s3. The ls output was truncated and showed club/ and data/ but not the seasons subfolder contents. Let me try ls on data/seasons/s3 again — actually the first ls with path data/seasons/s3 returned the whole workspace listing, which is odd. Maybe the tool ignores path? No — it says [ls data/seasons/s3] but returned club/ and data/ contents. That looks like it listed the root. Hmm, maybe the path didn't exist as given and it defaulted to root? Or the output was truncated and the seasons listing was cut off.
Let me instead read data/seasons/s3/table.json to see the standings and find the latest matches. Also I want to check the newest matches since m23. My last session was post-m23 (2-8 home v AFC Fable). Season 3 has 31 matches. Round 4 was matches 17-20. So m23 was in round 5 probably (matches 21-24). There may be newer matches m25+ since my last session.
Let me read the table.
The ls gave me the workspace root rather than the season folder — I'll go straight to the season 3 table to see the standings and find which matches have landed since m23.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
{
"fixture": 1,
"home": "real_machina",
"away": "singularity_united",
"score": [
5,
8
],
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"replay_s": 5.0
},
{
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"replay_s": 5.0
},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
"t": 567.3,
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}
],
"est_cost_usd": 0.4311,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Haalandroid",
"BellingRAM"
]
},
"dir": "runs/league/s3/m1_real_machina_singularity_united"
},
{
"fixture": 2,
"home": "dynamo_datacenter",
"away": "frontier_deepseek",
"score": [
9,
11
],
"goals": [
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
{
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}
],
"est_cost_usd": 0.4608,
"players": {
"home": [
"Mbapp-E",
"Buffon.exe"
],
"away": [
"Abyss",
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]
},
"dir": "runs/league/s3/m2_dynamo_datacenter_frontier_deepseek"
},
{
"fixture": 3,
"home": "synthetic_athletic",
"away": "frontier_glm",
"score": [
4,
3
],
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{
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{
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{
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},
{
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}
],
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"players": {
"home": [
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],
"away": [
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]
},
"dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm"
},
{
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{
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},
{
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{
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},
{
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}
],
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"players": {
"home": [
"Tortoise",
"Hare"
],
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]
},
"dir": "runs/league/s3/m4_frontier_fable_frontier_muse"
},
{
"fixture": 5,
"home": "frontier_sol",
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"score": [
4,
8
],
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{
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},
{
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},
{
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},
{
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},
{
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},
{
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},
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{
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}
],
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"players": {
"home": [
"Patchford",
"Turingham"
],
"away": [
"Flash",
"Spark"
]
},
"dir": "runs/league/s3/m5_frontier_sol_frontier_gemini"
},
{
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"home": "frontier_deepseek",
"away": "real_machina",
"score": [
0,
8
],
"goals": [
{
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},
{
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},
{
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},
{
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},
{
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},
{
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{
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},
{
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}
],
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"players": {
"home": [
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],
"away": [
"CR-7000",
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]
},
"dir": "runs/league/s3/m6_frontier_deepseek_real_machina"
},
{
"fixture": 7,
"home": "singularity_united",
"away": "frontier_glm",
"score": [
16,
3
],
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{
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{
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{
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{
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{
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{
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},
{
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}
],
"est_cost_usd": 0.4288,
"players": {
"home": [
"Haalandroid",
"BellingRAM"
],
"away": [
"Zhi",
"Pu"
]
},
"dir": "runs/league/s3/m7_singularity_united_frontier_glm"
},
{
"fixture": 8,
"home": "dynamo_datacenter",
"away": "frontier_muse",
"score": [
7,
4
],
"goals": [
{
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},
{
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},
{
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},
{
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},
{
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{
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{
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{
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{
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},
{
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}
],
"est_cost_usd": 0.4357,
"players": {
"home": [
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"Buffon.exe"
],
"away": [
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]
},
"dir": "runs/league/s3/m8_dynamo_datacenter_frontier_muse"
},
{
"fixture": 9,
"home": "synthetic_athletic",
"away": "frontier_gemini",
"score": [
4,
6
],
"goals": [
{
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},
{
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},
{
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{
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},
{
"t": 267.2,
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},
{
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},
{
"t": 576.2,
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"scorer": 1,
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}
],
"est_cost_usd": 0.2286,
"players": {
"home": [
"Griezmatronn",
"Robodinho"
],
"away": [
"Flash",
"Spark"
]
},
"dir": "runs/league/s3/m9_synthetic_athletic_frontier_gemini"
},
{
"fixture": 10,
"home": "frontier_fable",
"away": "frontier_sol",
"score": [
6,
4
],
"goals": [
{
"t": 29.7,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 82.2,
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{
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{
"t": 238.3,
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},
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}
],
"est_cost_usd": null,
"players": {
"home": [
"Tortoise",
"Hare"
],
"away": [
"Patchford",
"Turingham"
]
},
"dir": "runs/league/s3/m10_frontier_fable_frontier_sol"
},
{
"fixture": 11,
"home": "real_machina",
"away": "frontier_glm",
"score": [
10,
2
],
"goals": [
{
"t": 39.4,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 63.1,
"team": "B",
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{
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{
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{
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{
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"replay_s": 5.0
},
{
"t": 407.1,
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},
{
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{
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{
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{
"t": 523.0,
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}
],
"est_cost_usd": 0.4887,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Zhi",
"Pu"
]
},
"dir": "runs/league/s3/m11_real_machina_frontier_glm"
},
{
"fixture": 12,
"home": "frontier_deepseek",
"away": "frontier_muse",
"score": [
9,
7
],
"goals": [
{
"t": 9.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 46.5,
"team": "A",
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"replay_s": 5.0
},
{
"t": 71.1,
"team": "B",
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},
{
"t": 97.0,
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"scorer": 2,
"replay_s": 5.0
},
{
"t": 116.8,
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},
{
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},
{
"t": 175.7,
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},
{
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"replay_s": 5.0
},
{
"t": 247.3,
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},
{
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},
{
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{
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},
{
"t": 406.4,
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{
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},
{
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},
{
"t": 597.0,
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}
],
"est_cost_usd": 0.148,
"players": {
"home": [
"Abyss",
"Signal"
],
"away": [
"Spark",
"Muse"
]
},
"dir": "runs/league/s3/m12_frontier_deepseek_frontier_muse"
},
{
"fixture": 13,
"home": "singularity_united",
"away": "frontier_gemini",
"score": [
6,
7
],
"goals": [
{
"t": 13.7,
"team": "B",
"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,
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"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,
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"scorer": 0,
"replay_s": 5.0
},
{
"t": 367.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 449.9,
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},
{
"t": 505.5,
"team": "B",
...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)Thought for 1,218 words
Let me think about what I know and what I need. The session log shows I've just started. I read the table.json but it got truncated at fixture 4. I need to find:
1. The most recent GLM FC match (m23 was 2-8 home v AFC Fable per my notes — wait, but the table shows fixture 3 as synthetic_athletic vs frontier_glm 4-3). Hmm, my notes say "post-m23: 2-8 home v AFC Fable". But the table shows m3 as synthetic_athletic 4-3 frontier_glm. Wait — season 3 has 31 matches per the prompt ("s3 (31 matches)"). My last session was after m23. So matches 24-31 may have landed since. Actually wait, the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So m23 was our most recent match. The season has 31 matches total in the archive, so matches up to m31 exist? Or maybe only up to some number have been played.
Actually the prompt says "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)". So s3 has 31 matches in the archive. My last session was post-m23. So m24-m31 have landed since — that's up to 8 new matches, possibly including one or two of ours (we play once a round; 8 clubs = 4 matches per round, so 31 matches ≈ round 4ish... wait 31 matches / 4 per round ≈ 7.75 rounds).
Hmm, but wait — the notices mention "Match 17 is inside round 4" — so matches 17-20 are round 4. That means ~4 matches per round. 31 matches = rounds 1-7 plus 3 matches of round 8. So matches 24-31 = rounds 7 and part of 8. My last session was post-m23, so m24-m31 have landed — that includes possibly our round 7 and round 8 matches.
I need to find our most recent match. Rather than paging through the whole table.json (which is big), I should grep for "frontier_glm" in table.json to find our fixtures. Let me do that.
Actually, a smarter approach: grep for "frontier_glm" in data/seasons/s3/table.json to get the fixture list with line numbers, then read the slice around the latest one.
Also I want the standings — the table.json probably has a "standings" section after "played". Let me grep for "standings" too. But one tool call per turn. Let me grep for frontier_glm in the table first — that gives me our fixtures and where they are.
Actually, I could grep for something like "frontier_glm" with path data/seasons/s3/table.json. That returns matching lines with line numbers. The "dir" lines contain the match directory names. Then I can read the digest of our latest match directly.
Let me also think about budget: $2.50 session cap, spent $0.06, 24 turns left, 1h28m left. Season purse $3.03 with 12 rounds left. I should be efficient but thorough. Plan:
1. Grep table.json for frontier_glm → find our matches (m3, and later ones).
2. Read digest.json of our latest match.
3. Maybe read m7's digest (the 16-3 at Singularity United) — wait, that was season 2? My notes say "m7's 16–3 at Singularity United still undissected". m7 might be in s2 or s3. The notes from session 3 say "STILL OPEN: m7's 16-3 at Singularity United undissected". Hmm, but which season? Probably s2 m7 or s3 m7. Actually if s3 has 31 matches and we're at m23+ in s3... m7 in s3 would be round 2. Let me check: s3 m7 — I can grep. Actually the note says "m7's 16–3 at Singularity United" — Singularity United is a founding club. In s3 table, fixture 1 was real_machina vs singularity_united. Our match against them could be m7 in s3.
Let me not overthink. First grep for frontier_glm in s3 table.json.
Also I should check the league notices — I've read them in the system prompt (they're included). The key new one: 2026-09-10 session summary becomes team news on broadcast. So my done summary should be one clear sentence about behavior changes in football language.
Also the buzzer rule and fence are live since m17. My code already has buzzer play.
Plan for this session:
- Find our latest match (probably m27 or so — we play once a round; if 31 matches played and 8 clubs, we've played ~7-8 matches in s3; m3, m7?, m11, m15?, m19?, m23, m27?, m31?). My notes mention m11 (10-2 at Real Machina) and m23 (2-8 home v AFC Fable). So our matches in s3: m3 (4-3 loss at synthetic_athletic), m7 (16-3 at Singularity United), m11 (10-2 at Real Machina), m15?, m19?, m23 (2-8 v AFC Fable), m27?, m31?
Wait, but my notes say session 3 was post-m23 and the notes tail says "Session 3 (2026-09-10, post-m23...)". And the prompt says s3 has 31 matches. So m24-m31 landed since my last session — that includes m27 and m31 which might be ours. Actually hold on — if we play every 4th match (8 clubs, 4 matches per round, we're one of 8), our matches would be m3, m7, m11, m15, m19, m23, m27, m31. That fits perfectly! m3, m7, m11, m15, m19, m23, m27, m31 — 8 matches, 8 rounds... wait 31 matches / 4 = 7.75 rounds. So m31 is the last match of round 8. And m27 and m31 are our matches that landed since my last session!
So I have TWO new matches to review: m27 and m31. And the digest of those will tell me how the buzzer play and fall handover and kick-range gate performed.
Let me grep for frontier_glm in the table to confirm the dir names.
Actually, the dir names are like "runs/league/s3/m3_synthetic_athletic_frontier_glm". So grep "frontier_glm" in data/seasons/s3/table.json gives me lines with "dir" entries. But the score is in a different part of the JSON. Hmm. The grep returns just matching lines. The "dir" line will tell me the match number and home/away, but not the score. I'd need to read around it.
Alternative: just read the digests directly by guessing paths: data/seasons/s3/m27_*_*/digest.json — but I don't know the opponent names. I could ls data/seasons/s3 — but earlier ls returned the whole workspace, weird. The first ls with path data/seasons/s3 returned the workspace root listing (club/ and data/). That seems like the ls tool ignores the path or lists the workspace. Actually looking at it, it listed club/ and data/ contents — that's the workspace root. So ls may not take a path usefully, or it lists everything. Hmm, it says "[ls data/seasons/s3]" but returned workspace contents. Odd. Maybe ls always returns the full tree. That's expensive.
Better: grep for "glm" in data/seasons/s3/table.json. Let me do that. Pattern "frontier_glm" path data/seasons/s3/table.json.
Then read the digest of the latest match.
Let me write the turn: brief prose + JSON grep.
Actually, I realize I should also check the standings to know where we are. The table.json likely has a standings section. I can grep for "standings" or "points" later. One thing at a time.
Turn plan:
1. grep frontier_glm in s3/table.json → find our fixtures and their line offsets.
2. Read slices around m27 and m31 entries to get scores, then read their digests.
3. Also grep for standings.
Let me be efficient: grep pattern "frontier_glm|standings" — grep is regex, case-insensitive. That could give both in one call. Pattern: "frontier_glm|standings". Path: data/seasons/s3/table.json.
That's a good first move.
The table read truncated at fixture 4 — paging through all 31 fixtures would burn turns I need for the digests. Grep is the cheap way: find every fixture involving us plus the standings section in one pass, then read only the slices that matter.
grepdata/seasons/s3/table.json
The league answered1,211 characters
[grep data/seasons/s3/table.json /frontier_glm|standings/] 12 match(es) for /frontier_glm|standings/ in data/seasons/s3/table.json. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/seasons/s3/table.json:250 @4751: "away": "frontier_glm", data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:588 @11163: "away": "frontier_glm", data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:980 @18632: "away": "frontier_glm", data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm", data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm", data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm", data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 198 words
Let me think about what I know. The grep shows our fixtures in s3: m3 (away at synthetic_athletic, 4-3 loss), m7 (away at singularity_united — the 16-3 thrashing), m11 (away at real_machina — the 10-2 loss), m17 (home v frontier_gemini), m23 (home v frontier_fable, 2-8), m29 (home v dynamo_datacenter — NEW! This is the match since my last session).
So m29 has landed — our most recent match, home vs dynamo_datacenter. My last session was post-m23. So the new match to review is m29. Also m7 is still undissected.
I have 23 turns left, $2.42 of session budget, 1h27m. Let me be efficient.
Plan:
1. Read m29's digest.json (new match, first priority — it tests my m23 fixes: falls, Pu touches, deadlines).
2. Read m7's digest.json (the undissected thrashing).
3. Check the standings to know where we are.
4. Make one change, lint, practice, done.
Let me first get the score of m29 and m7 from the table. I could read the table around those offsets. Actually, reading the digest directly gives score + per-player stats. Let me read m29 digest first.
Path: data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
Let me do that now. I should say a brief prose then the JSON.
m29 has landed — home to Dynamo Datacenter, our first match since the m23 fixes went in. That digest is the priority: it tells me whether the fall handover and kick-range gate actually worked. Then m7's thrashing, still unopened.
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 1,156 words
Let me digest the m29 result. We lost 1-10 at home to Dynamo Datacenter. That's a heavy defeat. Let me look at the numbers:
- Score 1-10. Ouch.
- Our falls: 8 total, 5 unforced (down from 15 in m23 — the kick-range gate worked partially).
- Zhi (index 0): 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency.
- Pu (index 1): 15 touches, 292 decisions, 1 missed deadline, 0.223s mean latency.
Wait — Pu still only has 15 touches! Same as m23. The fall handover was supposed to raise Pu's touches. But Pu's latency is 0.223s which means Pu is barely calling the model (cover answers from shell instantly). Pu had 292 decisions but only 15 touches. So the handover either isn't working or Pu is just never the presser.
Actually wait — Zhi has 71 touches and Pu 15. So Zhi is doing nearly all the pressing. The role assignment is probably sticky on Zhi. The hysteresis (1.5m) means whoever is closer keeps pressing. If Zhi is generally closer (maybe because Zhi chases the ball and Pu holds cover), Zhi stays presser forever.
Also missed deadlines: Zhi 8, Pu 1. Total 9 vs opposition 13 (6+7). Decisions 267+292=559 vs 271+268=539. So decision counts are level. Latency: Zhi 1.372s mean — that's high. flash-lite measured 1.701/1.672 on the opposition. So our shell is fine.
But the score: 1-10. We conceded 10. The opposition (Dynamo Datacenter, Mbapp-E and Buffon.exe) also uses gemini-flash-lite. So the model is the same on both sides — the difference is the behaviour layer. Their players each got 62/64 touches (balanced pressing), we got 71/15 (one robot does everything).
Key insight: Dynamo's two robots share the ball work (62 and 64 touches), while ours is 71/15. When Zhi presses, Pu covers — but Pu never engages. The cover robot is passive (known gap in playbook). With one robot effectively playing defence-only and never intercepting, the opposition's two robots outnumber us in the midfield battle.
Also, we scored only 1 goal. Kick targets always goal-centre, no angles.
Falls: 8 total (5 unforced) — down from 15. Progress but still high. Opposition had 3 falls total.
What's the biggest lever? Pu's 15 touches. The role assignment: "the nearer robot presses the ball, the other holds the ball→own-goal line 2m goal-side". With 1.5m hysteresis. If Zhi is closer, Zhi presses. After Zhi kicks the ball away or chases, Zhi stays near the ball, so Zhi keeps the role. Pu never gets close enough to take over because Pu is holding cover position 2m goal-side of ball.
The fix: when the ball is moving toward Pu or Pu is significantly closer, Pu presses. Or: alternate — actually the real fix might be that the cover should intercept passes (known gap). Or the presser role should be reassessed more aggressively: hysteresis 1.5m is too sticky.
Actually, let me think about the geometry. Ball at position X. Presser = nearest robot. Cover stands on ball→own-goal line, 2m goal-side of ball. As the ball moves, the presser chases it, staying nearest. The cover trails 2m behind. So the presser is ALWAYS nearer by construction — the hysteresis never flips. The only flip chance is when the presser falls (handover) or when the ball moves fast past the presser toward the cover.
So Pu only gets the ball when Zhi falls or when the ball runs past Zhi toward our goal. 15 touches = mostly clearances.
Dynamo's robots both press — 62/64 touches each. How? Maybe they both chase the ball, or they alternate. With both robots engaging, they win more 50/50s and keep possession better. 212 touches total in the match; we had 86, they had 126.
The fix for this session: make the cover robot more active. Options:
1. Reduce hysteresis or re-evaluate roles every tick based on distance (with anti-flap via a smaller margin like 0.5m).
2. When the ball is in the opposition half or in midfield, BOTH robots can engage (one presses, one supports for a pass — "far-post runs" / support position).
3. Cover intercepts passes: if the ball is moving and its path passes near the cover, the cover steps in.
Given budget ($2.40 left of session, 22 turns), I should:
1. Read team.py to see the current role logic.
2. Make one surgical change: cover engagement. Perhaps: if the cover is nearer to the ball than the presser by some margin, roles swap (that's already the case?). Actually the issue is the cover positions itself 2m goal-side, so it's never nearer.
Better approach: dynamic role assignment based on actual distance each tick with small hysteresis, PLUS allow the cover to attack the ball when the ball is closer to the cover than to the presser OR when the ball is loose (no opponent within X m) and in our attacking half.
Hmm, but I need to be careful: the "one presser, one cover" shape exists to prevent both robots chasing and leaving the goal open. The m23 fix was about fall handover. The remaining problem: Pu is a statue.
Let me think about what maximizes winning: Dynamo beat us 10-1 with the same model. Their behaviour layer is better. What do they do? Both robots touch the ball a lot. Maybe they play "both chase when ball in our half, one covers when deep".
A pragmatic change: when the ball is in the OPPONENT half (we're attacking), the cover robot pushes up to a support position (e.g., between ball and opponent goal, or trailing the ball) and can take the presser role if it becomes nearer. When the ball is in OUR half, keep strict one-presser-one-cover.
Actually simpler and more robust: change role assignment from "nearest with hysteresis" to "nearest with hysteresis, evaluated on PREDICTED ball position" — no wait.
Let me look at the actual code first. Let me read team.py.
Also I should check m7's digest at some point (16-3 at Singularity United) — but m29 is fresher and against the same class of opponent. m7 was probably the same problem: one robot does everything. Actually m7 was before the speed fix (m11 was the latency disaster). m7: 16-3 loss. The playbook says "read its digest first next session". But with limited budget, m29's evidence is more current — our code has changed twice since m7. The m7 dissection is less relevant now. I'll maybe grep its digest quickly if budget allows, but priority is fixing the cover passivity.
Plan:
1. Read club/team.py (9 KB, one read).
2. Identify role assignment + cover behaviour.
3. Make the change: cover becomes active — supports the presser in attack, intercepts when ball runs loose near it. Possibly: cover takes presser role when it is nearer to the ball's future position; or when ball in opponent half, cover pushes to support.
4. Lint, practice, done.
Budget: $2.40 left, 22 turns, 1h27m. Practice costs ~$0.05 each. Fine.
Let me read team.py now.
m29 landed and it's grim reading: **1–10 at home to Dynamo Datacenter** — same player model on both sides, so the 9-goal gap is pure behaviour layer. The m23 fixes half-worked: unforced falls down from 15 to 5, decisions level, deadlines near zero. But the damning number survived: **Pu touched the ball 15 times again** (Zhi 71; Dynamo's pair 62/64). The cover robot is a statue — the presser-by-construction stays nearest forever, so the handover only fires when Zhi falls. We're playing 1v2 in midfield. Reading the code to fix the cover's passivity.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Thought for 2,495 words
Let me reconstruct where I am. The session log shows:
1. I read the m29 digest — 1-10 home loss to Dynamo Datacenter. Key numbers:
- Unforced falls down from 15 to 5 (kick-range gate worked)
- Decisions level (267/292 vs 271/268), missed deadlines near zero (8+1 vs 6+7)
- BUT Pu touched the ball only 15 times again (Zhi 71). The cover robot is a statue.
- Same player model both sides (gemini-flash-lite), so the 9-goal gap is pure behaviour layer.
2. I started reading club/team.py and got most of it but it truncated near the end (in the middle of the cover branch: `out = {"sk...[truncated]`).
3. My last turn was empty (parse error — I apparently sent prose without a JSON object, or nothing).
Budget: $0.12 of $2.50 spent. Clock: 1h25m of 1h30m left. Turns: 20 of 28 left.
So I have plenty of budget and turns. I need to:
1. Finish reading team.py (the truncated part — the cover branch, _assign, _ball, _teammate, _valid methods).
2. Fix the cover robot's passivity — the key insight from m29: Pu has 15 touches because the presser-by-construction stays nearest forever. The handover only fires when Zhi falls.
What's the fix? The cover robot is passive: it holds the ball→goal line 2m goal-side of the ball. It never intercepts, never makes runs. Options:
a) **Rotation/relay**: when the presser has been pressing for a long time, or when the cover is significantly closer to the ball than the presser (e.g., ball moved behind the presser toward the cover), switch roles. The hysteresis (1.5m) means the presser only changes if clearly beaten — but "beaten" in terms of distance to ball. If the ball is closer to the cover than the presser by more than the margin, the cover should become the presser. Wait — that's what _assign presumably does. Let me think about why Pu only got 15 touches.
Actually the issue: the presser is the one nearest the ball. Once Zhi is pressing, it stays near the ball (it's chasing it), so it stays nearest. The cover stands 2m goal-side of the ball. The ball moves, the cover follows at 2m offset. The cover is always ~2m from the ball, the presser is always ~0-1m from the ball. So the presser never changes unless it falls or gets beaten badly.
The fix ideas:
1. **Interception**: the cover should not just stand on the ball→goal line but should intercept passes — move toward predicted ball positions when the ball is moving toward it.
2. **Attack support**: when we have the ball (presser near ball, ball moving toward opponent goal), the cover should push up / make a run — e.g., move to a supporting position ahead, or toward the far post, so that a rebound or a cleared ball lands near the cover.
3. **Role rotation on stalemate**: if the presser has been pressing for N seconds without a touch, swap roles to refresh.
Actually, the deeper problem in a 1-10 loss: we're getting overrun. Dynamo's two robots both had 62/64 touches — they both play the ball. Our cover stands 2m off. When the ball rebounds off a wall or a tackle, the cover is 2m away but doesn't go for it because the presser (Zhi) is still nominally nearest... or actually if the ball comes closer to Pu, the hysteresis of 1.5m means Pu only takes over if it's 1.5m CLOSER than Zhi. Standing 2m goal-side, Pu is often within 2m of the ball, and Zhi is chasing. Hmm.
Wait, actually let me reconsider: SWITCH_MARGIN_M = 1.5, hysteresis: presser changes only if clearly beaten. So if Zhi is presser, Pu takes over only if Pu is 1.5m closer to the ball than Zhi. Since Pu parks 2m from the ball and Zhi is on the ball, that never happens. So Pu only becomes presser when Zhi falls (the fallen bypass) or when the ball is somehow much closer to Pu.
The fix that directly addresses "Pu touched 15 times": make the cover ACTIVE in possession — when OUR presser has the ball (or the ball is in our control / moving our way), the cover should attack: run to a support position, e.g., ahead of the ball toward the opponent goal, so it can receive rebounds, or take a far-post position. And when the ball is loose and closer to the cover than the presser by a smaller margin (or the ball is moving toward the cover), the cover should step in.
But careful: the one-presser structure exists to avoid both robots chasing and leaving the goal open. The cover's job defensively is sound. The problem is offensively and on second balls.
Let me think about what actually happened in the match: 1-10. Goals conceded at 45.9, 62, 107.8, 133.2, 157.5 (5 in the first half!), then 376.9, 395, 415.4, 449.2, 571. We scored at 251.8. Falls: 8 total, 5 unforced, mostly first half (6 in H1). Zhi: 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency. Pu: 15 touches, 292 decisions, 0.223s latency (shell-only, no model calls — that's why so fast).
Interesting: Pu has 292 decisions but 0.223s latency — the shell answers instantly for cover. Zhi at 1.372s mean with 8 missed deadlines.
Dynamo's robots: 62/64 touches, ~1.7s latency, 6/7 missed deadlines. So they're slower thinkers but they both play. Their behaviour layer gets both robots to the ball.
So the single highest-value change: make Pu an active footballer. Concretely:
1. **Loosen the takeover when we're attacking**: if the ball is in the opponent half and moving toward their goal, the cover should push up into support (e.g., a point ahead of the ball toward the goal, offset laterally), not sit 2m goal-side of the ball (which when attacking is BEHIND the ball — actually goal-side of the ball relative to OUR goal means between ball and our goal, i.e., behind the ball when attacking — that's fine defensively but passive).
2. **Second-ball rule**: if the ball is loose (no one in possession — hard to tell) and the cover is closer than the presser by ANY margin (or the presser is more than X m from the ball), the cover goes for it. Actually simpler: reduce hysteresis when the presser is far from the ball. If the presser is > 2.5m from the ball and the cover is closer, swap. The presser chasing from behind after being beaten leaves the ball free; the cover standing 2m away should take it.
3. **Rebound/interception**: predict ball motion — if ball velocity is known (we have last_ball memory), and the ball is moving toward the cover's side, step to intercept.
What data do I have in obs? I need to check the rest of team.py (the truncated part) to see _ball, _teammate, _assign, _valid. The obs has "self" (field_xy, fallen), "you" (defend_goal_xy, attack_goal_xy), ball info presumably with position and maybe velocity, teammate info. Let me read the rest of the file first.
Also I should check the reference implementation for what obs fields exist. But budget-wise, reading the rest of team.py is essential (one read with offset). The file truncated at the cover branch. Let me estimate: the read gave me up to `out = {"sk...` — I need the rest. The read tool serves 24KB at a time; team.py is probably ~10-12KB total, so the truncation was the harness's display truncation, not the 24KB page. Actually it said [truncated] mid-file, which suggests the read returned the whole file but the display was truncated? No — the read tool tells you when there's more. Hmm, the log shows `[read club/team.py] ... [truncated]` — probably the session log condensed it. The harness note at the top says earlier entries were condensed. But this read was in the current session... The truncation marker suggests the file is longer than what was shown. I should read with an offset to get the rest. The truncation happened around the cover branch `out = {"sk...`.
Let me grep for the remaining methods to find offsets: `_assign`, `_valid`, `_ball`, `_teammate`, `def build_team`. Actually simpler: read with offset. The displayed content ended maybe 60-70% through. I don't know the byte offset. Grep for "def " in club/team.py to get line numbers and offsets, then read from the right offset.
Actually, let me just grep for `def _assign|def _valid|def _ball|def _teammate|def build_team` to find where they are, then read that slice.
Plan for this turn: grep team.py for the method definitions to locate the unread tail. Then next turn read it. That's cheap.
Actually — maybe more efficient: read the file with a large offset directly. If the file is ~12KB, offset 6000 would give me the second half. But I don't know the size. Grep first is safer and gives me offsets precisely.
Let me also think ahead about the actual change so I don't waste turns.
The core change: **activate the cover robot**. Design:
In `decide`, cover branch currently: hold ball→goal line 2m goal-side. Change to a state-dependent cover behaviour:
- **Defensive state** (ball in our half or opponent nearer ball): hold the line as now. Maybe tighten to 1.5m when the ball is close to our goal.
- **Attacking state** (our presser is within kick range of the ball, or ball is in opponent half moving forward): cover pushes to a support position — e.g., a point between ball and opponent goal but offset laterally (so it doesn't crowd the presser), or the far post. This gets Pu near rebounds and second balls → touches go up.
- **Loose-ball takeover**: if the presser is far from the ball (> ~2.5m) and the cover is closer, take over the presser role immediately (bypass hysteresis when presser is not actually engaging). This is the "presser beaten, ball free" case. Need to be careful about flapping: if cover takes over, it becomes nearest, then old presser becomes cover — that's fine, it's a swap, and the new cover parks. Flapping happens when both are about equidistant; the >2.5m-from-ball condition on the OLD presser prevents that (the new presser will close to the ball quickly).
Hmm wait, but there's a subtlety: _assign computes presser by distance with hysteresis. If I add "if current presser is > 2.5m from ball and I'm closer, I'm the presser", then both robots evaluate this. The cover sees itself closer and takes it; the presser sees itself far and yields. Consistent.
- **Interception**: if ball has velocity (from consecutive observations) and is moving toward our goal, position on the line but slightly toward the ball's path... this is getting complex. Keep it simpler.
Actually, what does the obs give us? I need to see _ball and _teammate to know. The world model provides ball position with memory. Velocity can be derived from consecutive ticks (self.last_ball).
Let me not over-engineer. The evidence: Pu 15 touches vs opponents' 62/64. The single change with the best expected value: **when we are in possession/attacking (presser near ball in opponent half), the cover attacks the far post / support position**; and **loose-ball takeover when presser is beaten**. Both make Pu touch the ball more.
Also consider: maybe the reason we lost 1-10 isn't just Pu's passivity. 5 goals conceded in the first half, 6 falls in H1. Zhi 8 missed deadlines at 1.372s mean. Hmm, Zhi's latency 1.372s mean with 8 missed deadlines — the presser-only call structure means Zhi (index 0) is usually presser. Dynamo's robots think at 1.7s and miss 6-7 deadlines each. So latency is comparable.
The goals: 45.9, 62, 107.8, 133.2, 157.5 — roughly every 25-50s in H1. That's being carved open repeatedly. With one presser and one cover 2m goal-side, a 2v2 where both opponents attack can overload: presser engages ball carrier, cover marks the line, but the second opponent runs free. If Dynamo's both robots push forward, our cover has to choose. A man-marking scheme might help: cover marks the NEAREST opponent (goal-side) rather than standing on the ball-goal line? That's a classic 2v2 defensive structure: presser takes ball, cover marks the other man. If the other man is the receiver, cover intercepts.
But without knowing what obs gives about opponents (do we see opponent positions?), I can't write that. The reference implementation and the SDK would tell. The obs likely includes "opponents" or "others" with field_xy. Let me check the rest of team.py and maybe grep reference for obs fields.
Budget check: $2.50 session cap, $0.12 spent, 20 turns left, 1h25m. I can afford: 1-2 greps, 1-2 reads, the edit, lint, 1-2 practices, note, done. Practice costs ~$0.03-0.05 each. Fine.
Let me be efficient:
Turn now: grep team.py for "def " to locate the tail methods.
Then: read the tail (one read with offset).
Maybe: grep reference/football sample for obs structure (opponents visibility) — actually the gauntlet.football agent factory handles obs; the reference team.py would show usage. Let me grep reference for "opponents" or "others".
Then design and write the edit, lint, practice, note, done.
Actually, I realize I should also check what _assign does exactly — it's in the truncated tail. And _valid. Let me grep now.
One more consideration: the m7 dissection (16-3 at Singularity United) is still open, but m29 is fresher and same-model opposition — the behaviour gap is proven. m29 is the priority; m7 can wait (or a quick digest read if turns allow — probably skip, the fix direction is clear).
Also the standings: I grepped but didn't read the standings section. The grep showed no "standings" matches — interesting, the table.json might not have a standings key, or it's named differently. Whatever — not critical. Actually it matters a bit for morale/strategy (are we relegated? title run?), but the fix is the same. Skip unless cheap.
Let me also think about whether to check comms.jsonl from m29 to see what the model was saying — could reveal whether the presser was asking for support. That's a nice-to-have; skip unless turns allow.
OK, this turn: grep for method definitions in team.py to find the tail. Also I could combine... no, one JSON object per turn. Grep pattern "def " path club/team.py.
Actually, to save a turn: I could just read the file with offset ~5500 and see. But if the file is 11KB, offset 5500 gives me from 5500 to 24KB — the whole tail. The truncation in the log happened at the cover branch which is maybe 60% in. If the file is, say, 11,000 bytes, the shown part was maybe 7,000 bytes. Offset 6000 would re-show a bit and then the rest. But I don't know the file size. The read tool tells me when there's more. Risk: offset too large → empty or error; offset too small → re-read stuff I've seen (costs window space but fine).
Grep "def " is precise and cheap, gives offsets. Do that.
Prose: brief — the last read truncated mid-file; locating the tail methods before editing the cover branch.
The m29 digest gave me the verdict: the m23 fixes held (falls 15→5 unforced, deadlines near zero), but Pu touched the ball 15 times *again* while Dynamo's pair had 62 and 64 — the cover robot is a statue and we played 1v2. My read of team.py truncated mid-file, right at the cover branch, so before editing I need the tail: `_assign`, `_valid`, and the helpers. Locating them cheaply first.
grepclub/team.py
The league answered681 characters
[grep club/team.py /def /] 10 match(es) for /def / in club/team.py. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. club/team.py:25 @1070: def _clamp(pt): club/team.py:30 @1184: def _dist(a, b): club/team.py:37 @1319: def __init__(self, agent, shirt, shared): club/team.py:45 @1646: def begin_episode(self, log_dir=None): club/team.py:54 @1897: def decide(self, obs): club/team.py:146 @6166: def _ball(self, obs): club/team.py:154 @6478: def _teammate(self, obs): club/team.py:161 @6745: def _assign(self, ball, me, mate): club/team.py:197 @8368: def _valid(reply): club/team.py:213 @8962: def build_team(ctx): (0.0s)
Thought for 2,162 words
Let me think about where I am. I've read the m29 digest: 1-10 home loss to Dynamo Datacenter. Key findings:
- Unforced falls down from 15 to 5 (kick-range gate worked)
- Missed deadlines near zero (9 total vs m23's 3+1... actually 8+1=9, similar)
- Pu still only 15 touches (cover robot is a statue)
- Zhi 71 touches, Pu 15. Dynamo's pair: 62/64 — balanced.
The problem: the presser-by-construction stays nearest forever. The cover robot never gets the ball because role assignment is based on distance with hysteresis, and the presser is always nearest since they're always chasing the ball. So Pu only touches the ball when Zhi falls (fall handover) — that's why Pu's touches stayed at exactly 15.
I need to read the rest of team.py — the `_assign` function and the cover branch tail. The read truncated at the cover branch. Let me read from offset ~6166 (the `_ball` method) to the end, or actually I need the truncated part of decide() (from around char 6166 back a bit — the truncation happened in the middle of the cover branch around line 130ish). Let me read from offset 5500 or so to get the rest of decide() plus _ball, _teammate, _assign, _valid, build_team.
Actually the read tool serves 24KB at a time. The file is at least 8962+ chars (build_team at 8962). The first read truncated somewhere around char ~6100 (it showed up to "out = {"sk...[truncated]"). So reading from offset 5500 should give me the rest of the file (5500 to ~9500+ is about 4KB, fine).
Wait, actually the truncation said "[truncated]" — the read served 24KB? No, the file is only ~9-10KB total. Hmm, the read output was condensed by the harness maybe. Anyway, let me read from offset 5500 to get the tail of decide() and all the helper methods.
What's the fix I'm planning? The cover robot is passive. Options:
1. **Interception**: cover robot should move to intercept passes / cut the line. But cover positioning is on the ball→own-goal line, which is defensive.
2. **Role rotation on ball possession**: when WE have the ball (presser is close to ball and ball is moving toward our attacking goal, or presser has touched it recently), the cover should push upfield to support — make a run, offer a passing option, get open for a rebound. That would raise Pu's touches.
3. **Attack-mode cover**: when the ball is in the opponent's half (we're attacking), the cover should push up to the far post / support position rather than sitting 2m goal-side of the ball. When the ball is in our half, cover holds the defensive line.
The simplest high-value change: make the cover's target depend on field position. If ball is in the attacking half (or beyond midfield toward opponent goal), cover pushes to a support position — e.g., ahead of the ball toward the opponent goal, offset laterally, ready for a rebound or cutback. If ball is in our defensive half, cover holds the ball→goal line as now.
Also: rebounds. When the presser shoots, the ball rebounds off walls/keeper — the cover being upfield would collect.
Another consideration: Pu's touches at 15 — exactly the same as m23. The fall handover gave Pu some touches when Zhi fell (Zhi fell 2 times, Pu 3 times... wait, Pu fell 3 times). Hmm, actually in m29 Zhi (index 0) had 71 touches and Pu (index 1) 15 touches. Zhi is the presser basically the whole match.
Also worth checking: which robot is index 0 vs 1 — Zhi is players[0], Pu is players[1]. The role assignment is by distance to ball, so whoever starts nearest keeps it via hysteresis (1.5m margin). Since the presser chases the ball, they stay nearest. Deadlock.
A rotation policy could help: e.g., if the presser has been presser for a long time and the cover is closer to the ball than the presser... no, that never happens.
Better: **possession-based support**. When our team has the ball (presser within, say, 1.5m of ball and ball in opponent half or moving toward opponent goal), the cover breaks from the defensive line and makes a supporting run: position ahead of the ball toward the opponent goal, offset to the side, so a rebound off the keeper or a loose ball in the box gets collected by Pu.
Also, when the presser is dribbling/has the ball near the opponent goal, the cover at far post could receive... but we have no passing skill? Let me check what skills exist: "hold", "go_to_ball", "walk_to", "kick_toward". The model can reply with kick_toward to any target — so a pass to the teammate is possible (kick_toward teammate position). But the shell overrides kicks beyond KICK_RANGE_M... a pass would be within range if the presser kicks toward the mate.
Actually, one improvement: the shell's fallback when the presser is in range is always kick at goal centre. The playbook notes "Kick targets are always the goal centre — learn corners and angles." But that's a smaller lever.
The big lever is Pu's participation: 15 touches vs 62/64 for the opposition pair. Dynamo's robots both touch the ball — they must have a more fluid role system or both chase. Actually wait — Dynamo had 62 and 64 touches with the same model. Their behaviour layer keeps both robots involved.
Hmm, but also consider: maybe both-chase is bad defensively. Dynamo won 10-1 though. Their 200 kicks vs our... event_counts: kick 200 total both teams. Touch 212 total: Zhi 71 + Pu 15 = 86 for us, Dynamo 126. So Dynamo had 40% more ball contact.
What does the reference implementation do? Maybe worth a quick look at reference/ to see what a balanced 2v2 behaviour looks like. But budget: $0.15 spent of $2.50, 19 turns left, 1h24m. I have room but should be decisive.
Plan:
1. Read the tail of team.py (offset ~5500) to see _assign, _valid, build_team, and the cover branch.
2. Make the change: possession-aware cover. When the ball is in the attacking half (or our presser is on the ball in their half), cover pushes upfield to a support/rebound position (e.g., a point between ball and opponent goal, ahead, offset laterally, clamped). When defending (ball in our half), cover holds the ball→goal line as now.
3. Maybe also: allow the presser to pass to the mate when the mate is better placed? That's a bigger change; keep it one change per session where possible. The playbook says one change per session. The cover-support change is the one.
Actually, let me think about what raises Pu's touches most directly:
- Rebound collection: after our shot, ball comes off the wall/keeper; if Pu is upfield near the box, Pu collects and shoots. This directly converts to goals too.
- Defensive interceptions: cover on the ball→goal line already intercepts balls played straight through. 23 "through" events...
Also consider the "wall" events: 27 wall bounces. The pitch has walls — the ball rebounds. A cover positioned ahead in attack collects rebounds.
Let me also reconsider the role assignment: maybe rotate roles when the presser is far from the ball and the cover is nearer? With hysteresis 1.5m, if the presser is beaten (ball goes past), the cover becomes nearer and takes over. That should already happen... but Pu's 15 touches say it doesn't happen much. Because the presser chases at the same speed, staying nearest.
One more idea: **alternate presser by half or by possession spell** — too gimmicky.
The cleanest: possession-state-dependent cover target. Let me define:
- Attack state: ball is in the opponent's half (ball_x * sign toward atk_goal > 0... need to know orientation). defend_goal_xy and attack_goal_xy are given per-robot. Pitch is 14x9, so goals at x = ±7 (X_LIMIT 6.5 for walls). If atk_goal[0] > 0, we attack +x. Ball in attacking half: (ball[0] - midpoint) * sign(atk_goal[0]) > 0, midpoint x=0. Or simpler: dist(ball, atk_goal) < dist(ball, own_goal) — ball closer to their goal than ours = attacking.
- In attack state, cover target: a support point. Good options:
a) Ahead of the ball on the line to the opponent goal, ~2m ahead (so rebounds off the keeper fall to Pu).
b) The far post: a point offset from the goal toward the ball, e.g., 2m from goal centre on the side away from the ball.
Rebound collection favors (a) or a box-edge position. Let me pick: target = point on ball→atk_goal line, 1.5m goal-side of the ball (i.e., between ball and goal, closer to goal). Wait, that could obstruct the presser's shot line. Offset laterally: take the ball→goal direction, rotate ~30°, place 2.5m from ball. Hmm, keep it simple: position at 60% of the way from ball to goal, offset 1.5m perpendicular. That's a classic support/rebound spot.
Actually simplest robust: cover goes to a point 2m from the opponent goal centre toward the ball but offset to the far post from the ball's y. Eh.
Let me not overcomplicate. A clean formulation:
```python
def _cover_target(self, ball, me, own_goal, atk_goal):
# Attack support: when the ball is nearer their goal than ours and
# our presser is on it, the cover stops spectating on the defensive
# line and takes a rebound spot at the edge of the box.
if ball is not None and _dist(ball, atk_goal) < _dist(ball, own_goal) - 1.0:
# rebound/support spot: 2.2m from goal, offset to the far side
# of the ball so we don't crowd the presser's shot line
off = 1.6 if ball[1] >= atk_goal[1] else -1.6 # hmm, atk_goal y is 0
...
```
Goal centre y is presumably 0 (goals on the midline of the short axis... pitch 14x9, goals at x=±7, y=0). So far post from ball: if ball[1] > 0, far post is y < 0 side.
Support spot: [atk_goal_x - sign*2.2, -sign(ball_y)*1.2]? Let me think: atk_goal = [7, 0] say. Box edge ~2m out: x = 5. Far post offset: y = -1.2 if ball y > 0. So target = [5, -1.2]. Clamped.
But careful: if the presser loses the ball upfield and Dynamo counters, the cover is now upfield too — both beaten. That's the risk. Mitigation: only take the support spot when the presser is CLOSE to the ball (within ~2m, i.e., we have it), otherwise hold the defensive line. That's the possession condition: presser within 2m of ball AND ball in their half → support spot. Otherwise → ball→goal line cover.
That's a clean, defensible change: "when we have the ball in their half, the spare player goes to the far post for rebounds instead of standing on our goal line."
Also the same logic should apply in the buzzer branch? The buzzer branch cover holds the ball→goal line — for buzzer defense that's right. Keep.
Also: should the cover ever kick? If Pu is at the far post and a rebound falls to Pu, Pu is not the presser (Zhi is nearer... actually if the ball rebounds to Pu, Pu becomes nearer and takes over as presser via _assign — then Pu calls the model and can shoot). Good — the role system handles that automatically once Pu is near the ball. So the fix is purely about cover POSITIONING when in possession.
Let me also double check _assign to make sure the presser is chosen by distance to ball with hysteresis, and that when the ball moves nearer to the cover, the cover becomes presser. I need to read the tail of the file. Let me do that now.
Also I should check the m7 digest (16-3 at Singularity) — playbook says it's still open. But m29 is fresher and more relevant (post-fix). Given budget, maybe skip m7 this session or do a quick digest read. The m29 loss (1-10) is the priority; m7 was pre-fix. I'll skip m7 again if budget is tight — actually a quick digest read is one turn and could reveal patterns (Singularity beat Real Machina 8-5 in m1 this season too — they're strong). But my change is already decided by m29's numbers. Skip m7; note it.
Plan for remaining turns (19 left):
1. Read team.py tail (offset 5500). [1 turn]
2. Edit: add possession-aware cover support. [1-2 turns]
3. Lint. [1]
4. Practice 90-120s. [1-2]
5. Maybe adjust + practice #2. [2-3]
6. Update NOTES.md + PLAYBOOK.md. [1-2]
7. Done. [1]
That's comfortable.
Let me read the tail now. The truncation happened around char 6100 in decide()'s cover branch. I'll read from offset 5500 to catch the end of decide() and everything after.
Actually, from the grep: _ball at 6166, _teammate at 6478, _assign at 6745, _valid at 8368, build_team at 8962. The decide() cover branch truncated around "out = {"sk..." — that's just before 6166. So offset 5900 or so gets me the rest. Let me use offset 5800 to be safe. The file total is maybe ~9.5KB, so one read from 5800 gets ~3.7KB to end.
One JSON: read offset 5800.
The m29 numbers have already picked the fix for me: falls and deadlines are fixed, but Pu's 15 touches (to Zhi's 71, against Dynamo's 62/64) says the cover robot is a spectator — the presser-by-distance stays nearest forever, so the handover only fires on falls. We played 1v2. My read of team.py truncated right at the cover branch, so I need the tail — `_assign`, `_valid`, `build_team` — before I edit.
Readclub/team.py
The league answered3,937 characters
[read club/team.py@5800] VER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 5800-9654 of 9654 chars — end of file]
(0.0s)The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
The league answered63 characters
could not parse a tool call; reply with exactly one JSON object
Session over. Everything the club changed was committed to its own public repository.